diff --git a/.github/workflows/build_tests.yml b/.github/workflows/build_tests.yml
index 6e28fe818..b9391bc6d 100644
--- a/.github/workflows/build_tests.yml
+++ b/.github/workflows/build_tests.yml
@@ -18,11 +18,12 @@ jobs:
     strategy:
       matrix:
         python-version: ['3.9', '3.10', '3.11', '3.12']
-        os: [windows-latest, macOS-latest, ubuntu-latest] 
+        os: [windows-latest, macOS-13, ubuntu-latest]
+      fail-fast: false
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
@@ -36,7 +37,7 @@ jobs:
         python setup.py bdist_wheel
         ls dist/*
     - name: Save wheel
-      uses: actions/upload-artifact@v3
+      uses: actions/upload-artifact@v4
       with:
         name: wntr_${{ matrix.python-version }}_${{ matrix.os }}.whl
         path: dist/wntr*
@@ -47,23 +48,21 @@ jobs:
     strategy:
       matrix:
         python-version: ['3.9', '3.10', '3.11', '3.12']
-        os: [windows-latest, macOS-latest, ubuntu-latest]    
+        os: [windows-latest, macOS-13, ubuntu-latest]
+      fail-fast: false
     steps:
     - name: Set up Python 
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Download wheel
-      uses: actions/download-artifact@v3
+      uses: actions/download-artifact@v4
       with:
         name: wntr_${{ matrix.python-version }}_${{ matrix.os }}.whl
-    # - name: Discover
-    #   run: |
-    #     ls .
     - name: Install wntr
       run: |
         python -m pip install --upgrade pip
-        pip install wheel numpy scipy networkx pandas matplotlib setuptools
+        pip install wheel "numpy>=1.2.1,<2.0" scipy networkx pandas matplotlib setuptools
         pip install --no-index --pre --find-links=. wntr
     - name: Usage of wntr
       run: |
@@ -74,11 +73,12 @@ jobs:
     strategy:
       matrix:
         python-version: ['3.9', '3.10', '3.11', '3.12']
-        os: [windows-latest, macOS-latest, ubuntu-latest]
+        os: [windows-latest, macOS-13, ubuntu-latest]
+      fail-fast: false
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
@@ -94,33 +94,35 @@ jobs:
         coverage run --context=${{ matrix.os }}.py${{ matrix.python-version }} --source=wntr --omit="*/tests/*","*/sim/network_isolation/network_isolation.py","*/sim/aml/evaluator.py" --append -m pytest --doctest-glob="*.rst" documentation
       env:
         COVERAGE_FILE: .coverage.${{ matrix.python-version }}.${{ matrix.os }}
-        
     - name: Save coverage
-      uses: actions/upload-artifact@v2
+      uses: actions/upload-artifact@v4
       with:
-        name: coverage
+        name: .coverage.${{ matrix.python-version }}.${{ matrix.os }}
         path: .coverage.${{ matrix.python-version }}.${{ matrix.os }}
+        include-hidden-files: true
 
   combine_reports:
     needs: [ pytest_coverage ]
     runs-on: ubuntu-latest
     steps:
     - name: Set up Python 
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: 3.11
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
     - name: Install coverage
       run: |
         python -m pip install --upgrade pip
         pip install -r requirements.txt
         python -m pip install -e .
-    # pip install coveralls
+        pip install coveralls
     - name: Download coverage artifacts from test matrix
-      uses: actions/download-artifact@v2
+      uses: actions/download-artifact@v4
       with:
-        name: coverage
+        pattern: .coverage.*.ubuntu-latest # coverage from other OS cause problems
     - name: Setup coverage and combine reports
+      run: coverage combine .coverage.*.ubuntu-latest
+    - name: Create coverage report
       run: |
         echo "[paths]" > .coveragerc
         echo "source = " >> .coveragerc
@@ -129,55 +131,21 @@ jobs:
         echo "    D:\\a\\WNTR\\WNTR\\wntr" >> .coveragerc
         echo "    /home/runner/work/WNTR/WNTR/wntr" >> .coveragerc
         echo "    /Users/runner/work/WNTR/WNTR/wntr" >> .coveragerc
-        coverage combine
-    - name: Create coverage report
-      run: |
+        echo "    ${{ github.workspace }}/wntr" >> .coveragerc
         coverage report
         coverage json --pretty-print
         coverage html --show-contexts
     - name: Save coverage JSON
-      uses: actions/upload-artifact@v2
+      uses: actions/upload-artifact@v4
       with:
-        name: coverage
+        name: coverage-json
         path: coverage.json
     - name: Save coverage html
-      uses: actions/upload-artifact@v2
+      uses: actions/upload-artifact@v4
       with:
-        name: coverage
+        name: coverage-html
         path: htmlcov
-
-  combine_reports_upload_coveralls:
-    needs: [ pytest_coverage ]
-    runs-on: ubuntu-latest
-    continue-on-error: true
-    steps:
-    - name: Set up Python 
-      uses: actions/setup-python@v4
-      with:
-        python-version: 3.11
-    - uses: actions/checkout@v2
-    - name: Install coverage
-      run: |
-        python -m pip install --upgrade pip
-        pip install coveralls
-        pip install -r requirements.txt
-        python -m pip install -e .
-    - name: Download coverage artifacts from test matrix
-      uses: actions/download-artifact@v2
-      with:
-        name: coverage
-    - name: Setup coverage and combine reports
-      run: |
-        echo "[paths]" > .coveragerc
-        echo "source = " >> .coveragerc
-        echo "    wntr/" >> .coveragerc
-        echo "    wntr\\" >> .coveragerc
-        echo "    D:\\a\\WNTR\\WNTR\\wntr" >> .coveragerc
-        echo "    /home/runner/work/WNTR/WNTR/wntr" >> .coveragerc
-        echo "    /Users/runner/work/WNTR/WNTR/wntr" >> .coveragerc
-        coverage combine
     - name: Push to coveralls
-      run: |
-        coveralls --service=github
+      run: coveralls --service=github --rcfile=.coveragerc
       env:
         GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 609502d22..a976f3e41 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -10,48 +10,72 @@ on:
 jobs:
 
   wheels:
+    name: Build distribution 📦 on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [windows-2019, macOS-11, ubuntu-20.04] 
+        os: [windows-latest, macOS-13, macos-13, ubuntu-latest] 
     steps:
-    - uses: actions/checkout@v3
+    - name: Harden Runner
+      uses: step-security/harden-runner@91182cccc01eb5e619899d80e4e971d6181294a7 # v2.10.1
+      with:
+        egress-policy: audit
+    - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
     - name: Build wheels
-      uses: pypa/cibuildwheel@v2.11.1
+      uses: pypa/cibuildwheel@79b0dd328794e1180a7268444d46cdf12e1abd01 # v2.21.0
       env:
         CIBW_ENVIRONMENT: BUILD_WNTR_EXTENSIONS='true'
-        CIBW_BUILD: cp37-* cp38-* cp39-* cp310-* cp311-*
+        CIBW_BUILD: cp38-* cp39-* cp310-* cp311-* cp312-*
         CIBW_SKIP: "*-win32 *-manylinux_i686 pp* *-musllinux*"
-    - uses: actions/upload-artifact@v3
+    - uses: actions/upload-artifact@50769540e7f4bd5e21e526ee35c689e35e0d6874 # v4.4.0
       with:
+        name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
         path: ./wheelhouse/*.whl
 
   source:
+    name: Make SDist artifact 📦
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
-      - uses: actions/setup-python@v4
+      - name: Harden Runner
+        uses: step-security/harden-runner@91182cccc01eb5e619899d80e4e971d6181294a7 # v2.10.1
+        with:
+          egress-policy: audit
+
+      - uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+      - uses: actions/setup-python@v5
         with:
           python-version: '3.11'
-      - name: build the sdist
-        run: |
-          python -m pip install --upgrade build
-          python -m build --sdist
-      - uses: actions/upload-artifact@v3
+
+      - name: Build SDist
+        run: pipx run build --sdist
+    
+      - uses: actions/upload-artifact@50769540e7f4bd5e21e526ee35c689e35e0d6874 # v4.4.0
         with:
+          name: cibw-sdist
           path: dist/*.tar.gz
 
   publish-to-pypi:
+    name: Publish Python 🐍 distribution 📦 to PyPI
     needs: [wheels, source]
     runs-on: ubuntu-latest
+    environment:
+      name: release
+    permissions:
+      id-token: write
     if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
     steps:
-      - uses: actions/download-artifact@v3
+      - name: Harden Runner
+        uses: step-security/harden-runner@91182cccc01eb5e619899d80e4e971d6181294a7 # v2.10.1
+        with:
+          egress-policy: audit
+
+      - uses: actions/download-artifact@fa0a91b85d4f404e444e00e005971372dc801d16 # v4.1.8
         with:
-          name: artifact
+          pattern: cibw-*
           path: dist
+          merge-multiple: true
 
-      - uses: pypa/gh-action-pypi-publish@release/v1
+      - uses: pypa/gh-action-pypi-publish@0ab0b79471669eb3a4d647e625009c62f9f3b241 # release/v1
         with:
           user: __token__
           password: ${{ secrets.PYPI_WNTR_API_TOKEN }}
diff --git a/.gitignore b/.gitignore
index 5aee8f154..681f47bcb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@ build/
 wntr.egg-info/
 dist/
 docker/
+/.vscode
 
 *.pyd
 *.pyc
diff --git a/documentation/attention.rst b/documentation/attention.rst
index 230bc4c97..5f18bb2eb 100644
--- a/documentation/attention.rst
+++ b/documentation/attention.rst
@@ -1,4 +1,4 @@
 .. attention:: 
-    Version 1.1 is now available.  
+    Version 1.2.0 is now available.  
     See `release notes <https://usepa.github.io/WNTR/whatsnew.html>`_
     for more information.
\ No newline at end of file
diff --git a/documentation/framework.rst b/documentation/framework.rst
index 85ebb5a27..3061d2813 100644
--- a/documentation/framework.rst
+++ b/documentation/framework.rst
@@ -34,7 +34,7 @@ run simulations are described in more detail below, followed by a list of softwa
    :class:`~wntr.scenario`                            Contains classes and methods to define disaster scenarios and fragility/survival curves.
    :class:`~wntr.sim`		                          Contains classes and methods to run hydraulic and water quality simulations using the water network model.
    :class:`~wntr.library`	                          Contains classes and methods to help build water network models.
-   :class:`~wntr.metrics`	                          Contains functions to compute resilience, including hydraulic, water quality, water security, and economic metrics. Methods to compute topographic metrics are included in the wntr.network.graph module.
+   :class:`~wntr.metrics`	                          Contains functions to compute resilience, including topographic, hydraulic, water quality, water security, and economic metrics.
    :class:`~wntr.morph`	                              Contains methods to modify water network model morphology, including network skeletonization, modifying node coordinates, and splitting or breaking pipes.
    :class:`~wntr.gis`	                              Contains geospatial capabilities, including a function to convert the water network model to GeoDataFrames.
    :class:`~wntr.graphics`                            Contains functions to generate graphics.
diff --git a/documentation/gis.rst b/documentation/gis.rst
index bbf5959ca..e57d6886d 100644
--- a/documentation/gis.rst
+++ b/documentation/gis.rst
@@ -112,12 +112,13 @@ For example, the junctions GeoDataFrame contains the following information:
     :skipif: gpd is None
 
     >>> print(wn_gis.junctions.head())
-       node_type  elevation  initial_quality                   geometry
-    10  Junction    216.408        5.000e-04  POINT (20.00000 70.00000)
-    11  Junction    216.408        5.000e-04  POINT (30.00000 70.00000)
-    12  Junction    213.360        5.000e-04  POINT (50.00000 70.00000)
-    13  Junction    211.836        5.000e-04  POINT (70.00000 70.00000)
-    21  Junction    213.360        5.000e-04  POINT (30.00000 40.00000)
+         node_type  elevation  initial_quality                   geometry
+    name                                                                 
+    10    Junction    216.408        5.000e-04  POINT (20.00000 70.00000)
+    11    Junction    216.408        5.000e-04  POINT (30.00000 70.00000)
+    12    Junction    213.360        5.000e-04  POINT (50.00000 70.00000)
+    13    Junction    211.836        5.000e-04  POINT (70.00000 70.00000)
+    21    Junction    213.360        5.000e-04  POINT (30.00000 40.00000)
 
 Each GeoDataFrame contains attributes and geometry:
 
@@ -333,21 +334,23 @@ and then translates the GeoDataFrames coordinates to EPSG:3857.
 	
     >>> wn_gis = wntr.network.to_gis(wn, crs='EPSG:4326')
     >>> print(wn_gis.junctions.head())
-       node_type  elevation  initial_quality                   geometry
-    10  Junction    216.408        5.000e-04  POINT (20.00000 70.00000)
-    11  Junction    216.408        5.000e-04  POINT (30.00000 70.00000)
-    12  Junction    213.360        5.000e-04  POINT (50.00000 70.00000)
-    13  Junction    211.836        5.000e-04  POINT (70.00000 70.00000)
-    21  Junction    213.360        5.000e-04  POINT (30.00000 40.00000)
-	
+         node_type  elevation  initial_quality                   geometry
+    name                                                                 
+    10    Junction    216.408        5.000e-04  POINT (20.00000 70.00000)
+    11    Junction    216.408        5.000e-04  POINT (30.00000 70.00000)
+    12    Junction    213.360        5.000e-04  POINT (50.00000 70.00000)
+    13    Junction    211.836        5.000e-04  POINT (70.00000 70.00000)
+    21    Junction    213.360        5.000e-04  POINT (30.00000 40.00000)
+
     >>> wn_gis.to_crs('EPSG:3857')
     >>> print(wn_gis.junctions.head())
-       node_type  elevation  initial_quality                          geometry
-    10  Junction    216.408        5.000e-04  POINT (2226389.816 11068715.659)
-    11  Junction    216.408        5.000e-04  POINT (3339584.724 11068715.659)
-    12  Junction    213.360        5.000e-04  POINT (5565974.540 11068715.659)
-    13  Junction    211.836        5.000e-04  POINT (7792364.356 11068715.659)
-    21  Junction    213.360        5.000e-04   POINT (3339584.724 4865942.280)
+         node_type  elevation  initial_quality                          geometry
+    name                                                                        
+    10    Junction    216.408        5.000e-04  POINT (2226389.816 11068715.659)
+    11    Junction    216.408        5.000e-04  POINT (3339584.724 11068715.659)
+    12    Junction    213.360        5.000e-04  POINT (5565974.540 11068715.659)
+    13    Junction    211.836        5.000e-04  POINT (7792364.356 11068715.659)
+    21    Junction    213.360        5.000e-04   POINT (3339584.724 4865942.280)
 
 Snap point geometries to the nearest point or line
 ----------------------------------------------------
diff --git a/documentation/morph.rst b/documentation/morph.rst
index 8d9220828..be88b3ed1 100644
--- a/documentation/morph.rst
+++ b/documentation/morph.rst
@@ -82,6 +82,7 @@ This initial set of operations can generate new branch pipes, pipes in series, a
 This cycle repeats until the network can no longer be reduced.  
 The user can specify if branch trimming, series pipe merge, and/or parallel pipe merge should be included in the skeletonization operations.  
 The user can also specify a maximum number of cycles to include in the process. 
+The user can also specify a list of junctions and pipes which should be excluded from skeletonization operations.
 
 .. only:: latex
 
diff --git a/documentation/whatsnew/v1.2.0.rst b/documentation/whatsnew/v1.2.0.rst
index 0814855d9..59fa5c836 100644
--- a/documentation/whatsnew/v1.2.0.rst
+++ b/documentation/whatsnew/v1.2.0.rst
@@ -1,11 +1,18 @@
-v1.2.0 (main)
+v1.2.0 (June 18, 2024)
 ---------------------------------------------------
 WNTR version 1.2.0 includes the following updates:
 
-* Added basic and geospatial jupyter notebook demos, updated documentation, dropped Python 3.7 and 3.8 from testing https://github.com/USEPA/WNTR/pull/419
-* Fix: plot_network bug due to changed networkx draw function behavior https://github.com/USEPA/WNTR/pull/417
-* Fix: Addressing bug caused when units="SI" in a call to write_inp() https://github.com/USEPA/WNTR/pull/410
-* Added EpanetException class and subclasses that allow for cleaner error reporting during IO https://github.com/USEPA/WNTR/pull/381
-* Added google analytics key https://github.com/USEPA/WNTR/pull/406
+* Added setuptools and removed readthedocs config https://github.com/USEPA/WNTR/pull/396
 * Documentation updates to install WNTR without Anaconda https://github.com/USEPA/WNTR/pull/403
-* Added setuptools and removed readthedocs config https://github.com/USEPA/WNTR/pull/396
\ No newline at end of file
+* Added google analytics key https://github.com/USEPA/WNTR/pull/406
+* Added EpanetException class and subclasses that allow for cleaner error reporting during IO https://github.com/USEPA/WNTR/pull/381
+* Fixed bug caused by units="SI" in a call to write_inp https://github.com/USEPA/WNTR/pull/410
+* Fixed bug caused by changes in NetworkX draw function https://github.com/USEPA/WNTR/pull/417
+* Added basic and geospatial jupyter notebook demos https://github.com/USEPA/WNTR/pull/419
+* Dropped Python 3.7 and 3.8 from testing https://github.com/USEPA/WNTR/pull/419
+* Resolved deprecation/future warnings and included GeoPandas in windows build test https://github.com/USEPA/WNTR/pull/423
+* Added nodes/pipes to excluded in skeletonization https://github.com/USEPA/WNTR/pull/384
+* Fixed bug related to link colormap https://github.com/USEPA/WNTR/pull/429
+* Fixed bug in GIS I/O caused by index names https://github.com/USEPA/WNTR/pull/395
+* Fixed bug in network_animation https://github.com/USEPA/WNTR/pull/405
+* Updated workflow and testing to hold numpy < 2.0 and use macOS-13 https://github.com/USEPA/WNTR/pull/430
diff --git a/examples/demos/basic_demo.ipynb b/examples/demos/basic_demo.ipynb
index 37de5b2ce..a9c36a7da 100644
--- a/examples/demos/basic_demo.ipynb
+++ b/examples/demos/basic_demo.ipynb
@@ -6,18 +6,23 @@
     "tags": []
    },
    "source": [
-    "# WNTR Basic Tutorial"
+    "# WNTR Basic Tutorial\n",
+    "The following tutorial illustrates basic use of WNTR, including use of the `WaterNetworkModel` object, the ability to read/write model files to other formats, run hydraulic and water quality simulations, compute resilience metrics, define and use fragility curves, skeletonize water network models, identify network segments associated with isolation valves, and assign geospatial data to junctions and pipes."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
     "tags": []
    },
    "source": [
     "## Imports\n",
     "Import WNTR and additional Python packages that are needed for the tutorial\n",
-    "- Numpy is required for define logic operations\n",
+    "- Numpy is required to define comparison operators (i.e., np.greater) in queries\n",
     "- Scipy is required to define lognormal fragility curves\n",
     "- NetworkX is used to compute topographic metrics\n",
     "- Geopandas is used to load geospatial data\n",
@@ -26,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,6 +43,14 @@
     "import wntr"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Units\n",
+    "WNTR uses SI (International System) units (length in meters, time in seconds, mass in kilograms).  See https://usepa.github.io/WNTR/units.html for more details."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -56,148 +69,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create water network model from an INP file\n",
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
     "wn = wntr.network.WaterNetworkModel('../networks/Net3.inp')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Nodes': {'Junctions': 92, 'Tanks': 3, 'Reservoirs': 2},\n",
-       " 'Links': {'Pipes': 117, 'Pumps': 2, 'Valves': 0},\n",
-       " 'Patterns': 5,\n",
-       " 'Curves': {'Pump': 2, 'Efficiency': 0, 'Headloss': 0, 'Volume': 0},\n",
-       " 'Sources': 0,\n",
-       " 'Controls': 18}"
-      ]
-     },
-     "execution_count": 195,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Basic description of the model\n",
+    "# Print a basic description of the model.  The level can be 0, 1, or 2 and defines the level of detail included in the description.\n",
     "wn.describe(level=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['add_control',\n",
-       " 'add_curve',\n",
-       " 'add_junction',\n",
-       " 'add_pattern',\n",
-       " 'add_pipe',\n",
-       " 'add_pump',\n",
-       " 'add_reservoir',\n",
-       " 'add_source',\n",
-       " 'add_tank',\n",
-       " 'add_valve',\n",
-       " 'assign_demand',\n",
-       " 'control_name_list',\n",
-       " 'controls',\n",
-       " 'convert_controls_to_rules',\n",
-       " 'curve_name_list',\n",
-       " 'curves',\n",
-       " 'describe',\n",
-       " 'fcv_name_list',\n",
-       " 'fcvs',\n",
-       " 'from_dict',\n",
-       " 'from_gis',\n",
-       " 'get_control',\n",
-       " 'get_curve',\n",
-       " 'get_graph',\n",
-       " 'get_link',\n",
-       " 'get_links_for_node',\n",
-       " 'get_node',\n",
-       " 'get_pattern',\n",
-       " 'get_source',\n",
-       " 'gpv_name_list',\n",
-       " 'gpvs',\n",
-       " 'head_pump_name_list',\n",
-       " 'head_pumps',\n",
-       " 'junction_name_list',\n",
-       " 'junctions',\n",
-       " 'link_name_list',\n",
-       " 'links',\n",
-       " 'name',\n",
-       " 'node_name_list',\n",
-       " 'nodes',\n",
-       " 'num_controls',\n",
-       " 'num_curves',\n",
-       " 'num_junctions',\n",
-       " 'num_links',\n",
-       " 'num_nodes',\n",
-       " 'num_patterns',\n",
-       " 'num_pipes',\n",
-       " 'num_pumps',\n",
-       " 'num_reservoirs',\n",
-       " 'num_sources',\n",
-       " 'num_tanks',\n",
-       " 'num_valves',\n",
-       " 'options',\n",
-       " 'pattern_name_list',\n",
-       " 'patterns',\n",
-       " 'pbv_name_list',\n",
-       " 'pbvs',\n",
-       " 'pipe_name_list',\n",
-       " 'pipes',\n",
-       " 'power_pump_name_list',\n",
-       " 'power_pumps',\n",
-       " 'prv_name_list',\n",
-       " 'prvs',\n",
-       " 'psv_name_list',\n",
-       " 'psvs',\n",
-       " 'pump_name_list',\n",
-       " 'pumps',\n",
-       " 'query_link_attribute',\n",
-       " 'query_node_attribute',\n",
-       " 'remove_control',\n",
-       " 'remove_curve',\n",
-       " 'remove_link',\n",
-       " 'remove_node',\n",
-       " 'remove_pattern',\n",
-       " 'remove_source',\n",
-       " 'reservoir_name_list',\n",
-       " 'reservoirs',\n",
-       " 'reset_initial_values',\n",
-       " 'sim_time',\n",
-       " 'source_name_list',\n",
-       " 'sources',\n",
-       " 'tank_name_list',\n",
-       " 'tanks',\n",
-       " 'tcv_name_list',\n",
-       " 'tcvs',\n",
-       " 'title',\n",
-       " 'to_dict',\n",
-       " 'to_gis',\n",
-       " 'to_graph',\n",
-       " 'valve_name_list',\n",
-       " 'valves']"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "# List properties and methods associated with the WaterNetworkModel (omitting private underscore names)\n",
     "[name for name in dir(wn) if not name.startswith('_')]"
@@ -205,24 +101,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Basic network graphic\n",
+    "# Plot a basic network graphic\n",
     "ax = wntr.graphics.plot_network(wn)"
    ]
   },
@@ -238,19 +123,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Node names ['10', '15', '20', '35', '40', '50', '60', '601', '61', '101', '103', '105', '107', '109', '111', '113', '115', '117', '119', '120', '121', '123', '125', '127', '129', '131', '139', '141', '143', '145', '147', '149', '151', '153', '157', '159', '161', '163', '164', '166', '167', '169', '171', '173', '177', '179', '181', '183', '184', '185', '187', '189', '191', '193', '195', '197', '199', '201', '203', '204', '205', '206', '207', '208', '209', '211', '213', '215', '217', '219', '225', '229', '231', '237', '239', '241', '243', '247', '249', '251', '253', '255', '257', '259', '261', '263', '265', '267', '269', '271', '273', '275', 'River', 'Lake', '1', '2', '3']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print the names of all junctions, tanks, and reservoirs\n",
     "print(\"Node names\", wn.node_name_list)"
@@ -258,17 +135,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tank names ['1', '2', '3']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print the names of just tanks\n",
     "print(\"Tank names\", wn.tank_name_list)"
@@ -276,27 +145,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'wntr.network.elements.Tank'>\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Tank '1', elevation=40.203120000000006, min_level=0.030480000000000004, max_level=9.784080000000001, diameter=25.908, min_vol=0.0, vol_curve='None'>"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get a tank object\n",
     "tank = wn.get_node('1')\n",
@@ -306,50 +157,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['add_leak',\n",
-       " 'bulk_coeff',\n",
-       " 'coordinates',\n",
-       " 'demand',\n",
-       " 'diameter',\n",
-       " 'elevation',\n",
-       " 'get_volume',\n",
-       " 'head',\n",
-       " 'init_level',\n",
-       " 'initial_quality',\n",
-       " 'leak_area',\n",
-       " 'leak_demand',\n",
-       " 'leak_discharge_coeff',\n",
-       " 'leak_status',\n",
-       " 'level',\n",
-       " 'max_level',\n",
-       " 'min_level',\n",
-       " 'min_vol',\n",
-       " 'mixing_fraction',\n",
-       " 'mixing_model',\n",
-       " 'name',\n",
-       " 'node_type',\n",
-       " 'overflow',\n",
-       " 'pressure',\n",
-       " 'quality',\n",
-       " 'remove_leak',\n",
-       " 'tag',\n",
-       " 'to_dict',\n",
-       " 'to_ref',\n",
-       " 'vol_curve',\n",
-       " 'vol_curve_name']"
-      ]
-     },
-     "execution_count": 201,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# List properties and methods associated with the tank (omitting private underscore names)\n",
     "[name for name in dir(tank) if not name.startswith('_')]"
@@ -357,20 +167,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original max level 9.784080000000001\n",
-      "New max level 10\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Change the max level of a tank\n",
     "print(\"Original max level\", tank.max_level)\n",
@@ -380,26 +181,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Add a junction\n",
-    "wn.add_junction('new_junction', base_demand=0.0, demand_pattern=None, elevation=0.0, coordinates=None, demand_category=None)"
+    "# Add a junction to the WaterNetworkModel\n",
+    "wn.add_junction('new_junction', base_demand=0.0, demand_pattern=None, elevation=0.0, coordinates=None, demand_category=None)\n",
+    "print(wn.junction_name_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Remove a junction (as a node)\n",
-    "wn.remove_node('new_junction')"
+    "# Remove a junction from the WaterNetworkModel\n",
+    "wn.remove_node('new_junction')\n",
+    "print(wn.junction_name_list)"
    ]
   },
   {
@@ -412,39 +215,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Link names ['20', '40', '50', '60', '101', '103', '105', '107', '109', '111', '112', '113', '114', '115', '116', '117', '119', '120', '121', '122', '123', '125', '129', '131', '133', '135', '137', '145', '147', '149', '151', '153', '155', '159', '161', '163', '169', '171', '173', '175', '177', '179', '180', '181', '183', '185', '186', '187', '189', '191', '193', '195', '197', '199', '201', '202', '203', '204', '205', '207', '209', '211', '213', '215', '217', '219', '221', '223', '225', '229', '231', '233', '235', '237', '238', '239', '240', '241', '243', '245', '247', '249', '251', '257', '261', '263', '269', '271', '273', '275', '277', '281', '283', '285', '287', '289', '291', '293', '295', '297', '299', '301', '303', '305', '307', '309', '311', '313', '315', '317', '319', '321', '323', '325', '329', '330', '333', '10', '335']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Print the names of all links\n",
+    "# Print the names of all pipes, pumps, and valves\n",
     "print(\"Link names\", wn.link_name_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Head pump names ['10', '335']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print the names of just head pumps\n",
     "print(\"Head pump names\", wn.head_pump_name_list)"
@@ -452,17 +239,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Links connected to node 229 = ['261', '263', '271']\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get the name of links connected to a specific node\n",
     "connected_links = wn.get_links_for_node('229')\n",
@@ -471,29 +250,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'wntr.network.elements.Pipe'>\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Pipe '105' from '101' to '105', length=774.192, diameter=0.30479999999999996, roughness=130.0, minor_loss=0.0, check_valve=False, status=Open>"
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get a pipe object\n",
     "pipe = wn.get_link('105')\n",
@@ -503,47 +264,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['bulk_coeff',\n",
-       " 'check_valve',\n",
-       " 'cv',\n",
-       " 'diameter',\n",
-       " 'end_node',\n",
-       " 'end_node_name',\n",
-       " 'flow',\n",
-       " 'friction_factor',\n",
-       " 'headloss',\n",
-       " 'initial_setting',\n",
-       " 'initial_status',\n",
-       " 'length',\n",
-       " 'link_type',\n",
-       " 'minor_loss',\n",
-       " 'name',\n",
-       " 'quality',\n",
-       " 'reaction_rate',\n",
-       " 'roughness',\n",
-       " 'setting',\n",
-       " 'start_node',\n",
-       " 'start_node_name',\n",
-       " 'status',\n",
-       " 'tag',\n",
-       " 'to_dict',\n",
-       " 'to_ref',\n",
-       " 'velocity',\n",
-       " 'vertices',\n",
-       " 'wall_coeff']"
-      ]
-     },
-     "execution_count": 209,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# List properties and methods associated with the pipe (omitting private underscore names)\n",
     "[name for name in dir(pipe) if not name.startswith('_')]"
@@ -551,20 +274,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original diameter 0.30479999999999996\n",
-      "New diameter 10\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Change the diameter of a pipe\n",
     "print(\"Original diameter\", pipe.diameter)\n",
@@ -574,26 +288,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Add a pipe\n",
-    "wn.add_pipe(name=\"new_pipe\", start_node_name=\"10\", end_node_name=\"123\", length=304.8, diameter=0.3048, roughness=100, minor_loss=0.0, initial_status='OPEN', check_valve=False)"
+    "# Add a pipe to the WaterNetworkModel\n",
+    "wn.add_pipe(name=\"new_pipe\", start_node_name=\"10\", end_node_name=\"123\", length=304.8, diameter=0.3048, roughness=100, minor_loss=0.0, initial_status='OPEN', check_valve=False)\n",
+    "print(wn.pipe_name_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Remove a pipe\n",
-    "wn.remove_link(\"new_pipe\")"
+    "# Remove a pipe from the WaterNetworkModel\n",
+    "wn.remove_link(\"new_pipe\")\n",
+    "print(wn.pipe_name_list)"
    ]
   },
   {
@@ -603,11 +319,11 @@
    },
    "source": [
     "## Demands and Patterns\n",
-    "Junctions can have multiple demands which are stored as Timeseries objects in a `demand_timeseries_list`. Each Timeseries contains a base value, pattern, and category.  Patterns contain multiliers and the pattern timestep.  \n",
+    "Junctions can have multiple demands which are stored as Timeseries objects in a `demand_timeseries_list`. Each Timeseries contains a base value, pattern, and category.  Patterns contain multipliers and the pattern timestep.  \n",
     "\n",
-    "The following illustrates how to\n",
+    "The following example illustrates how to\n",
     "* Compute expected demand (which accounts for base demand, demand patterns, and demand multiplier)\n",
-    "* Compute average expected demand (average value for a 24 hour period, also accounts for base demand, demand patterns, and demand multiplier)\n",
+    "* Compute average expected demand (average value for a 24 hour period -  accounts for base demand, demand patterns, and demand multiplier)\n",
     "* Add demands to a junction\n",
     "* Modify demand base value and pattern\n",
     "* Remove demands from a junction\n",
@@ -616,202 +332,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
-   "metadata": {},
-   "outputs": [
-    {
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-      "text/plain": [
-       "          10        15   20        35   40   50   60  601   61       101  ...  \\\n",
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-       "14400.0  0.0  0.039116  0.0  0.112995  0.0  0.0  0.0  0.0  0.0  0.009108  ...   \n",
-       "\n",
-       "         257  259  261  263  265  267  269  271  273  275  \n",
-       "0.0      0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "3600.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "7200.0   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "10800.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "14400.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
-       "\n",
-       "[5 rows x 92 columns]"
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Compute expected demand\n",
     "expected_demand = wntr.metrics.expected_demand(wn)\n",
@@ -820,89 +343,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10    0.000000\n",
-      "15    0.016666\n",
-      "20    0.000000\n",
-      "35    0.108389\n",
-      "40    0.000000\n",
-      "dtype: float64\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Compute and plot average expected demand and \n",
+   "outputs": [],
+   "source": [
+    "# Compute and plot average expected demand \n",
     "AED = wntr.metrics.average_expected_demand(wn)\n",
     "print(AED.head())\n",
-    "ax = wntr.graphics.plot_network(wn, node_attribute=AED, node_range=(0,0.025))"
+    "ax = wntr.graphics.plot_network(wn, node_attribute=AED, node_range=(0,0.025), title='Average expected demand (m$^3$/s)')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['10', '20', '40', '50', '60', '601', '61', '120', '129', '164', '169',\n",
-      "       '173', '179', '181', '183', '184', '187', '195', '204', '206', '208',\n",
-      "       '241', '249', '257', '259', '261', '263', '265', '267', '269', '271',\n",
-      "       '273', '275'],\n",
-      "      dtype='object')\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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CEQn+dyY2NhZBQUH5vuDXqVMH3bt3R7169WRzwf+WKlWAx48BDw/g/XugfXs42Noi9ONHzJ07F2vXrsWOHTuwaNEijBo1Sn4XcWKskHhkQIaWL1+OGTNm4Mcff8SBAwd4aVP2XR4eHujduzfi4uKgra1d7P39c8H/5x5+fr7h16pVC/Xq1YOlpSWqV69efBf8IggJCcGECRNw8uRJmJubY/369ejcubPgAYuxsoLDgIy5u7tj6NChcHBwwLlz53jYkn2Tj48PmjVrhoCAANSrl9cUuYL77wX/+fPnCA4ORlhYGBITE7OP++eC/889/NJ+wc+Pe/fuYdy4cbh37x6aNGmCLVu28JshGcsHDgPF4MKFC+jWrRuqV6+Oa9euQU9PT+iSWCkVEhICMzMznD9/Hu3bt8/3ebGxsTkm7eX3gv/lPXwtLa3i+EiCIyKcPHkSEydORGhoKFxdXbFu3TpUqVJF6NIYK7U4DBSTe/fuoV27dtDU1ISXlxfMzc2FLomVQunp6VBWVsb27dsxYsQIAIBUKkVkZCRCQ0MREhKC4OBgvHz5Em/evMHbt2+RlZWFly9fZrfx5QX/nyH98n7Bz4/MzExs2bIF8+bNQ3JyMsaOHYsFCxagQoUKQpfGWKnDYaAYBQcHw8nJCSkpKbh69SoaNmwodEmsFNLS0oKxsTE0NDTw/v17REVFITMzM3u/qqoqDA0NYWxsDFNTU7x69Qq+vr5wdnaGu7s7DA0NBay+9EtMTMTChQuxYcMGqKioYO7cufjll194Tg9jX+AwUMwiIyPh5OSE0NBQnDp1Cm3atBG6JFaKeHl5wcnJCVpaWmjevDnMzc1hYWEBCwsLmJqaomrVqtDW1s41EW7nzp0YP348TExMcPr0adSpU0egT1B2vHv3DpMnT4aHhwdMTEywZs0a/PjjjzzJkDFwGCgRiYmJ6NChA+7evQt3d3f07dtX6JJYKZCYmIiaNWvi48ePaN26Nc6cOVOg8x8/fowuXbrg/fv32LJlC4YOHVpMlZYvDx8+xLhx43Dr1i1YW1tjy5YtsLe3F7osxgTFr/EqAZqamrh27Ro6duyIfv36Ye3atUKXxEqBMWPGIC4uDlZWVvj48WOBz69bty4CAgLwww8/YNiwYejTpw9SUlJkX2g5Y2VlBW9vb5w7dw6JiYlo2rQpOnfujFevXgldGmOC4TBQQpSUlODp6YmRI0di0qRJ+O2338CDMvLr5MmT2L9/P37//XdUrlwZ8fHxhWpHXV0dhw8fxrZt2+Dp6YkGDRrg6dOnMq62fHJxccGzZ8+wfft2+Pr6wtLSEqNHj0ZsbKzQpTFW4jgMlCCxWAw3NzcsWLAAq1evRv/+/XNMFGPyITo6GkOGDIGjoyPGjRsHXV3dQoeBf4wcORL+/v7IysqCjY0Ndu/eLaNqyzeJRIIRI0YgNDQU06dPx59//omqVati6dKlSEtLE7o8xkoMh4ESJhKJMHfuXOzYsQOHDh2Ci4sLD+3KESLCwIEDkZmZiUOHDkEkEqFixYo51gYorHr16iEwMBCdO3fG0KFD0b9/f3z6z2t/Wd7U1NSwePFivHnzBt27d8ecOXNgbm6Offv28QgekwscBgQyfPhwnDhxAjdv3kSzZs14aFJOuLu746+//oKbm1v2m/b09PSQlJQkk4uOuro6PDw84ObmBg8PDzRo0ADPnj0rcrvyolKlSnB3d0dAQABq1aqFAQMGoH79+vDy8hK6NMaKFYcBAXXp0gXXrl3Dy5cvYWtrW+rfXMeK5u3btxg3bhy6du2Kfv36ZW/X19dHZmamzEaIRCIRRo8eDT8/P2RkZMDa2hp//vmnTNqWF3Xq1MHVq1dx5coVSKVSODk5wdnZGc+fPxe6NMaKBYcBgTVt2hR3795FSkoKGjVqhMePHwtdEisGRITevXtDRUUl1/38ihUrAgDi4uJk2meDBg0QGBgIFxcXDB48GAMGDODbBgXUunVrBAYGYs+ePQgICEDdunUxZMgQfPjwQejSGJMpDgOlQK1atXD//n1UqFAB9vb2uHHjhtAlMRlbt24dfHx84O7uDl1d3Rz7dHR0AMg+DACAhoYGjh07hi1btuDw4cOwsrLib7cFJBaLMWjQILx58wbz5s2Dh4cHHB0dMWvWrBxvemSsLOMwUEoYGxvj3r17qFWrFtq2bYtjx44JXRKTkefPn2P69OkYPHgwOnbsmGv/P2GgMGsN5IdIJMKYMWNw7949pKamomHDhti3b1+x9FWeqaioYM6cOQgJCUGrVq2wcuVKVK1aFevWrUNGRobQ5TFWJBwGShFtbW3cvHkTbdq0Qc+ePbFlyxahS2JFlJmZiZ49e0JfXx+bNm3K85jiHBn4kpWVFR4/fox27dphwIABGDRoEFJTU4u1z/JIT08Pbm5uCA4ORqtWrTBp0iSYm5vjwIED/OQBK7M4DJQyKioqOHPmDAYOHIhx48Zh9uzZ/A9MGbZgwQIEBgbi8OHDUFdXz/OYf8JATExMsdejqamJEydOYOPGjTh48CCsrKwQFBRU7P2WR6ampjh27BgePHgACwsL9OvXD3Xr1sWlS5eELo2xAuMwUApJJBLs3r0bM2fOxJIlSzBs2DBkZWUJXRYroPv372PZsmWYOHEimjVr9tXjlJSUoKysXGKT0kQiEcaPH589cdXKygr79+8vkb7LIysrK3h5eeHq1auQSCRo164dmjVrhvv37wtdGmP5xmGglBKJRFiyZAk2bdqEPXv2oGvXrjykW4akpqaiZ8+eqFatGpYvX/7d4zU1NREdHV0Clf2rYcOGePLkCdq2bYv+/ftjyJAh/HesCFq1aoVHjx7h8OHDCAsLQ6NGjfDDDz/wOw9YmcBhoJQbN24cDh8+jEuXLsHR0bHIy9aykjF58mSEhobi6NGjUFJS+u7xQoSBf/o9efIk1q9fj/3798Pa2hrBwcElXkd5IRKJ0KtXL7x69QqbNm2Ct7c3LC0tMXz4cH4ckZVqHAbKgJ49e+LSpUt48uQJ7Ozs8P79e6FLYt9w7do1uLm5Ye7cuWjQoEG+ztHS0hJsFUqRSIRffvkFvr6+SExMhJWVFQ4dOiRILeWFgoICxo0bh7CwMMyaNQuHDh2CqakpZsyYIZOlp0sSv6NBPnAYKCNatmwJX19fxMXFwcbGhp8Vl7XgYOCvv4CoqCI1k5CQgJ9++gnW1taYOXNmvs/T0dEp9qcJvsfGxgZPnjxBq1at0LdvXwwbNowvBEWkpqaGBQsWICQkBEOGDMGqVatQtWpVrFmzBunp6UKX912pqamwtrbGrFmzhC6FFTMOA2VIvXr14O/vD1VVVTRu3Bi+vr5Cl1T2ZWUBgwYBlpZAx45AlSrA6tXAyZPAggXAiROfj8mn0aNH4+PHjzhy5AgkEkm+z9PR0SkVt4C0tLRw+vRprF27Fu7u7rC2tsbLly+FLqvMq1ixIrZs2YKXL1/C2dkZv/32G6pVq4b9+/dDKpUKXd5XjRw5EsHBwejRo4fQpbDiRqzMiYmJISsrK1JWVqZTp04JXU7ZtnMnEfDtn9atidLSvtvU8ePHCQBt3ry5wGUMGzaMzMzMCvMJio2fnx8ZGxuTmpoaHTp0SOhyypWHDx+Sk5MTAaBatWrR+fPnhS4pl127dhX67zMrezgMlFHJycnUqlUrkkgktGPHDqHLKbu6dft+GACI9uzJ+/zQUCIPD4q9coW0tbXJycmJpFJpgcuYOnUq6erqFvHDyN7Hjx/JxcWFANDw4cMpNTVV6JLKlWvXrlH9+vUJADVt2pT8/PyELomIiAICAkhZWZl+/PHHQv19ZmUPh4EyLCMjg/r06UMAaOHChfx/2sIYNixfYcDX2pouXLhAcXFx/567ZAmRRJJ9zGkFBQoPCSlUGStXriQlJSXZfCYZk0qltGrVKlJQUKC6devSy5cvhS6pXJFKpXTkyBEyNTUlANS5c2cKDg4WrJ7ExEQyNTUlCwsLSkpKEqwOVrI4DJRxUqmUJk2aRABo1KhRlJWVJXRJZYu/P5Gi4nfDwBQlJQJAAKhq1ao0oXXrvI/duLFQZezZs4cA0KdPn2T8AWXnzp07ZGRkRGpqauTh4SF0OeVORkYGbd68mSpWrEgSiYSGDh1KERERJVqDVCqlrl27koqKCj19+rRE+2bC4jBQTqxatYpEIhF169aN0vJxf5v96627O10BKEVXl8jVlcjUNOcFvnp1yoqNpadPn9Iff/xBAwcOpJX6+nmGgZe1a9ONGzfo48ePBarh9OnTBIDev39fPB9SRuLi4qh9+/YEgEaOHMl/14pBcnIyzZ8/n9TU1EhZWZlmz55NiYmJJdL3mjVrCADt27evRPpjpQeHgXJk//79pKCgQE2aNCn1F5XSZMWKFaSgoPDvP7jR0bRMXZ38zc2JFiwgio7OfdLevXmGgZ0iUfYIgpGREbVt25amT59Onp6e9ObNm6/eyrl58yYBoMePHxfjJ5UNqVRKv//+O0kkEqpXrx69fv1a6JLKpenTpxMAUlBQIB0dHdq4cSNlZGQUW3++vr4kkUho2LBhxdYHK704DJQzy5cvJzU1NVJUVKRBgwbx/d18aNKkCTk4OGT/HhYWRgDo6NGjXz8pJYXIzCxnGFBWpvS7d+nhw4e0a9cuGjVqFNnZ2ZGWllZ2QFBXVycbGxsaOnQo7dixg/z9/Sk1NZUCAwMJAHl7e5fAJ5YNX19fMjQ0JHV19W//WbECO3/+PIlEIpowYQK9efOGXF1dCQCZmZmRp6enzOcHxcTEUKVKlahevXo82iOnOAyUI+np6WRubk716tWjmTNnUoUKFUgkElHnzp3J399f6PJKpfj4eFJQUKDVq1dnbztw4AAB+P792tBQOlaxIoWoqxN17kzk45PnYVKplMLCwuj06dM0a9Ys6tChA1WuXJlE/x9FkEgkVLVqVQJAw4YNK1MBLi4ujtq1a5c9Z4UvJEX3+vVr0tTUpObNm1NmZmb2dj8/P7K3tycA1KhRI/L19ZVJf1lZWeTk5ESampr05s0bmbTJyh4OA+XI8uXLSSQSZV/4k5OTae3atWRkZEQAyMHBgS5dusRPHXzBw8ODANCrV6+ytw0ZMoRMTEy+e+6nT59IQUGBfv/990L1nZiYSD4+PrRhwwbq06cPaWpq5rjF0Lt3b9q/fz99+PChUO2XFKlUSsuXLyeJREL169enyMhIoUsqs1JSUqhmzZpkaGhI0XncnpJKpXTmzBmysLAgANSpU6ciP3kwd+5cAsBrlsg5DgPlREREBKmpqdHAgQNz7cvIyKC9e/eSpaUlAaDatWvTgQMHcnzrkFc9evQgc3PzHNtq1qxJrq6u3z33+vXrBIAePHggk1qWL19OYrGYtmzZQkOGDMl+1AwAVa9enUaPHk1nz54tsclkBXX79m0yNzenWbNmCV1KmSSVSqlHjx6kpKRE9+/f/+axmZmZtG3bNtLT0yOJREIjRozIMzx8z+XLl0kkEtGkSZMKWzYrJzgMlBO9evUiTU3Nb/6DIJVK6dy5c9S4cWMCQCYmJrRhw4ZS/ThbccrKyqIKFSrQ+PHjs7clJiaSWCymTZs2fff8mTNnkrq6usxCVUpKCuno6FDPnj2zt71794527dpF3bt3Jz09vewJZdbW1jR9+nTy9vam9PR0mfQvC127dqUGDRoIXUaZ9PvvvxMA2r17d77PSU5Opjlz5pCqqiqpq6vT/PnzKSUlJV/nvn//nnR0dKhJkyb8xYBxGCgPbt++TQBo3bp1+T7n7t275OLiQiKRiHR0dGju3Lk5F9SRA76+vgSAvLy8srddvnyZAFBgYOB3z7e3tydHR0eZ1vTP6EBeQ79SqZSePn1Ka9asoTZt2pC6ujoBIBUVFWrZsiWtWLGCAgICBL0NNGPGDNLS0hKs/7Lq2rVrJJFIaNSoUYU6/8OHDzR06FCSSCSkr69PO3fu/OaaIxkZGWRra0u6urolvpYBK504DJRxWVlZVLduXbK0tCxUug8KCqL+/fuToqIiqaqq0tixY+nt27fFUGnpM3nyZNLQ0MjxuNaMGTNIXV39u4s3paenk4qKCi1YsECmNeU1OvA1GRkZdOfOHZo7dy7Z2dmRoqIiASAdHR3q0qULbdu2jUIKuSJiYe3fv58AyF2wLIqwsDDS1tYmOzu7Io/yvHjxgjp06EAAqEaNGvTXX3/ledyECRNILBbT1atXi9QfKz84DJRxW7ZsyfXttjDCw8NpwoQJpK6uThKJhHr16lXuVyCztLSkLl265Njm4OBALVq0+O65d+7cIQB0+/Ztmdf1rdGBb0lJSaGLFy/S+PHjqXbt2tlPK1SuXJn69+9PHh4eFBMTI/N6v+Tn50cA6O7du8XaT3mRmppK9evXJ319fZlOvPT29qaGDRtmTxz+cl7LPy/UWrhwocz6Y2Ufh4EyLC4ujipUqEDdunWTWZvx8fG0ePFiqlixIolEImrbti3dunVLZu2XFm/fviUAtHfv3uxtmZmZpKamRrNnz/7u+YsXLyZlZeVieZSuIKMD3xITE0NHjhyhAQMGUJUqVQgAiUQiqlWrFo0bN44uXLiQ7/vL+RUfH08AyN3dXabtllf9+vUjBQWFYgmVUqmUPDw8qEqVKiQSiejHH38kb29vUldXp7Zt2/JTRSwHDgNl2PDhw0lFRaVYhvVTU1Np69at2c+/W1tb08mTJ8vNPyCbNm0isVic45vyw4cPCUC+hk6dnJyocePGxVZfYUcHviUkJIS2b99OXbt2JR0dnezJiLa2tjRnzhzy9fWVyQp3Ojo6NGXKFBlUXL5t2rSJAJCbm1ux9pOenk7r1q0jbW1tUlVVJUNDQ4qNjS3WPlnZw2GgjHr06BGJxWKaP39+sfaTlZVFR48ezX7NarVq1eiPP/4oVTPYCyQri+jmTZpoZUWNra1z7Fq/fj1JJBJKTk7+ThNZpK6uTtOnTy+2MmU1OvA1UqmUAgMDacWKFeTk5ESqqqrZKyS2bt2aVq9eTU+ePClU+LOysqLOnTsXQ9Xlh4+PDykoKOT5KHBxSUhIIC8vL54wyPLEYaAMkkqlZGdnR5UrVy6x98tLpVK6du0aOTo6EgAyMDCgFStWlK1XnL58SVSzZvbywfFaWkT/PM+dnEyD27Wj2rVrf7eZgowgFMWKFStkPjrwNenp6XTr1i2aMWMG2djYkIKCAgGgihUrkqurK/3xxx/5HoHq2bMn1apVq5grLrsiIiJIT0+PrKyseMVGVmpwGCiD/pmxffr0aUH6f/jwIXXr1o3EYjFpamrSlClTKCoqSpBaCsTZOffLherWJZo2jUhNjQigd9raRN+5f7t69WpSUFCQ+f32/yru0YFvSUpKonPnztGYMWOoRo0aOV7fPHjwYDp27NhXnxiYN28eqaurl5tbSrKUnp5OjRo1Ih0dHXr37p3Q5TCWTUREBFZmJCcnw9zcHPXr18eVK1cErSUkJASLFy/G3r17QUTo168f5s6dCzMzsxKvJSMjA7GxsYiOjkZMTAyioqIQGRmJiIgIREZGIi4iAodOnYI4P43p6gKhoYC6ep67XVxcEBERgQcPHsj0M+Rl5cqVmDFjBl68eAELC4ti7+9roqKicOXKFZw6dQrXr19HeHg4RCIR6tSpA2dnZ3Tp0gUODg5QUVHB0aNH0bNnT3z48AH6+vqC1VwaDR8+HHv27MGVK1fQsmVLocthLBuHgTJm8uTJ2LhxI54+fSroxeFLMTExWLVqFbZs2YKkpCR07twZCxYsQMOGDQvVXkZGBmJiYnJc2P+5qEdGRiIqKgrR0dGIjY3Fx48fER8fj+Tk5FztiEQiaGpqokKFCtDV1sbNp0+hmZmZ4xgpkGdAeL1yJYx+/hkqKio5thMRKlasiEGDBmHt2rWF+nwF8enTJ5iYmKBt27bw8PAo9v7y6/Xr1zh//jzOnDmDW7duIT4+HkpKSrCxsUHDhg2xdetW3Lx5E82bNxe61FJj165dGDZsGNasWYNff/1V6HIYy4HDQBny8uVL1KxZE7/88gvWrFkjdDm5pKSkwM3NDatWrUJERASaNWuGuXPnom7dutnf2qOjoxEREYHw8HB8+PABUVFRiImJyXFhT0lJydW2WCzOvrDr6OigYsWK0NPTg4GBAQwMDGBkZIRKlSplb69YsSK0tbUhkUj+bWTOHGDx4hztSvX1IY6KytWfK4CTIhFMTExgaWmJ+vXrw9bWFlpaWvjhhx/w119/oUOHDrL+I8zTP6MDz58/R/Xq1Uukz4KQSqUICAjA2bNn8ddff+Hu3btQUlKClpYWpk+fjqFDh0L9K6Ms8sLf3x9NmzaFq6srDh06BJFIJHRJjOXAYaAMiY2NRdWqVVGpUiXMnDkTAwYMgJKSktBl5ZKZmYkDBw5g8eLFiIyMREJCQo79YrEYWlpa0NbWhra2do4Le6VKlWBoaJjjwq6np4cKFSpALM7XIP/XSaXAmjXArl1ARgbQty9gZASMHZvzMH193PPwwN/Pn8Pf3x+BgYEICgpCdHQ0AMDIyAjPnj2DlpZW0erJp0+fPqFy5cpo06ZNqRod+Jr09HRcu3YNq1evxuXLl6Guro5BgwZhypQpMDU1Fbq8EhcdHY169epBV1cX9+/fzzXaxFipINRkBVZwUqmULl26lD2jv2LFijR//vxSu/SrVCqlo0ePkpmZWfZjie7u7qXvpSgLFhBpa3+eUGhr++8TBv/x8eNHMjc3p6ZNm5Zwgf8+WRAUFFTifRfF69evaezYsaSurk4ikYjatWtH169fl5vJhRkZGdS0aVPS0tIq8aWhGSsIDgNlVGBgIPXu3ZsUFBRIRUWFRo4cSa9fvxa6rDxJpVK6ePEiNWrUKPt1vAcPHvzu+v8lKj2d6DuhKjAwkACQh4dHydT0hZSUFNLV1RXkyQJZSEpKonXr1mW/ltnS0pK2bdtW7t+Y+fPPP5NIJKKLFy8KXQpj38RhoIwLDw+nSZMmkaamJolEIurUqRPduXNH6LK+6tq1a2Rvb08AyMzMrHSOFHzF6NGjSUtLS7Bnw8vq6MCXpFIp/fXXX9mjW1paWjRp0qRy+Zjdvn37CAAtWbJE6FIY+y4OA+VEUlISrVmzhoyNjQkA2djYkKenZ+n69v2FW7duUYsWLQgAValShXbu3CmTpXCLS3p6OlWoUIFGjhwpWA1lfXTgv168eEFDhw4lFRUVkkgk1KVLF/Lx8RG6LJl49OgRqaioUNeuXeXmlggr2zgMlDOZmZnk4eFBDRo0yF4kZuPGjcW+QE5h3b17l1q3bk0AyNjYmLZs2VIqV2U7duwYAaBHjx4JWkd5GB34r48fP9KKFSuyg6yLi0uZfg9GXFwcmZiYUPXq1cvWCp1MrnEYKMe8vb2pXbt2JBKJqEKFCjRt2jT68OGD0GXl6f79+9S+ffvspY7XrVtXYkst54eTk1O+lioubuVtdOBLmZmZ5OnpSc7OzgSA6tWrR3/99VeZCgVZWVnUsmVLUldXL1eBjZV/HAbkQFBQEA0ePJiUlJRISUmJ+vfvT8+fPxe6rDwFBARQly5dSCQSkZ6eHq1cuVLwUY3379+TWCymdevWCVrHP8rj6MCX/plXUK9ePQJAjRo1omvXrgldVr5MmTKFRCIRnTlzRuhSGCsQDgNyJDo6mmbNmkU6OjokEomoTZs25OXlVSq/eT179oy6d+9OYrGY7OzsaMaMGYKNaixYsICUlJRKzSOc5Xl04EtSqZROnDhBlpaWBICaNm1KN2/eLJV/X4mIjh49SgBo9uzZQpfCWIFxGJBDnz59Ijc3t+zn/+vWrUv79+8vlRP4goODadCgQdmjGv369aOnT5+WWP9SqZSqVq1KXbt2LbE+86O8jw58KSsriw4dOkTm5uZUq1Ytqlq1Kk2bNq1UjW49ffqUVFVVqV27dqV20i5j38JhQI5lZWXRqVOnqHHjxgSADA0NaeXKlZSYmCh0ablERUVlj2oAICcnJ7p8+XKxf0u8desWASh1z4nLy+jAlzIzM+mvv/4iV1dXUlVVJQBUs2ZNWrRoEYWGhgpWV0JCApmZmZGpqSnFx8cLVgdjRcFhgBERkZ+fH3Xt2pXEYjGpq6vTL7/8Uiqf/U5NTaXt27dTtWrVshev2b17N6WnpxdLf3369KFKlSqVym97gwcPpoYNG5baYfPilJKSQocPH6Z27dqRoqIiAaCGDRvS2rVrKTIyssTqkEql1L59e1JVVS3RESvGZI3DAMshJCSExowZQ6qqqiSRSKhHjx708OFDocvK5Z9JZs2aNSMApKenRwsWLKDY2FiZ9ZGUlEQqKio0bdo0mbUpK2/evCF1dXXq0KGD0KUILj4+nnbt2kXNmzcnsVhMYrGYmjZtSjt27KCPHz8Wa99z584lAHTkyJFi7Yex4sZhgOXp48ePtGTJEtLX1ycA1KxZMzp//nyp/Bb66NEj6tWrV/bSzMOGDaOXL18Wud0dO3YQgFK3zHN6ejo1bNiQDAwMZBp+yoOoqCjasGEDNWrUiEQiESkoKFCbNm3owIEDlJycLNO+zpw5QyKRiCZPnizTdhkTAocB9k3p6en0559/Us2aNbPfK7Bz585SuTBQeHg4/fbbb6SlpUUikYjat29P3t7ehW7PxsaGmjRpIsMKZePXX38lsVhMN2/eFLqUUu3t27e0dOlSqlOnDgHIXhHw5MmTRf77GxwcTBoaGtSyZcsys5w2Y9/CYYDlS1l6Y2JycjJt2LCBqlSpQgCofv36dODAgQI9LREUFEQA6M8//yzGSgvu/PnzJBKJaN68eUKXUqYEBQXRrFmzsp+g0dTUpL59+9KVK1cKfDFPSkqi6tWrk4mJCY/MsHKDwwArsMePH1OfPn1K/RsTs7Ky6MSJE2Rra5v9tMSKFSsoISHhu+f++uuvpK6uLviCR1+KiIggHR0dcnBwKJUTGssCqVRKDx8+pAkTJpChoSEBIF1dXRo+fDjdvn37u7fBpFIp/fDDD6SsrFwq59IwVlgcBlihlaU3Jvr5+dEPP/xAEomE1NTUaOzYsV99HC0zM5P09PRowIABJVzl12VlZVGzZs1IW1ubwsPDhS6nXJBKpeTj40PDhg0jXV1dAkBGRkY0YcKEr76DYunSpQSA9u3bV8LVMla8OAywIitLb0wMCwuj8ePHk7q6OkkkEuratSvdu3cvxzFnz54lAHT37l2BqsxtwYIFBIDOnTsndCnlUmZmJl2+fJn69OlDGhoaBIDMzc1p9uzZFBwcTEREly5dIrFYTOPGjRO4WsZkj8MAk5l/3phoZWVV6t+YmJiYSCtXriQjI6Ps9e//CTAdOnQgCwuLUvPkxK1bt0gikdCECROELkUupKWl0cmTJ6lLly6koqKSvbiRuro62dvbl8qVOhkrKhERERiTsVu3bmHhwoW4dOkStLS0MHr0aEyePBn6+vpCl5ZDVlYWjh07hiVLluDRo0cwNDTEhw8fsHTpUkybNk3o8vDx40fUrl0bBgYG8PPzg6KiotAlyZXk5GScPHkSu3btQp06dTB37lzo6ekJXRZjMsdhgBWr4OBgLFmyBAcPHgQRoVevXpgzZw4sLS2FLi0XHx8f9O3bF+Hh4Th48CB69OghaD1EhI4dO+LGjRt4/PgxzMzMBK2HMVZ+iYUugJVv1atXx+7du/Hu3TtMnToVZ8+eRa1atdC2bVvcuHEDpSmL1qhRA5GRkVBTU8O6deuELgcbN27E+fPnsWvXLg4CjLFixWGAlYiKFSti0aJFeP/+PbZs2YKXL1+iZcuW6NKlC44dOwapVCp0iZgzZw4kEgmWLFkCb29v/P3334LV8ujRI/z2228YNGgQevfuLVgdjDH5wLcJmCCkUinOnDmDpUuX4s6dOzA3N8e8efPQr18/KCgolHg9YWFhsLCwwJQpU7BgwQJUqVIFjRs3xsmTJ0u8luTkZNSrVw8KCgp49OgRVFVVS7wGxph84ZEBJgixWIyuXbvC19cXN27cQJUqVTB48GCYmppi06ZNSEtLK9F6pk6dCjU1NUyfPh0KCgqYMmUKTp8+jdevX5doHQAwYsQIhIeH4+TJkxwEGGMlgsMAE1yLFi3g5eUFf39/WFlZ4ZdffoGJiQmWL1+O5OTkYu//6dOnOHz4MGbOnAlNTU0AwOjRo6GpqYmFCxcWe/9fcnd3x8GDB7Fu3TrUqVOnRPtmjMkvvk3ASp1nz55hzpw58PT0hLq6On7++Wf89ttv0NbWLpb+XFxccP/+fYSGhkJZWTl7+6xZs7Bq1SqEhYXBwMCgWPr+UlBQEKysrNCuXTt4enpCJBIVe5+MMQZwGGClWEhICObPn4/9+/dDQUEBI0aMwMyZM1GpUiWZ9eHn5wc7Ozts27YNI0eOzLEvNjYWJiYmGDduHFatWiWzPvOSnp6Ohg0bIiEhAU+ePIGWllax9scYY1/iMMBKvYiICCxZsgQ7d+6EVCpF//79MW/ePFStWrXIbTdr1gzv3r1DcHBwnhMXR48ejf379+P9+/fZtxCKw5gxY7Bjxw74+PigcePGxdYPY4zlhecMsFLP0NAQGzduxPv37zF58mQcOXIE1apVQ+/evfHixYtCt3v16lX4+PhgxYoVX32CYdasWfj06RM2bdpU6H6+5+TJk9i6dSuWLFnCQYAxJggeGWBlTlJSEjZs2IDVq1cjLi4OLi4uWLJkCRo2bJjvNogIVlZWyMzMxOPHj795f75nz564fv063r17ByUlJRl8gn+9e/cOderUQaNGjXDlyhWeJ8AYEwSPDLAyR0NDAzNnzsS7d++wfv16PHjwANbW1nBycoKPj0++2jhx4gQCAgKwdu3a716AFyxYgOjoaOzZs0cG1f8rKysL3bp1g6KiIjw8PDgIMMYEwyMDrMzLzMzE3r17sXDhQrx58wa2trZYsmQJnJ2d87zAZmVlwdLSEnp6erhz506++mjbti2Cg4Px6tUriMWyydDTp0/HypUrcfnyZbRu3VombTLGWGHwyAAr8xQUFDBkyBC8fPkSR44cQVJSEtq3b4/69evj+PHjuZY6dnd3x6tXr7B+/fp897Fw4UKEhITgxIkTMqn56tWrWLlyJaZOncpBgDEmOB4ZYOUOEeH8+fOYM2cO/P39Ua1aNcybNw8//fQTpFIpzMzMULduXVy6dKlA7drY2CAzMxMPHz4s0pB+dHQ0ateuDTMzM/j6+kIikRS6LcYYkwUOA6xcu3nzJmbPno0bN27A2NgYTZs2xfHjx/Ho0SPUq1evQG2dPn0aXbt2xbVr1+Dk5FSoeogIbdq0gZ+fH548eYLKlSsXqh3GGJMlvk3AyrX/LnV87tw5SCQSHDp0CNHR0QVqq3PnzrCwsMDcuXMLXc+LFy9w69YtuLm5cRBgjJUaHAaYXLCxscG5c+fQuXNniMVirFy5EiYmJujfvz+ePn2arzZEIhHmzJmDmzdv4uHDh4WqQ0tLC4qKili6dCni4uIK1QZjjMka3yZgcsXf3x+2trY4ePAgnjx5gs2bNyM2NhaOjo6YPXs22rZt+835AJmZmahcuTKaNGlS6Ncb379/H05OTjAxMcGtW7egq6tb2I/DGGMywSMDTK7Y2NjA3Nwc+/btw8KFC/H+/Xvs3LkT4eHhaNeuHSwtLbFjx46vvkJZQUEBv/32G86cOYM3b94UuoYbN27g3bt3cHBwQExMTBE+EWOMFR2HASZXRCIRBg8ejEuXLiE+Ph7KysoYNmwYnj9/jsuXL6Ny5coYOXIkjIyMMHv27DznFYwdOxYaGhpFer1xw4YNcfPmTYSHh8PBwaHA8xcYY0yWOAwwuTN06FBkZGTg0KFD2dtEIhHatGmDa9eu4dmzZ+jcuTN+//13mJiYYMCAAXj27Fn2sWpqahg7diz279+PqKioQtdhZWUFb29vREZGciBgjAmK5wwwuWRvb4/MzEz4+fl99Zjo6GisX78eW7ZsyTWvIDY2FpUrV8b48ePx+++/F6mWwMBAtGjRAnp6evDx8YG+vn6R2mOMsYLiMMDk0u7duzF06FC8fv0aZmZm3zw2LS0N+/btw4oVKxAUFITq1atj2rRpuH37Njw8PBAeHg4NDY0i1fP48WO0aNECurq68PHxgYGBQZHaY4yxguDbBEwu9ezZE8rKyti5c+d3j/1yXsGlS5dgYmKCESNG4NixY0hOTsaKFSuKXE/dunXh7e2N2NhY2NvbIzIysshtMsZYfnEYYHJJQ0MDLi4ucHd3R34Hx0QiEdq2bYvr16/j6dOn6NKlC4DPb0DMzMwsck116tTBrVu3EB8fD3t7e0RERBS5TcYYyw8OA0xujR49GmFhYbh7926Bz61Vqxb27t2La9euITAwEJ6enjKpqXbt2vDx8UFiYiLs7e0RHh4uk3YZY+xbeM4Ak1tZWVmoVKkSOnfujD179hS6HSsrK6ioqOT7dcj58eLFCzRr1gyqqqrw9fWFsbGxzNpmjLH/4pEBJrckEgn69u2L48ePIz09vdDt/Pbbb7h79y6ePHkis9osLS3h4+OD1NRUNGnSBO/evZNZ24wx9l8cBphcGzVqFBITE3H27NlCt9G7d2/o6Ohg5cqVMqwMqFGjBnx8fJCWlgZ7e3u8fftWpu0zxtg/OAwwuVavXj1YWlrCzc2t0G0oKSlh2LBhOHz4MBISEmRYHVC9enXcvn0b6enpsLe3R1hYmEzbZ4wxgMMAYxg6dCiuXbuG2NjYQrcxceJEpKen5+tRxYKysLDA7du3kZmZCXt7e4SGhsq8D8aYfOMwwOTe4MGDIZVKsW/fvkK3YWJiAmdnZ2zYsCHfjyoWRLVq1eDr6wupVAp7e3uEhITIvA/GmPziMMDkXqVKldCsWbMif6ufOnUqQkJCcPnyZRlVlpOZmRl8fX0BfF5O+fXr18XSD2NM/nAYYAyfJxIGBAQgKCio0G20atUK5ubmRX5XwbeYmprC19cXYrEY9vb2ePXqVbH1xRiTHxwGGAPQvXt3qKmpYceOHYVuQyQSYeLEibh8+XKx3tevWrUqfH19oaioiKZNm+Lly5fF1hdjTD5wGGAMgKqqKrp06YK9e/dCKpUWup2hQ4dCVVUVa9eulWF1uVWpUgV37tyBkpISmjZtiuDg4PydmJr6+Ycxxr7AYYCx/xszZgwiIiLg7e1d6DY0NDTQt29f7Nq1C6nFfNE1MTGBr68vVFRUYG9vjxcvXnz94Ph44KefAE1NQEsL6NcPkPFjkIyxsovDAGP/16JFC1SqVAlbt24tUjtTpkxBQkICDh48KKPK8vb27VtcvHgR9evXx8ePH9G9e3cEBATkffDw4cDBg0BmJpCRARw4AIwcWaz1McbKDn43AWNfWLhwIQ4ePIjNmzejdevWhW7HwcEBiYmJX784F0JUVBSuXr2KM2fOwMvLK3sBIgsLCzRt2hTe3t6IiorCuXPn4Ojo+O+JCQkgXV2IsrJytJcpEmF49+7IUlVFu3btMGDAAJnVyhgrWzgMMPaF1NRUtGrVCvfu3cOOHTswZMiQQrXj6emJ7t274+7du7CzsytUG/Hx8bh+/TrOnDmDa9euZU8UrFKlClq2bInOnTujdevW0NfXzz6+bdu2ePToETZv3gyJRIILFy4g0Nsbj969yzUMmAWgSY0aSADQo0cPLFu2rFB1MsbKPg4DjP1Heno6BgwYAA8PD8yaNQuLFi2CSCQqUBtZWVkwNjaGo6Mjjhw58nljfDwwYwbg6fn5vv3YscCECdnnpKSkwNvbG2fOnMGVK1fw9OlTEBH09fXh6OiITp06wdnZGZUrV/5qv6mpqfjhhx8QFBSE169fw9zcHM2bN8fSgABU/vvvnAd37QqcPFmgz8UYK6eIMZaLVCqlGTNmEADq06cPpaenF7iN+fPnk6KiIkVFRX3e4OxMBOT4CR4/nqZNm0Y2NjakoKBAAEhbW5s6depEmzdvpuDgYJJKpQXqNywsjPr370+BgYH/boyMJOrQgUgk+vzj4kL04UOBPxNjrHzikQHGvmHnzp0YPXo07O3tce7cOWhpaeX73OjoaBgbG2PWrFmY16cPUKtWrmNeArBSV4e9vT1cXFzQoUMH1KlTp8AjEV+6f/8+GjVqBH9/f9jY2OTc+eEDIBIB/7+1wBhjAKAgdAGMlWbDhw+HqakpunXrhkaNGuHq1auoUqVKvs7V09ND165d4ebmhtnOzpDkcYxphQqIj4mBRJLX3mJgYFAy/TDGyhR+tJCx73B2dsbdu3eRmJgIa2trPHjwIN/nTp06FZGRkTgTHo5kXd1c+xW6dy+5IMAYY1/BYYCxfKhbty7+/vtvGBgYwMHBAadPn87XeY0bN0bdunUxc+5cdIyPR4yKyr87mzcHivE9Bowxll8cBhjLJ0NDQ9y7dw/NmjVDt27dsHHjxm8en5aWhrNnz0JDQwPPnz9HcsOG0IyOBm7eBB49+vy/FSuWUPWMMfZ1PGeAsQJQV1fHhQsXMHLkSPzyyy8IDg7G2rVrIRZ/ztWJiYk4c+YM9u/fj2vXriElJQXGxsZQUFBAw4YNoaSu/nlEgDHGShEeGWCsgCQSCXbu3Illy5Zh48aN6NSpEzZt2gQnJydUrFgRP/30E4KCgjBhwgQEBATg7du3GDp0KDw8PIr9fQWMMVYY/GghY0Xg4eGB8ePHIyoqCtbW1ujVqxd69+4Nc3PzHMe9ePECNWvWxO7duzF48OBirembjxYyxlgeOAwwVkSrVq3ClClTcO/ePdja2n71uMaNGyMjI6NATyMUBocBxlhBcRhgrIgyMzNRs2ZNaGlp4f79+19dMOjAgQPo168fAgMDUbdu3eIpxssLiZMnI9nfHyqOjtDetAmoX794+mKMlRscBhiTgQsXLqBDhw7Yt28f+vXrl+cx6enpqFSpErp3744//vhD9kUEBgK2tkBa2r/bdHWBZ894xUHG2DfxBELGZKB9+/Zo06YNJk2ahE+fPuV5jJKSEgYOHIjDhw8jJSVF9kVs354zCABAbCywf7/s+2KMlSscBhiTETc3N8TExGDx4sVfPWbChAlITk7Gvn37ZNbvy5cvsWbNGlw4fDjvA2JjZdYXY6x84tsEjMnQ+PHjsXPnTrx8+RImJiZ5HtOsWTMkJCQgICCgUH2kpqbi+vXrOHr0KC5evIiwsDBIJBJMMTXFslevcp9w797n2weMMfYVPDLAmAwtXboUqqqq+Pnnn796zK+//orAwMACPVXw6tUrrF27Fo6OjqhQoQJcXFxw6tQpNG/eHMeOHUNcXByWBQcD48cD/18AKRVA0pw5HAQYY9/FIwOMydiWLVswbtw43Lp1Cw4ODrn2Z2RkwNrAALOqVUNfV1egRw+gdu0cx6SmpsLLywtHjhzJ8e3f2toaP/zwA7p164a6devm/eRCaCgS/PxQs1cvTFi8GNOnTy+uj8oYKyc4DDAmY1lZWahTpw6UlJTw8OHD7KWKs928ifTWraGUmfn5d7EY2LULr1q0wMmTJ3HixAncuXMHaWlp0NfXR9u2bdGjRw84OztDS0sr33W4uLjgxYsXCA4O/urjjowxBnAYYKxYXL9+Ha1atcIff/yBoUOH5txpZwf4+eXYFCsWw0gqRdb/v/137doV3bp1Q7169Qp9IT937hzmduqE4xMmoKqjI5Jbt4a6tnYhPxFjrDzjMMBYMenYsSPu3r2LN2/eQEND4/NGqRSQSPI8/uLatbAfOrRA3/6/RTpxIsTr12f/HiAWQ8XXFzXs7GTSPmOs/OAJhIwVky1btiAhIQHz58//d6NYDFha5j5YXR3thg2TWRDA33/nCAIAUF8qhcWpU7JpnzFWrnAYYKyYmJmZYfz48diwYQNCQkL+3bFkSfaM/3+kT54MaGrKrvObN/PcLPb2ll0fjLFyg8MAY8Vo4cKF0NTUxNixY//d+OOPgI8PMHIkEl1d0VUsxsKsLJn2KzUzy3N7xle2M8bkG4cBxoqRhoYGVq5ciXPnzsHLy+vfHU2aANu2QfP4cZiMHInVq1cjMjJSJn0mJSWh48aN8P3P9gQArl5eeP/+vUz6YYyVHzyBkLFiJpVK0aBBA2RlZeHx48e5HjWMjY1F1apV0b17d7i7uxes8ZQU4MCBzy8jatwYrxo2RPtOnfD27Vsc2rEDP4SFAdevA+bmeNq+PVqOGgUAuHjxIho2bCibD8gYK/M4DDBWAm7duoXmzZvDzc0No0ePzrV/6dKlmDNnDgIDA1H7PwsQfVVCAtC8OfDFssZXJBIMqVQJ5y5cQL169XKd8v79e7Ru3RohISHw8PBAly5dCv2ZGGPlB4cBxkpIt27dcOPGDbx58ybXUwNpaWkwMzNDnTp1cOXKlfw1+PvvwNSpuTYn/vknNAcO/OppSUlJ6NatG65evYrVq1fj119/LdDnYIyVPzxngLESsnHjRiQnJ2P27Nm59ikrK2PlypW4evUqrl+/nr8G/f3z3Kz54sU3T9PQ0MCFCxcwYsQITJo0CaNHj0aWjCcwMsbKFg4DjJWQKlWqYNKkSXBzc8PLly9z7e/Xrx/q1KmDcePGQSqVfre9aCOjvHfUrfvdcyUSCbZt24Y1a9Zg+/bt6NChA5KSkr57HmOsfOIwwFgJmjNnDnR0dDBmzJhc+8RiMTZt2oQnT57gwIED32zn2LFjsNm2DSH/Wc3QVyTCNV3dfNfz66+/4sSJE/D29oadnd2/TxqEhgJRUfluhzFWtvGcAcZK2N69ezFw4EBcvHgRzs7Oufa3bt0aT58+xZs3b6CsrJxjX2ZmJiZPnowNGzagXbt2OLJtG7Q8PIAnT5BhY4N2+/fjTkAAbty4AdsCvLr477//hpOTE5pUrIjjampQDwz8vDCSqyuwe7dsF0RijJU6HAYYK2FEBGtrayQlJeHZs2dQUFDIsf/JkyeoX78+Fi9ejBkzZmRvj4yMxA8//IB79+5hwYIFmDVrVq6XGCUlJcHBwQEhISHw9fXN95MJDx48QPPmzXE7NRUN/nuLYuRIYNu2wn1YxliZwGGAMQHcu3cPjRs3xvr16/HLL7/k2j9w4EAcP34coaGh0NXVxc2bN+Hq6gqpVIqjR4+idevWX207NjYWdnZ2SExMxL1792BqavrNWh4/fgwHBwe00NPDmVevch+gqfn5MUbGWLnFYYAxgfTq1QsXLlxASEgItP/zauHIyEiYmZlh0KBBqFatGmbOnAkrKyucPn0axsbG3237/fv3sLOzg1gshp+fHypVqpTncUFBQbC3t4eOjg7uHTwIncaNcx+kqwvExBTmIzLGygieQMiYQNavX4/09HRMnz49175KlSph5MiR2L59O6ZNm4YRI0bA19c3X0EAAIyNjXHjxg2kpKTA0dERHz9+zHVMSEgImjdvDg0NDdy6dQs6dnZAixa5jnvZsmWBPxtjrGzhMMCYQIyMjDB16lTEb9+OxNatP0/WO3kSqampWLBgAbZv3w5VVVUoKSmhQ4cOUFRULFD7FhYWuH79Ot6/f482bdogJTIS8PQELl3C+7AwNGvWDBKJBLdu3fp35ODIkc91SCQgdXUcq1wZDc+ehY+PTzH8CTDGSg1ijAkmfd48IiDHz041NZKIxTRixAh6/fo1tWnThkQiEc2ZM4ekUmmB+/Dx8SEXRUVKkEiy+3ipoEA2urr06tWrvE9KSyPKzKTk5GSysbEhTU1NCgwMLNqHZYyVWjxngDGhfPoEVKoEJCbm3lW3LlRv3AB0dSGVSjFt2jSsWrUKnTp1wuHDh6Gurp7/fjIzkWpoCJX/3PdPatsWGpcufff0uLg4NG7cGB8/foS/vz+qVq2a/74ZY2UC3yZgTCjR0XkGAQBQffwYWLQIwOfFiH7//XccOnQIV65cgbW1Nd68eZO/Pk6cABwdcwUBANC4dStfTejo6MDLywsKCgpo0aIFoqOj89c3Y6zM4DDAmFBMTIBq1b6+/+LFHL/27t0bd+7cQVJSEho0aIDLly9/u/0//vh8///27bz3Gxrmu1RjY2N4eXkhISEBTk5OSPxKiGGMlU0cBhgTilgMuLkBSkp57pbm8e6BBg0aICAgAPXq1UP79u2xatUqfPVO39Kl3+7/t98KVK6lpSUuXbqEV69eoUOHDkhLSyvQ+Yyx0ovDAGNCatcOCAsDLCxybM4CMDUyMs+XB1WsWBE3btzAiBEjMGXKFPTt2zfvC3NoaN59NmwIuLsDY8cWuFxbW1ucOnUKd+/eRc+ePflth4yVEzyBkLHSICEBWLYM+OsvwMgIfo6OaLFwISwsLHDt2jXo6+vnedrOnTsxduxY1KpVC+fPn89ehyA1NRXvjI1hEReX8wRdXSAiAijgY4r/dfjwYfTt2xeDBg3Crl27ci2LzBgrWzgMMFZK+fv7w9nZGerq6rh+/Tos/jN68I87d+6gc+fOkEqlOH36NBwcHJDp44O4li2hn5mZ8+AqVb4+YlBAmzdvxvjx4zFt2jQsX75cJm0yxoTBYYCxUuzly5dwcnJCcnIyLl++DBsbmzyPi4iIgIuLC2ICAnDf2Bh6YWFfbzQqCtDTk0l9c+fOxaJFi7BmzRr8+uuvMmmTMVbyeM4AY6WYhYUF/P39YWxsjObNm+PChQt5HmdoaAhfX18c/U4QiAbQpnt3nDhxApn/HTUohAULFmDUqFGYPHky9u/fX+T2GGPC4DDAWClnYGAAX19f2NraolOnTnB3d8/zOGVlZdhFRn6zrReurgiPjoarqysMDQ0xZcoUhISEFLo2kUiELVu2oFu3bhg0aBDOnz9f6LYYY8LhMMBYGaChoYErV67A1dUVgwYN+vo9el3dvLc7OwNHj8Lh+HE8efIEfn5+cHFxwebNm2Fubo4WLVrg2LFjyMjI+Hx8cjKwcSMwcODniY3feGuhWCzG4cOH0bx5c7i6uuLOnTtF/LSMsZLGcwYYK0OICBMnTsSGDRswfvx4rF+/HmKxOHvfuRYt0Om/Kwv26AEcPZpne0lJSdi3bx82bdqEx48fQ1dXF8P698ei69eh/OjRvwdWqwbcvQtUrPjV2pKTk9GsWTO8evUKd+7cQe3atYv8eRljJYPDAGNl0IoVKzBjxgy4urri4MGDUFJSwowZM7B8+XJc6NgR7V6/BlJSgJ49gQULADW177b54MGDz+Hi4EHsSk/Ptf+miwv+atgQbdu2RevWrfNsIzY2Fra2tkhOToa/vz8qV65c5M/KGCt+HAYYK6P27duHIUOGoKWdHaZKJNDw9oZOzZqovXUr4ORU6HbTf/sNSqtX59p+UCTC2AoVMHnyZMyePfur5799+xaNGjWCmpoa/Pz8UPEbowmMsdKBwwBjZdilS5cg6tABbaXSfzdKJJ8XL3J2Llyjx49/vrXwX2vWAPl8fPDZs2do0qQJzMzM4OPjU7C3LDLGShxPIGSsDHPW0ckZBAAgKwtYubLwjf7ww+dlkr/UsCEwfHi+m6hVqxYuXbqEFy9ewMXFBel53HZgjJUeHAYYK8u+spogFWWVQYkEOHsWOHIEmDIF2LPn85sPNTUL1Ezjxo1x4sQJ3L59G71794b0v6GFMVZq8G0CxsqyDx8+LzH8n2/eJ42NYXv3LkxMTAQq7F8HDhxA//79MWzYMGzfvp3fY8DKLCLCq1ev8OzZM3Tq1EnocmRKQegCGGNFYGAAbNgAjBv3+fYAgIQqVTA1IQHvatbEunXrMGzYMEEvwD/99BM+fPiAX3/9FZUqVcLixYsFq4WxgoiIiMDdu3fh5eUFX19fBAQEIDExERKJBElJSVBRURG6RJnhkQHGyoO3b4GLFwFDQ6B9e8TGx2P48OHw9PSEk5MT9u/fn/1GQ6HMnDkTy5Ytw4YNG/Dzzz8LWgtj/5WQkIB79+7B29sbt27dwt9//42oqCgAgLa2Nho0aAAHBwc4OjrCzs4OejJ6v0dpwWGAsXLs+PHjGD58ONLT07F+/XoMHTpUsFECIsLw4cOxe/duHDhwAH369BGkDsbS0tLw8OFDeHt7w9vbG/7+/ggLCwMRQVVVFXXq1EGTJk3QsmVL2Nvbo0qVKuX+9haHAcbKudjYWAwbNgwnTpxAq1atsG/fPsFGCaRSKVxdXXHu3DmcO3cOzoV9/JGxfMrKysLz589x+/ZteHl5wc/PD0FBQcjMzISCggJq1KgBOzs7ODo6omnTpqhZsyYkEonQZZc4DgOMyYmjR49i5MiRSE9Px4YNGzBkyBBBvu2kp6ejTZs28PPzw40bN2BnZyfT9sePHw8DAwPMnTtXpu2y0o+IEBoaijt37sDLywt37tzBkydP8OnTJ4hEIlStWhU2NjZo0aIFmjVrBisrKygrKwtddqnAYYAxORITE4OhQ4fi1KlTaNOmDfbu3QsjI6MSryMpKQlNmzZFWFgY7ty5g5o1a8qk3eXLl2PGjBlYtWoVJk+eLJM2WcEREc6ePYvbt2/j9evXUFZWzv5RUVHJ9b9f/igpKWX/KCsr5/nf//z+6dMn3L9/Hzdu3ICPjw8ePXqEjx8/AgD09fVhbW0NBwcHtGjRAra2ttDS0hL2D6YU4zDAmBw6cuQIRo4ciczMTGzcuBGDBg0q8VGC6Oho2NraIjU1Fffv3y/SrQsiwv79+zFw4ECMGzcOGzdulGGlLL+kUimOHz+O2bNn4/nz57Czs0NCQgIyMjLy/MnMzPz3TZmFoKOjg7i4OGhoaKBBgwawt7dHy5Yt0bhxYxgaGsrwk5V/HAYYk1PR0dEYOnQoTp8+jbZt22Lv3r0l/g9oaGho9je2e/fuQUdHB8DnCV7R0dHZP1FRUQgPD0dERAQ+fPiADx8+ICYmBrGxsYiLi0N8fDwqV6iAicrK+LlRI4hq1QJ+/hngFyWViKysLBw4cADz5s3D69evYWdnh6VLl6JNmzbfDZlEhMzMTKSnp2f/pKWl5fnfX/6elpaG9evX4/bt25g4cSJWrVoll/f6ZYXDAGNyzsPDA6NGjRJslODJkydo3Lgx9PX1kZqaivj4eHz69CnXcQoKCtDS0oK2tjZ0dXWhp6cHPT09VKpUCcb6+ui4eDFqJSX9e4KBAeDn93lRJlYsMjIysHv3bixatAhv375FixYtsHTpUjRv3rxE+iciLF68GPPmzYOjoyM8PT2zAyUrGA4DjDFER0djyJAhOHPmDJydneHu7l6iowTOzs54+PAh+vbtC0NDQxgZGcHAwCD7gq+npwdNTc2vhxQPD6B379zbp04FVqwo3uLlUGpqKrZt24Zly5YhMjISzs7OWLp0KWxtbQWp56+//kKvXr2gra2Nv/76C/Xq1ROkjjKNGGPs/w4dOkQVKlQgTU1N+vPPP0kqlRZ7n1lZWaSjo0Pjxo0rdBtJ06YRAbl/fvhBdoUySkpKomXLlpGuri6JRCLq0qULPXr0SOiyiIgoKCiILCwsSEVFhTw8PIQup8zhFxUxxrL17t0bQUFBcHR0xKBBg9ChQwdERkYWa5+PHj1CXFwcXF1dC3zu3bt30aNHD3RftSrvAxwcilgdAz6vzjdv3jwYGxtj9uzZaNOmDZ49e4ZTp06hfv36QpcHAKhevTr+/vtvtGrVCr169cLUqVP55VgFIXQaYYyVTgcPHiQtLS3S0tIid3f3YhslmD9/PikrK1Nqamq+jk9NTaVdu3ZR3bp1CQAZGhrSokWLKLV795yjAjY2RAkJxVKzvIiJiaEpU6aQuro6KSgo0MCBA+n169dCl/VNUqmU5s6dSyKRiFq3bk0fP34UuqQygcMAY+yrPnz4QB07diQA1L59e4qMjJR5H7a2tuTo6Pjd40JDQ2nixIlUoUIFAkDNmjUjT09PyszMzD7m4Zo1NBugd2vXEqWny7xWeREREUHjx48nFRUVUlJSolGjRtHbt2+FLqtATp06RWpqalS1alV68uSJ0OWUehwGGGPfdeDAgexRgr1798pslCAxMZEUFBRo1apVee6XSqV06dIlcnZ2JpFIRGpqajR8+HB6/vx5nsefPXuWANC7d+9kUp+8CQsLo2HDhpGSkhKpqqrShAkTiiUAlpTnz5+TmZkZqaqq0vHjx4Uup1TjMMAYy5fIyEhycXEhANShQweZXCROnjxJAHJd3BMSEmjt2rVkZmZGAMjc3JzWr19PiYmJ32zv0KFDBIAS+PZAgbx69Yr69+9PCgoKpKGhQdOmTaPY2Fihy5KJhIQEcnZ2JgA0c+ZMysrKErqkUonDAGOsQPbt25c9SrB///7CNfL0KdGIERRoZEQrNTRIGhf3/81PaejQoaSqqkpisZjat29PV65cyfdIxPbt2wlAjlsH7OuePXtGPXr0ILFYTBUqVKD58+dTfHy80GXJXFZWFs2YMYMAULt27Tgs5oHDAGOswCIjI6lDhw4EgFxcXOjDhw/5P/npUyJNzRyT/WKrVaNmTZoQANLW1qbJkydTWFhYgetauXIlKSsrF/g8efP3339Tp06dSCQSUcWKFWnFihWUnJwsdFnF7ujRo6Sqqkrm5uZfvdUkr/jRQsZYgRkYGODcuXNwd3fHrVu3UL16dRw8eDB/J69bByQm5tik8+oVGoWHY8+ePYiIiMCqVatQuRBLCSckJEBVVbXA58mLu3fvom3btmjYsCH8/f2xfv16vHv3DlOnToWamprQ5RW7Hj16wN/fH1lZWbC2tsaVK1eELqnU4DDAGCsUkUiEAQMG4MWLF7C3t8dPP/2Ezp07Iyoq6tsnvnqV5+b1EyZg0KBBRXqlbHx8PIeBPNy4cQPNmzdHkyZN8OLFC+zYsQOhoaH4+eef5e4VvrVr18ajR4/Qrl076OvrC11OqcFhgDFWJJUqVcL58+fx559/4saNG6hRowYOHz789RMcHfPe3rJlkWtJTEyUi2+4+UFEuHjxImxtbdGyZUuEh4dj3759eP36NYYPHw5FRUWhSxRMhQoV4OnpiQYNGghdSqnBYYAxVmQikQgDBw7Eixcv0LhxY/Tp0wddunRBdHR07oMnTADs7HJva9SoyHUkJiZCXV29aI2EhwMfPhS5FqEQEU6ePIkGDRqgffv2SElJwbFjxxAUFIR+/frxm/1YnjgMMMZkxtDQEBcuXMCePXvg5eWF6tWr5x4l0NQEbt/Gqw0bMBFAoLv753kEMlCkMPD2LeDkBBgbA4aGQNeuQFycTOoqCVKpFIcOHUKtWrXQrVs3KCgo4OzZs3j8+DG6d+8OsZj/uWdfx387GGMyJRKJMGjQILx48QJ2dnbo06cPunbtmnOUQCIBOnXCegCRxsYy6zs5OTnvMJCYCOzeDaxdCwQH531yv36Al9fn/yYCTp8Gxo2TWW3FJTMzE7t374aFhQX69u0LXV1dXL16Fffv30fHjh1L9HXUrOziMMAYKxaGhoa4ePEidu/ejWvXrqFGjRo4cuRI9n5NTU0AQFJSksz6TEpKym4327NnQI0awNChwKRJQM2awPbt2bszMjLw4to14MaNXO1leXjgoZ8fPn36JLMaZSU9PR1bt26Fqakphg4dCjMzM/j4+OD27dto1aoVhwBWIApCF8AYK79EIhEGDx6M9u3bY8CAAejVqxe6du2KXbt2QUNDA8DnJwBkJSUlJbvdbNOmAV++eVEqRdr48eh/7BjuBwcjNDQUFTMz8R65vx2lZ2WhkZ0dpCIRDA0NUb16ddSpUwfW1taoX78+atWqBV1dXZnVnx+fPn2Cm5sbVqxYgQ8fPqB9+/Y4ffo0bGxsSrQOVr6IiIiELoIxVv4REfbs2YNffvkFioqK2LZtG/r06YN169bh559//ua5GRkZiI2NRUxMTPbPhw8fEBkZiQ8fPuDDhw+IiYnBnTt3oKioCA0NDaSmpiItLQ0vExKgl0ebwy0skNa0KWxsbGBtbY2mS5ZA+fLlHMekDRyI+6NHIzAwEPfv38eTJ08QFBSEiIgI/PNPp7a2NqpVq4bBgwdjxIgRUFFRkdUfWQ5JSUnYsGEDVq9ejbi4OHTt2hWLFy9GvXr1iqU/Jl84DDDGSlR4eDj69++Pq1evQiKRoHPnzmjevDkiIyMRHR2NqKgoxMbGIjY2FvHx8YiPj//qML26ujq0tLSgra0NXV1dVK5cGQoKClBXV4eGhgY0NDQwevduGIWF5TiPxGKIXr8Gqlb9d2NsLDBmDHD8OKCgAPTvD6xfD+TxqOKnT58QFBSEx48f48GDB3j48CHu3buHCRMmYN68eTL984qPj8eqVauwYcMGJCcno2fPnli0aBGqV68u036YfOMwwBgrcUSE3bt3Y/78+QgLC4OioiK0tLRQoUIF6OjoQFdXF3p6etDT04OBgQEMDQ1hYGCAihUrZv/o6OhAQSEfdzrPngV++AHIyvp32/DhwI4deR+flgaIxUABn8Pv168fzp07h/fv38tk4aPo6GgsX74cW7duRXp6Ovr164cFCxag6pcBhjEZ4TDAGBNMZmYm0tLSoKamVrwT3m7fBtzcPj8q2K0bMGTI5wu+DAUHB8PS0hJr1qzBxIkTC91OREQEFi1ahF27doGIMHToUMyZMwdGRkayK5ax/+AwwBhjMtKlSxf4+fkhNDS0wCv8vXv3DnPmzMG+ffugqKiI0aNHY8aMGdDTy2vGA2OyxY8WMsaYjCxatAgRERHYu3dvvs+JjIzEqFGjYGZmhsOHD2PixIl49+4dVq9ezUGAlRgOA4wxJiMNGzaEo6MjFi1aBKlU+s1jY2NjMXHiRJiammLfvn0YNWoUPn36hOrVq0NbW7tkCmbs//g2AWOMyZC3tzdatGiBY8eOoXv37rn2JyQkYPny5Vi/fj2ysrIwZswYzJkzB7q6umjatClSU1Px4MEDASpn8ozDAGOMyZiNjQ3S09MREBCQPTEyJSUFa9aswe+//45Pnz5hyJAhWLhwISpVqpR93r59+zBgwAA8f/4clpaWQpXP5BDfJmCMMRlbuHAhHj9+jCtXriAtLQ2rV69GlSpVMH/+fHTt2hWvXr3Ctm3bcgQBAPjxxx+hrq6OLVu2CFQ5k1c8MsAYYzJGRKhVqxYyMzORlJSEqKgouLq64vfff0e1atW+ee7gwYNx5swZREZG8uuGWYnhkQHGGJMxkUiERYsWISYmBtWqVUNgYCCOHTv23SAAAOPHj0dMTAzOnz9fApUy9hmPDDDGWDGQSqWoVasWNDQ04O/vn+9FlYgINWrUQPXq1TkQsBLDIwOMMVYMxGIx1q1bhwcPHuDkyZP5Pk8kEmHEiBG4fPkyoqOji7FCxv7FIwOMMVZMiAhNmzbFhw8fEBQUlO85AFFRUTAyMsKyZcswZcqUYq6SMR4ZYIyxYiMSibBhwwa8fv0au3btyvd5+vr6aN26NXZ87WVKjMkYjwwwxlgx69SpE+7du4eQkJB8v9Hw5MmT6NatG/z9/WFjY1PMFTJ5xyMDjDFWzNatW4eYmBisWbMm3+d06tQJurq62Lx5czFWxthnHAYYY6yY1ahRA/3798fy5cvx8ePHfJ2joKCAfv36wcPDA6mpqcVbIJN7HAYYY6wErFixAhkZGZg3b16+zxk3bhySkpLg6elZjJUxxnMGGGOsxPz222/YuHEjXr16BRMTk3ydY2VlBU1NTXh7exdzdUye8cgAY4yVkLlz50JFRQVTp07N9zmjR4+Gj48P3r59W4yVMXnHYYAxxkqIlpYWZs6ciYMHD+Lp06f5Oqdfv35QUlLC9u3bi7k6Js/4NgFjjJWgtLQ0mJqaokGDBrh48WK+zunRowfu3LmDsLCwfC9rzFhB8MgAY4yVIGVlZSxbtgyXLl3C7du383XOzz//jHfv3uHmzZvFXB2TVzwywBhjJUwqlaJmzZrQ0tKCn5/fd7/tExEqV64MBwcHHDlypISqZPKERwYYY6yEicVirF27Fvfv38fp06e/e7xIJMKQIUNw+vRpJCYmlkCFTN7wyABjjAmAiNCkSRPExsbi+fPn332JUVhYGExNTeHm5oZRo0aVUJVMXnAYYIwxgfj6+qJp06bYuXMnhg0b9t3jmzVrhuTkZPz999/FXxyTKxwGGGNMQB06dMCDBw8QEhICFRWVbx67f/9+9O/fH8+fP4elpWUJVcjkAc8ZYIwxAa1btw7R0dFYu3btd4/t0aMH1NTUsGnTphKojMkTDgOMMfYdUqkUGRkZxdJ2rVq18NNPP2HZsmWIj4//6nFpaWlwd3eHkpISDh06hC5duuD8+fPIysoqlrqYfOEwwBhj3+Ho6IiOHTsWW/srVqxAWloaFixYkGtffHw8Fi5cCGNjY4waNQrW1tbo06cPAgIC4OLiAiMjI0yePBnBwcHFVh8r/zgMMMbYt1y9iuVBQVh+9y6wdi1QDCMExsbGGDduHDZv3oz3798DAN6+fYtx48bByMgIixYtQtu2bfHkyRNcvXoVGzZswOvXr3H79m20a9cO27ZtQ40aNWBjY4Pt27fz44eswHgCIWOMfc3Jk4CrK/DlP5N9+gAHD8q8q/j4eFSpUgUtW7aEsrIyTpw4ARUVFQwdOhTTp0+HsbHxV8/99OkTjhw5Ajc3N9y5cwfKysro1KkTxo8fj5YtW/ISxuy7OAwwxtjXNG4M3LuXe3tQEFC9usy7W7FiBZYsWQKJRIKpU6di3Lhx0NLSKlAboaGh2LZtG/bs2YP379/D2NgYgwcPxqhRo1C1alWZ18zKBw4DjDH2NZUqAR8+5N5++TLQpo3Mu0tLS4OZmRksLCzg7e1dpLaICDdu3MCmTZtw5swZpKWloUmTJhg9ejR69eoFVVVVGVXNygOeM8AYY1/j5JRrU4ayMmBnVyzdKSsrY8OGDbh16xbOnDlTpLZEIhFatmyJI0eOICoqClu3bkVaWhoGDx4MPT099OvXD7dv3wZ/H2QAjwwwxtjXvX4NtGoFhIQAADJFIgwnQrPt2zFixIhi6ZKIYG9vj8jISAQFBUFRUVGm7QcHB8PNzQ179+5FVFQUTE1NMWzYMAwfPhxGRkYy7YuVHTwywBhjX2NuDjx/Dhw5AmzbBvGbN8j46SeMGjUKW7duLZYuRSIRtm/fjtDQ0HwtRFRQ1atXx+rVqxEeHo7z58+jfv36WLRoEUxMTODo6IjDhw8jLS1N5v2y0o1HBhhjrACkUikGDRqE/fv3Y/PmzRgzZkyx9NO/f3+cOHECISEhqFixYrH08Y+PHz/C3d0d27dvx+PHj6Gjo4N3797xvAI5wmGAMcYK6P379+jSpQsePXqE2rVro3v37mjbti1sbW2/+36B/Prw4QPMzc3Ru3dv7Nq1SyZt5sfjx4/h5eWFsWPHllifTHgcBhhjLB9SU1Ph6ekJNzc33Lp1CxKJBBoaGkhOToZYLEZqaioUFBRQt25dtGjRAu3atUOzZs2gq6tb6D6XzJsHLFyIKcbGUFJRAQYOBGbNAhQUZPjJGOMwwBhjX0VE8PX1xZYtW+Dp6Ynk5GTUr18fw4cPx4ABA/D27VtYWVlh5cqVaNOmDa5evYorV67gzp07iI2NBQCYm5ujadOmaNeuHVq2bAlTU9N8LQJ0+fJlfHB1xU9JSTl3TJz4eSVExmSIwwBjjP3H27dvsWPHDuzZswehoaHQ19dHv379MHr0aNSsWTPHsT179sTly5cRFhYGDQ0NAJ9DxJs3b3D9+nVcunQJt2/fxps3bwAAenp6aNy4Mdq0aYNWrVqhQYMGEIvFSExMxMePH/H8+XPMmzcPAbdvIxqA8n+LU1MDYmMB5Vx7GCs0DgOMMQYgJSUFR44cwdatW3Hnzh0oKirCxcUFY8eORZs2bSCRSPI8LyQkBNWrV8fUqVOxZMmSr7YfExMDb29vXLp0Cd7e3qgQGIjFWVmwFYnwiAizAFz54ngDAJFfayw+HijgyoSMfQuHAcaYXAsODsasWbNw+vRpfPr0CTY2Nhg5ciT69u2b76WAx4wZgz///BMhISHQ19f//gkhIaA6dSBKScnelAbAGkDNbt0wa9YsVKlSBfqdO0Ps55fzXCcn4Nq1fH8+xvKD1xlgjMm1mJgY3Lx5Ez///DNevnwJf39/jBo1qkDvBFi8eDFEIhFmzZqVvxPc3XMEAeDz7QCvQYPg6ekJW1tbVKpUCWJ39xzvQHgK4OagQfmui7H84pEBxpjcI6Iiv9lvzpw5WL58OYKCgmBmZvbtg6dOBX7/Pff2UaOA/y5mJJUCvr4gsRh248YhIjISwcHBMnuEkTGARwYYY0wmr/idPn06NDU1MXny5G8el5aWhhXBwXnv7NEj9zaxGHBwgMjeHnv37UNERARmz55d5HoZ+xKHAcYYkwF1dXXMnz8fnp6eePjwYZ7HPHv2DFZWVph16hQud+wIUlf/vENFBViwAHB2/mYftWvXxqRJk7Bu3To8fvxY1h+ByTG+TcAYYzKSkZGBatWqoVq1avDy8sreTkTYunUrfv31VxgYGMDT0xONGjX6/FTAs2dAjRpAPhcnSk1NhaWlJfT09ODn5wexmL/TsaLjv0WMMSYjioqKWLFiBW7cuIHr168D+Lzuf5cuXTB27Fi4urriyZMnn4MAAFSoADRpku8gAAAqKirYtWsXHjx4UGwvS2Lyh0cGGGNMhqRSKerVqwcFBQVs2LABvXv3RlJSErZv345+/frJrJ9evXrh3LlzePnyJSpVqiSzdpl84jDAGGMyduHCBXTo0AEAYG1tDU9PT5iamsq0j+joaFhYWMDJyQknT56UadtM/nAYYIyxYnDy5Ek8fvwYU6dOhUIxvVjojz/+wPDhw3H27Fl07NixWPpg8oHDAGOMlVFEhKZNmyI0NBTBwcFQU1MTuiRWRvEEQsYYK6NEIhHc3d0RHR2NGTNmCF0OK8M4DDDGWBlmaWmJqVOnYvPmzXj06JHQ5bAyim8TMMZYGZeeno6aNWtCU1MTf//9N689wAqM/8YwxlgZp6SkhD179iAgIAAbN24UuhxWBvHIAGOMlRM//fQTTp48ieDgYBgZGQldDitDOAwwxlg5ERsbCwsLCzg4OODs2bNCl8PKEL5NwBhj5YSuri7WrVuHc+fO4dSpU0KXw8oQHhlgjLFyhIjQokULBAcH4+XLl1D/582IjH0Djwwwxlg5IhKJ8Oeff+Ljx484duyY0OWwMoJHBhhjrBx6+/YtKleuLHQZrIzgMMAYY4zJOb5NwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnOMwwBhjjMk5DgOMMcaYnPsfuOtWVmnP2yoAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Identify junctions with zero demand\n",
     "zero_demand = AED[AED == 0].index\n",
     "print(zero_demand)\n",
-    "ax = wntr.graphics.plot_network(wn, node_attribute=list(zero_demand))"
+    "ax = wntr.graphics.plot_network(wn, node_attribute=list(zero_demand), title='Zero demand junctions')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Demands: [<TimeSeries: base_value=6.30901964e-05, pattern_name='3', category='None'>]>"
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get the demands on Junction 15\n",
     "junction = wn.get_node('15')\n",
@@ -911,22 +378,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Pattern '3', multipliers=array([620., 620., 620., 620., 620., 360., 360.,   0.,   0.,   0.,   0., 360., 360., 360., 360., 360.,   0.,   0.,   0.,   0.,   0.,   0., 360., 360.])>"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get the pattern associated with the demand\n",
     "pattern = wn.get_pattern(junction.demand_timeseries_list[0].pattern_name)\n",
@@ -935,83 +391,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<Demands: [<TimeSeries: base_value=0.005, pattern_name='New', category='None'>]>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Modify the base value of the demand\n",
     "junction.demand_timeseries_list[0].base_value = 0.005\n",
     "\n",
-    "# Add a pattern\n",
+    "# Add a new pattern to the model\n",
     "wn.add_pattern('New', [1,1,1,0,0,0,1,0,0.5,0.5,0.5,1])\n",
     "\n",
-    "# Modify the pattern of the demand\n",
+    "# Use the new pattern to modify the junction demand\n",
     "junction.demand_timeseries_list[0].pattern_name = \"New\"\n",
     "print(junction.demand_timeseries_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<Demands: [<TimeSeries: base_value=0.005, pattern_name='New', category='None'>, <TimeSeries: base_value=0.015, pattern_name='1', category='None'>]>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Add a demand\n",
+    "# Add a demand to Junction 15\n",
     "junction.add_demand(base=0.015, pattern_name='1')\n",
     "print(junction.demand_timeseries_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: >"
-      ]
-     },
-     "execution_count": 220,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot original and modified expected demands\n",
     "new_expected_demand = wntr.metrics.expected_demand(wn) \n",
     "\n",
     "plt.figure()\n",
     "ax = expected_demand.loc[0:48*3600, \"15\"].plot()\n",
-    "new_expected_demand.loc[0:48*3600, \"15\"].plot(ax=ax)"
+    "new_expected_demand.loc[0:48*3600, \"15\"].plot(ax=ax)\n",
+    "tmp = ax.set_xlabel('Time (s)')\n",
+    "tmp = ax.set_ylabel('Expected demand (m$^3$/s)')"
    ]
   },
   {
@@ -1026,29 +447,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'wntr.network.elements.HeadPump'>\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get a head pump object and plot the head pump curve\n",
     "pump = wn.get_link('10')\n",
@@ -1058,22 +461,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(0.0, 31.6992), (0.1261803928, 28.041600000000003), (0.2523607856, 19.2024)]"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get the head curve and print the points\n",
     "pump_curve_name = pump.pump_curve_name\n",
@@ -1083,46 +475,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Modify the curve points and replot the pump curve\n",
+    "# Modify the curve points and re-plot the pump curve\n",
     "curve.points = [(0.10, 20)]\n",
     "ax = wntr.graphics.plot_pump_curve(pump)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Add a tank volume curve to the model and assign it to a tank\n",
     "wn.add_curve('new_tank_curve', 'VOLUME', [\n",
@@ -1150,44 +520,16 @@
    "source": [
     "## Controls\n",
     "\n",
-    "Controls define conditions and actions that operate pipes, pumps, and valves.  WNTR includes support for EPANET controls and rules (both are stored as WNTR controls). Following EPANET, controls are evaluated after each simulation timestep, while rules are evaluated after each rule timestep (see `wn.options.time`). The method `convert_controls_to_rules` can be used to convert controls to rules, which can help avoid unintended behavior when controls and rules are both used in complex simulations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['control 1',\n",
-       " 'control 2',\n",
-       " 'control 3',\n",
-       " 'control 4',\n",
-       " 'control 5',\n",
-       " 'control 6',\n",
-       " 'control 7',\n",
-       " 'control 8',\n",
-       " 'control 9',\n",
-       " 'control 10',\n",
-       " 'control 11',\n",
-       " 'control 12',\n",
-       " 'control 13',\n",
-       " 'control 14',\n",
-       " 'control 15',\n",
-       " 'control 16',\n",
-       " 'control 17',\n",
-       " 'control 18']"
-      ]
-     },
-     "execution_count": 225,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+    "Controls define conditions and actions that operate pipes, pumps, and valves.  WNTR includes support for EPANET controls and rules (note that both are stored as WNTR controls). As with EPANET, controls are evaluated after each simulation timestep, while rules are evaluated after each rule timestep (see `wn.options.time`). The method `convert_controls_to_rules` can be used to convert controls to rules, which can help avoid unintended behavior when controls and rules are both used in complex simulations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "# Get a list of control names\n",
     "wn.control_name_list"
@@ -1195,19 +537,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print all controls\n",
+    "for name, controls in wn.controls():\n",
+    "    print(name, controls)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IF TANK 1 LEVEL ABOVE 5.821680000000001 THEN PIPE 330 STATUS IS OPEN PRIORITY 3\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get a specific control object\n",
     "control = wn.get_control('control 18')\n",
@@ -1216,19 +561,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "IF TANK 1 LEVEL ABOVE 5.821680000000001 THEN PIPE 330 STATUS IS OPEN PRIORITY 1\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Modify the control priority\n",
     "control.update_priority(1)\n",
@@ -1237,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -1248,383 +585,397 @@
     "action = wntr.network.controls.ControlAction(pump, 'status', 1)\n",
     "condition = wntr.network.controls.SimTimeCondition(wn, '=', '121:00:00')\n",
     "control = wntr.network.controls.Control(condition, action, name='new_control')\n",
-    "wn.add_control('NewControl', control)"
+    "wn.add_control('NewControl', control)\n",
+    "print(wn.control_name_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
     "# Remove a control\n",
-    "wn.remove_control('NewControl')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "control_1_Rule IF SYSTEM TIME IS 01:00:00 THEN PUMP 10 STATUS IS OPEN PRIORITY 3\n",
-      "control_2_Rule IF SYSTEM TIME IS 15:00:00 THEN PUMP 10 STATUS IS CLOSED PRIORITY 3\n",
-      "control_3_Rule IF SYSTEM TIME IS 25:00:00 THEN PUMP 10 STATUS IS OPEN PRIORITY 3\n",
-      "control_4_Rule IF SYSTEM TIME IS 39:00:00 THEN PUMP 10 STATUS IS CLOSED PRIORITY 3\n",
-      "control_5_Rule IF SYSTEM TIME IS 49:00:00 THEN PUMP 10 STATUS IS OPEN PRIORITY 3\n",
-      "control_6_Rule IF SYSTEM TIME IS 63:00:00 THEN PUMP 10 STATUS IS CLOSED PRIORITY 3\n",
-      "control_7_Rule IF SYSTEM TIME IS 73:00:00 THEN PUMP 10 STATUS IS OPEN PRIORITY 3\n",
-      "control_8_Rule IF SYSTEM TIME IS 87:00:00 THEN PUMP 10 STATUS IS CLOSED PRIORITY 3\n",
-      "control_9_Rule IF SYSTEM TIME IS 97:00:00 THEN PUMP 10 STATUS IS OPEN PRIORITY 3\n",
-      "control_10_Rule IF SYSTEM TIME IS 111:00:00 THEN PUMP 10 STATUS IS CLOSED PRIORITY 3\n",
-      "control_11_Rule IF SYSTEM TIME IS 121:00:00 THEN PUMP 10 STATUS IS OPEN PRIORITY 3\n",
-      "control_12_Rule IF SYSTEM TIME IS 135:00:00 THEN PUMP 10 STATUS IS CLOSED PRIORITY 3\n",
-      "control_13_Rule IF SYSTEM TIME IS 145:00:00 THEN PUMP 10 STATUS IS OPEN PRIORITY 3\n",
-      "control_14_Rule IF SYSTEM TIME IS 159:00:00 THEN PUMP 10 STATUS IS CLOSED PRIORITY 3\n",
-      "control_15_Rule IF TANK 1 LEVEL BELOW 5.21208 THEN PUMP 335 STATUS IS OPEN PRIORITY 3\n",
-      "control_16_Rule IF TANK 1 LEVEL ABOVE 5.821680000000001 THEN PUMP 335 STATUS IS CLOSED PRIORITY 3\n",
-      "control_17_Rule IF TANK 1 LEVEL BELOW 5.21208 THEN PIPE 330 STATUS IS CLOSED PRIORITY 3\n",
-      "control_18_Rule IF TANK 1 LEVEL ABOVE 5.821680000000001 THEN PIPE 330 STATUS IS OPEN PRIORITY 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Convert controls to rules.  \n",
-    "wn.convert_controls_to_rules()\n",
-    "for name, controls in wn.controls():\n",
-    "    print(name, controls)"
+    "wn.remove_control('NewControl')\n",
+    "print(wn.control_name_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert controls to rules.  This can help avoid unintended behavior when controls and rules are both used in complex simulations.\n",
+    "wn.convert_controls_to_rules()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Model I/O\n",
-    "Water network models can be converted to/from dictionaries, NetworkX graphs, Geopandas GeoDataFrames, and EPANET INP files, JSON files, and GeoJSON files"
+    "## Queries\n",
+    "Queries return attributes of nodes and links.  Comparison operations (like >, =) can be used to return a subset of attributes that meet specific criteria."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Convert the WaterNetworkModel to/from a dictionary\n",
-    "wn_dict = wn.to_dict()\n",
-    "wn2 = wntr.network.from_dict(wn_dict)"
+    "# Return all pipe diameters (no comparison operator used in the query) \n",
+    "all_pipe_diameters = wn.query_link_attribute('diameter')\n",
+    "all_pipe_diameters.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Return pipes diameters > 12 inches\n",
+    "large_pipe_diameters = wn.query_link_attribute('diameter', np.greater, 12*0.0254)\n",
+    "print(\"Number of pipes:\", len(all_pipe_diameters))\n",
+    "print(\"Number of pipes > 12 inches:\", len(large_pipe_diameters))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot pipes diameters > 12 inches\n",
+    "ax = wntr.graphics.plot_network(wn, link_attribute=large_pipe_diameters, node_size=0, link_width=2, title=\"Pipes with diameter > 12 inches\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {
     "tags": []
    },
-   "outputs": [],
    "source": [
-    "# Convert the WaterNetworkModel to a graph\n",
-    "G = wntr.network.to_graph(wn)"
+    "## Coordinates\n",
+    "Node coordinates can be obtained using a node query.  Node coordinates can also be modified using functions in `wntr.morph`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Convert the WaterNetworkModel to a collection of GeoDataFrames\n",
-    "wn_gis = wntr.network.to_gis(wn)\n",
-    "wn2 = wntr.network.from_gis(wn_gis)"
+    "# Get node coordinates\n",
+    "coords = wn.query_node_attribute('coordinates')\n",
+    "coords"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Create an INP file from the WaterNetworkModel\n",
-    "wntr.network.io.write_inpfile(wn, 'Net3_modified.inp', units='LPS')\n",
-    "wn2 = wntr.network.read_inpfile('Net3_modified.inp') "
+    "# Rotate node coordinates counterclockwise by 30 degrees\n",
+    "wn_rotated = wntr.morph.rotate_node_coordinates(wn, 30)\n",
+    "ax = wntr.graphics.plot_network(wn_rotated)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 235,
+   "cell_type": "markdown",
    "metadata": {
     "tags": []
    },
+   "source": [
+    "## Loops and generators\n",
+    "Loops and generators are commonly used to modify network components or run stochastic simulations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Create a JSON file from the WaterNetworkModel\n",
-    "wntr.network.write_json(wn, 'Net3.json')\n",
-    "wn2 = wntr.network.read_json('Net3.json')"
+    "# Loop over tank names and objects with a generator\n",
+    "for name, tank in wn.tanks():\n",
+    "    print(\"Max level for tank\", name, \"=\", tank.max_level)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loop over tank names and then get the associated tank object\n",
+    "for name in wn.tank_name_list:\n",
+    "    tank = wn.get_node(name)\n",
+    "    print(\"Max level for tank\", name, \"=\", tank.max_level)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pipe breaks and leaks\n",
+    "Pipes can be split (adding 1 junction to the model) or broken (adding two junctions to the model) using the `split_pipe` and `break_pipe` functions.  While a split pipe retains the network connectivity, a broken pipe does not connect across the break.  By default these functions return a copy of the WaterNetworkModel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split pipe 123 and add a leak to the new node which starts at hour 2 and ends at hour 12\n",
+    "wn = wntr.morph.split_pipe(wn, pipe_name_to_split='123', new_pipe_name='123_B', new_junction_name='123_node')\n",
+    "leak_node = wn.get_node('123_node')\n",
+    "leak_node.add_leak(wn, area=0.05, start_time=2*3600, end_time=12*3600)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Create GeoJSON files from the WaterNetworkModel\n",
-    "wntr.network.write_geojson(wn, 'Net3')\n",
-    "geojson_files = {'junctions': 'Net3_junctions.geojson',\n",
-    "                 'tanks': 'Net3_tanks.geojson',\n",
-    "                 'reservoirs': 'Net3_reservoirs.geojson',\n",
-    "                 'pipes': 'Net3_pipes.geojson',\n",
-    "                 'pumps': 'Net3_pumps.geojson'}\n",
-    "wn2 = wntr.network.read_geojson(geojson_files)"
+    "# Break pipe 121\n",
+    "wn = wntr.morph.break_pipe(wn, pipe_name_to_split='121', new_pipe_name='121_B', \n",
+    "                           new_junction_name_old_pipe='121_node', new_junction_name_new_pipe='121B_node')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Queries\n",
-    "Query methods return attributes of nodes and links.  Logical operations (like >, =) can be used to return a subset of attributes that meet specific criteria."
+    "# Model I/O\n",
+    "A `WaterNetworkModel` can be converted to and from the following data formats and file types: \n",
+    "* EPANET INP file\n",
+    "* dictionary\n",
+    "* JSON file\n",
+    "* NetworkX graph\n",
+    "* Geopandas GeoDataFrame\n",
+    "* GeoJSON and Shapefile file"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20     2.5146\n",
-       "40     2.5146\n",
-       "50     2.5146\n",
-       "60     0.6096\n",
-       "101    0.4572\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 237,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Return all pipe diameters\n",
-    "all_pipe_diameters = wn.query_link_attribute('diameter')\n",
-    "all_pipe_diameters.head()"
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
+    "wn = wntr.network.WaterNetworkModel('../networks/Net3.inp')"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 238,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20     2.5146\n",
-       "40     2.5146\n",
-       "50     2.5146\n",
-       "60     0.6096\n",
-       "101    0.4572\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "# Return pipes diameters > 12 inches\n",
-    "large_pipe_diameters = wn.query_link_attribute('diameter', np.greater, 12*0.0254)\n",
-    "large_pipe_diameters.head()"
+    "## EPANET INP files\n",
+    "WaterNetworkModel objects are commonly built from EPANET INP files.  WaterNetworkModel objects can also be saved as an EPANET INP file. Note that model attributes that are not EPANET compatible will not be saved in the INP file (i.e., leak attributes)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Plot large pipes\n",
-    "ax = wntr.graphics.plot_network(wn, link_attribute=large_pipe_diameters, node_size=0, link_width=2, title=\"Pipes with diameter > 12 inches\")"
+    "# Create an EPANET INP file from a WaterNetworkModel\n",
+    "wntr.network.write_inpfile(wn, 'Net3_LPS.inp', units='LPS')\n",
+    "\n",
+    "# Create a WaterNetworkModel from an EPANET INP file.  Note, this is equivalent to running `wn = wntr.network.WaterNetworkModel('Net3_LPS.inp')`\n",
+    "wn2 = wntr.network.read_inpfile('Net3_LPS.inp') "
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
-    "## Coordinates\n",
-    "Node coordinates can be obtained using a node query.  Node coordinates can also be modified using functions in `wntr.morph`"
+    "## Dictionaries\n",
+    "Dictionaries offer a convenient Python format to store all the information in a WaterNetworkModel object. The dictionary can be saved to a file (typically a JSON file) to save the model. Unlike an EPANET INP file, dictionaries can contain custom model attributes."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10         (9.0, 27.85)\n",
-       "15       (38.68, 23.76)\n",
-       "20       (29.44, 26.91)\n",
-       "35       (25.46, 10.52)\n",
-       "40        (27.02, 9.81)\n",
-       "              ...      \n",
-       "River    (24.15, 31.06)\n",
-       "Lake       (8.0, 27.53)\n",
-       "1         (27.46, 9.84)\n",
-       "2         (32.99, 3.45)\n",
-       "3        (29.41, 27.27)\n",
-       "Length: 97, dtype: object"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# Get node coordinates\n",
-    "coords = wn.query_node_attribute('coordinates')\n",
-    "coords"
+    "# Convert the WaterNetworkModel to a dictionary\n",
+    "wn_dict = wn.to_dict()\n",
+    "print(wn_dict.keys())\n",
+    "\n",
+    "# Create a WaterNetworkModel from a dictionary\n",
+    "wn2 = wntr.network.from_dict(wn_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## JSON files\n",
+    "JSON files can be created directly from a WaterNetworkModel object and hold the same information as the dictionary representation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "# Rotate node coordinates counterclockwise by 30 degrees\n",
-    "wn_rotated = wntr.morph.rotate_node_coordinates(wn, 30)\n",
-    "ax = wntr.graphics.plot_network(wn_rotated)"
+    "# Create a JSON file from the WaterNetworkModel\n",
+    "wntr.network.write_json(wn, 'Net3.json')\n",
+    "\n",
+    "# Create a WaterNetworkModel from a JSON file\n",
+    "wn2 = wntr.network.read_json('Net3.json')"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "source": [
-    "## Loops and generators\n",
-    "Loops and generators are commonly used to modify network components or run stochastic simulations"
+    "## NetworkX graphs\n",
+    "Graphs facilitate topographic analysis using NetworkX. WaterNetworkModel objects are represented as a MultiDiGraph, which can have multiple edges between nodes and are directed (from start node to end node). Note that WaterNetworkModel objects cannot currently be created from NetworkX graphs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max level for tank 1 = 10\n",
-      "Max level for tank 2 = 12.28344\n",
-      "Max level for tank 3 = 10.820400000000001\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Loop over tank names and objects with a generator\n",
-    "for name, tank in wn.tanks():\n",
-    "    print(\"Max level for tank\", name, \"=\", tank.max_level)"
+    "# Convert the WaterNetworkModel to a MultiDiGraph\n",
+    "G = wntr.network.to_graph(wn)\n",
+    "print(G)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GeoPandas GeoDataFrames\n",
+    "GeoDataFrames store network attributes and geospatial geometry of junctions, tanks, reservoirs, pipes, pumps, and valves.  GeoDataFrames can be used in geospatial analysis, such as spap and intersection with other geospatial data. Note that WaterNetworkModels created from a collection of GeoDataFrames will not contain patterns, curves, rules, controls, or sources. The GeoDataFrames can be saved to file (typically GeoJSON or Shapefile) and loaded into GIS software platforms for further analysis."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max level for tank 1 = 10\n",
-      "Max level for tank 2 = 12.28344\n",
-      "Max level for tank 3 = 10.820400000000001\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Loop over tank names and then get the associated tank object\n",
-    "for name in wn.tank_name_list:\n",
-    "    tank = wn.get_node(name)\n",
-    "    print(\"Max level for tank\", name, \"=\", tank.max_level)"
+    "# Convert the WaterNetworkModel to a collection of GeoDataFrames\n",
+    "wn_gis = wntr.network.to_gis(wn)\n",
+    "print(wn_gis.junctions.head())\n",
+    "#print(wn_gis.tanks.head())\n",
+    "#print(wn_gis.reservoirs.head())\n",
+    "#print(wn_gis.pipes.head())\n",
+    "#print(wn_gis.pumps.head())\n",
+    "#print(wn_gis.valves.head())\n",
+    "\n",
+    "# Create a WaterNetworkModel from a collection of GeoDataFrames\n",
+    "wn2 = wntr.network.from_gis(wn_gis)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Pipe breaks and leaks\n",
-    "Pipes can be split to add a node, or broken into two none conntected segments using the `split_pipe` and `break_pipe` functions.  By default these functions return a copy of the network model."
+    "## GeoJSON files and ESRI Shapefile files\n",
+    "GeoJSON and ESRI Shapefile files can be created directly from a WaterNetworkModel object.  The files can be loaded into GIS software platforms for further analysis. Note that column names longer than 10 characters will be truncated when saved to Shapefile. \n",
+    "\n",
+    "WaterNetworkModels can also be created from GeoJSON files or Shapefiles. A specific set of column names are required to define junctions, tanks, reservoirs, pipes, pumps, and valves (see the use of `valid_gis_names` below). Model attributes including controls, patterns, curves, and options need to be added separately."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Split pipe 123 and add a leak to the new node\n",
-    "wn = wntr.morph.split_pipe(wn, '123', '123_B', '123_node')\n",
-    "leak_node = wn.get_node('123_node')\n",
-    "leak_node.add_leak(wn, area=0.05, start_time=2*3600, end_time=12*3600)"
+    "# Create GeoJSON files from the WaterNetworkModel\n",
+    "wntr.network.write_geojson(wn, 'Net3')\n",
+    "\n",
+    "# Create a WaterNetworkModel from GeoJSON files\n",
+    "geojson_files = {'junctions': 'Net3_junctions.geojson',\n",
+    "                 'tanks': 'Net3_tanks.geojson',\n",
+    "                 'reservoirs': 'Net3_reservoirs.geojson',\n",
+    "                 'pipes': 'Net3_pipes.geojson',\n",
+    "                 'pumps': 'Net3_pumps.geojson'}\n",
+    "wn2 = wntr.network.read_geojson(geojson_files)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Break pipe 121\n",
-    "wn = wntr.morph.break_pipe(wn, '121', '121_B', '121_node', '121B_node')"
+    "# Compare model attributes of the original model with the model built from Shapefiles (note the absence of patterns and controls)\n",
+    "print(wn.describe(level=1))\n",
+    "print(wn2.describe(level=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create Shapefiles from the WaterNetworkModel.  The following code will produce UserWarnings: \"Column names longer than 10 characters will be truncated when saved to ESRI Shapefile.\"\n",
+    "wntr.network.write_shapefile(wn, 'Net3')\n",
+    "\n",
+    "# Create a WaterNetworkModel from Shapefiles\n",
+    "shapefile_dirs = {'junctions': 'Net3_junctions',\n",
+    "                  'tanks': 'Net3_tanks',\n",
+    "                  'reservoirs': 'Net3_reservoirs',\n",
+    "                  'pipes': 'Net3_pipes',\n",
+    "                  'pumps': 'Net3_pumps'}\n",
+    "wn2 = wntr.network.read_shapefile(shapefile_dirs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compare model attributes of the original model with the model built from Shapefiles (note the absence of patterns and controls)\n",
+    "print(wn.describe(level=1))\n",
+    "print(wn2.describe(level=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print valid GeoJSON or Shapefiles column names required to build a model\n",
+    "column_names = wntr.network.io.valid_gis_names()\n",
+    "print(\"Junction column names\", column_names['junctions'])\n",
+    "print()\n",
+    "print(\"Tank column names\", column_names['tanks'])\n",
+    "print()\n",
+    "print(\"Reservoir column names\", column_names['reservoirs'])\n",
+    "print()\n",
+    "print(\"Pipe column names\", column_names['pipes'])\n",
+    "print()\n",
+    "print(\"Pump column names\", column_names['pumps'])\n",
+    "print()\n",
+    "print(\"Valve column names\", column_names['valves'])"
    ]
   },
   {
@@ -1645,13 +996,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Create water network model from an INP file\n",
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
     "wn = wntr.network.WaterNetworkModel('../networks/Net3.inp')"
    ]
   },
@@ -1661,27 +1012,17 @@
     "tags": []
    },
    "source": [
-    "## Simulation options"
+    "## Simulation options\n",
+    "Simulation options include options related to simulation time, hydraulics, water quality, reactions, energy calculations, reporting, graphics, and user/custom options."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Options(time=TimeOptions(duration=604800.0, hydraulic_timestep=3600, quality_timestep=300, rule_timestep=360, pattern_timestep=3600, pattern_start=0.0, report_timestep=3600, report_start=0.0, start_clocktime=0.0, statistic='NONE', pattern_interpolation=False), hydraulic=HydraulicOptions(headloss='H-W', hydraulics=None, hydraulics_filename=None, viscosity=1.0, specific_gravity=1.0, pattern='1', demand_multiplier=1.0, demand_model='DDA', minimum_pressure=0.0, required_pressure=0.07, pressure_exponent=0.5, emitter_exponent=0.5, trials=40, accuracy=0.001, unbalanced='CONTINUE', unbalanced_value=10, checkfreq=2, maxcheck=10, damplimit=0.0, headerror=0.0, flowchange=0.0, inpfile_units='GPM', inpfile_pressure_units=None), report=ReportOptions(pagesize=0, report_filename=None, status='YES', summary='NO', energy='NO', nodes=False, links=False, report_params={'elevation': False, 'demand': True, 'head': True, 'pressure': True, 'quality': True, 'length': False, 'diameter': False, 'flow': True, 'velocity': True, 'headloss': True, 'position': False, 'setting': False, 'reaction': False, 'f-factor': False}, param_opts={'elevation': {}, 'demand': {}, 'head': {}, 'pressure': {}, 'quality': {}, 'length': {}, 'diameter': {}, 'flow': {}, 'velocity': {}, 'headloss': {}, 'position': {}, 'setting': {}, 'reaction': {}, 'f-factor': {}}), quality=QualityOptions(parameter='TRACE', trace_node='Lake', chemical_name='CHEMICAL', diffusivity=1.0, tolerance=0.01, inpfile_units='mg/L'), reaction=ReactionOptions(bulk_order=1.0, wall_order=1.0, tank_order=1.0, bulk_coeff=0.0, wall_coeff=0.0, limiting_potential=0.0, roughness_correl=0.0), energy=EnergyOptions(global_price=0.0, global_pattern=None, global_efficiency=75.0, demand_charge=0.0), graphics=GraphicsOptions(dimensions=['6.157', '-1.553', '46.703', '32.613'], units='NONE', offset=['0.00', '0.00'], image_filename=None, map_filename=None), user=UserOptions())"
-      ]
-     },
-     "execution_count": 247,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print the WaterNetworkModel options\n",
     "wn.options"
@@ -1689,17 +1030,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 248,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Change the simulation duration to 4 days\n",
-    "wn.options.time.duration = 4*24*3600"
+    "wn.options.time.duration = 4*24*3600 # seconds\n",
+    "print(wn.options.time)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 249,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -1707,8 +1049,9 @@
    "source": [
     "# Change the simulation to use pressure dependent hydraulic analysis\n",
     "wn.options.hydraulic.demand_model = 'PDD'\n",
-    "wn.options.hydraulic.required_pressure = 20 \n",
-    "wn.options.hydraulic.minimum_pressure = 2"
+    "wn.options.hydraulic.required_pressure = 20 # m\n",
+    "wn.options.hydraulic.minimum_pressure = 2 # m\n",
+    "print(wn.options.hydraulic)"
    ]
   },
   {
@@ -1720,7 +1063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 250,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1731,7 +1074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -1743,98 +1086,94 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "tags": []
    },
    "source": [
-    "## Simulation results"
+    "## Simulation results\n",
+    "Simulation results are stored in an object which includes a dictionary of DataFrames for nodes and a dictionary of DataFrames for links.  Each DataFrame is indexed by time (in seconds) and the columns are node or link names."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 252,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'Tank level'}>"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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3kFmWSZugNiJfoZ4CBaB/dH/+mPQHvSJ6UaovZeqqqaQXp1vRYsl/WZ68nBc3iRy+WzrewgPdH2jQ569scyW/TPiFZn7NSC1O5ZFVj8jKHztEihSVUBSFD3Z+wI8JPwLw8qCX6RHR45zbajVaHun1CJ9f+jnBnsEczj3M5IWTWXJyiQ0tlgBsz9jOo6sfRa/ouTzucp7r/1yDBYqJSN9IPrnkE3zcfNiWsY2XN78sk2ltQF55Hvcuu5fU4lSa+zXny9FfEuQV1OD9hHmH8ckln9A2uC3ZZdk8uPJBWWJuIzae3siT657EqBi5uu3VPNn3yUZdh/FB8Xx26WcEeASwN2svz254Vuao2BlSpKjErD2zmH1wNgAvDHyBifETL/qZwc0G8/vE3+kd2ZtSfSlPrnuSj3d9bG1TJdUk5CTw8KqHqTRWMiJ2BK8NeQ2dVtekfbYPac/7I95Hp9Hx94m/+Wr/VxayVnIuSqtKeXDFg5woOEGETwRfjfmKcJ/wRu/Pz8OPTy/51Fyi/Niax2TDMCuzJ3MP01ZPQ2/UM7bVWF4Y8EKjFwoAcYFxfDjyQ9y0bixLXibvqXaGFCkq8MXeL/hy35cAPN3vaa5rd129PxvpG8nXY77m3m73AiKr/ZfDv1jFTmdie8Z2ThWeuuA2VYYqMkszz/leSmEK96+4n5KqEvpE9uG94e9ZrM39kGZDeKaf6JA5c/dMFicutsh+JXWpMlQxbfU0DuQcIMgziK9Gf3VWuXhjiPKNYuYlM/F282Zz+mZe3/K69IhZieN5x3lo5UOUG8oZ3Gwwbw55s8kLBYC+UX15edDLAHxz4Bv+OPpHk/cpsQxSpNiY2QdmM2vPLED0Y7i5480N3oeb1o2Hez7MIz0fAeDNbW+y9tRai9rpTBzLO8adS+/k6gVXsyhx0Tm3OZJ7hKsWXMUlv1/C9DXTOZF/wvxeVmkW9y6/l9zyXDqEdDB3kbUk13e4nts63QbAcxufY1Oa7I3TUBRFYVHiIhYnLkZv1Nd5z6gYeXbDs2xO34y3mzefXvIprYNaW+zYnUM7886wd9Cg4c9jf/LNgW8stm9XIqcsh493fcyK5BVnhV3Si9O5b8V9FFYW0i28GzOGz8BdZ7l5WJPiJ5nzWl7b8hobTm+w2L4ljUeKFBvyy+FfmLFzBgAP93yY27vc3qT93d31bq5pew1GxcgT657gYM5BC1jpfGxO2wxAuaGcp9c/zbvb3zU/xBRF4c+jf3Lz4pvNycjLk5dz1d9X8dS6pziQfYD7V9zP6eLTxPrH8tmln+Hv4W8VO6f3mc7olqOpMlbxv1X/k82malFQUcCMnTPOK94KKwt5dM2jPL3+aZ5a/xRX/HUFC08sxGA0oCgKb217iyVJS3DTuvHhiA/pGt7V4jaOiB3BU/2eAsRQO+nhrMu3B75l7B9j+f3o7+fM+9h1ZheTF07mq/1f8eiaR5m8cDKrUlahKIrII1p+L5mlmbQObM0noz7Bx93H4jY+0P0BJrSegEEx8OjqR9mesd3ix5A0DFmCbEEKKgoI8Ag4Z3x0wYkFPLvhWQDu6XoPj/R6xCLHrDJWMXXlVDalbSLMO4wfL//xrAohV2fa6mmsTFlJ++D2HMk7AkD/qP68MvgVZu2excJE0T10aLOh3NX1Ln449AMrUuo2zwvzDuP7cd8T6x9rVVurDFX8b/X/WH96PT5uPnw15iu6hXez6jEdgQ92fsC3B74FYHjz4Tze53FaBbYCRK7Q9DXTSS1OxU3rhp+7H/kV+QC0CmhFt/BuLDixAIC3h77N5a0vt6qtH+36iK/3fw3AK4Ne4aq2V1n1eI7CmD/GkF4iKqB6hPfghYEv0Da4LYqiMOfgHD7a9REGxUAzv2bkV+RTUlUCQKfQTiiKQkJuAlG+UcwdN5co3yir2VllqGLammmsS12Ht5s3X47+8rxFDZK6WOP5LUWKhfgx4Ufe2vYWfaP68lTfp2gf0t783srklUxfOx2jYuTmjjfzVN+nmpTo9V+KK4u57d/bOJp3lPjAeL4b953suVGNoiiM+G0EueW5zB03l6yyLJ7d8Cxl+jI0aFBQ0Gl0TO05lTu73Gke5Z6Qk8Anez5hbepa/N39mX3Z7Drn1JqU68uZunIqWzO24u/hzzdjvqFjaEebHNteuXXxrezJ2mP+2U3rxs0dbibGL4b3d7xPpbGSGN8Y3h/xPq0DW/PT4Z+Yc3BOncnFT/d7ulHh1YZiai3wQ8IPaNDw5tA3Gd96vNWPa89klGQw+o/RaDVavHRelOpLcdO4MaXzFBILEllzag0Al8ddzosDX6TSUMl3h77jx4QfzV19gzyD+G7cd7QOtFyY7nxUGCqYunIqW9K34Ofux9djv6ZzqOyDczGkSLFTssuyGT9vPKV60eJcq9FyXbvrmNpjKodyDjF11VSqjFVcEX8Frwx+xfwgtCQZJRncvOhmMssy6RrWlS9Hf9mgvg/OSmJBIlf8dQWeOk8237gZd507x/OO87/V/yOlKIVw73DeGfbOedvZJxYk4uvmS6RvpE3tLq0q5YEVD7ArcxdBnkF8O/Zb2ga3takN9kKFoYKBPw2kyljFrFGz+PXIr6w/vb7ONsObD+f1Ia/XEefFlcX8dPgn/jr+F1e3vZq7u95tM5tNE5V/O/obOo2Od4e/y+iWo212fHtjycklPLnuSTqFduKjkR/x1ra3WJmy0vy+u9bdXERQewGXW57LnINz2JO5hyf7PkmXsC42s7n2NRjoGcg3Y76x2ULFUZEixU55dfOr/Hb0NzqGdCTWP5ZlycsACPAIoMpYRZm+jNEtR/POsHdw07pZzY6jeUe5a+ld5Ffk0yO8B1+M/sIqcVtH4o+jf/Dy5pfpE9mH2ZfNNv++sLKQtafWMrjZYLvtMllcWcw9y+7hQM4BAjwC+GDEB/SL7qe2WTZn15ld3PbvbYR4hbBm8ho0Gg3rU9fzzvZ3SC1K5eFeD3N759utIv6bglEx8sLGF/j7xN+4ad14e+jbjGk1Rm2zVOGNrW/w8+GfubnjzTzd72kAVqWs4p3t7+CudeftYW/TKbSTylaeTUlVCfcuu5d92fvMYy2kR+X8yI6zdsiJ/BP8eexPAJ7s+yTvj3ifb8d+S7vgdhRWFlKmL2Nws8G8PfRtqwoUENNavxz9Jf4e/uzJ2sODKx90+QF2u87sAqBXZK86vw/wCGBi/ES7FSggenB8PvpzuoV1o7CykPuW3+eSpZG7M3cD0DOip3mVPbT5UP6+8m/W37C+TpjOntBqtLw86GXGtRqH3qjnsbWP8eW+L12yPLn2OTQxqsUolly9hL+v/NsuBQqAr7svn43+jI4hHcktz+X2JbezNGmp2ma5FPZ3ZTsYH+z8AINiYFTsKHPIoG9UX36d8CsvDRTt7D8Y8YFFS+UuRMfQjiLUUz2S/JHVrt3qeVemECm9I3qrbEnjCPQM5Jux3zAubhx6Rc/Lm1/m7W1vYzAa1DbNZuzJ3APUfcCBEAH2HtLUaXW8MfQNbul4CyD64Dy9/mmXuiaLK4s5mncUOPscajQauxSYtQnwCOCbsd+Yp9I/vvZxPt/7uUuKTTWw7/8dds7W9K2sTV2Lm8aNR3s/Wuc9N60b17S7hkd6PYK3m7dN7eoS1oXPLv0MHzcftqZv5X+r/2fOlHclMkoyOF18Gq1GS/eI7mqb02i83Lx4e+jbTO0xFYAfEn5g6qqp5JXnqWyZ9TEqRnZnnb0KdyTctG481e8pnh/wPG4aNxafXMxdS+8iuyxbbdNswr6sfRgVI838mhHhE6G2OY3C38OfWaNmmcXmJ3s+4al1T7mU2FQLKVIaiVEx8t6O9wCY3H6yuRzSXugR0YNPLvkEbzdvNqVt4pbFt5BalKq2WTZl55mdAHQI6YCvu6/K1jQNjUbDfd3v4/3h7+Ol82LD6Q2M/XMsM3bOILc8V23zrEZSQRIFFQV46bzoGOLYFU6T20/mi9FfEOARwL7sfVy/8HrmH5vv9G30HV1kmtBpdTzV7yleHPgibho3liQt4cZFN7IyeaWc92NFpEhpJP8k/sPh3MP4uftxf/f71TbnnPSJ6sPXY74mzDuM4/nHuXHRjS7VnMicjxLR6yJbOg5jWo1hzrg5dAzpSJm+jNkHZnPZn5cxY8cMcspy1DbP4pjCdV3CutgsZGpN+kX346fxP9EqoBWZZZm8sOkFJs6fyB9H/3DaKdi7zziHSDFxbbtr+XLMlwR6BnI8/zjT1kzjuoXXsSxpmRQrVkCKlEZQri83D6G6p9s9BHsFq2zR+ekW3o2fx/9Mp9BO5Ffkc++ye/ntyG9qm2UTzPkokY6Zj3I+Ood25tcJvzJz1Ew6hXYSYuXgbK74+wrSitPUNs+inCvh0tFpGdCS3yb+xuN9HifUK5TTxad5efPLjJ8/npXJKy++AweiyljFvux9gHOdw75Rffnnyn+4p+s9+Lr7cjTvKI+tfYxrFlzDoZxDapvnVEiR0gg2p23mTOkZInwibNIcqqlE+UYx57I5ospA0fPqlld5Yu0TdebTOBv55fkczz8OnF3Z4wxoNBpGxI7gl/G/MGvULFoGtKSgosDpWrGfL2nW0fF28+a2zrex5JolPNn3ScK8w0gvSefxdY87Va7K0dyjlOnL8PfwJz4oXm1zLEqQVxCP9HqEpdcs5f7u9+Pv7s/x/OM8v/F5mVRrQaRIaQSmzpdDmg2x+KA5a+Ht5s3bw942DyX8N+lfrvz7Sh5d/ahTzvwxrcDjAuPsusy4qWg0GobHDmd67+kAzD8+n0pDpcpWWYbssmxSilLQoHHoxOcL4e3mza2dbmXJ1UvoHNoZvVHPX8f/Utssi2G6DnuE97D7Kp7GEugZyEM9HmLBVQvw1HlyNO8oe7P2qm2W0+Cc/2usjGl11yO8h6p2NBSNRsM93e7h1wm/cmmLSwFYkbKCG/65gfuX3+9UKzhTqMeZ8lEuxLDmw4j0iSS/It/cTNDRMV1nbYLbEODhGI0dG4uXmxc3drgREA0InaXE3HQdOpsn7FyEeYdxWavLAFwmpG4LpEhpIJWGSg5kHwAc98LrFNqJD0Z+wF9X/MXE1hPRaXRsTNvIjB0z1DbNYpiSZp0tH+V8mEreAX4/8rvK1lgG8wMu3DGvs4YyttVYAjwCOF18+rzTnh0JRVGcNlx3Pq5vfz0AS5OWkl+er64xToIUKQ0kITeBSmMlwZ7BtAxoqbY5TSI+KJ43hr7B12PExFZnubBKq0rNyWvOmI9yPq5pew06jY5dmbvMzbMcGbPH0kUm0Hq5eXFFmysA51iJny4+TVZZFm5aN5vO3FGTLmFd6BjSkUpjJX+f+Fttc5wCKVIaiOnG2T2iu0UnGatJ78je5gvLGeLh+7L3oVf0RPpEEuMbo7Y5NiPCJ4KRsSMBx/emlOnLSMhJAFxLaF7X7joA1p1eR3pxusrWNA1TPkqn0E54uXmpbI1t0Gg0TG4/GRBCU5YkNx0pUhqIo+ajXIg6F9ZRx7+was/rcRYhWV9M53Fh4kKHntt0IPsAekVPhHeESwnNuMA4+kf1x6gY+eOYY89pMpePu0i4zsTlcZfj5+5HSlEKW9O3qm2OwyNFSgNQFMUp+zZAzYV1qugUW9K2qG1OkzDnozjovJ6m0D+6Py0DWlJSVcLik4vVNqfRmK+zyJ4uJzSvay+8KfOOzXPobrS1z6Er4ePuw4TWEwDnCNupjRQpDSC1OJWc8hzctG50DnOucd0+7j5MjJ8IwK9HflXZmsZTri83l/+5StJsbbQarTlk8NuR3xy2X4OzLgbqw6gWowjzDiO7LJvVKavVNqdRFFQUmPsUOZPXub6YPJqrT60mszRTZWscGylSGoAp1NMptJPD9EdpCJPbiQtrTeoaMkoyVLamcew6s4tyQzkRPhFO1zyqvlwRfwUeWg8SchPMlWiOhFExsjdTCE1XFCnuWneuanMV4LgrcdNCoVVAK0K9Q1W2xva0DW5Lr4heGBQDfx77U21zHBopUhqAs8dY2wS3oXdkb4yK0WEvrI1pGwEYHDPY5cIEJoK8ghjbaizgmF6xY3nHKKoqwtvNm3bB7dQ2RxWua3cdWo2WrRlbOVlwUm1zGsy29G2Aa4pMEyZvyh9H/0Bv1KtsjePSYJGybt06Jk6cSExMDBqNhr/++qvO+4qi8NJLLxETE4O3tzcjRozg4EHn6Ghq6jTrzCWRpjr/P4/+6ZDx8I2nhUgZ1GyQypaoi+kGueTkEodzN5uEZt+ovrhp3VS2Rh2i/aIZ2mwo4JhC03QOB8YMVNkS9RjdcjTBnsFklmay+pRjhu3sgQaLlJKSErp3786sWbPO+f4777zDjBkzmDVrFtu3bycqKorRo0dTVFTUZGPVpLCykON51TFWJxYpl7a4lBCvELLKslhzao3a5jSIjJIMThScQKvRMjDadW+OAN3Du9MzoieVxkrmHJyjtjkNYtNp0chsUIxrC82bOtwEiAWDI024PlNyhuP5x9Ggcenr0EPnwbXtrgXgq31fOWx+mNo0WKSMGzeO1157jauvvvqs9xRF4cMPP+TZZ5/l6quvpkuXLnz33XeUlpby008/nXN/FRUVFBYW1vmyR/Zn7UdBIdY/ljDvMLXNsRruOneubivOraOt4ExelC5hXQj0DFTZGnXRaDTc3+1+QPRMcZSRB6VVpeZOs4NjBqtsjboMjBlIl9AulBvK+e7Qd2qbU29M3XK7hHUhyCtIXWNU5tZOt+Lt5k1CbgLrT69X2xyHxKI5KSdPniQjI4MxY8aYf+fp6cnw4cPZtOncbZ7ffPNNAgMDzV+xsbGWNMli1B6U5exc2+5aNGjYmr7VoSYlm1zMQ2KGqGyJfTAwZiBdw7pSbijn+4Pfq21OvdiesZ0qYxXN/Jo5fEfnpqLRaLi/uxCavxz+hbzyPJUtqh8mkeLqnjCAYK9gbmh/AwCf7/1celMagUVFSkaGqAiJjIys8/vIyEjze//lmWeeoaCgwPx16tQpS5pkMVwhH8VEM79m5s6lX+3/SmVr6ofeqDf3d3H1fBQTdR5yRxzjIWcSmoNiBrls4nNthjUfRseQjpTpy5h7aK7a5lwUg9FgFimDm7m2J8zElM5T8NJ5sT97v1PMZLI1Vqnu+e/NRVGU895wPD09CQgIqPNlb+iNevZl7QNcQ6QA3Nf9PkAkXiYVJKlrTD04kH2AoqoiAjwC6BLqGnNC6sPQZkMd6iEnH3B10Wg05mvxp8M/UVBRoLJFF+ZgzkEKKwvxd/ena1hXtc2xC8K8w8wN+qQ3peFYVKRERUUBnOU1yczMPMu74kgczTtKmb4MP3c/2gS1Udscm9AptBPDmw/HqBgdwpuy4fQGQIQ4dFqdytbYD7W9Kfb+kDtVdIrkwmTcNG70j+qvtjl2w8jYkbQLbkdJVQk/JvyotjkXxOQJGxAzwGUrs87FHZ3vwEPrwZ6sPWzL2Ka2OQ6FRUVKXFwcUVFRLF++3Py7yspK1q5dy6BBjuuCNw8VDO+OVuM6rWVMD7dFiYtIKUxR2ZoLY16Bu3iy5blwlIecqaqnW3g3/Dz8VLbGftBqtNzb7V4Afjj0A0WV9lspKSuzzk24TzjXtLsGEN4USf1p8BO3uLiYPXv2sGfPHkAky+7Zs4eUlBQ0Gg3Tpk3jjTfeYP78+Rw4cIDbb78dHx8fbrrpJkvbbjNcbWS8iS5hXRjSbAgGxcDX+79W25zzkleeZ+6sKm+OZ6PRaLivmwgZ2PNDztyIT4Z6zmJ0y9HEB8ZTVFXETwnnrpRUm4KKAvZli7C4XCyczZ1d7sRd686OMzvYnrFdbXMchgaLlB07dtCzZ0969hSdBKdPn07Pnj154YUXAHjyySeZNm0aDz74IH369OH06dMsW7YMf39/y1puQ0xJs67YPdHkTVl4YiGpRakqW3NutqRvQUGhTVAbIn0dN6xoTS5tean5IWeP3pQqY5XZDS4fcGdT25vy/aHvKakqUdmis9mavhWjYqR1YGui/aLVNsfuiPKNMo87+GLfFypb4zg0WKSMGDECRVHO+pozZw4gVm0vvfQS6enplJeXs3btWrp0cdxExoySDNJL0tFqtC6ZCNY9vDuDYgahV/R2600x5aMMaSZLj8+HVqM1J2B+f/B7u8tN2Zu5l5KqEoI9g+kY2lFtc+ySsa3G0iqgFYWVhXYpNGXp8cW5q+tduGnc2Jq+lR0ZO9Q2xyFwnQSLRmIalNU+uD0+7j4qW6MOD3R/AIC/T/xNWnGaytbURVEUeXOsJ2NbjaVtcFuKqor47qB9NQczncOBMQNdKu+rIei0OrNnc86BOXYlNBVFkeG6ehDjF8NVbYU3ZebumbLSpx7Iu8FFqJ0066r0iOhB/+j+6I16vtn/jdrm1OFo3lGyy7LxdvOmV2Qvtc2xa7QaLVN7TAXgh4Qf7KrVuskbJh9wF2Zc3DjaBLWxO6F5suAkGSUZeOo86RPZR21z7Jp7u92Lh9aDXZm7zMJOcn6kSLkIJk+KqyXN/heTN2Xe8XmcLj6tsjU1mB5ufSL74KnzVNka+2dk7Ei6hHahTF9mN+G7nLIcEnITAOkNuxhajZapPe1PaJquw96RvfFy81LZGvsmyjeKGzqILrTSm3JxpEi5AOX6chJyxM3TFZNma9M7sjcDoweiN+r5bM9naptjZmnSUkB05pRcHI1Gw8M9HwbgtyO/kVFy7k7QtmRz+mZAhFSdeS6WpRgVO8ruhKYMuTaMu7rehY+bD4dyDrEyZaXa5tg1UqRcgIM5B9EreiK8I4j2ldnqj/R6BICFiQtJzE9U2Ro4knuEhNwE3LRuXNbqMrXNcRgGxgykd2RvKo2VdlFlYOqtIUM99cPehGa5vpwdZ0QSqKzMqh8hXiHc0ukWAGbtnoXBaFDZIvtFipQLYM5Hiegu54gg+qaMih2FUTEya88stc3h7xN/AyKE4erTVhuCRqPhkZ5CcP517C9OFao3L6vKUGWeDisfcPWnttBUuznYmtQ1VBgqiPGNIT4oXlVbHInbOt+Gv4c/JwpOsPjkYrXNsVukSLkA5qGCLjD5uL5M7TkVDRqWJy/nYM5B1eyoMlaxKHERAFfEX6GaHY5Kr8heDG42GL2i59O9n6pmx7rT68ivyCfMO0wmPjeAOkLz+F+qdoRecHwBAONbj5eLuQYQ4BHAnV3uBODTPZ9SZaxS2SL7RIqU86AoCnszZdLsf2kb3JbxrccDIulLLdanrie3PJdQr1AZJmgkppDBosRFHMs7pooNfx8X3rCJrSfKWS8NpFdkL3NHaLWEZlZplrlC5Yo2crHQUG7qcBMhXiGkFqfy1/G/1DbHLpEi5TykFKWQV5GHh9aDjiGyuVRtHuz+IG4aNzae3sjOMztVscH8cIuXD7fG0jm0M6NbjkZB4b0d79m8yiC3PJf1qSLUMyl+kk2P7SyYhObixMUcyjlk8+MvSlyEUTHSI7wHLQNa2vz4jo6Puw/3dL0HgM/2fEZpVanKFtkfUqScB1M+SpewLrjr3NU1xs6IDYg1NyT6eNfHqjzc1qWuA+TDrak82utR3LXubErbZP43tRWLExejV/R0Du1Mm2DXmC5uaTqFdmJc3DgUFN7e9rZNr0VFUcx5YdKL0ngmt59MrH8sWWVZdlOtZU9IkXIeTPko3SNct4nbhbiv233mhkSmHgm2ovbDrW1wW5se29mIDYjl1k63AvDujnepMtguLr7ghMhlkEKzaUzvPR0vnRe7MnexLHmZzY57KPcQx/OP46nzZGyrsTY7rrPhofPgsT6PAfDdwe/sdkaaWkiRch7Mk49l0uw5ifSNNDckmrFzBnqj3mbHlqs3y3Jvt3sJ9QoluTCZnw7bZsJu7fLxy+Mut8kxnZUo3yju6HIHADN2zKBcX26T45pCrqNajMLfw3EHyNoDo2JH0T+6P5XGSmbsnKG2OXaFFCnnoLCykBP5JwDXbod/Me7tdi+BnoEczz/O70d/t8kxD+ce5nDuYdy17vLhZiF83X35X6//AfD53s9t0sXUJDRHNB8hy8ctwB1d7iDSJ5K0kjSbtMuvNFSay2ZldV3T0Wg0PNn3SbQaLcuTl7M9Y7vaJtkNUqScg31Z+1BQaOHfglDvULXNsVsCPQPNs2Bm7Z5Ffnm+1Y9pWr2NjB1JoGeg1Y/nKlzR5go6hnSkuKrY6j1w6pSPS2+YRfB282Z67+kAfHPgG86UnLHq8dalrqOgooAI7wgGRA+w6rFchXbB7biu3XUAvL3tbdngrRopUs6BOdQjS48vyrXtrqVtcFsKKwutXgZZZZAPN2uh1Wh5ut/TAMw7No8juUesdqyNpzeSW55LiFeILB+3IOPixtEjvAdl+jI+2vWRVY9Vu7pOp9VZ9ViuxEM9HsLfw58jeUeYf3y+2ubYBVKknANz0qwM9VwUN60bT/V9ChAtuq3Zb+OTPZ+QV5FHuHe4nBFiBXpF9uKyVpdhVIy8vd16lSKmhNnxrcfjrpWVc5ZCo9HwVD9xLS5MXMi+rH1WOU52Wba5S/CkNjLp2ZIEewXzYPcHAdGHqqiySGWL1EeKlP+gN+rZn7UfkJ6U+tI/uj+XtrgUg2Kw2sPtx4Qf+ebANwBM6z1N9kaxEtN7T8dT58n2jO3m4Y2WJL88n9WnVgMyl8EadAnrYv53fWvbWxgVo8WPsThxMQbFQLewbrQObG3x/bs613e4nrjAOHLLc/lsr3WGua5LXcfJgpNW2belkSLlPxzPP06pvhQ/dz/iA+Ucivoyvc90PLQebE3fan4IWYolJ5fw9ra3AXik5yOyZNWKRPtFc1fXuwBRkmzp5lJ/HPsDvVFPh5AOtA9pb9F9SwT/6/U/fN192Z+93+JdTLPLspmbMBeQpePWwl3rbvZO/5TwE8fzjlt0/7szdzN9zXRuXXIrSQVJFt23NZAi5T+Y8lG6hXeTsdYGEOsfy22dbwPg3e3vUmmotMh+N6dt5v82/B8KCjd2uJG7u95tkf1Kzs+dXe6kuV9zMkszLToleV3qOmbtFkm5pgRBieUJ9wnnge4PAPDhzg8pqCiwyH5Lqkp4cMWDZJRk0MK/hXk8hsTyDG42mEtaXIJBMfDGtjcs5p1OLEhk6sqpVBgq6Bnek+b+zS2yX2siRcp/2JW5C5D9URrD3V3vJsI7gtTiVIuUQR7KOcS01dPQG/WMbTWWp/o+JQeY2QBPnac5ifb7Q99bxC18IPsAj699HINiYFL8JClSrMxNHW+iTVAb8iryLDJjq8pYxfQ100nITSDEK4TPL/0cPw8/C1gqOR9P9H3CHHr9N+nfJu8vqzSLB5Y/QGFlId3CuvHO8HccImwuRUotjIqRrelbAegX3U9laxwPH3cfpvcRZZBf7vuStOK0Ru+rXF/Ow6seplRfSv+o/rwx5A3p2bIhw2OHM6z5MPRGPW9ufbNJK7lThad4aOVDlOnLGBg9kJcGvSTFppVx17rzf/3/D4Dfj/5OQk5Co/elKAovbXqJTWmb8Hbz5pNLPiE2INZSpkrOQzO/ZmbP8Xvb36OkqqTR+yquLObBlQ+SVpJGy4CWzLpkFt5u3pYy1apIkVKLo3lHyS3PxcfNh27h3dQ2xyG5PO5y+kT2odxQzjvb32n0fv489ieZpZnE+Mbw4cgP8dB5WNBKSX14uu/TeGg92Jy+mZUpKxu1j9zyXB5Y+QC55bl0COnAByM/kBU9NqJvVF/GtRqHUTHy+tbXG51EO3P3TBacWIBOo+O94e/RJayLhS2VnI87utxBrH8smWWZfLG3caHXKoPwgh3OPUyIVwifXfoZwV7BFrbUekiRUotNaZsAcXHLG2nj0Gg0PNv/WXQaHStTVpqn3DaEKkMVsw/MBkR+hHQrq0NsQKy53fo729+hTF/WoM9XGCp4eNXDJBcmE+Mbw6eXfIqvu681TJWch+l9puPt5s3erL3m0u+GsOTkEr7a/xUALwx8gWHNh1naRMkFqB16nXtoLon5iQ3ex9vb32Zz+ma83bz59JJPifV3LC+YFCm12Jy2GYCBMQNVtsSxaRPchls63gKIMsiGJtEuTFzImdIzhHuHc2XbK61goaS+3NX1LmJ8Y0gvSW/whNY/jv7Bvqx9BHgE8NmlnxHuE24lKyXnI8o3ivu73w/ABzs/oLCysN6fNRgN5kTnu7vezdVtr7aKjZILM6z5MEY0H4Fe0Tc4ifZU0SnzyJL3hr9H57DO1jLTakiRUk25vpxdZ0TS7MBoKVKaygM9HiDcO5yUohTmHJxT78/pjXq+2S/6odzW+TY8dZ5WslBSH7zdvHmi7xOAWMnVd65PlaHKfN4f6fkIrYNkPw21uLXjrea+Gw0RmsuTl5NSlEKQZxD3dL3HihZKLsaT/Z40t3hYl7qu3p+bc2AORsXI4GaDHdYLJkVKNbsyd1FprCTCJ4K4wDi1zXF4fN19ebzP4wB8te8rThefrtfnliUtI6UohUDPQFkBYidc0uISuoR2oUxfZm6odzEWnVxERkkGoV6h0humMu46dx7r/RgAPyf8THZZ9kU/oyiKWdDc1PEmfNx9rGqj5MLE+sdyc8ebAdF5uz7elKzSLHNr/bu7OG7rBilSqtmStgWAQTGDZOWBhRgXN46+UX1FEu22iyfRGhWjOf59S8db5I3RTtBoNEztKQZJ/nr414sOrzMqRr498C0AUzpPkd4wO2BY82F0C+tGuaHc7Km8EOtPr+dI3hF83Hy4qcNNNrBQcjHu6HIHPm4+JOQm1CuRfe6huVQZq+gZ0ZPekb1tYKF1kCKlms3p1fkoMtRjMTQaDf/X7//QaXSsOrWK3Zm7L7j9mlNrOJ5/HF93X27scKNtjJTUi0Exg+gV0YtKY6VZSJ6PVSmrOFlwEn8Pfya3m2wjCyUXQqPR8FDPhwD49civZJRkXHB7kxdlcvvJctq4nRDsFcytnW4FhDflQlOSCyoK+PXIr4DIJ3LkhbcUKUBOWQ6Hcw8DYg6NxHK0CW7DlW2uBEQp4/nclIqi8NU+8fC7of0N8sZoZ9T2pvx57M/z9sCpHSa4of0NsjLLjhgYPZDekb2pMlbx5b4vz7vdzjM72Z25G3etO1M6TbGhhZKLMaXzFPw9/Dmef/yCs7V+PvwzpfpS2gW3Y2izoTa00PJIkQJsSRehng4hHQj1DlXZGufjvm734a51Z3vGdrZmbD3nNpvTN3Mg5wBeOi/zakFiX/SN6kv/6P7ojfrztsvfnL6ZgzkH8dJ5cUunW2xsoeRCaDQapvYQQnP+sfmkFqWeczuTp+zKNlfKiiw7I8AjgNs73w7Ap3s/RW/Un7VNaVUpPyb8CDi+FwWkSAFqlR7LUI9ViPaLZnJ74fY/lzelqLKIN7e+CcA17a6RQtGOMT3k/j7+N8mFyWe9b8p3uKbdNYR4hdjUNsnF6RPVh0Exg9Arej7f+/lZ7yfkJLDx9Ea0Gq25R47Evri5480EewaTXJjMwhMLz3r/z2N/kl+RTwv/FoxpOUYFCy2Ly4sURVHM+SgDYgaobI3zcnfXu/HSebEva1+dEjqD0cDT658mqTCJCJ8IWepo5/SI6MHQZkMxKIazxsjvy9rHtoxtuGncuK3TbSpZKLkYJqG5MHHhWXOZTKG6y1pd5nBNv1wFX3df86TyL/Z9QZWhyvxepaHSXPp/R5c7nGKUiP1PF7IyJwtOklmaiafOk14RvdQ2x2kJ8w7jpo438e2Bb5m1ZxZDmw9Fq9Eyc/dM1qWuw1PnyccjP5ZeFAfgoZ4Psf70ehYnLia9OB2tRotOoyO1WIQPJsRPINovWmUrJeeja3hXRjQfwZrUNby65VXaBbfjRP4JEvMTySzLBDA/BCX2yeT2k5lzcA6ni0/z7MZncde6k1KYQnJhMnkVeUR4RzApfpLaZloElxcpJi9Kr4heeLl5qWyNc3NH5zv47chvHM49zIrkFaJxW3XfjZcHveyQ3RBdkc6hnRndcjTLk5ebp4abcNO4yTCBA/BQz4dYk7qG7Rnb2Z6xvc5717a7lnbB7VSyTFIfvN28uafrPby57U2WnFxS5z2tRsvUnlOdZt6ZFCmyFb7NCPIKYkqnKXy691Pe3/E+ueW5gHBLjm89XmXrJA3htcGvMSl+EpWGSowYMRqNGDESFxBH60DZXdbe6RDSgcd6P8b2M9uJC4gjPiie+KB4Wge2lhVZDsJ17a7jVNEpiiqLaBnQktiAWFr4t6CFfwunOocapSkz2K1AYWEhgYGBFBQUEBAQYNVjVRmqGPzLYMr0Zfw+8Xc6hHSw6vEkYmT4ZfMuo6CiAIAhzYYwa9Qsp4idSiQSiStjjee3SyfO7snaQ5m+jBCvEOnetBF+Hn7c1UXEu1sFtOLtYW9LgSKRSCSSc+LS4Z5/T/4LiNW8VuPSes2mTOk0hWjfaPpE9SHAw7reMolEIpE4LlZ5MhcVFTFt2jRatmyJt7c3gwYNYvv27Rf/oA2pNFTyb5IQKc6SBe0o6LQ6Lou7jDDvMLVNkUgkEokdYxWRcvfdd7N8+XLmzp3L/v37GTNmDJdeeimnT9dvEq4tWJe6jsLKQiJ9Iukb1VdtcyQSiUQikfwHi4uUsrIy/vzzT9555x2GDRtGmzZteOmll4iLi+Ozzz67+A5sxIITCwCY0HqCDPVIJBKJRGKHWDwnRa/XYzAY8PKq23PE29ubDRs2nLV9RUUFFRUV5p8LCwstbdJZ5JXnsf70egAmxk+0+vEkEolEIpE0HIu7EPz9/Rk4cCCvvvoqaWlpGAwGfvjhB7Zu3Up6evpZ27/55psEBgaav2Jjrd+K+d+kf9Eb9XQK7UR8ULzVjyeRSCQSiaThWCXOMXfuXBRFoVmzZnh6evLxxx9z0003odOdXWr6zDPPUFBQYP46deqUNUyqg2ko08TW0osikUgkEom9YpUS5Pj4eNauXUtJSQmFhYVER0dz/fXXExcXd9a2np6eeHp6WsOMc3Ky4CT7s/ej0+gYFzfOZseVSCQSiUTSMKyaMerr60t0dDR5eXksXbqUK664wpqHqxcmL8rgZoPlMDuJRCKRSOwYq3hSli5diqIotG/fnuPHj/PEE0/Qvn177rhD3cFjRsXIosRFgEyYlUgkEonE3rGKJ6WgoICHHnqIDh06MGXKFIYMGcKyZctwd3e3xuHqzc4zO0krScPP3Y8RzUeoaotEIpFIJJILYxVPyuTJk5k8ebI1dt0k/kn8B4Cxrcbi5eZ1ka0lEolEIpGoict0MSvXl7MsaRkgGrhJJBKJRCKxb1xmwGBOeQ6dQztzuvg0vSJ7qW2ORCKRSCSSi+AyIqWZXzO+Hvs1Zfoy2QZfIpFIJBIHwOWe1t5u3mqbIJFIJBKJpB64nEiRSCQSiUTiGEiRIpFIJBKJxC6RIkUikUgkEoldIkWKROIMVJVBwWm1rZBIJBKL4jLVPRKJ01JZAt+OhYz9EDsAet8Ona8Ed5kk7jAYjbDuXTBWQfcbITRebYskDSX/FKx4EcLaQ89bILCZ2hY5BRpFURS1jahNYWEhgYGBFBQUEBAQoLY5Eol9oyjw591w4I+6v/cKFA+7PndBeDt1bJPUn9VvwNq3a36OGwa9boOOE8HNdlPiJY2kqkwsFNL3ip81Wmg7RpzDtmNA5xr+AGs8v6VIkUgcmS2fwb9Pg9YNrv0Wso/Czu+hIKV6Aw10vQ5GPC1X5/bKkX/h5+vF6+b9IHU7UH1b9g6BfvfAgAfBO0gtCyUXQlHg74dgz4/gEwrhHSF5Q837/jEw+H/Cw+nu3ONYpEiRWAdFgbyTENQStDq1rZHUl+RN8N1EMOrhsrdgwAPi90YDJK6GbV/D0SXidxoddL8Bhj0BIXHq2SypS84J+HIkVBRAv3vh8nchPwV2/wC75kJRmtjOKxAGToX+94OXvC/aFdu/gUXThffk1r+g9XDIPga7voM9P0FpjtjOPxqGPga9pjitd0yKFIl1WPwkbPtCXERdroGu10J0D9Bo1LZMcj4K0+GLYVCSKTwlV3917vOVthvWvAVH/xU/a91g4EMw8lmnvVE6DJUl8PVoyDwIsf3htn/AzaPmfYMeDi+ENW9DVoL4nXcwDHpYCBZ5/tTn1HaYPU7kEl36MgyZVvd9fYUQKuveg8JU8buA5jDiKeh5q9PdY6VIkViefb/BvHvO/n1oW+h+vchp8AmxvV2S86OvhO8mwKmtENEZ7l4OHr4X/kzqDpH3cGKl+DmiM1z9BUR1tb69krNRFHHd7f8dfCPgvnUQEH3ubY1GODRfiM3so+J3EZ3gqi8gupvtbJbUpTgTvhguvF0dJ8LkuecXHfoK2PU9rJ9R4x1rPRKu+MSpEmylSJFYlswE+GoUVJXCkOnQvI+4aR5ZAvpysY27L/S5Q6y+A2LUtdfZUBQoPC1i1tp6dAOoKILdP8KWTyE/GTwD4d7VDcs1SfgHFv4PSrNB6w4j/0/Ey2WYz3aU5cH692HTTBGGu20htBp88c8ZDbD/D1j2LJRkCa/YiKdh8KMuk5hpN5zaLnLBTu+AsHZw98r6heGqymH7V7DqddCXiWv48neg2/VO4VWRIkViOSqKRCw855hQ9Lf8WfOgqiiChIXiYZixX/xO5yGqRYZOh+BWqpntNFSVw7y7xb+zV6Bw97cYIEqIw9uDYhQPJcUAFcWw9yfYMUfkLoBw+1/zDbS5pOHHLs6Cf6bB4X/Ez1HdoFlv8IsE/0jwi4KYHlKU1od9v0PaLvHv12LghVfFuYki0Xn3D2JhADD2TRj4YMOOWZItzl/CQvFzsz5w2ZsQHCcSN+sjeCWCyhJY+aoI18QOENdgUOy5tzUa4PAi2DxLeDEBPPzgnlXimm0I2cdh/n1C5IDwxAx6RFyDfpEOm2ArRYrEMigK/H47HPoLApoJV7Nv2Lm3O75SrPpSNonfuXnDJc+LBD65+m4cFUXwy81wcm3DPxvaRlR6dL8RPHwab4OiwN6fYclTUFF49vs6T3GeBzwoz/O5UBTR12T163V/HxgrBKd/VF2hWXC6Oi+o+nYb2QWGPCpywBqzglYUEapd/ESNcAXhmfGLEA+6rtfK83chyvLgx8mQuq3u7wOaCa+yh1/N+TMahBjNSxLbaN2h22RxDsPaNu74Bj1s/ECE8Yz6uu95BUJgC5Hj0tj/IyogRYrEMtQuW71jCcT2u/hnkjcJF6WptK55P7jy08ZfoK5KaS78eC2c3ilugpO/F16RU1shZTOkbIXiDLGtRicqBrRu4qY58CFoO9ayK+XCNDi6FIoyxHGLMyH3ZE2iZmx/uPIzWb5cG6NRhFy2fCp+7jABCk4Jr6NivPBn244R5zFuuGUePAWpQmimbKmuIvnP7bx5X7jiU9kr578UnYG5V4mkZa9A6DpZeDXS9wlRcj68gqDvXaISyz/KMrak74UVLwnvSvEZMFTUfb/jJBg/A/zCLXM8KyJFiqRpZB8Xyn3Pz+JCvOxtGHB//T+vKLBzDix7HiqLwM1L5DR0nCj6OXgFOoziV4XCdHFjzEoQ/163/CHCBP/FaFTXZa8oIslv6bPV59kbLn0R+t0nQwkGPSx4WITfoG7pd0WRSFBO3S5ea3VCaGp1ohKn/XiI6GBF26pEKKj4jLBh5SvCSya9YnXJS4LvrxRtF/wi4ZZ5ENVFvFdZIhYQ6XuF90SjrTmPPiHQYfzFk9SbgqJAeYE4hwfnV3ch1osw3vj3ofNV1ju2BZAiRVJDeQFs/lQka0V3F1UaXoHn3jZtD2yYAYcWYF5p9bwVJs1snKjIPwULH4ETq+r+3nQhB8eJh1qrIQ3ft7OSlwTfTRIJr/7Rop+CNR9YliA/Bf6eWhOWGvAQXPaGujapSVU5/HEnHFkk/q9f8Qn0uFFtq85PwWlxnR5fIX6O7Q83/Ay+oerapSaZCUKgFGeIvlBT/oKQ1mpbdX7S98FfD8CZA+LnbtcLz6adik0pUiSCkhz44WpI31P398FxItPcWAWVpWJVUFlUE0cFaDdOJL/WJ8RzIRRFJABu/FB4CKpKzt6mz10w+mXw9G/asRydnBNCoBSminM05W8Ibqm2VfVDUUR4cOkzotLriWPWXUnaK5Wl8MtNokmezhOumwMdLlfbqoujKLB7Lvz7f+JeMHAqjH394p9zRtL3wfdXQFmu6Ap76/zzl33bE/pK4VFZ/77wgN/4K7S/TG2rzokUKRKRO/D9FZB1WLgAWwwUF5+5Dfo50GhF8tWQRyGys3XsqioXF39JNuz4RoSFQCQSTvwQ2lxqnePaO9nHRVfYojQhIG9baLlYtq1QFPi4hxC7V38N3a5T2yLbUlkCP10PSeuFULvpFzFbx5FI+Ad+vVmEN6Yn2O1K3Gqk7RYelPJ8iOkpQjyO1v/p3/+DLZ+IkM91c9S25pxY4/kti+sdibxkIVDyToreGlP+qil9K82FjH0i6dHdG9x9xIrXw1e4Na29YnD3AvcYUbY68SPofLWI3ecnww/XuGaoIOuoECjFGRDeAaYsECW+joZGI9zMa9+Gfb+4lkipKBICJXmjSHS+5U9RpupotB0j8qCKz0DimsaVrjsqqTtFLlhFgUgkvuXP84fG7Znu1wuRcnixCPc74t/QCFw8C86ByDoK315WM2PnziV1a/N9QqD1CNF4rfsN0GmSuBG1GKCOS7P1cHhwM/R/ANCIiyv7mO3tUIvMwzBnvBAoEZ1Ey3NHFCgmulUPwDuxSlRGuALlhfDDtUKgeAaIPCJHFCgg2u13uVq83verurbYklPbYO6VQqDEDhAeFEd9uEd1E4sdQ0V1fqFrIEWKI5CxX8yHKEqDsPZw57+O0VDNwxfGvQVtR4uf9/6irj22oqpMrNxKMiGyqxAoDlA+eEFC40XTMMUIB/5U2xrrU1kq8r5ObREPtSl/QWxfta1qGiahmbBQNAh0dk7vqvagFELLwdUeFAdOITB5NMGlhKYUKfZO6g6xIi/NFlU8dyxxvE6g5gvrN1Fe6+zs/UUIyoDmcNsC56mm6H6D+L7PBcTmjm9FGa9XkAjTnatU3NFo3ldUslSVis6pzs6y56GyGFoNhZt/B08/tS1qOt0mi+9J60WVpQsgRYo9k7RR5KCUF4jmaVMc9IHXYTx4+Ivk3pTNaltjXYxGUQ0Dot25oyXnXYjOV4vGcul7RTjLWTHoYevn4vXoV8SIAGegzkrcyYVm2h7ReFLrJgYxOktFWmBzIbpAzFlzAaRIsVeOrxAJp6aVwK3zwTtIbasah7s3dL5CvHb2m+OJlZB9RIiynreqbY1l8Q2FNtWhO2d2Nx9eKDrI+oTWrFydha7VSc+Ja0SloLNi6gbc+SqnmjIM1A352FdxrlWQIsUeOboMfr5RTMlsO8Y5XJXdqkMFB/8W5crOyuZZ4nuvKY4d/z4f3atvkPt/d97Q3eZPxPe+dwuB7UyExguvrGIUE5WdkcK0mrypAQ0c3ugIdJokun1nHRZeTSdHihR7w1AF/zwKhkoxs+H6H53jRtlysOiZUlEAR5eobY11OHNQrFA1Wuh/n9rWWId240SlS8GpmqGTzsSpbSIXRechRIoz0t3JQz7bvhKt5FsOhma91LbG8ngFQvtx4vW+39S1xQZIkWJvHPpbdCb1DYervxKlg86AVlvjat7rpKGCzdUu5o6THKejbENx94JO1aE7Z6zWMnnCuk0W04Sdkc5Xiym+GfvhzCG1rbEslSUi6Rmc04tiwuSZ3v+7yKFyYqRIsScUpeYm2e9e8UBwJkzVIceXi860zkRxJuyvXtUMfEhdW6yN6TwecrLQXV6yKM8F537A+YSIMDI4X27Rnp9EV9nguBpvgzPS5hKRM1WSKby3TowUKfZEymbRvtnNC/rcqbY1lie8vWhJbdTDgXlqW2NZtn8tQnTN+zZ9LpK902JQdeiu0LlCd1u/ELkarUdab3yEveCMuUW1K+ucfeKzzl2MOgHnDdtVI0WKPbGp2ovS/QbwDVPXFmvRzQl7bVSVCZECzr0CN+GMobvyQtj1vXg9cKq6ttiCtmNFbkPhaUhap7Y1luHYUsg9If6uHjepbY31Md1LDy8S4xucFClS7IWcE3BksXg9wInDBV2uEWPuT+90njb5+36D0hzhXeg4SW1rbIOzhe52zxVTgsM7uMZcG3cvkZsCsOdndW2xFKaqrN63O341ZH1o1gtC24rmfIf+VtsaqyFFir2w5VNAESuc8HZqW2M9/MJrJiI7Q+JlVRmse0+87n8f6FxkZmd4e4ju4Ryhu6ryumECjUZde2yFyduQsMDxV+KpO0QXVq0b9HPSyrr/otFAjxvFa2cRmudAihR7oDQXdv8oXg9yAVez6cLaPRf0lera0lQ2fiw66QY0hz53qW2NbXGWNvmbZ4mSav9o52vediGa94WQ+OqVuAMPrDMaYcmT4nW3652veduF6HYDoBHddfOS1LbGKkiRYg/s+FY0bovqWtPy2JnpMEE8EIrPwMH5alvTePJTYMMM8XrMq+Dho649tqbLtY4fuis4DevfF69Hv+ocPYnqS+2V+F4HXonv/Vn8H/Twh0teUNsa2xLYTEycB6ftmWJxkaLX63nuueeIi4vD29ub1q1b88orr2B0lgxyS6OvgG1fitcDH3YNV7POHfpWex22fua4rZ2XPQ/6cmg5RLTfdjX8wmvyNxy1lHX588KT0GIgdL1WbWtsj2klnrRelGA7GuUFsOJF8Xr4k+Afpa49atC9ltB01HvpBbC4SHn77bf5/PPPmTVrFgkJCbzzzju8++67zJw509KHcg72/CQ8Cv7RrvWg630H6DxFyXXqdrWtaTgn18Ohv0R32XFvu4a4PBe154g42kIkaaNon67Rwrh3XPMcBsVC3DDx2hGF5tp3oCRLJJD2v19ta9Sh40Tw8IPcRDi1VW1rLI7FRcrmzZu54oorGD9+PK1ateLaa69lzJgx7Nixw9KHcnxKc2HlK+L1oIedp7tsffANg27VZaympEVHwaCHJU+J133uhKgu6tqjJu0vF272/BQ4tUVta+qPQV+Tx9D7dojupqo5qmJKoHW0lXjWkZpp1Ze95Vr3z9p4+NZ0gd7zk7q2WAGLi5QhQ4awcuVKjh49CsDevXvZsGEDl19++Tm3r6iooLCwsM6Xy7DyZSjLhfCOosOsq2Fa+Rz6W+QGOAo7Z0PmQfAOhpHPqm2Nunj4iIFn4Fgr8Z2z4cwB8AqCUc+rbY26OOJKXFHEQsGoF0K57aVqW6QuppDPwfmi4tCJsLhIeeqpp7jxxhvp0KED7u7u9OzZk2nTpnHjjTeec/s333yTwMBA81dsbKylTbJPTm2Hnd+J1xNmiDwNVyOqq8jnUAw1zdDsnZIcWPWaeD3qOdFi3NUxhXwOzneMNvmlufIc1qbOSvxHdW2pL4cXQeJqETIe+7ra1qhPy8EQ2EJ0gTb123ISLC5Sfv31V3744Qd++ukndu3axXfffcd7773Hd999d87tn3nmGQoKCsxfp06dsrRJ9odBD4umAwp0vwlaDlLbIvUwTQveOccxVgC754rZIJFdRF6NRFSkBTQTSYzHlqptzcVZ9548h//FvBL/y/6vQ0URCc8gwuQhrdW1xx7QamtGHThZzxSLi5QnnniCp59+mhtuuIGuXbty66238uijj/Lmm2+ec3tPT08CAgLqfDk9O76BjH2iffPoV9S2Rl06jBcrgLJcMUfE3jm+QnzvfbtzzwZpCI7UJl9RRMIzCC+KqzTfuxi1V+KHF6ltzYU5c1CEpty8YcijaltjP5iE5omVUJShri0WxOIipbS0FK227m51Op0sQTZRlFHjar7kRVHG6cpoddDvHvF6y+f2nbhXUSSGQIJrtE5vCKbGbseWQVmeurZciMwEMa/GzQtaj1DbGvtBq605h/beM8W0UIgb5hrt7+tLaDzE9hdDMh1hwVdPLC5SJk6cyOuvv86iRYtISkpi/vz5zJgxg6uucqHy2gux7DmxWonpJVbjEuh1K7j7iGTU5I1qW3N+EteKRL2Q1tLF/F8iOkJYezBWiX8ne8X0gGs1xLUat9UHkzfs5DqoLFHXlgthOodtXDxZ9lx0qe71c9QBwq71xOIiZebMmVx77bU8+OCDdOzYkccff5z77ruPV1991dKHcjyyj1crXI1IlpXhAoF3cM2ws4R/1LXlQsib44WJHym+J65W144LcXy5+N5mtLp22CNhbcWQTEMlJG9S25pzI72ZF8b0b5Kyxb6FZgOwuEjx9/fnww8/JDk5mbKyMk6cOMFrr72Gh4eL1rDX5ui/4nvr4RDTU11b7I12Y8R3kxCwNxQFjq8Ur+UD7ty0rhYpJ+xUpFQUQ7LpASeF5lloNDVC017P4cl1Nd7M0Hi1rbE/QlqL3CJjlf0KzQYiZ/fYEtMqru1Yde2wR+KGizkwOcfsc1BW9lExSFDnCa0Gq22NfdJqsJhCm58sEhvtjaT14uYd3Eo+4M6HWWiuUteO83HM5AmTIvOcaDQQP0K8ttdz2ECkSLEVFcWiDTdAW7kSPwvvIJH0BTUeC3vC5OFpOUj0lZCcjac/NO8nXtvjSrz2A84VW+DXh9YjAA1kJUBhutrW1KWON1OKlPMSP0p8t8drsBFIkWIrTq6ttYpro7Y19okpnmqPIR+TTVJgXhh7zUtRlFr5KPIBd158QiCmh3iduEZNS84m+1gtb+YQta2xX+KGY7dCsxFIkWIrji0T39uOkau482F6eCSuBX2lurbUprK0xgsmH3AXxhQuOLkOjAZ1balNznExX0jnIZrPSc6PeSVuZ+ECk8iU3swL4xNSk/Nob4uFRiBFii1QFDhmWomPUdcWeyaqG/iGQ1WJfQ2rS9oAhgpR+RDWTm1r7JuYnuAZKLrPpu1W25oaTJ6wFgNlb42LYRKaiWvsa7K1rK6rP/aeAN0ApEixBZkJUJgqGkhJN+X50WprbkD2FPKpfXOUXrALo3ODuGpPhT3dIGXCZf2J7Sf6FpVkit5F9kBtb6YMuV4cexWajUCKFFtgCvW0GiobSF0Ms0ixo+RZuYJrGPaWl1JVVtMkUD7gLo5brZwPexGa0pvZMGL7gbuvfQnNRiJFii0wreJkqOfitB4JaODMAShMU9saUUqbe0KU1sYNU9sax8C0iju1TVS1qU3SRtCXiyGI4R3UtsYxaG1nQtO8ULhEejPrg1utVgn2IjQbiRQp1qa8oCa/oq1ciV8U31Bo1ku8tgdvismGFgPBywWGX1qCkNYQZGooZQdjDsxVPfIBV29MybPJm6CqXF1boJZIkZ6wemPvPW/qiRQp1iZxjeiQGNpGznupL6YbkT3kpdRewUnqh0ZjX91n5QOu4YS3B/9o4YEytaFXC+nNbBwmoZmy2T6EZiORIsXa1C49ltQPcynyajDo1bNDXyFKaWvbJKkf9pKXkntSlB9r3cQ4Ckn90GjspxTZ5M2MHSC9mQ3BnoRmE5AixZrUKT2Wq7h606wXeAWJUNnpnerZkbQBqkrBLwoiu6hnhyNibih1WN3cIpMXpXk/8ApUzw5HxF7yUo7VCtdJ6o89Cc0mIEWKNcnYD8UZopyvpZz3Um+0upqLy5RPoAamceftxspchoZiL51LTUM928l5WQ2m9QjxPWM/FGeqY0NlqejWDdDuMnVscGTsRWg2ASlSrIkp1BM3XGRbS+pPW5XzUhSl1gNO3hwbhdp5KRXFNeE6eQ4bjl84RHUVrxPXqmPDybUiXBHYAiI6qmODI1NHaGapakpjkSLFmphLj2Wop8HEV7t203ZDWZ7tj591REzz1XnKXIbGYkpyPLVVneMnrgFDJQS1FPF5ScOJq/6/r1YH6NqeMOnNbDh+4RDRWbxW6zpsIlKkWIuSbEjdJl7LpNmG4x8pVk8AmYdtf3zTzTFumJwT0lhMeTz5KcJtb2tqe8LkA65xmDwpWUdsf2xFqQm5tpeesEYTVX0dZqtwDi2AFCnW4ui/oBjFRR4Uq7Y1jklEdeOtrATbH7t2PoqkcfiGgXcIoEDOMdse22isCbfKB1zjMXmgslRYKGTsg6J00Tm1pRwn0mjM51CKFEltjiwR39uPV9cOR8Z0cdnak1KaW+PeliKl8Wg0NR1ebX2DTN8DxWfAw08mrTcFUwv6kiwoybHtsY9Ue8LiR4K7l22P7UyYr0EVhKYFkCLFGlSV1ZR8dbhcXVscmfDqRDlbX1zHVwovWERn0TlV0njCqx9ytj6HR2s94GTSeuPx8K25BmwdLpCVWZbBLFKOOuSwQSlSrEHiGtFfI6A5RHVT2xrHJUKlFYC8OVoOtTwpsjLLcqixEi86A2m7xOu28jpsEkEtRQGAvgwKUtS2psFIkWINjiwW39uPkwl7TSGsOtxTfEaEYGyBoaqmN4t8wDUdNeLhhWmQvhfQyKR1S6DGOTTlE8X0Ekn0ksajc4OwtuK1A+alSJFiaYzGmliqDPU0DU+/mgofW11cp7aKTrfeIdC8j22O6cyYVuG5iWLMgC0wPeCa9Qa/CNsc05lRw5MiPWGWxZzfp0IRQhORIsXSnN4JJZngGSAz0i2BrSt8TDfHtmNE51tJ0/CPFteCYoCcE7Y55hH5gLMotg7ZVZXXNACUIVfLoFbY1QJIkWJpjiwS39tcCm4e6triDNi6wkeWHlsWjca2ZaxVZTVt+OU5tAymCp+idCjLt/7xkjdAVYkQuNHdrX88V0DNUvImIkWKpTlcnY/SQZYeWwRbVvjknIDso2Jirml2kKTp2DKn4eR6kSAY0KymEZmkaXgFiH9PENeHtZEzsyxPbU+KoqhrSwORIsWS5JwQZXpaN+FJkTQdW1b4mHIZWgwE7yDrH89VsGVOg2yjbh1stRKXM7OsQ0hr8VyqKoGCVLWtaRBSpFgSU1VPy8HyIWcpbFnhY17ByZujRbFVPLx2G3V5Di2Lrc5hZoIYo+DmVTM3SNJ0dO4Q2ka8drC8FClSLIkM9VgeW1X4GI2QukO8lgMFLYtpFZ5zXJR4W4vcRChMFT0hTMMNJZbBVp6UpA3ie8vB4OFj3WO5Gg6alyJFiqUoyalppd5+nLq2OBu2qPDJOwmVRWIFZ8qDkViGgOZi/oqxCnJPWu846XvE98jO4O5tveO4IrbypJjOoSz/tzwO2h5fihRLcWxprYGCspW6RbFFhU/abvE9srNofiSxHFqtbdrjp+0R32N6WO8YroqpwqfgFFQUWe84pnMY3cN6x3BVHHTQoBQpliJpo/jeZrS6djgjtqjwMa3g5M3ROthiJS7PofXwCQG/6s6v1qrwqSqrucal0LQ8DlrhI0WKpTizX3yXF5flsUWFj1yFWxdrx8MVpboVPvIcWgtrr8TPHBRN/3wjRI8UiWUJbQMaLVQUQFGG2tbUGylSLIFBXxOKkL0ZLI+1K3wUBdL3iddyFW4dTKs4a03SzUsS4wx0HjKnyFpYO6fBFHKN7i7Lx62Bm6coRQaHyktxHZFiqIKULbBrruX3nXMMDBXg4QdBrSy/f1fH2hU+eSfF6kLnUXMjllgW0yo8+xgYDZbfvynUE9FJdnq2Ftb2pJjOofSEWQ8HbI/vOiKlJAu+HQsLH7F84ldGdagnsrNIEpRYHmtW+JhCPZGd5QPOWgS1FJVT+nLIT7b8/mW4zvpY3ZNSHa6T3kzr4YBlyK7zRA2IEVU3Sq1+GJbCJFJkqMd6WLPCRyZcWh+tzrrj4uU5tD4mkZKXDJWllt13VXnNAkQKTeshPSl2TuwA8T1li2X3e+aA+B7ZxbL7ldRgzQofmXBpG6y1ElcU6UmxBb5h4BMKKCLEbUkyD4JRDz5hNXOCJJbH7ElJcJgKH9cSKS36i++nLCxSpCfF+lirwqf2A05OXLUu1sppyE+B8nzQuoucFIn1sNZKvPY1KJNmrUdoW0ADZXlQkq22NfXCxUTKQPE9dYeoyLEERWdEvotGK2+Q1sRaFT75yfIBZyus5Ukxd5rtJCoYJNbDWjkNMmnWNnj4QHBL8dpB8lIsLlJatWqFRqM56+uhhx6y9KEaTnhH8AyEyuKaEE1TMfVHCYmXsyasibUqfMxJs/IBZ3XMIuWomJVkKaQnzHZY3ZPSw7L7lZyNg7XHt7hI2b59O+np6eav5cuXA3DddddZ+lANR6uF2L7i9amtltmnOdQj81GsjjUqfNJlRYHNCI4THquqEjEI0FLIpFnbYQ1Pir5CTD8G6UmxBQ7WHt/iIiU8PJyoqCjz1z///EN8fDzDh9vJZFlLJ89mVHtkZD6K9bFGhY/5ASdX4VZH52b5cfEyada2mFbhuYlCXFiCMwfF8EnvEAiMtcw+JefH1T0ptamsrOSHH37gzjvvRHOeZKiKigoKCwvrfFmVFrVEiiWym82VPVKkWB1ThU/aLsvsTz7gbI9JaGbss8z+Ck5BWS5o3SCis2X2KTk/fpHgFShaOVgqZF67uk4mzVofV/ek1Oavv/4iPz+f22+//bzbvPnmmwQGBpq/YmOtrKSb9RY3tKI0cYNrClVlooMmyHCPLWg9XIQLUrfDiVVN3598wNmeuKHi+7avRW+MpmJ6wEV0BHevpu9PcmE0Goir9opvmmmZfUpvpm0xTbQ26q070dpCWFWkfPPNN4wbN46YmJjzbvPMM89QUFBg/jp1qonC4WJ4+EBUN/E6pYl5KZkJYiCWT6gciGULAptDv3vE6+UvNj35Uj7gbE+PW0QfjKI02PFN0/cnEy5tz/CnAA0cnF9zDTUFeQ5ti6c/PJEIT50Ur+0cq4mU5ORkVqxYwd13333B7Tw9PQkICKjzZXVMpchN7ZdSu4mbdFPahqGPg2eACBcc+KNp+5JVIbbH3av6IQesn9H0lZxchdueqC7Q9VrxetVrTduXvhIyD4nXMuRqO3xD1bag3lhNpMyePZuIiAjGjx9vrUM0HlNTt6Ymz8qkWdvjGwpDHhWvV77atJCBrApRhx43iWmspdmw5fPG76dOTlFPi5gmqScjnhFh0mPLIHlz4/eTeQgMleAVJOY7SST/wSoixWg0Mnv2bG677Tbc3NyscYimYarwOXNQjHdvLLLTrDoMeAD8Y6AgBbZ/3bh9yAeceujcYeSz4vWmmY1vzld4WggdjU4Mh5TYjtB46HmreL3y5cYXIcikWclFsIpIWbFiBSkpKdx5553W2H3T8Y8UPRtQRBJmY1AUIXJAzuyxNe7eMPL/xOt174oWzw2lMM2qD7h5u1J59Z9DzFp1jLlbkvlnXxobj2eTlF1ClcGCjcwclc5Xi2TligLY9HHj9lEnp8jbcrZVozjIbBPVGP4k6DwhZTMcX9m4fVg5XFdUXkWlXl5vjoxV3Bxjxoyx/wu8xQDIOylCPm0ubfjn81PEDVbrXpMtLbEdPW6CzZ+Ixm4bPoDRrzTs86abY3gHiz/g1h7NYvpv508o1Gk1xAZ70zLUl/ZR/twztDXh/i7W7VarhVHPwi83wdYvoP8DYvHQEKyYcHk4o5Cbv9pKSaWeIG8PgnzcCfR2J8TXg9gQH1qE+NAq1JeWoT7EBHmj07qgFyAgRiSyb54lvCnxo8R5bQhWPIdfrUvk9cWiSZyvh44gHw8Cvd0J8/ekZYgPLUPFOWwV5kNcmJ9rnkMHwA5jMTYitj/s/bnxeSmmpNmIDuDmYTm7JPVDq4PRL8NPk0VeQ997IKie5esnVsOS6uRNC4d6CsqqeOoP0QNkaNswYgK9ySutJL+0ipySClLzyqjQG0nKKSUpp5S1R7NYciCd7+7oR+twP4vaYve0v1y0BDi9EzbMgHFv1/+zRWfg8D/itYUTLiv1Rh79dS85JZUAZFSVk1F4/tyn6EAvPrulNz1igyxqh0MwZDrsnCMS2RP+hs5X1f+zJ1bV3EctfA4PpRXyztKaZmUllQZKKss4nV8G6Wdv3zE6gG9v70N0oOU9cpKm4boixVThc3onGKpEnLwhmPJRZBM39Wg7BloOgeQNsO0LGHORSoOKYlj+Qk3pa3ArGDrdoia9vPAgGYXlxIX58uWtffD20NV532hUyCyqICmnhKTsEj5be4LknFKu/Xwz39zWh54tgi1qj12j0cCo52HulbDjWxg8DQLqUcp/YjXMu0cM9nT3Ff8PLMisVcdISC8k2MedH+7uj9EI+WVCaGYXV5CSW0pyTilJOSWk5paRXlDOjV9u4ZObezKqQwO9QY6ObygMnApr34I1b0GnKy+eW6KvhNWvwcaPxM8tBlWH3y1Dpd7I9N/2UGVQGNs5krev6UZ+aRX5ZVXkl1ZyprCc5Jyac3giq5iE9EKu/nQTc+7oR/so+y/LdSVcV6SEtRMZ5eX5YhXQrHfDPi9n9qiPRgP97xMi5cB8uPSV87ubkzbAXw+IMB0Iz8ulL4nBhRZi6cEM5u06jVYD713X7SyBAqDVaogK9CIq0IsBrUO5tFMkd87Zzr7UAm78aguf3NSLSzq60IOu9Qho3lfkhh2cBwMvMIjUoIc1b8L69wFFTK2+bk7NVFcLsPdUPp+sOQHAa1d2pXNM4AW3L67Q8+CPu1h3NIt7vt/J61d24YZ+LSxmj0Mw8EEhOLIOizyhC3lFchPhj7tqukb3vVssLiyYNPvxymMczigixNeD16/qSpCPB0E+5/d2p+aVcvvs7RzPLOa6zzfx5ZQ+DGjtOCW6zo5Vm7nZNVqtCPlA45q6nbFe+XGVwcjKhDP8sCWZGcuO8PSf+7hj9jamfLuNqT/t4v/m7+etJYf5fO0JTuWWWvz4DkXbMeDhLwbWpW479zaZh+H7K4RACWwBUxbA+PcsKlByiit4dr4QrvcOi6d3y5B6fS7Mz5Of7xnA8HbhlFcZuef7Hfy0NaVBOV15JZU89NMuxnywlrzqEIXDoNFA18ni9YF559+uNBe+mwDr3wMU6H073LOqpsW3BSivMvDY73sxGBUmdo9hfLeLe3X8PN345rY+XNOrOQajwtPz9vPhiqMNzslTFIXVRzLZl5rfSOtVxCsQ2lV7sw5e4BweWw6fDxUCxSsIrv8Rxr9v0ZywPafy+WytSWR2Iczv4rlezYN9+OP+gfRpGUxhuZ4p32xj0b5zxIQuQlF5FXM2niQlx8XvyRbGdT0pIJJnjy0V2ekDH6z/58oLIS9JvLZwZY/RqHDrN1vZkli/ssxPVx/n81t7Myg+zKJ2OAzuXtBxgsgv2v9HzWym2mz7QrSAbjUUbvzZ4l0WFUXhub8OkF1cSbtIPx4d3bZBn/f1dOPr2/rwzLz9/LEzlf+bv59VhzN59crOF42R70jK5eGfd5NeIHImlh7McLyVfKcrYMmTcHoH5CWf2zOy5i1xnXr4w8QPa5qJWZAZy49yPLOYcH9PXplU/4ovd52W967rRnSgF7NWH+fDFcdIyi7hpUmdL7iCN1FUXsWz8w+wYG8a/l5u7HjuUjzdzvbC2TWdr4ZDf4sutJe+fLZnxKCHhf+DymJoORiu/lJ0kLYg5VUGHvttDwajwqTuMVzetf5dwIN8PPjh7v5M+2UP/x7MYOrPuziUHs/Do9ri5X7xc7EvNZ+pP+0mJbeU1Uey+O7Ofk35UyS1cF1PCtTkpaRsblidf/JG8T2gGfjUb8VcX37dcYotibl4u+u4tGMkN/dvwfTR7Xjr6q68d113XpzYiUcvbcddQ+Lo0izArPx/32HlcQL2TJdrxPdDf4mbYW3KC2Hvr+L18Kes0gZ6/u7TLDmQgZtWw4zJPRr1gHHXaXn32m48PqYd7joNKxLOMHrGOr7fnITRePb/TaNR4ZPVx7n+yy2kF5TjVl2ZsP54dpP/HpvjHwmthojXB+ef/X5FsRChIMI7VhAoO5Jy+Wp9IgBvXtWVYN+GJcNrNBoeH9ue167sglYDf+1J49IZa1mwN+2CXpUDpwuYOHMDC/amAVBUrmdnciNK6tWm7RiRH5SfIvL8/suxpaKvjU8o3DLP4gIF4P1lRziRVSJE5hUNbyvg5a7jk5t7cdvAligKfLL6BJd/tJ4tiTnn/YyiKHy74STXfLaJlGqv9pbEHMqrDI3+OyR1cW1PSrNe4OYtEvCyj9bfdbxplvjekEz2epBdXMFbS0RG+mNj2nH30NYX3L68ysDjv+/ln33pPPHHPlJyS5k+ut15J06fj7JKAzuT8+gXF4KHmwPq1tYjxJj3kixIWg/xI2ve2/crVJVAWPuaB6EF+fdABk9WV/NMHdWGLs0unMNwITQaDVNHtWVM5yie+nMfu1PyeeHvg/y1+zRX9WqO0ahQZTCiNyqsP5bFxuPi5nlljxiu6tWc277dxsbj2RiMiuOVU3a+Spy7g/NgyLS67+3/DSoKIbSNKHO1MOkFZUz7dQ+KAtf0as6lnRqfE3TLgJZ0jPbn6T/3cyyzmEd+3s28Xam8dmUXmgf7mLdTFIXvNyfz+qIEKg1GYgK9iA7yZmdyHhuOZTueZ9TDB9qPE6MqDsyD5n3qvm9qutjzVqvMyfpnXxpfbzgJwFtXd62XB+tc6LQaXprUmQGtQ3lxwUESs0u44cstXN8nlv+7vCOBPqLAQlEUcksqeerP/axIOAPA2M6R7DmVz5nCCrYn5TK0bbhl/jgXx7VFipsnxPaFk+vEDbI+IiV1h0jU1LrDgAaEiOrBG4sSKCirolN0ALcPanXR7b3cdXx8Q09ahvrwyeoTzFx1nOScUt66pis+HvU7tUfPFPHADzs5kVXCU5d14IER8U38K1RA5y5CBjtnw4E/a0SKotTcHPvebfGOlov2pfPIL7vN7uWpI9tYZL/tIv354/5B/LAlmXf+PcyulHx2peSftZ2Xu5ZXrujCdb1FPoS/pxv5pVUcOF1Ad0crh+10BSx+QiRe5pwQHU2h+hxWV2P1uavhfTguwun8Mm78cgupeWXEhnjzwsROTd5n75YhLHpkKJ+vPcGsVcdZcySLS95fS6ivB5UGBb3RSKXeSGmlWG1f2jGS967rxqrDmexMzmP9sWyevKzJZtieLlcLkXJwvkiGNZ2rnBPVU8s10OcOix/27z2nebRaZN4yoEWTE881Gg3jukYzqE0Y7/x7mB+3pvDrjlPM330arRb0BgV9Le+mh07Ls+M7MmVgS578Yx+/70xl/bFsKVIshAMumy1Mq+rR8Ukb67f9xg/F926TIbCZxczYdCKbebtPo9HAG1d3xU1Xv1Oj1Wp4YmwH3rmmG25aDQv2pjF6xjqWHsy4aPLeHztTmTRrAyeySgDMKwKHxBTySVgA+grxOmmDqDhw94Xu11v0cH/vOW0WKFf3bMYH1/eo9zmrDzqthtsGtWL59OHcMqAFYztHMr5bNFf0iOGaXs25bWBLFk4dwuQ+sWg0Gtx0WgbGi4qEDY4Y8vENg7hh4nXtkM+prSJJ3c0betxo0UOeyi3l+i82k5JbSosQH365dyCB3g1sRXAePNy0PHJJW5ZMG0q/uBAq9EbSCsrJLq4gv7SK0koDHjotL0zoxFdTehPk48GQNsJ7ciCtgFxHS4AG0RTTM0BMuD5Vqxhhx7fie9sxouzfgszblcqjv+7BqMDkPs15eZLlcgQDvd15/aqu/H7/QNpE+FFpMFJeZawjUNpE+DHvwUHcNqgVGo2GIW3FOVx/zAGvQTvFtT0pIJK4QDzQFOXCq+3sY5BQ3UBq0CMWM6FCb+C5+aJa6Jb+LRvVFGpy31iah3jzxO/7OJ1fxn1zdzKyfTgvT+pCi1CfOtuWVRp44e8D/L4zFYC+rYLZnpTHnlP5FJRVWexGbVNaDgL/aChKF6u29uNqvCjdJosKBAsxb1cqj/++F6MC1/VuzlvXdLNaeCUmyJvXrqxfBdnQduEsO3SGdUezeMhCXh2b0uVqSFwtRMqwx8XvTOew6zXgbbkeMqdyS7nhyy2czi+jVagPP90zgJggyzfyig/345d7BnA0s4gqvYKbToO7Tou7TkOIrwf+XjXXWkSAFx2i/DmcUcTG49lM7B5jcXusipsndBgv8ocOzoOWA6GqDHb/IN7ve7dFD/f7jlM8+ec+FAVu7BfL61d2RWuF67BvqxCWThvG6bwyNBrM589NpyXAy61OeN0kNBPSC8ksKifC3/KhLVdDelKa9RbzJ0oyIef4hbfdNBNQoN040WnWQnyxNpHEbJHw9fjYxpdUDooPY8X04Tw0Mh53nYbVR7IY/cFapv+6h//9spu7v9vOjV9u4ZL31/D7zlS0GnhsdDt+vXcgrcN9MRgVNp84f5KYXaPV1eQIHfgTCtNrOpJa8Oa44Vg2j1ULlBv7teBtKwqUhjK0+ga5KyWPkgr9Rba2QzpMEJN1zxyArKNQnAUH/xLvWfAcpuQID8rp/DJah/nyy70DrSJQTGi1GjpEBdC1eSAdowNoE+FHy1DfOgLFxFDzSjzLavZYlc5Xi++H/gajQeSnlOdDUAtoc4nFDvNbLYFyc/8WVhMoJnRaDS1CfYgN8SEq0ItQP08Cvd3Pyv8L9fOkS7MAADY6okfTDpEixd0LYqvLxZLWn3+7ooyaCoPB/7PY4ROzipm1Woij5yd0arIXw9tDxxNjO7Dkf8MY3CaUCr2RebtP8/eeNFYkZLI5MYe0gnLC/Dz54e7+PHxJW7RajfkB57A3R6gJ+RxeDFs/F2XHLQZarOGeoii8u+yIOcHy9Su7WPXG2FBahvoQG+JNlUFh60kHFJs+ITWJsQfnwe7vwVglFhIWHF/w+O97SSsoJz7cl1/uHUBUoP2sdk15DOuPZdv//LNz0XqE6IFSfEZUQZo8YX3uFAsJC5BRUM5z8w+gKHDbwJaiosqOrsMhbWrOoaTpyHAPiJBP0nqRl9LnPJObt3wGhkrRAK7lQIsctqi8ivvm7qRSb2Ro2zAm1qN5VH1pE+HHD3f1Z0VCJofSCvH11OHr6Yavpxv+nm70ahlcRxANbRvOd5uTHTOfwUSz3hDUEvKTaybrWtKLcjybvafy8XLX8szlHezqxggi4W9o23B+2prCuqPZjtmivfNVcGyZ8IZVlYnfWfAcbk3MYVtSLh46Ld/f1Z+IAPsRKIC5wi69oJwTWSW0iXCweU5uHtBxIuyeCyteFo3bdB6iqsdCfLkukUqDkb6tgnlpUucGVzNam2Ftw/h87Qmz0LQ3+xwN6UmBmtJUU17KfykvrEn+GjzNIoc0GBWm/bKHY5nFRAZ48t513S3+n1mj0TC6UyT/u7Qtdw9tzY39WjCpewwjO0Sc5bEZEB+Km1ZTPdOixKJ22AyNpsabohjBN1zcMC3EzFXC43VTv5b16mSpBg7vEeswXjzUso9CwSmRh2LBUn+T1/K6Ps1pZsUQT2PxctfRr5XoveSw57BLdcjn9A7xvfNVIjHaAmQXV/DTtmQAHh7V1i4FQO9WwXi5a8kqquDImSK1zXF4pEgBUdOv84DiDDFb4r/snC36NIS1h3aWqQ18f9kRVh7OxMNNy5e39iFS5RWdX7V3BRzcTWkSKQC9pohkPguwNTGHbSfFCvzeYRfuX6Mmg+LD0GrgRFYJafllapvTcLwCRZWIiZ63WKxt+u4UUd7rptVw/3D7LbUf6ugVIq2GgU8tUdLnLovt+psNJymvMtK9eaD538ne8HTT0T+uutLOUc+hHSFFCoibYPO+4vV/81IqimHzJ+L14Ecs0qfh7z2n+bR6iNk713Szm54Wwxw9aQ8gsrOYjOwdfP7QXSOovQK3pxyG/xLo40635kGAA98gTcmXYNlzWO0Ju6pnM2JDfC6ytXqYyli3JOZQqTeqbE0j0LlBp0nidWTXmpy/JpJfWsn3m5IAmGqnXhQTJgG1zlGvQTtCihQT5lLk//RL2TBDJIEFt4Ku1zX5MPtS880dSu8fHs+VPS3Xa6WpmJL2Nh3PQW9wwJsjiJDPrfNh2gGLtd52lBW4iWHmG6SDis2OE0Slz7AnIcQyXqsDpwtYeTgTrQa7b1jYMSqAMD8PSisN7EpxwBb5AEMfg46TxABBC4mJ2RuTKKk00DE6gEs7Rlhkn9ZiWDtxL90qW+Q3GSlSTJwrLyUvqaYF/pjXmxw6KK7Qc+/3O6nQG7mkQwRPNKHc2Bp0aRZIoLc7RRV69jriNFYTbh4WnXD8yWrHWIGbGFp9g9x4PPucc3/sHndvuOFHGPWsxXb56RpxDid0i6F1uH0no2q1GnO/DYf1agY2h+vnQov+FtldUXkVszeKtvdTR7axay8KQNsIPyIDPKnQGx1zFpMdIUWKieZ9Rav7ojTIExcDy18AQ4XohNlhfJMP8fuOU2QUltMixIcPb+hhN/01TOjq3BylmxLgYFoBKxIcYwVuokdsEH6ebuSVVnEwrVBtc1Tn2JkilhzIAHCYJndDqr2aDhuyszBztyRTWK4nPtyXy7pEqW3ORdFoNOZSZIf1aNoJUqSY8PCpGYqVtEF8HfobNFq47K0muyyNRoXvquOp9wxrfc5GTvaAwyftWRiTF8URVuAm3HVaBrQWiXvyBgmfrjmBoogBcO2jLD8F2xqYrsN9pwvIc8QW+RaktFLP1+vFwvGhkW3sbnF3Poa1E+dQCs2mIUVKbUx5KSfXwb9Pi9e9bxfJmE1kzdFMknJKCfBy45pe9pOH8l9MSXumFvmuzMG0AodbgZuQN0hBUnYJf+85DcDUkW1Vtqb+RAZ40S7SD0WBTY7aBdpC/LQ1hdySSmJDvJnkQKMCBld7pQ+mFZJdXKGyNY6LFCm1MeWlHPgTMvaDZyCMtExcfPbGJABu6Nei3hOK1aB5sI/jt8i3AIqi8Oo/h1AUmNg9xmFW4CZMSdDbk3LJL3Xdlfi7S49gVGBE+3C6Nrfc/CZbYDqHa45kqmyJehSUVpkr6x4c0caiQzytTZifJ52iRYv8dUelR7OxOM4ZtwWx/cTsEKW6smXEUxZpQnTsTBHrj2Wj1cCUgS2bvD9rM8zcmtt1L6xlh86wJTEXTzctT11mXwnO9SEuzJcOUf7ojQr/VnuDXI3tSbks2p+OVgNPXWa5WVu24pIOooJlecIZxyxFtgAfrzpGfmkV7SL9uK63Zar1bImpCmnx/nSVLXFcpEipjYevaK0OENoG+t5jkd3Ors5FGdMpiubB9l8dYkqedegW+U2gUm/kzcUJANw9NM4hztm5ME3R/Wef690gjUbhCQO4vm8sHatXtI5E/9ahhPl5kl9a5ZLD6k5ml/D95iQAnh3fyaG8KCZM1+Dao1kUlLp2+LyxON5ZtzZ974HAFjDxY1HK2kTySyuZtysVgDsGt2ry/mxB7Rb5SdkO2iK/CXy/OYmknFLC/T15YIRj5aLUZmI3cYPcdCKbrCLXion/tec0+1IL8PN0Y/pox/OEgai2G99VVLIs3JemsjW2543FCVQZFEa0D2d4dVm9o9E20p/2kf5UGRSWHnJNj2ZTkSLlv3S7Dh7dD60GW2R3v2w/RXmVkU7RAfSLC7HIPq2Nn6cb/VsLWxcfcK1VeG5JJR+tPAbA42Pa4edpv/lDF6NFqA/dmwdiVGCJC53H0ko97/x7BIAHR8YT7m+fc5bqg2klvuzgGZdqCrbpRDbLD51Bp9Xw3PiOapvTJCZ2F4NjF+51PaFpCaRIsSJ6g9Hcxvn2wa3svgFRbUyr8IV7XefhBvDhiqMUlevpFB3Atb1j1TanyUyoPo//uNB5/HJdIhmF5TQP9ubOwXFqm9MkerUIJjrQi+IKPWtdJPnSYFR49R8Rbr25fwvaRDhW0vp/mWD2aOaQI6t8GowUKVZk2aEzpBWUE+rr4VClcwCXdYnCTashIb2Q45muMcnz2JkiftyaAsDzEzo5TD+GCzG+m1jFbUvKJb3AAQcONpCMgnK+WCuGhD49rgNe7jqVLWoaWq2GCd1cayX+x85TJKQX4u/lxrRL26ltTpNpFeZL12aBGIyKuaWBpP5IkWJFvt0gGhDd1L+Fw90sg3w8zPMnXMWb8u7SIxiMCmM6RTIwPlRtcyxCTJA3fVuJ6daLXCCB9p2lhymrMtCnZTDju0arbY5FMIV8ViZkUlqpV9ka61JcoefdpUcB+N8lbQnxbXpeoD0gQz6NR4oUK7EzOZcdyXl46LTcOsD+y47PhfnC2peGojjgDJgGkF9ayarDoh+Fvc1Uaiomd/NCJxcpp/PLmL9bNG57fkInhwqvXoiuzQJpEeJDWZWBlQnO3TNl/q5UsosraBXqw5SBrdQ2x2KMr74GtyXlcqawXGVrHAspUqzE59Uu56t6NiMiwEtlaxrHpR0j8XTTkphVwqF0554Bs/zQGfRGhQ5R/rSNdOwY+H8Z1zUKrQb2nsonJadUbXOsxpL96SgK9IsLoXtskNrmWAyNRuMyK/FF1f1Ebu7fEg8353k8NQvypnfLYBTFNTyalsR5/hfYEcczi1h+6AwaDdw73DKj5tXA38udUdUNpZw95GNqtnS5k4QIahPh72UOX/2z33kfcqZ4v7OEeWpj8oatOZJFYblz9tvIKqpg28lcQAhrZ8OcW+SC5eRNQYoUK/DlOuFFGd0xkngHGUp3Pkzx8IV7nTfkU1BWZW5c54wiBWqFfJxUbKYXlLEzOQ/AIabkNpQOUf60ifCj0mBk+cEzaptjFZYdysCoQPfmgQ7bQPFCjO8ajUYDu1PyOZXrvB5NSyNFioXJKCg3x8XvHxGvsjVNZ1SHCHw9dJzOL2P3qXy1zbEKKw6docqg0C7SjzYRji0qz8dlnWtXaxWrbY7FMbX+79MymEgHDa9eCI1GU9MWwElX4iZv5jgnXShEBHgxIE54NBfJNvn1RooUC/PtxpNUGRT6xYXQq0Ww2uY0GS93HWM6V3e9dNJ4uKnR2bguznlzBAj29TBPuHbG87hkvxApzuoJA5hQnZey4Vg2eSXONTQyp7iCLYki1HO5E1+HE1wkt8iSSJFiQQrKqvipus/G/Q6ci/JfTEl7i/alYzA6V8inqLyKdUedO9Rj4ooeYiX++45TTnUeMwvL2Z4sHnDOGOoxER/uR6foAPRGxem8KcsPncFgVOjSLIAWoc4X6jExrks0bloNB9MKOZLhGv2nmooUKRbkx63JFFfoaR/pz8j2EWqbYzGGtAkn0NudzFqJbc7CyoRMKg1G4sN9aRfpnKEeE+O6RBPk405aQTlrjjhPKeu/BzNQFOjZIoiYIG+1zbEq1/URk4B/2priVDlipvCHM3szAUJ8Pbi0YyQgnheSiyNFioUorzLw7YYkAO4d1tppejQAeLhpGdfFOQed1a7qcaZzdi683HXmcfemzrrOgPkcOvkDDuDqXs3xctdyOKPInCjs6OSVVLLpRA7g/N5MgJsHtABg3q7TlFQ4d3M+S2AVkXL69GluueUWQkND8fHxoUePHuzcudMah7Ib/t5zmuziCmICvZjUw7Fa4NcHU5XPon3pTjPorLhCz5rqeSjOvoIzcWM/cYNcfSST1DzHrzBw9rLV/xLo7W4eseEsQnN5ggj1dIwOIC7MV21zrM7g+DBahfpQXKGXuSn1wOIiJS8vj8GDB+Pu7s6SJUs4dOgQ77//PkFBQZY+lF1h6gR5Y78WuOucz0E1oHUozYK8KSircppmRKsOZ1KpNxIX5kvHaOdq4HY+Wof7MbhNKIoCv2w7pbY5TWbpQecuWz0XN/cXHawX7U8n1wkSaGs8Yc4vMkHMY7qpv1gs/LA12anCdtbA4k/Tt99+m9jYWGbPnk2/fv1o1aoVl1xyCfHxjl+Oez4URWFHtet1UBvnmPnyX3T/ubCcgSXmOHiU04d6amN6yP2y/RSVeqPK1jQNc2WWC4QJTHSPDaJrs0Aq9Ub+2OnYQrOgtIqN1T2KXOkcXts7Fg83LQdOF7IvtUBtc+wai4uUBQsW0KdPH6677joiIiLo2bMnX3311Xm3r6iooLCwsM6Xo3Eiq5jckko83bR0bRaktjlWY3KfWNx1Gnan5HMwzbEvrNJKPaurk0ddIQ5em9GdIgn39yS7uILlhxy3MZirlK2ei5urFww/bU3B6MCVWisSnL9H0bkI8fUwd0aWCbQXxuIiJTExkc8++4y2bduydOlS7r//fh555BG+//77c27/5ptvEhgYaP6KjY21tElWZ9tJ4UXpERvkVPMm/ku4vydjq3um/LDFsePhqw5nUl5lpEWID51jAtQ2x6a467Tc0FdcZ458g1zmImWr52JSjxj8Pd1Iyik1J506Is48juJimITmgr1pFJQ656gDS2DxJ6rRaKRXr1688cYb9OzZk/vuu4977rmHzz777JzbP/PMMxQUFJi/Tp1yPPfl9iSxmusXF6KyJdbnluqJzn/vOU2Rg84QqdAbmLFcjIOf2N35q3rOxQ39WqDVwKYTOZzIcrwOtIqiMG9XKuA6Sc+18fFw4+pezQDHFZpp+WWsP+YaPYrORe+WwXSI8qe8ysi83alqm2O3WFykREdH06lTpzq/69ixIykp5155e3p6EhAQUOfL0TBVF/Rt5fwipX9cCG0i/CitNJjb/zsaX61LJDGrhDA/T+4d5ry5UheiWZC3uZfPzw5YJbL04Bm2J+Xh4ablyp7N1DZHFW6qzi1adugMZwrLVbam4byxOIFKg5F+rUJo60KhHhMajcbsTfnRyfreWBKLi5TBgwdz5MiROr87evQoLVu2tPSh7IK0/DJO55eh1UCvlo7fBv9i1L6wftjieJnpKTmlzFx1HIDnJ3Qk0NtdZYvUw9Sv4Y9dqQ5VVl5eZeCNxQkA3Du0Nc2cvIHb+Wgf5U/fVsEYjAq/bncsD/TWxBz+2ZeOVgMvTurkkt5MgCt7NsPHQ8fxzGK2OlmjTEthcZHy6KOPsmXLFt544w2OHz/OTz/9xJdffslDDz1k6UPZBaZQT+eYQPw83VS2xjZc3as53u46jp4pZnuS4zSUUhSFFxccoEJvZHCbUHO/CVdleLsImgV5k19a5VAPuW83niQlt5QIf08ecIIhnk3BFH79eVuKw1RqGYwKLy08BIiwY+eYQJUtUg9/L3eu6CE8gd9vTlLXGDvF4iKlb9++zJ8/n59//pkuXbrw6quv8uGHH3LzzTdb+lB2gSuFekzUbij1wxbHiYcvPZjB6iNZeOi0vHJFF5ddvZnQaTXmSd0frTxGoQPkGGUWlvNJtSfs6XEd8HWRhcH5uKxLFOH+nqQXlDvMtfjzthQS0gsJ8HLj8THt1TZHdaYMFEJz8f4M9jrppPmmYJVSlAkTJrB//37Ky8tJSEjgnnvuscZh7AJXSpqtjWkFt+RAOtnFFSpbc3FKKvS8XL16u294a+LDXS8Gfi5u6BtLfLgvuSWVfLr6hNrmXJR3lx6hpNJA99ggruzhmrkotfF00zF9dDsAPl51zO6rRPJLK3l/mUgHmD66HSG+HipbpD4dowO4ujqv6vXFCQ4XQrc2zlsvawPySio5ekZURvRt5fz5KLXp2jyQbs0DqTIo/LLN/hMvP1xxlPSCclqE+PDQyDZqm2M3uOu0PDOuIyDCKKdy7bdV/r7UfP6oruh5cWIntFrX9oSZuK53c9pF+pFfWsWs1cfUNueCfLD8KHmlVbSL9DMvdCTw+Nj2eLpp2XYyl2UO3LvIGkiR0gRMXWbjw30J9fNU2Rrbc8fgVgB8sS7Rrttzn84v49uNSQC8fEVnvNx16hpkZ1zSMYKBrUOp1Bt5b9mRi39ABRRF4ZWFh1AUuKpnM3q1cK1FwYVw02n5v8uF0PxuUzIpOfYpNI9kFPFDdSXZixM74+aE40MaS0yQN3cPjQPgrSWHHSa/yBbI/yVNwFVDPSYmdW9Gp+gAisr1fLTiqNrmnJedyXkYjArdmgeay24lNWg0Gp4d3xGNBv7ek8YeO4yLLzt0hh3JeXi763jqsg5qm2N3jGgfwdC2YVQajLz972G1zTknH644isGocFnnKAa3CVPbHLvjgRFtCPPz4GR2CT85aO8bayBFShNwxaTZ2ui0Gp4bL1ZwP2xN4XimfTYFO5QmRi10bea6VQQXo0uzQK4yxcUXHbK7uPj6Y2Ja9Q39YokK9FLZGvvk/y4XQnPR/nR2JttfOavJ82zyGEjq4ufpxrRLRX7RRyuPUVBm3/lFtkKKlEZSWqnnwGkxv8ZVRQrAoDZhXNoxEoNR4c3q3hX2xqF0IVI6uVj7+4byxNj2eLlr2Z6Ux9KD9hUXNwnNHrFB6hpix3SMDmBybzHu4LVF9pWAmV1cQVZRBRqNsFNybm7oG0vbCD/ySqv4ZPVxtc2xC6RIaSR7UvLRGxWiA71oHuyazaRMPHN5B9y0GlYezmRDdZtre8L0gOskb44XJDrQm3uGtgbgrSUJVOjto8Gb0ahwOKMIkOfwYjw2ph3e7jp2p+Tzz750tc0xk1C9UGgV6uvyZeMXonZ+0ZyNSXadyG4rpEhpJNuSakI9rt5vIz68JlP/tUWHMNjRVNbMonKyiyvQaqBDlHzAXYz7hscT7u9JUk4pX68/qbY5AKTkllJaacDTTUtcmK/a5tg1EQFe3DfcJDQPU1qpV9kigWmh0DHaX2VL7J8R7cMZ3CaUSoORV/85pLY5qiNFSiMxJc32ddGk2f/yv0vaEuDlxuGMIv7caT/Dskw3x7gwX7w9ZFXPxfDzdOPZ6pXczFXHSM1TfyVnCte1j/KXFSH14L5h8TQL8uZ0fpndhAxMnhTpCbs4Go2GFyZ0RqfVsOzQGVYfzlTbJFWRV3wjqDIY2ZWcD0A/F85HqU2wrwcPj2oLwLvLjlBSYScrOHM+ikyarS9X9IihX1wI5VX2sZIzPeA6Sk9YvfD20PHCRDHk9ct1iXYx5TohXYTrZD5K/Wgf5c8dg1oB8NLCgw41W8vSSJHSCA6lFVJWZSDQ290lp3eejymDWtIixIesogo+XmUfTaVkPkrD0Wg0vHpFF3RaDUsPnmH1EXVXcgky8bnBjOkUyYj24VQZFF5acFDVJNryKgPHq4WSFCn1Z9rodkQGeJKcU8oXaxPVNkc1pEhpBAerH3zdY4Nk18taeLrpeLF6BffN+pMcPVOkskXyAddY6qzkFqi7kqvJZ5DnsL5oNBpentQZDzct649ls3h/hmq2HM8sxmBUCPJxJ1qWj9cbP083nhsv7qefrjlut036rI0UKY3gULooPZZJYGdzScdILu0Yid6o8NxfB1RdwZVW6knMLgGkJ6Ux1F7JfblOnZVcfmklaQXlAHSQ11uDaBnqy/3DxQDJV/85pFoI9lCtcJ2rFxk0lAndohkUH0qF3sjLCw+qbY4qSJHSCGQI4cK8OLETXu5iDsVfe06rZseRjCIUBcL9PQn3d72xBU3Fz9ONZ6tXcp+sPq5KOaQplyE2xJsAL3ebH9/ReXBEPLEh3mQUlvPxSnVCsOacInm/bDAajYZXruiCu060eFjhgnN9HFakGAwGysvLbf5VUlpGQXEpzfx1dAj3stpxjEbHnd0QG+JjTqJ9fVGCap0TD8mKgiYzsVs0A1uLldxbKrRbPySTZpuEl7uOlyd1BuCbDSc5nmn7EKx5USdDro2iTYQfdw0RZeWumETrcF11FEUhIyOD/Px8VY6vNxh5ekgoWg0YCjI5WWid42i1WuLi4vDwcMxR5vcMbc28XamcyCphxrIjvHxFF5vbIG+OTUej0fD8hE5c/vF6/j2Qwen8MpoF2a55oVyFN51RHSK5tGMEKxIy+WxNIu9P7m6zYyuKUuscynBdY3nkkjb8tfs0qXllLNibxuQ+sWqbZDMcTqSYBEpERAQ+Pj42j3EWllVi9CvHy11Hy1DrNJYyGo2kpaWRnp5OixYtHDKO6+Gm5dUrunDT11uZuyWZa3vH0rW5bcuApSfFMnSKCWBg61A2J+Ywd3MyT4+z3YA/KVIsw9RRbVmRkMnCvWk8c3kHwmw0tT2toJzCcj1uWg1tZCVko/HxcOOOwa14c8lh5mxM4rrezR3yudAYHCrcYzAYzAIlNDQUb29vvLy8bPpl1LqjcfPA18d6x/bx8SE8PJzS0lL0evvoN9IYBrUJ44oeMRgVeP5v2ybRGowKh6vzGaQnpencMbgVAL9sT6Gs0jbu5iqDkWNnROlqZ3kOm0SP2CB6xAZRaTDy09YUmx3X5M1sE+GHp5tsptgUru8bi5e7lkPphWxPylPbHJvhUCKlqkrkNvj4+KhmQ1mVyBXxdrfuBWcK8xgMjh1/fPbyjni769hzKp/NiTk2O25STgllVQa83XW0spLHy5W4pGMkzYO9yS+t4m8bJUOfyCqm0mDE39PN5edjWQKT0PxhSzKVetvkvMlOs5YjyMeDq3o2B2D2RvsYWWELHEqkmFDTzWVKWvKyskhxFldeRIAX1/RuBsDsjUk2O65pBdch2h+d7GXTZHRaDbcNbAXAnE1JNvGKmR5wHaL9neZ6UJNxXaKJ8Pcks6iCJQdsM3xQhussi0loLj0o8sNcAYcUKWpRZTBSZRArEGuLFGfi9uqmYCsSztisIZHMR7E8k/vE4u2u43BGEVsSc61+PFnqb1k83LTmQaC2WjAckiLForSL9Gdwm1CMCszdnKy2OTZBipQGYPKieLrp5Oq8AbSJ8Gdo2zAUBb7fnGSTY8rKHssT6ONeyytmfXeznPdieW7s1wIPnZY9p/LZnWLdvIbiCj3J1YsSWdljOW4fFAfYNj9MTaRIaQBl5lCP/GdrKHcOFhfWrztO2aTzpfSkWAdTyGdFwhmrNnerW7oqz6GlCPf3ZGL3GAC+25Rk1WMdyRDnLzLAk1AbVRO5AqM6RBAbIvLD1GyWaSvk07YBlFfaJmnWGRneLpzWYb4Ulev5c1eqVY+VWVROVlEFWg10kE3ALErbSOEVMyowd4v13M2ZRRXklFSi1Yg5QhLLYQq/LtqfTmZhudWOI2cuWYc6+WEbbZMfpiZSpDQAsyfFQ4qUhqLVarit+uY4Z2MSRqP1LixTmCAuzBdvea4sjukh98u2FEorreMVM3nCWof7yfwvC9O1eSB9WgZTZVD4wYrlyIdMLQCkSLE41/WJxcdDx5EzRTatmlQDpxYpI0aM4JFHHuHJJ58kJCSEqKgoXnrpJfP7M2bMoGvXrvj6+hIbG8uDDz5IcXGx+f05c+YQFBTEP//8Q/v27ekRF8lj992GsbKc7777jlatWhEcHMzDDz9cp1S4srKSJ598kmbNmuHr60v//v1Zs2aNDf9y++Sa3s3x93QjMbuEtceyrHacmnwU2zaPcxVGto+gZagPheV6PlphnXkwMtRjXW6vrhL5aWuy1cZWyHNoPQK93bmmlyhH/mzNCasu+tTGqUUKwHfffYevry9bt27lnXfe4ZVXXmH58uWAaD3/8ccfc+DAAb777jtWrVrFk08+WefzpaWlfPzxx8ye+wOfzv2dHZs3cP1117J48WIWL17M3Llz+fLLL/njjz/Mn7njjjvYuHEjv/zyC/v27eO6667jsssu49gxdQZ82Qt+nm5cV93OeY4VqwtkPop10Wo1PDG2PQBfrEvktx2nLH4MWdljXcZ2jqJ5sDfZxZXcMXubxfPEDEaFwxlSpFiT2we3wk2rYf2xbF5YoO7EeWvi9CKlW7duvPjii7Rt25YpU6bQp08fVq5cCcC0adMYOXIkcXFxjBo1ildffZXffvutzuerqqr47LPPaN+pG70HDGbcxCvZsGED33zzDZ06dWLChAmMHDmS1atXA3DixAl+/vlnfv/9d4YOHUp8fDyPP/44Q4YMYfbs2Tb/++2N2we1QqOBtUezOJ5ZfPEPNIJDaQWArOyxJhO6xfDwqDYAPDt/P1ss7HKW816si7tOy1dT+hDo7c6ulHzu+X6HRQfXJeWUUF5lxMtdS1yYbKZoDeLD/ZhxfQ80GvhhSwrvLTuitklWwSVESm2io6PJzMwEYPXq1YwePZpmzZrh7+/PlClTyMnJoaSkxLy9j48P8fHx5gs4KiqSVq1a4edXM4ciMjLSvM9du3ahKArt2rXDz8/P/LV27VpOnDhh7T/X7mkR6sMlHSIBuPnrLTz1xz4W7k0jp7jCIvvPK6nkRJY4f12byXCPNXn00naM7xZNlUHh/h92cjK75OIfqgdllQbzvqQnxXp0jA7guzv74euhY9OJHB76cZe5D1RTMYnM9lEBsl2DFZnUPYbXr+wKwCerT/DFWud7xji9SHF3d6/zs0ajwWg0kpyczOWXX06XLl34888/2blzJ5988glQ036/9udN7fA93HTn3SeI4YA6nY6dO3eyZ88e81dCQgIfffSR1f5OR2LapW0J8HLjTGEFv+44xcM/76b3ayu45rNNTS5r3Z4kmoy1jfAjxNcxJ0g7Clqthvev60732CDyS6u4a8528ksrm7zfQ+kFGBUI8/Mg3F+WrlqTHrFBfHN7XzzdtKw8nMm0X/dgsEB+w95T+YCcuWQLburfwjz0880lh/l5m+1mM9kCpxcp52PHjh3o9Xref/99BgwYQLt27UhLSzvntoqimD0pbhdZFfTs2RODwUBmZiZt2rSp8xUVFWXxv8MR6dIskC3/dwmz7+jL3UPi6FBdYrozOY//m7+/SbHVbSeFSOkXF2IRWyUXxstdx1dTetMsyJvE7BIe+GFXkx9yW6vPYZ+WIbIdvg0Y0DqUL27tjbtOw6J96by04GCT92m+DlvJ69AW3D88ngdHxAPwf/P3s/zQGZUtshwuK1Li4+PR6/XMnDmTxMRE5s6dy+eff37ObSv1RoyKglajuajrsl27dtx8881MmTKFefPmcfLkSbZv387bb7/N4sWLrfGnOCQ+Hm6MbB/BcxM68e+0YSx/dBgeblrWH8vm3wMZjd7vtiQpUmxNhL8X39zeB18PHZsTc5q8kpNC0/aMaB/BzBt7AqL/zc7kxo89KK7Qc6A68VmeQ9vxxNj23NS/BYoCz/91wCZNM22By4qUHj16MGPGDN5++226dOnCjz/+yJtvvnnObctqDRWsz8pu9uzZTJkyhccee4z27dszadIktm7dSmxsrEX/BmeibaQ/9w9rDcCr/xxqVP+N4go9B+XNURU6RAXweHXFz3vLjpBX0riwj8GosLN6DL08h7blsi7RTO4jylpf+Ptgoz1iu5LzMBgVmgd7ExMkp1fbCo1GwwsTOtEixIeMwnJmrT6utkkWQaPYWd1SYWEhgYGBFBQUEBBQN55ZXl7OyZMniYuLw8vLy2Y2ZRSUkVlUQYivB82DfWxyTLX+VjUpqzQw+oO1pOaV8eCIeJ68rEODPr/uaBZTvt1GbIg3658cZSUrJedDbzAy/uMNHDlTxC0DWvBadUJfQzhwuoAJMzfg5+nG3hfHyKRLG5NdXMHI99ZQVK7ntSu7mAcSNoT3lh5h1urjXN2rGTMm97C8kZILsvzQGe75fgfuOg3LHh1u0+qqCz2/G4vLelIagilpVrbDty7eHjpenNgZgK/WJ3Iiq2ElyjVx8FCL2ya5OG46LS9NEufvp60pHKwuBW8IpnPYp1WwFCgqEObnyfTR7YDGe8RM57C/9ISpwqUdIxjeLpwqg8LLCw86fP8UKVLqQXmtcI/EulzaMYKR7cUF9tKChl1g8uaoPgPjQxnfLRqjQoPPH8h8FHvg1gEtaR/pT35pFe8vb1jvjfIqA3uqK3v6xcnFghpoNBpenNgJd52GNUeyWJmQqbZJTUKKlIugNxjNvQOkSLE+Go2GlyZ1NifRLqlnEm3dm6N8wKnJs5d3xNtdx/akPP7ec+6KuXOhKIo58VkKTfVw02l5+QrhEftxawoHTtffI7b3VD6VBiPh/p60CrVNaFxyNq3D/bhriMjxe+WfQxZt1GdrpEi5COXm/iha6X62ES1Dfesk0VbqL95gal9qgfnm2FLeHFUlJsibqdXdaN9YnEBxPasMTmQVk1tSiaeblq7NgqxooeRiDGgdyoRu0SgN9IjV9oTJ8nF1eXhUGyIDPEnJLeXr9Ylqm9NopEi5COX66lCPm/Si2JIHRrQhzM+D9ILyek353HZSbCNvjvbB3UPjaBnqQ2ZRBbNW1a/KwNQfpVeLYDzc5K1JbZ4dLzxiO5LzWLgvvV6fMbcAkP1RVMfX043/u7wjALNWH+dMYbnKFjUOeSe4CDIfRR28PXSM6Sya3y09ePGQz1aZj2JXeLrpeLb6BvnL9hT09Wi3LvNR7IvoQG/uqfZo/rEz9aLb6w1GdibL8nF7YlL3GLrHBvH/7d17cFPXnQfw75UtybKQFRs/hXm4kECMbUIMBZMQXsHAAEmGdCYQhjrpbHbIYEqHdCeFdAPNpGtmtu02s2loSlg2zDZ1JgVSdknApsE4WWwCNi4GE9YN5m1jB/zCD1mWfvuHkUDGbyTrSvp+ZjSDpeN7zv35IP10z+O22xz4nwEmmmrj8SRl69atUBTF7eHPO606h3vCtMznhltmctc9fvIrbvR5K/JOuwOlfHNUnfmTYhEZrkVDqw0n7ux90hsRwfELTDTV5tnHLACAom+/Q1O7rc+yZ683obXDjoiwUEyM440h1UBRFDw7petvmDeAL3tq5JVP3smTJ6O6utr1KC8v90Y1Xnfvdvi8kjL8Zo2PhkkfirpmK07dmRTbk4rqJrR02GE2aPFILN8c1SI0RIMFj3YlmnkVfb9BXq1vQ01TO0I1CqaOiRyO5tEAjI8ZgfExRtjsgoLzdX2WvfdKmIbz91Rj4Z0veycu3sKtIW6y6EteSVJCQ0MRHx/vesTExHijGq/rsHdth68oCvQcIx92ulAN5k6KBdD3h5zzzXH6uEi+OaqM82pY3tkbfU6+dA7XpSaaYdDxC4GaLEzuuhLe3zfx4xyuU6XRUeFIToiAQ4C/nvO/e/p45ZO3srISFosFSUlJWLlyJS5c6H1msdVqRVNTk9tDLVxDPaEaTsb0kUWT+/+Q45ujes1+OAZhWg2uNbThXHVzr+XunfhM6pJ55/9gwfk6WDt7XsrqcIjrDuTcH0V9Ft4zdO5vPJ6kzJgxA7t378ahQ4ewY8cO1NTUYNasWbh5s+cVGjk5OTCbza6Hmu5v48mhnsLCQixfvhwWiwWKouDTTz994GMGgzmPxEAXokHVdy34e+39O9DyzVHdDLoQzH6460rqQK6GcT6K+jyW+BBiTHrctnai+ELPNx78v9pmNLbZEK4LwWSLZ7ZDJ89xJpqFlXVo6/CvPVM8nqQsWbIEzz//PFJTU/H000/jwIEDAIAPP/ywx/KbNm1CY2Oj63HlyhVPN2nI7iYpDx6mlpYWTJkyBe++++4DHyuYmMK0eGJCV/KR18O3gMra22hotcGg5ZujWt075NOT2qZ2XLzZCkUB0scySVEbjUZxfRPvbcjHmWSmj42ENoRD42qTnBCBUQ8Z0G5z4MvKvucWqY3Xe5PRaERqaioqKyt7fF2v1yMiIsLtMRgigtaOTq88GlptaLfZ4eiljsFs+b1kyRK8/fbbWLFixaDOj9DnUmTnMAHfHNVrwaNx0ChdE5yv3Gq973Xn3hqPxkfAbNAOd/NoAPpbaecacuX+KKqkKIrrakpPX/bULNTbFVitVpw7dw6zZ8/2yvHbbHYkv3nIK8fuT8VbixCu83oIg97Tj8Zhs1KO01cbcb2hzXX795rGu7cjzxjPoR61ijLqMG1cFL6uuoXD527g5SeS3F7n/ijqlzF+JEboQ1HbbMXfrja4rcASkbuT1/k3VK3M5Hjs+t+L+Ou5G+i0OxDqJ1/qPN7Kn/70pzh69Ciqqqpw/Phx/OAHP0BTUxOysrI8XRUFiRiTHul33hSdE79aOzrxD7tP4EaTFY/EjcAPMwZ/S3kaPr0N+TS22nD4zt+U81HUSx8agjkTnXOL3P+Gn5ZdQ12zFboQDR4b/ZAPWkcDMX1cJB4K16K+1YaTl/ret0hNPH4Z4OrVq1i1ahW+++47xMTEYObMmSguLsbYsd75EDFoQ1Dx1iKPH7ehxYarDa0I14fie9HGXuum4ZE5OQ4nL9Ujr6IGa2aOxcaP/4Yz15oQZdRhZ9Z0mMI4TKBmmcnxePvAOXx98RbqWzoQadSho9OBtf9VguuN7bCYwzD7Ef/cqiBYZCbH4cDpauSdrcHriycB6Np74/U/d+2D9cpTSdxPSsVCQzRYMCkOe0qvIr/iBmZ+zz+uPns8ScnNzfX0IfukKIpXhlwa22wI04YgMlzHIR0VyEyOx7989g2KL9zC1v8+i4Nna6AL0eD9NekYHcUbCqrdmJHhmBRvwjc1zfjim1qseHwU3thXjqILN2HUhWDnS9MxQs//Z2o2b1IstCEKvq1rwbd1txGqUfCPu0+iw+7A4snxeG3hRF83kfqxMLkrScmrqMHPlz7qF1tr+MeglA/cu0cK+d64aCMmxplgdwh2F10CAOSsSMV0TtTzG/dOvnyv4Ft8UnIVGgV498XH8WgCV2apXUSY1vXt+88lV/Hyf55AfasNaYlm/NsLj3EjRT/w1CPR0IdqcOVWG76p6X3fIjXhJ3AvPL0d/u3bt1FWVoaysjIAQFVVFcrKynD58mWPHD8YODd2A4BX547H8+mJPmwNDZZzldZfv7mBfz10HgCw9ZnJmHdnV2FSP+ffcHvBt7hQ1wKLOQwf/HAadwn2E+G60Lv7FvWyJYDaMEnpQafdAZvdeWNBz/znO3nyJKZOnYqpU6cCADZu3IipU6fizTff9Mjxg8Hz6YmIDNdixeOj8E+ZvLTsbyZbImAxh8Fm71rC+vIT4/DDjHG+bRQNysJH735RGKEPxX+8PB2xEWE+bBEN1t2lyP5xw0EOAvfAOdSjC9EgxEOXMOfOnTuofVXofmNHGlH6zwv9YhyV7qcoCpamJWDHl1VYMCkWP1+a7Osm0SDFm8Mw++FoHL9wC//+4lRMiucwnb9ZMCnWtW9RbXM7Yk3qTjKZpPSgvZN3PlYrJij+bePCicgYPxJPTIj22BcAGl4fZE1Dc3snokfofd0UGoKRI/R4b3U6Hh/zkOoTFIBJSo88PR+FiLoYdCGYPymu/4KkWvrQEOhH8L3Rny1Oifd1EwaMc1J64FrZ44F79hAREdHQ8FO4GxHhlRQiIiIVYJLSjc3ugEMEiqJAxz1SiIiIfIafwt203Rnq0YdqoOEkTSIiIp9hktKNc6iH9+UhIiLyLSYp3dydj8LQEBER+RI/ie8hImjtuHMlhTcVJCIi8ikmKfew2QU2uwMKFIR7eLgnJycH06dPh8lkQmxsLJ577jmcP3/eo3UQEREFEiYp92jt6ATQNdTj6Tt6Hj16FOvWrUNxcTHy8/PR2dmJzMxMtLS0eLQeIiKiQMExjXs4h3rC9Z4Py8GDB91+3rVrF2JjY1FSUoKnnnrK4/URERH5O/9PUkQAW6tHDtXW0gLF1olwCNDh6P8XtOHAEJcpNzY2AgCioqKG9PtERESBzv+TFFsr8C8Wjxxq/GB/YfN1QGccdD0igo0bN+LJJ59ESkrKoH+fiIgoGPh/kuKHsrOzcfr0aXz11Ve+bgoREZFq+X+Sog3vuqLxgOpuW1HT2I6IMC3GjgwfeN2DtH79euzfvx+FhYVITEwc9O8TEREFC/9PUhRlSEMu3bUKIFoNDMYwQBfmgYa5ExGsX78e+/btQ0FBAZKSkjxeBxERUSDx/yTFQ1wre7y0idu6devw0Ucf4S9/+QtMJhNqamoAAGazGQaDwSt1EhER+TPukwLA1um4s4kbYNB5554927dvR2NjI+bOnYuEhATX4+OPP/ZKfURERP6OV1Jw7yZuIQjx8CZuTiLileMSEREFKl5Jwb1DPbzzMRERkVowSQHQ4uX5KERERDR4QZ+kOETQZuOVFCIiIrUJ+iSl3WaHiCBUo0AXGvThICIiUo2g/1Rutd4d6lGGeB8eIiIi8jwmKXdW9nCoh4iISF2YpHBlDxERkSoFdZJiszvQ4drEjSt7iIiI1CSok5Tm9q6hHr0XN3EjIiKioQnaJEVEUNdsBQA8ZND6uDVERETUXdAmKU3tNlg77QjRKBg5Quf1+rZv3460tDREREQgIiICGRkZ+Pzzz71eLxERkb8KyiRFRFDb1HUVZaRRjxCN98OQmJiIbdu24eTJkzh58iTmz5+PZ599FmfPnvV63URERP4oKGeL3rZ2os1mh0ZRED0MV1EAYPny5W4///KXv8T27dtRXFyMyZMnD0sbiIiI/InfJykigrbOtkH9zuVbLWi3dyLaqEeHox0djqHVbQg1DGkDOLvdjk8++QQtLS3IyMgYWuVEREQBzutJSk5ODjZv3owNGzbgt7/9rceP39bZhhkfzfD4cQfi+IvHEa4NH3D58vJyZGRkoL29HSNGjMC+ffuQnJzsxRYSERH5L69Oxjhx4gT+8Ic/IC0tzZvV+I2JEyeirKwMxcXFePXVV5GVlYWKigpfN4uIiEiVvHYl5fbt21i9ejV27NiBt99+21vVwBBqwPEXjw+obLvNjr/X3YYC4OE4E3QhD5ajGUINgyqv0+kwYcIEAMC0adNw4sQJvPPOO3j//fcfqB1ERESByGtJyrp167B06VI8/fTTfSYpVqsVVqvV9XNTU9Og6lEUZcBDLnVNLQgLMeChcB0eChv4MI23iIjbuRMREdFdXklScnNzUVpaihMnTvRbNicnB7/4xS+80Qw37TY7GttsAIBYk97r9XW3efNmLFmyBKNHj0ZzczNyc3NRUFCAgwcPDntbiIiI/IHHk5QrV65gw4YNyMvLQ1hYWL/lN23ahI0bN7p+bmpqwujRoz3dLOhCNUiMDEe7zY4w7fDfTPDGjRtYs2YNqqurYTabkZaWhoMHD2LhwoXD3hYiIiJ/4PEkpaSkBLW1tUhPT3c9Z7fbUVhYiHfffRdWqxUhIXeTBL1eD73e+1c2NIqCKOPw7InSk507d/qsbiIiIn/k8SRlwYIFKC8vd3vu5ZdfxqRJk/D666+7JShEREREvfF4kmIymZCSkuL2nNFoxMiRI+97noiIiKg3QXnvHiIiIlK/YdkWv6CgYDiqISIiogDCKylERESkSn6ZpDgcQ7wjoB8REV83gYiIyKf86i7IOp0OGo0G169fR0xMDHQ63ZDuQqx2IoK6ujooigKtVuvr5hAREfmEXyUpGo0GSUlJqK6uxvXr133dHK9SFAWJiYlcsk1EREHLr5IUoOtqypgxY9DZ2Qm73e7r5niNVqtlgkJEREHN75IUAK5hEA6FEBERBS6/nDhLREREgY9JChEREakSkxQiIiJSJdXNSXHuD9LU1OTjlhAREdFAOT+3PbnPl+qSlObmZgDA6NGjfdwSIiIiGqzm5maYzWaPHEsRlW1t6nA4cP36dZhMJo9v1NbU1ITRo0fjypUriIiI8Oix/QVjwBg4MQ6MAcAYAIyB04PGQUTQ3NwMi8UCjcYzs0lUdyVFo9EgMTHRq3VEREQEdUcEGAOAMXBiHBgDgDEAGAOnB4mDp66gOHHiLBEREakSkxQiIiJSpaBKUvR6PbZs2QK9Xu/rpvgMY8AYODEOjAHAGACMgZMa46C6ibNEREREQJBdSSEiIiL/wSSFiIiIVIlJChEREakSkxQiIiJSJSYpREREpEpBk6S89957SEpKQlhYGNLT0/Hll1/6ukkDsnXrViiK4vaIj493vS4i2Lp1KywWCwwGA+bOnYuzZ8+6HcNqtWL9+vWIjo6G0WjEM888g6tXr7qVqa+vx5o1a2A2m2E2m7FmzRo0NDS4lbl8+TKWL18Oo9GI6Oho/PjHP0ZHR4fHz7mwsBDLly+HxWKBoij49NNP3V5X2zmXl5djzpw5MBgMGDVqFN566y2P3GCrvzi89NJL9/WNmTNnBkwccnJyMH36dJhMJsTGxuK5557D+fPn3coEQ18YSBwCvS9s374daWlprp1QMzIy8Pnnn7teD4Z+0F8MArYPSBDIzc0VrVYrO3bskIqKCtmwYYMYjUa5dOmSr5vWry1btsjkyZOlurra9aitrXW9vm3bNjGZTLJnzx4pLy+XF154QRISEqSpqclVZu3atTJq1CjJz8+X0tJSmTdvnkyZMkU6OztdZRYvXiwpKSly7NgxOXbsmKSkpMiyZctcr3d2dkpKSorMmzdPSktLJT8/XywWi2RnZ3v8nD/77DN54403ZM+ePQJA9u3b5/a6ms65sbFR4uLiZOXKlVJeXi579uwRk8kkv/rVr7weh6ysLFm8eLFb37h586ZbGX+Ow6JFi2TXrl1y5swZKSsrk6VLl8qYMWPk9u3brjLB0BcGEodA7wv79++XAwcOyPnz5+X8+fOyefNm0Wq1cubMGREJjn7QXwwCtQ8ERZLy/e9/X9auXev23KRJk+RnP/uZj1o0cFu2bJEpU6b0+JrD4ZD4+HjZtm2b67n29nYxm83y+9//XkREGhoaRKvVSm5urqvMtWvXRKPRyMGDB0VEpKKiQgBIcXGxq0xRUZEAkG+++UZEuj4wNRqNXLt2zVXmT3/6k+j1emlsbPTY+XbX/cNZbef83nvvidlslvb2dleZnJwcsVgs4nA4vBYHka43pWeffbbX3wm0ONTW1goAOXr0qIgEb1/oHgeR4OsLIiKRkZHywQcfBG0/uDcGIoHbBwJ+uKejowMlJSXIzMx0ez4zMxPHjh3zUasGp7KyEhaLBUlJSVi5ciUuXLgAAKiqqkJNTY3buen1esyZM8d1biUlJbDZbG5lLBYLUlJSXGWKiopgNpsxY8YMV5mZM2fCbDa7lUlJSYHFYnGVWbRoEaxWK0pKSrx38t2o7ZyLioowZ84ctx0aFy1ahOvXr+PixYueD0A3BQUFiI2NxSOPPIJXXnkFtbW1rtcCLQ6NjY0AgKioKADB2xe6x8EpWPqC3W5Hbm4uWlpakJGREZT9oHsMnAKxDwR8kvLdd9/BbrcjLi7O7fm4uDjU1NT4qFUDN2PGDOzevRuHDh3Cjh07UFNTg1mzZuHmzZuu9vd1bjU1NdDpdIiMjOyzTGxs7H11x8bGupXpXk9kZCR0Ot2wxlFt59xTGefP3o7LkiVL8Mc//hFffPEFfv3rX+PEiROYP38+rFarq/5AiYOIYOPGjXjyySeRkpLidtxg6gs9xQEIjr5QXl6OESNGQK/XY+3atdi3bx+Sk5ODqh/0FgMgcPtA6KBK+zFFUdx+FpH7nlOjJUuWuP6dmpqKjIwMjB8/Hh9++KFrUtRQzq17mZ7KD6XMcFHTOffUlt5+15NeeOEF179TUlIwbdo0jB07FgcOHMCKFSt6/T1/jEN2djZOnz6Nr7766r7Xgqkv9BaHYOgLEydORFlZGRoaGrBnzx5kZWXh6NGjfdYZaP2gtxgkJycHbB8I+Csp0dHRCAkJuS97q62tvS/T8wdGoxGpqamorKx0rfLp69zi4+PR0dGB+vr6PsvcuHHjvrrq6urcynSvp76+HjabbVjjqLZz7qmM8xLrcPevhIQEjB07FpWVla62BUIc1q9fj/379+PIkSNITEx0PR9sfaG3OPQkEPuCTqfDhAkTMG3aNOTk5GDKlCl45513gqof9BaDngRKHwj4JEWn0yE9PR35+fluz+fn52PWrFk+atXQWa1WnDt3DgkJCUhKSkJ8fLzbuXV0dODo0aOuc0tPT4dWq3UrU11djTNnzrjKZGRkoLGxEV9//bWrzPHjx9HY2OhW5syZM6iurnaVycvLg16vR3p6ulfP+V5qO+eMjAwUFha6Lb/Ly8uDxWLBuHHjPB+APty8eRNXrlxBQkICAP+Pg4ggOzsbe/fuxRdffIGkpCS314OlL/QXh54EWl/oiYjAarUGTT/oKwY9CZg+MKhptn7KuQR5586dUlFRIT/5yU/EaDTKxYsXfd20fr322mtSUFAgFy5ckOLiYlm2bJmYTCZX27dt2yZms1n27t0r5eXlsmrVqh6X3iUmJsrhw4eltLRU5s+f3+Oys7S0NCkqKpKioiJJTU3tcdnZggULpLS0VA4fPiyJiYleWYLc3Nwsp06dklOnTgkA+c1vfiOnTp1yLRlX0zk3NDRIXFycrFq1SsrLy2Xv3r0SERHhkSXIfcWhublZXnvtNTl27JhUVVXJkSNHJCMjQ0aNGhUwcXj11VfFbDZLQUGB27LK1tZWV5lg6Av9xSEY+sKmTZuksLBQqqqq5PTp07J582bRaDSSl5cnIsHRD/qKQSD3gaBIUkREfve738nYsWNFp9PJ448/7rZ8T82c6/21Wq1YLBZZsWKFnD171vW6w+GQLVu2SHx8vOj1ennqqaekvLzc7RhtbW2SnZ0tUVFRYjAYZNmyZXL58mW3Mjdv3pTVq1eLyWQSk8kkq1evlvr6ercyly5dkqVLl4rBYJCoqCjJzs52W2LmKUeOHBEA9z2ysrJUec6nT5+W2bNni16vl/j4eNm6datHlhr2FYfW1lbJzMyUmJgY0Wq1MmbMGMnKyrrvHP05Dj2dOwDZtWuXq0ww9IX+4hAMfeFHP/qR6/07JiZGFixY4EpQRIKjH/QVg0DuA4qIB7bGJCIiIvKwgJ+TQkRERP6JSQoRERGpEpMUIiIiUiUmKURERKRKTFKIiIhIlZikEBERkSoxSSEiIiJVYpJCREREqsQkhYiIiFSJSQoRERGpEpMUIiIiUqX/B3wlkFYoN4/vAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "# Print available node results\n",
+    "results_EPANET.node.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print available link results\n",
+    "results_EPANET.link.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# View EpanetSimulator pressure results\n",
+    "results_EPANET.node['pressure'].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compare EpanetSimulator and WNTRSimulator pressure results\n",
+    "diff = results_EPANET.node['pressure'] - results_WNTR.node['pressure']\n",
+    "ax = diff.max(axis=1).plot(title='Max difference in pressure')\n",
+    "ax.set_xlabel('Time (s)')\n",
+    "ax.set_ylabel('Pressure difference (m)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Plot timeseries of tank levels\n",
     "tank_levels = results_EPANET.node['pressure'].loc[:,wn.tank_name_list]\n",
-    "tank_levels.plot(title='Tank level')"
+    "ax = tank_levels.plot(title='Tank level')\n",
+    "ax.set_xlabel('Time (s)')\n",
+    "ax.set_ylabel('Tank Level (m)')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 253,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: title={'center': 'Pump flowrate'}>"
-      ]
-     },
-     "execution_count": 253,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot timeseries of pump flowrates\n",
     "pump_flowrates = results_EPANET.link['flowrate'].loc[:,wn.pump_name_list]\n",
-    "pump_flowrates.plot(title='Pump flowrate')"
+    "ax = pump_flowrates.plot(title='Pump flowrate')\n",
+    "ax.set_xlabel('Time (s)')\n",
+    "ax.set_ylabel('Pump flowrate (m$^3$/s)')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot pressure at hour 5 on the network\n",
     "pressure_at_5hr = results_EPANET.node['pressure'].loc[5*3600, :]\n",
@@ -1848,12 +1187,12 @@
    },
    "source": [
     "## Reset initial conditions\n",
-    "Reset initial values, including simulation time, tank head, reservoir head, pipe status, pump status, and valve status.  This is required when using the WNTRSimulator multiple simulations. Note, the EPANETSimulator is autoamtically reset."
+    "Reset initial values, including simulation time, tank head, reservoir head, pipe status, pump status, and valve status.  This is required when using the WNTRSimulator in multiple simulations. Note, the EPANETSimulator is automatically reset."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -1882,13 +1221,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Create water network model from an INP file\n",
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
     "wn = wntr.network.WaterNetworkModel('../networks/Net3.inp')"
    ]
   },
@@ -1897,31 +1236,52 @@
    "metadata": {},
    "source": [
     "## Topographic\n",
-    "Topographic metrics describe the physical layout of the system. Note that some metrics require an undirected graph or a graph with a single edge between two nodes."
+    "Topographic metrics describe the physical layout of the system."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Convert the WaterNetworkModel to a MultiDiGraph, an undirected Graph, and an undirected simple graph (see NetworkX for more details)\n",
-    "G = wn.to_graph() # directed multigraph\n",
+    "# Convert the WaterNetworkModel to a MultiDiGraph\n",
+    "G = wn.to_graph() # directed multigraph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Some topographic metrics require an undirected graph or a graph with a single edge between two nodes\n",
     "uG = G.to_undirected() # undirected multigraph\n",
     "sG = nx.Graph(uG) # undirected simple graph (single edge between two nodes)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 258,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Articulation points\n",
+    "articulation_points = list(nx.articulation_points(uG))\n",
+    "ax = wntr.graphics.plot_network(wn, node_attribute=articulation_points, title=\"Articulation points\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Compute betweenness centrality\n",
-    "betweenness_centrality = nx.betweenness_centrality(sG)"
+    "betweenness_centrality = nx.betweenness_centrality(G)\n",
+    "ax = wntr.graphics.plot_network(wn, node_attribute=betweenness_centrality, title=\"Betweenness centrality\")"
    ]
   },
   {
@@ -1936,7 +1296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -1952,28 +1312,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Compute water service availability, defined as the ratio of delivered demand to the expected demand. \n",
     "expected_demand = wntr.metrics.expected_demand(wn)\n",
     "demand = results.node['demand'].loc[:,wn.junction_name_list]\n",
     "wsa = wntr.metrics.water_service_availability(expected_demand.sum(axis=0), demand.sum(axis=0))\n",
-    "ax = wntr.graphics.plot_network(wn, node_attribute=wsa, title='Water Service Availability')"
+    "ax = wntr.graphics.plot_network(wn, node_attribute=wsa, title='Water service availability')"
    ]
   },
   {
@@ -1988,7 +1337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -2001,31 +1350,21 @@
     "\n",
     "age = results.node['quality']\n",
     "age_last_48h = age.loc[age.index[-1]-48*3600:age.index[-1]]\n",
-    "average_age = age_last_48h.mean()/3600 # convert to hours"
+    "average_age = age_last_48h.mean()/3600 # convert to hours for the plot\n",
+    "ax = wntr.graphics.plot_network(wn, node_attribute=average_age, title=\"Average water age (hr)\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Compute the population that is impacted by water age greater than 24 hours\n",
     "pop = wntr.metrics.population(wn)\n",
-    "threshold = 24\n",
+    "threshold = 24 # hours\n",
     "pop_impacted = wntr.metrics.population_impacted(pop, average_age, np.greater, threshold)\n",
     "ax = wntr.graphics.plot_network(wn, node_attribute=pop_impacted, title=\"Population impacted by water age > 24 hours\")"
    ]
@@ -2043,39 +1382,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Fragility curves define the probability of exceeding a given damage state as a function of environmental change."
+    "Fragility curves define the probability of exceeding a damage state as a function of environmental condition.  Fragility curves are commonly used in earthquake analysis, but can be defined for other scenarios."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Create water network model from an INP file\n",
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
     "wn = wntr.network.WaterNetworkModel('../networks/Net3.inp')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "FC = wntr.scenario.FragilityCurve()\n",
     "FC.add_state('Minor', 1, {'Default': lognorm(0.5,scale=0.3)})\n",
@@ -2085,22 +1413,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 265,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Model peak ground acceleration from a magnitude 6.5 earthquake, assigning values to pipes.\n",
     "wn = wntr.morph.scale_node_coordinates(wn, 1000)\n",
@@ -2110,12 +1427,12 @@
     "earthquake = wntr.scenario.Earthquake(epicenter, magnitude, depth)\n",
     "distance = earthquake.distance_to_epicenter(wn, element_type=wntr.network.Pipe)\n",
     "pga = earthquake.pga_attenuation_model(distance)\n",
-    "ax = wntr.graphics.plot_network(wn, link_attribute=pga)"
+    "ax = wntr.graphics.plot_network(wn, link_attribute=pga, node_size=0, link_width=2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 266,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -2128,22 +1445,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 267,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot damage state (converted to numeric values) on the network\n",
     "priority_map = FC.get_priority_map()\n",
@@ -2167,63 +1473,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Network skeletonization reduces the size of a water network model with minimal impact on system behavior. \n",
+    "Network skeletonization reduces the size of a WaterNetworkModel with the goal of having minimal impact on hydraulics. \n",
     "\n",
-    "The skeletonization process retains all tanks, reservoirs, valves, and pumps, along with all junctions and pipes that are associated with controls. Junction demands and demand patterns are retained in the skeletonized model. Merged pipes are assigned equivalent properties for diameter, length, and roughness to approximate the updated system behavior. Pipes that are less than or equal to a user-defined pipe diameter threshold are candidates for removal based on branch triming, series and parallel pipe merge."
+    "The skeletonization process retains all tanks, reservoirs, valves, and pumps, along with all junctions and pipes that are associated with controls. Junction demands and demand patterns are retained in the skeletonized model. Merged pipes are assigned equivalent properties for diameter, length, and roughness to approximate the updated system behavior. Pipes that are less than or equal to a user-defined pipe diameter threshold are candidates for removal based on branch trimming, series and parallel pipe merge."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Nodes': {'Junctions': 3323, 'Tanks': 32, 'Reservoirs': 1},\n",
-       " 'Links': {'Pipes': 3829, 'Pumps': 61, 'Valves': 2},\n",
-       " 'Patterns': 3,\n",
-       " 'Curves': {'Pump': 60, 'Efficiency': 0, 'Headloss': 0, 'Volume': 0},\n",
-       " 'Sources': 0,\n",
-       " 'Controls': 124}"
-      ]
-     },
-     "execution_count": 268,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Create water network model from an INP file\n",
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
     "wn = wntr.network.WaterNetworkModel('../networks/Net6.inp')\n",
     "wn.describe(level=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 269,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Nodes': {'Junctions': 1121, 'Tanks': 32, 'Reservoirs': 1},\n",
-       " 'Links': {'Pipes': 1547, 'Pumps': 61, 'Valves': 2},\n",
-       " 'Patterns': 3,\n",
-       " 'Curves': {'Pump': 60, 'Efficiency': 0, 'Headloss': 0, 'Volume': 0},\n",
-       " 'Sources': 0,\n",
-       " 'Controls': 124}"
-      ]
-     },
-     "execution_count": 269,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Skeletonize the network using a 12 inch pipe diameter threshold\n",
     "skel_wn = wntr.morph.skeletonize(wn, 12*0.0254)\n",
@@ -2232,32 +1506,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot the original and skeletonized networks\n",
     "ax = wntr.graphics.plot_network(wn, node_size=0, title='Original')\n",
@@ -2266,7 +1519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 271,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -2282,30 +1535,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 272,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1df3affda50>"
-      ]
-     },
-     "execution_count": 272,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ra3E4HBk/lxI2CoVCoRgWuq6ze/durFYr48aNw+FwKINURQpCCCKRCI2NjezevZuZM2emxYSvP5SwUSgUCsWwiEQi6LrOxIkT8Xg8uV6OIk9xu93Y7Xb27t1LJBLB5XJl9HyqeFihUCgUIyLTd+CK0U823yPq3ahQKBQKhWLMoISNQqFQKBSKMYMSNgqFQqFQDIE9e/agaRobNmwY9HNWrVpFSUlJztfxUUAJG4VCoVB8JNm/fz//8R//kejomjx5Mtdffz3Nzc39Pm/ixIkcPnyY+fPnD/pcF198Mdu2bRvpkhWDQAkbhUKhGIhwB7z2E2jdk+uVKNLErl27OProo9m2bRuPPvooO3bs4IEHHuDll1/muOOOo6WlpdfnRSIRrFYrNTU12GyDbyx2u91UVVWla/mKflDCRqFQKAZAX/0r+OcPCP/zR7leSt4jhCAQiWX9jxBiSOu85pprcDgcvPDCC5x00klMmjSJc845h5deeomDBw/yne98B4ApU6bwwx/+kM9//vN4vV6+9KUv9ZoCevrpp5k5cyZut5tTTjmFhx56CE3T8Pl8QM9U1Pe//30WLVrEww8/zJQpU/B6vVxyySV0dHQk9nnuuec48cQTKSkpoby8nJUrV7Jz585hvzYfFZSPjUKhUAxA07qnqQJ279vPnFwvJs8JRuPMu+X5rJ93y+1n4XEM7iutpaWF559/njvuuAO3253yWE1NDZdeeimPP/44999/PwA//vGP+d73vsd3v/vdXo+3Z88eLrzwQq6//nq++MUvsn79er7+9a8PuI6dO3fy5JNP8swzz9Da2spFF13EXXfdxR133AGA3+/npptu4sgjj8Tv93PLLbfwyU9+kg0bNqgW+35QwkahUCj6w99MRfv7AOiRzhwvRpEOtm/fjhCCuXPn9vr43LlzaW1tpbGxEYBTTz01Rajs2bMnZf8HHniA2bNn8+Mf/xiA2bNn8/777ycESl/ous6qVasoKioC4LLLLuPll19OPO+CCy5I2f/BBx+kqqqKLVu2DKm+56OGEjYKRZrZ+uYztO1Zz7GXfAdN3VWNepo2PksFMs1hjwdzvJr8x223suX2s3Jy3nRhprXM8RBHH310v/t/+OGHHHPMMSnbli5dOuB5pkyZkhA1ALW1tTQ0NCR+3rlzJ9/73vd46623aGpqQtd1APbt26eETT8oYaNQpJNQO5Ne+CIFBNmx6VRmLDwh1ytSjJCWDc9QYfxbCZuB0TRt0CmhXDFjxgw0TWPLli2cf/75PR7/4IMPKC0tpaJCvvIFBQX9Hk8I0WNG1mBqfux2e8rPmqYlxAvAueeey8SJE/nNb37DuHHj0HWd+fPnE4lEBjz2Rxl1O6lQpJH4+kcoQH75BdsaBthbkffocWoa/5340SmUsBkLlJeXc8YZZ3D//fcTDKa+pnV1dTzyyCNcfPHFgx7oOWfOHNasWZOybe3atSNaY3NzM1u3buW73/0up512WiI9phgYJWwUinShx9HfeiDxYyyk6jFGO4c2v0Gx6OpSceqhHK5GkU5+8YtfEA6HOeuss3jttdfYv38/zz33HGeccQbjx48fsD4mmauvvpoPPviAb37zm2zbto0///nPrFq1CmDY085LS0spLy/n17/+NTt27OCf//wnN91007CO9VFDCRuFIl1sfwF7257Ej/FQR9/7KkYFB9c8BcBmbSYAbpSwGSvMnDmTtWvXMn36dC6++GKmT5/OVVddxSmnnMLq1aspKysb9LGmTp3KE088wd/+9jcWLFjAr371q0S7uNPpHNb6LBYLjz32GO+++y7z58/nxhtvTBQnK/pHE0Nt/s9T2tvb8Xq9tLW1UVxcnOvlKD6KPHQu7H4t8ePb877DsRd9I4cLUoyU7T9YzMz4Tl6Z/FVO3vtzufGWVlBF4QCEQiF2797N1KlTcblcuV5OXnHHHXfwwAMPsH///lwvJS/o772S7u9v9elUKNJB/WbY/Ro6VlbH58ltYRWxGc3s2bOTmXFphjblY59JbBdRf66WpMhj7r//ftasWcOuXbt4+OGH+fGPf8wVV1yR62V9JMnv0nWFYrTw1q8A2FT0Mba32jmOLYiw+gIczez495NMAXY5ZlEzcSa60LBogpC/E7ezaKCnKz5ibN++nR/+8Ie0tLQwadIkvva1r3HzzTfnelkfSZSwUShGir8J3vszAH91nMs4XgFAU3f2oxrnnpcBCE4+FZfdThAHBYQJBtpxl9XmeHWKfOOee+7hnnvuyfUyFKhUlEIxct79PcTDULuIlzun0ilk/lgJm9HLjsMtLIysA2DysZ/EYtEIIl/XSEClGBWKfEYJG4ViJMQi8M5vAYgv/TJ1HWECxhegNRbI5coUI2DDmy9QrAXpsHgpnCYdZUOafF1DStgoFHmNEjYKxUjY+jR01kFhNYcnnkNcF/iVsBn16B++AEBL7XKwSKv+kCaHJUaDStgoFPmMEjYKxUjYa7jSLriYA+1xAAJC+lbY40rYjEYOtAZYEHoHgMrFKxPbIxYpWKNBZbyoUOQzStgoFCPBL6f/UjKJA63Smt2M2Ki5QqOTQwf2MseyHx0Nz9wzE9sjFhmxUcaLCkV+MyJhc+edd6JpGjfccENiW319PZ///OcZN24cHo+Hs88+m+3bt/d7nFWrVqFpWo8/oZBy+VTkOf4m+XdBBQdaZYSmsMgLgFNXwmY00nHoAwCabDXg6XKfjVkNYaPa+BWKvGbYwmbNmjX8+te/ZsGCBYltQgjOP/98du3axVNPPcX69euZPHkyp59+On5//xeD4uJiDh8+nPJHOVkq8h5T2HgqEhGbyvJyAFxqYOKoJNy0F4AOV2pLd8zmAUBXwuYjgaZpPPnkk8N+/pQpU7j33nvTtp6RcPLJJ6cEIDLBSH9f6WRYwqazs5NLL72U3/zmN5SWlia2b9++nbfeeotf/epXHHPMMcyePZv777+fzs5OHn300X6PqWkaNTU1KX8UirzHTEUVVCYiNuOqTGGjIo6jEdG6D4BY4fiU7XGbjNiIsKqxGQs0NDRw9dVXM2nSJJxOJzU1NZx11lmsXr06J+v5/ve/z6JFizJy7L/97W/84Ac/yMix85FhCZtrrrmGFStWcPrpp6dsD4fDACmRFqvVisPh4I033uj3mJ2dnUyePJkJEyawcuVK1q9f3+/+4XCY9vb2lD8KRVaJRyHkk/8u6IrYTKiuBMAjgjA2RrF9pHD4DwKglUxM2a7bZcQG5U80JrjgggvYuHEjDz30ENu2bePpp5/m5JNPpqWlJddLSztlZWUUFX103LKHLGwee+wx1q1bx5133tnjsTlz5jB58mRuvvlmWltbiUQi3HXXXdTV1XH48OE+jzlnzhxWrVrF008/zaOPPorL5eKEE07otzbnzjvvxOv1Jv5MnDixz30ViowQaJZ/axZiDi91bTJCM6W2CgC7FicWUVGb0UZhqA4AV+WUlO3CViD/EVHdbv0iBET82f8zhJsIn8/HG2+8wd13380pp5zC5MmTWbp0KTfffDMrVqzo9Tm333471dXVbNiwAYA333yT5cuX43a7mThxItddd12/JRdtbW1cddVVVFVVUVxczKmnnsrGjRsBWWd62223sXHjxkSN6apVqwDYt28f5513HoWFhRQXF3PRRRdRX1+fOK4Z6Xn44YeZMmUKXq+XSy65hI6OriL35FTUK6+80mtN6+c///nE/v/4xz9YsmQJLpeLadOmcdtttxGLxRKPb9++neXLl+NyuZg3bx4vvvjioH/32WBIIxX279/P9ddfzwsvvNBr/Yvdbuevf/0r//Ef/0FZWRlWq5XTTz+dc845p9/jLlu2jGXLliV+PuGEE1i8eDE///nPue+++3p9zs0338xNN92U+Lm9vV2JG0V2Metr3GXU+2PEdIHdqjG5pjKxSzDQTpHTnaMFKoZKOBanMt4AFvDWTEt90CGFjUX5E/VPNAD/NS775/32ocRrNBCFhYUUFhby5JNPsmzZMpxOZ5/7CiG44YYbePLJJ3njjTeYOXMmmzZt4qyzzuIHP/gBDz74II2NjVx77bVce+21/P73v+/1GCtWrKCsrIxnn30Wr9fL//zP/3Daaaexbds2Lr74Yt5//32ee+45XnrpJQC8Xm+ibrWgoIBXX32VWCzGf/7nf3LxxRfzyiuvJI6/c+dOnnzySZ555hlaW1u56KKLuOuuu7jjjjt6rOX4449PCTRs3bqVj3/84yxfvhyA559/ns997nPcd999fOxjH2Pnzp1cddVVANx6663ous6nPvUpKioqeOutt2hvb894/c5QGZKweffdd2loaGDJkiWJbfF4nNdee41f/OIXhMNhlixZwoYNG2hrayMSiVBZWcmxxx7L0UcfPejzWCwWjjnmmH4jNk6ns983o0KRcQJmR1QlB1rkl934EjdOp5OwsOPUogQ72ykqrc7hIhVD4VBrkPGafF2LuwkbzWkIm6gSNqMdm83GqlWr+NKXvsQDDzzA4sWLOemkk7jkkktSGmJisRiXX345a9eu5d///jcTJkwA4Mc//jGf/exnE1/oM2fO5L777uOkk07iV7/6VY8b/3/9619s2rSJhoaGxPfWT37yE5588kmeeOIJrrrqKgoLC7HZbCn1pS+++CLvvfceu3fvTty4P/zwwxxxxBGsWbOGY46Rrti6rrNq1apEuumyyy7j5Zdf7lXYOByOxDmam5v50pe+xJVXXsmVV14JwB133MG3vvWtxGTyadOm8YMf/IBvfOMb3Hrrrbz00kts3bqVPXv2JH4f//Vf/zVgACObDEnYnHbaaWzatCll2xe+8AXmzJnDN7/5TaxWa2K71ytbXrdv387atWuHVLgkhGDDhg0ceeSRQ1meQpFdUlq9jfqaUg+apuHXXDiJEvKr2q/RRH3dAaZqEQA074SUxyxGNMCm/In6x+6R0ZNcnHcIXHDBBaxYsYLXX3+d1atX89xzz/GjH/2I3/72t4m0zI033ojT6eStt96ioqIi8dx3332XHTt28MgjjyS2CSHQdZ3du3czd+7clHO9++67dHZ2Um50TJoEg0F27tzZ5xq3bt3KxIkTU7IR8+bNo6SkhK1btyaEzZQpU1JqaGpra2loaOj3/x+NRrnggguYNGkS//3f/52y1jVr1qSIong8TigUIhAIsHXrViZNmpQQNQDHHXdcv+fKNkMSNkVFRcyfPz9lW0FBAeXl5Yntf/nLX6isrGTSpEls2rSJ66+/nvPPP58zz+wyurr88ssZP358ok7ntttuY9myZcycOZP29nbuu+8+NmzYwC9/+cuR/v8UisyRaPUuTwib8SUy7RTCDXSogYmjjLbDuwFotZZRakuNCFuchQDYlKN0/2jaoFNCucblcnHGGWdwxhlncMstt/DFL36RW2+9NSFszjjjDB599FGef/55Lr300sTzdF3n6quv5rrrrutxzEmTJvXYpus6tbW1Kekjk5KSkj7XJ4RA07QBt9vt9pTHNU1D1/U+jwvwla98hX379rFmzRpsti4poOs6t912G5/61Kd6PMflciF6qWXqbY25ZEjCZjAcPnyYm266ifr6empra7n88sv53ve+l7LPvn37sFi66pZ9Ph9XXXUVdXV1eL1ejjrqKF577TWWLl2a7uUpFOmjl1bvCaWGsLG4QYdIUEVsRhOhJilsOp01lHZ7zOaSwkY5So9d5s2bl+LF8olPfIJzzz2Xz372s1itVi655BIAFi9ezObNm5kxY8agjrt48WLq6uqw2WxMmTKl130cDgfxeLzHevbt28f+/fsTUZstW7bQ1tbWIyo0FH72s5/x+OOPs3r16h5RpMWLF/Phhx/2+X8z13To0CHGjZO1VLlqke+LEQub7gr0uuuu61XF9vece+65h3vuuWekS1EoskugKxV18JCRiiqTwiZiCJuYGpg4qtBb9wMQ6eZhA2BzS2GjHKVHP83NzXz605/myiuvZMGCBRQVFbF27Vp+9KMfcd5556Xs+8lPfpKHH36Yyy67DJvNxoUXXsg3v/lNli1bxjXXXMOXvvQlCgoK2Lp1Ky+++CI///nPe5zv9NNP57jjjuP888/n7rvvZvbs2Rw6dIhnn32W888/n6OPPpopU6awe/duNmzYwIQJEygqKuL0009nwYIFXHrppdx7772J4uGTTjppSHWrybz00kt84xvf4Je//CUVFRXU1ckuQLfbjdfr5ZZbbmHlypVMnDiRT3/601gsFt577z02bdrED3/4Q04//XRmz57N5Zdfzk9/+lPa29v5zne+M6y1ZAo1K0qhGC591NhA8lwhZeY2mrB3Sg8bSnqmExxuWcPgVMaLo57CwkKOPfZY7rnnHpYvX878+fP53ve+x5e+9CV+8Ytf9Nj/wgsv5KGHHuKyyy7jb3/7GwsWLODVV19l+/btfOxjH+Ooo47ie9/7HrW1tb2cTaZqnn32WZYvX86VV17JrFmzuOSSS9izZw/V1bK54IILLuDss8/mlFNOobKykkcffTTh5ltaWsry5cs5/fTTmTZtGo8//viw/+9vvPEG8XicL3/5y9TW1ib+XH/99QCcddZZPPPMM7z44oscc8wxLFu2jJ/97GdMnjwZkM09f//73wmHwyxdupQvfvGLvRYp5xJN9JYwG4W0t7fj9Xppa2ujuLg418tRfBR48CzY/xbxCx9i9p8cxHTB6ptPpdbrZt2PPs7iwL9ZO/8Wjr7wa7leqWKQvHLbGZws3uHg8bcz/szrUx7btnk9s/5yMp14KPx+375cHyVCoRC7d+9m6tSpagSOol/6e6+k+/tbRWwUiuFi1Ni0UkxMF9gsGlVF8gMbs5pzhVTEZrQQisapiMtOEm/N9B6PuwrkBddFSDlKKxR5jBI2CsVwMWpsDkVl7cW4EjdWi+wOiJutpxElbEYLB31dHjYFVVN6PO4qkKkoGzoiFs7m0hQKxRBQwkahGA6xCITaANgXliLG7IgCEHbTfl8Jm9HCoYYmSjX5emm91Ni4C7p8QiKqKFyhyFuUsFEohkNiTpSVXZ3SQ6I3YaNcakcPbYelUVpAKwBXzzy/x+UiLORrHfQrYaNQ5CtK2CgUw8Fs9faUcdAn0xJmRxQACft9NQl6tBBs3ANAu6v3zharRSOArKEKB5Q/UTJjpAdFkUGy+R5RwkahGA7J5ny+VHM+AM0p0xZWNTBx1BA3PGzCBX0PcAxp0o04rBylgS7H20BAvc8V/WO+R7q7JGeCtDsPKxQfCfxGKspTzoHG1HEKoOz3RyO2jgPyH96Jfe4T0twgUKMyDKxWKyUlJYm5RB6PJ+/s9RW5RQhBIBCgoaGBkpKSlJmSmUIJG4ViOBgRG72gkkPbTdfhrlSU1YjYKPv90UNhSHrTOCqm9LlPWHOBgKgyXkxgTooeaOii4qNNSUlJyuTyTKKEjUIxHIwam6C9hGhcethUF3UNTTTt9x26itiMBhIeNhYorp7W535RqxyVEQ+piI2JpmnU1tZSVVVFNBrN9XIUeYjdbs9KpMZECRuFYjiY5nyaF4DaEhc2a1fJmmm/79KV/f5o4EBrIOFh4+nFw8bEHJURC6mi8O5YrdasfnkpFH2hiocViuFg1Ng0xqWAmVDiSXnY4TGEjVCpqNHAgaY2qmkFevewMYnbpLDRw0rYKBT5ihI2CsVwMFJRh2My5TQ+qSMKwGnY77vVwMRRQWvdXiyaIKrZwVPR534xmxqVoVDkO0rYKBTDwUhF7fRLX5NJZakRG7dHChunFkXEItldm2LIBBp3A9DuqAFL35dF3RA2KONFhSJvUcJGkVWCkTif++3b/OqVnbleysgwUlHvtToAmFNTlPKwu8ib+LfyPMl/4i37AAj142EDINQMMIUi71HCRpFV1u9v5Y0dTdz38nZC0XiulzM8YmEIyzlRG5pl/f2cmlQLfrfLTUTIQspgZ1t216cYMqaHjejHwwaSR2Wo2ilF9qhvD/Hc+3XounJ4HgxK2CiySnswBkAwGmftntYcr2aYGHOihGalKe6iwGFNcR2GVPv9kLLfz3s8QcPDpnxyv/tpDkPYxFTxsCJ73PrUZr78x3d5bXtjrpcyKlDCRpFV2kNdPhf5/iH91wcN/GXt/p4PGPU1YUcpAguza4qwWHq6rQY1KXbUXKH8JhCJURGrB6CwHw8bAM2YAWaNqYiNInscbpdNCHualKAeDErYKLJKe7BL2Lz6YX4Lm+sfW8//e+I9djV2q6fwy46odmsJAHNqe06CBsN+H1Vjk+8caA12edhUTul3X6salaHIAcGIjHQ3dapGhMGghI0iq7SHYol/f1jfQV1bfrZDByPxxFp7pMwMYdOky4Lh7oXDJmHTzC2ohE0+c6Clk3GaMfvLO6HffS1GxEaNylBkE39Y1iM2doRzvJLRgRI2iqzSEUq1XH9tW35GbXzBrjujdfu6CRvDw+ZgRH7JdS8cNkm41AZVB00+01R/EKcWQ8cCxeP73deeGJWRn4JcMTYJGo0WjZ1K2AwGJWwUWcUsHi5yyW6iV/O0zqbV3yXAeggbI2JzwBA2s/uI2EStsjU4HlYRm3wm0CA9bDrsFWC197uvzSVfa6euIjaK7BFIpKKUsBkMStgosopZPHzWEXLK6xvbm4jF9VwuqVd8ga6IzfaGzpSiZ7N4uFkUM77Ejdfd+5dhl0utKvjLZ6LNg/OwAXB6ZMTGqRylFVlC1wWhqLxGqlTU4FDCRpFVzOLhj82soNhloy0YZeOB/PN5aQ10CRkhYMM+X9eDRrt3M8V9Rmugy6VWKPv9vMbaITvf9OL+PWwAnIajtBslbBTZIZjk99XUGUYI5WUzEErYKLKKWZBb6nHwsZmVQH7W2STX2EC3dJQRsWkRxX0WDgPEDZdaTbnU5i2RmI6j8yAA7gE6ogCcxnBTJ1GIxwbYW6EYOf5I1/ssGhe0BaP97K0AJWwUWcaM2BS77SyfJYcNvpqPwsaI2Nit0p9mXXLExqixaRZFfbZ6yyfLtIUWUamofGVbfQc1Qr7/vDX9e9gAuAu6Xu9oSNVOKTJPMJLq0K7SUQOjhI0iq5i1KsUuG8tnyYjNewd8tPrzy5/BXM/SqWUArN/XmrAzF6awwcvcfiI2wnCp1ZRLbd7y3oE2JmpS2GglA6eiPG4PMSEvm0G/EjaKzBPoLmxUAfGAKGGjyBq6LugMy7BqsdtOrdfNrOpCdAFv7GjK8epS8RmRpWVTy/E4rHSEYuxo7IRoCC0iv9A6LF6mVBT0eQzT88SqJkHnLR/uPcgMTaaiqDlywP0ddmtiVIYyXlRkgx7CRkVsBkQJG0XW6AjHMOvezHbvk2blZ52N2RVVXuhkwQQ5qXvd3taEh01UWKmqrMZu7fsjpDllNEe51OYv0X1rsGqCQMEEKKoZ1HOCmhOAkBI2iizQPRWl3IcHRgkbRdYw62tcdgtOm5x8fdKsKkDOjcqnan+zK6rUY2fxpFLAKCA20lAtFDFnXD/1NYDVJWts7ErY5CXBSJzqtg0AaBOPHfTzwkbEJqocpRVZIBBJLVJXEZuBUcJGkTW66mu6fF+OnlLKSfat/CD4X+zZ/HaultaDViNiU+JxJAkbXyJi0yy8zO3DcdjE5pYRG4ey389LthxuZzEfAuCadvygn2eOyoioiI0iC6hU1NBRwkaRNUzX4eIkQzvXtn/wW+tdnGl9l463H87V0nrQFoiioVPmiHHUpBIAdjR0EmiVU6CbRVG/HjYAduVSm9ds2t/MUZYdAGiTlg36eRGLjNjElD+RIgt0FzbKfXhglLBRZA0zYmPW17D2d/CXz2NHbo/kyUwlIQS+YJQ/O25nxmPLKRc+ppRLT5p9+6VLbTPFzKkdQNgYnicuMbqFzZ/X7Ofdva0D7zjKaNy5nkItRNhaAFVzB/28iDEqQ80AU2SDQCSGnRgzXe2AitgMBiVsFFkj4WHjtMFrP4ZnbgQEnQ5ZZ2OJ54eba3sohtDjHGPZhtVfB2/el0hH7dwj5woFbKVUFjr7PY7pUusaxS61Oxo6+MZf3+Nrf96Q66WkHfuhNQD4K48Ci3XQz4tZZSpKjcpQZINgJM73bQ/xHP/J8Zb3VcRmEChho8gapuvwFZ2/gX/+UG5c/v/YMPnzQP4IG18ggpeku/E1D3JcjQwHdxqpKGtRJZqm9XscV4GM2LiJgB7vd998pdnowDjoCyZ8fMYC7aEoUwKbAHBNO25Iz41bzRlgKmKjyDyBaJz5lt1Y0fmy9R80+yPEx9BnMRMoYaPIGu3BKHO1vZza+he54ey74NTvotlkzYI1nh93Ir5AlFIt6UsrFuTUpj8BUI6ca+UpGbg12F3gTTpEe3oXmSVCMTl8LxoXCW+fscD7B9s42rINAM/0E4b03LhNRmxQjtKKLBCMxClGvteWWzcxRRxINDcoekcJG0XW6AjFmKQ1yB/GHw3LvgKA5pBfFPkibFoDEUrMiI0mUxRlW//IZEcH5ZrshCmtqh3wOB5PkkvtKO2gSfbQGEu5/Z07tzNBa0LHAhOOHtJzhd0wZVTCRpEF/OEYRVpXnd7l1hdUOmoAlLBRZI32UJRyzYhcFFYltlvsUtjY9HxJRUUpMSM2NUfChKVosRDfLHo+EbGprR3Yft9h63KpDXWO0ohN0mThho78eH3SQWjXmwC0FM4EZ/9F4N0R5nDTmPInUmSeQCSWiNgAXGB9nZaW/HJqzzeUsMkzRCRA46aXID52wv4m7cEoZRhf8J7yxHaLQ35R2PT8uAvxJUdsPGVw8rcAOCPwv1RrPgAmTBhY2GiaRlAzhE1gdAqbYHRsRmyKG9cDEB2/dOhPNmaAWdSojI8E4Vic9w+25cxANB4O4NDk57DBWk2hFqJgy59zspbRghI2eca6P91C5V8vYMPTP8/1UtJOSsSmoDKx3WqkouwiP/LGrck1Nu5SmH4qTFiKXYRxalJwOr3VgzpWSJP/t+goTUWFxqCwae4MMzu6GQDvrKHV1wBohrCxqojNR4L/fmk7K3/+Bk9tOJST82thGSXWNSuvVnwGgMk7HwFdz8l6RgNK2OQbLbsACNdvy/FC0k97MEaFJj+kKcLGaQqb/Pji9AUiXakodxloWiJqA6Bb7ODs33XYJGgIm8goLR4OpqSi8uP1GSnv763jCG0vMPTCYQCryxA2ylH6I8G6fdLDaWdjbrrgrGF57YjZi9g17lzahZuS4D7Y+c+crGc0oIRNnmGPyDexLTT2DNHaQ1HK6S1iI1NRjjyK2KSkoiARtQGwFFRKsTMIIobnSSw0SiM2Y7B4uPGD1di1OD5bBXgHTil2x+JUM8A+SuxukvUtbTnqCrQa3wlxZzElJaU8ET9JPvDO/+RkPaMBJWzyDEdMfgHaI205Xkn6aQ9GKTO6iiioSGy3GRGbfBE2vmC3VBRIIXPqd0CzQPURgz5W1DK6XWrNdm8YO8XD2n45k6y1/KhBC9RkbMaoDHueFLsrMkdnOEZ9uxT0uRI29qi8Zgqnl8oiJ3+InyEf2P4iNO/MyZryHSVs8gxPXL6JndGxJWx0XdARjlGeSEV1CRu7S375O8kTYZNs0Ocu63pg2snwlTfhgt8M+lgx2+g2cxtr7d5CCKraNgJgmzz4+VDJ2N0yYqNmgI199jR1dSPlTNjEjCi300tFoZM9opZ3bEcDAtb8NidryneUsMkzPEJ+AXrio7Mmoy86IzE0oVOGGbHpSkXZnfLL304sLxx6WwORnhEbk6q5Pbf1Q8xwqRWjVdiMseLhw74AR+ofAFA576RhHaNL2KiIzVhnVx4IG1fMvBbJiA3AH/Sz5Lb1j+TFNTPfUMImj9DjOkVCfpAK9bElbNqDsm7Fqhktk0nt3g4jYgNANPd3wT5/ko+Np6z/nQdANzxPRquZW7KwaQ/FUrqkRiM7t66nRPMTwolr4qJhHcOcAeYexTPAFINjd2NuhU1cF7h1eS2yuEuoMObTvRicJXcIt0FkdN40ZRIlbPKIjs5OnJqcp1QkOiFHvgmZoD0Y62r1dpeC1Z54zGF0mQDokdwKm2hcpyMc6yoeHkJ0pjdMl1ptlF58wt2EzGiP2rR/+DoAhwrmprwHh4IzMbU9PKY+o4qe7Grq+ty250DYBKNxijVZpG7zlFBW4MCiQVjYEJrx9Z0HN4P5hhI2eURHW5ebpE3TiQV8uVtMmunLwwbA6bATFjYAIpHcdpq0BaPYiVGoGXfjIxQ2o93MLdhd2IxiK3ddF0zY95T896Sht3mbuAxhY9EEeo7fr4rMsrtbKirbJn2BcJfrsNVTgtWiUV7oBDR0qzT/VMKmJ0rY5BGdvqZuPzfmaCXppz2Y1OrtqUh5zGmzEMYBQDSY2y+KFNdhNHCVjOyARmuwJTZKU1GRVGHT0D56hc22tS+xUGwlImxMOOM/h32cgsKuEQxB/9hKGSu6EEKkpKKicdFD6GeaQKQrYqMZ1yIzHRW3Oo2FKWHTHSVs8ohgR6p3zZgSNqHeO6IAbBaNkClswrkVNq3Jc6LcJWAZ2UfE4pDCxjZKXWpDUdnu7bLL38Nojtjw7/8GYF3JWbjKJgz7ME67jYCQXyrBUToqQzEwTZ0ROsIxLBpYLdIWINt1NoFInGKMa4fLC5AoII5ajIhNTAmb7ihhk0eEO5pTfg61jSFhE+w7FaVpWlfEJpTriE2SOZ97ZIXDABaXIWxGqZmbWSw8qUwWQTe2j86C2Xj9B8xpex1daHDCtSM6lqZpBI3hphH/6DReVAzMLsNpeEKpB69b1mNlW9gEozGKNSNqZAobI2ITMa6ZREfnZzKTKGGTR0T9qRGbSMfYmeDal+uwSUSTF45YjruHZKu38WU10voawOaWaQvHKLXfDyaEjawVGq0Rm6YXfgzAK9rRLF48PP+aZEKa/HIJB0ZnUbhiYMz6mqkVBV3CJpD7iE1FkRQ0IVQqqi+UsMkj4oFUYRPzt+RoJeknpSuqWyoKIGp8UURDuf2QpsyJGmGrN3S51DpGqZlbKBrnOMtmFhbI9+Ko7IpqP0T5ricB2Dr9P3DYRn7ZSww3DalU1FglWdgU5yhik1xj0z1iExRGxEalonqghE0eIYKpbsP6GBI2HSldUT2FTcQQNvEct3unOxXlSLQGj86LT3n0EI/Y/4svfngVxfhH5SDM+OpfYhMx3tbnsOi4M9JyzIhR3xAdpaMyFANjmvNNqyzIWSoqEI4muqK619j4ddlJqlJRPVHCJo/QQr7UDcGxMwhzoFRUVJN3H/Ect8+2BnqZEzUCuoTN6Lv46LqgIt6IRRO4o61ca3ty9EVsgj7Emt8D8Cfbp1g2rXyAJwyOiOEoHQspYTNW6TUVlWVhEw75cWhGJ1a3iE2HbtbYjM76vUyihE0eYU5x9SELTq1jaMJ3aiqqp7CJWeSHVc/x3UfKnKg0pKJcHnkx8hACXR9g7/wiHNMpouui+Xnrc3g69qDro8iUbu3vsMX8fKBPpGTBxxPdLSMlZniIxJWwGZPE4jp7m6WwmVEY5szOp3ASybpJn254mcWxJDyxzIhNR8xqLHb03TRlGiVs8gh7VH7xN9jGyZ8jvhyuJr0EggFKzOr+XoWN/KLItfNwv3OihoGroMvzRERHl5dNKBqnkK7Xw6HF+Zb1EXw5mpkzZKIhxFu/AuB/Yis5d9H4tB06ZpVfMiI8ul5TxeA46AsSjQucNgs1r93MuQfv4SLrK1mP2OgBWZ4QshYlJtGbPjYdccM5WxUP92BEwubOO+9E0zRuuOGGxLb6+no+//nPM27cODweD2effTbbt28f8Fh//etfmTdvHk6nk3nz5vH3v/99JEsblThjshunwy09NhxjaMK3FpT1QkKz9mp6FzPMpkSOP6S+NKeiPAVFssUYCI8yM7dgNE6RWbhYPZ8YFs6wvot/60u5XdhgObwRzd9AkyhmbeEpLJ408tfTRLfJ4mF9lM4AU/SPWV8zv0ygbXsOgElaQ9aFjQjJ74CwrTCxzeu2Y7dqXcXDStj0YNjCZs2aNfz6179mwYIFiW1CCM4//3x27drFU089xfr165k8eTKnn346fn/fF4DVq1dz8cUXc9lll7Fx40Yuu+wyLrroIt5+++3hLm9U4olLYRP1TpY/x8aOsHGEpEdP3FXWq+ld3JI/wsabpjlRAG6HDb/heTLaXGqDyRGbcYv4h2MFAGWv3wrxWA5XNkjC8vNUJ8o4Z9EkLGlKQwHEzeGmoywKpxgcpuPwea51EI8AUK61Z13YmHWXEVtX5Ndi0agodCZMTVVXVE+GJWw6Ozu59NJL+c1vfkNpadfFf/v27bz11lv86le/4phjjmH27Nncf//9dHZ28uijj/Z5vHvvvZczzjiDm2++mTlz5nDzzTdz2mmnce+99w5neaMSIQQeY7K3pXw6AAX62DD/0nWBK2J0ePXSEQUgEvbguc0Xp6Si0lBjY7V0mbmNtohNKBqnSDMumk4vz1VcQasopKBtG6x7KLeLGwQhwxXYj4uVC2rTe/DEcFNVuDkWMYdfLg+/mthWQVv2hU1Yvoej9uKU7SnCRkVsejAsYXPNNdewYsUKTj/99JTt4bDsmHC5XIltVqsVh8PBG2+80efxVq9ezZlnnpmy7ayzzuLNN9/s8znhcJj29vaUP6MZfySO12jrK6ydCUCB8IOe3dkkmcAfiVFqdERphVW97qPbcj/QLRiJE47paW33BghqhrAJjq73aCga7yoedhbh8VZyT+wC+fO/7oCgL2drGwyHGmWUULe6OXK8N70Hd8iIjWWUjspQ9M/uJj+V+JjctjaxrSIHERuzoSTuSBU2lUVOlYrqhyELm8cee4x169Zx55139nhszpw5TJ48mZtvvpnW1lYikQh33XUXdXV1HD58uM9j1tXVUV1dnbKturqaurq6Pp9z55134vV6E38mTpw41P9KXuHzhxJhf68hbCwICI3+dFR7KEaF0RFlLexZOAwkJtVq8dxFbHzBCC7CuDTj4pWGVBRA2DBziwT6j8Bt2O/jF//cTiyeH91TwYhOYSJiU0RVkZM/xU+j0TUFAs3wyl05Xd9AxILy9x23e9C09KWhgKSp7SoVNRbZ3ejn49a30dATbdblWhttweymYG1GQ4nu7CZsCp2JMTSqK6onQxI2+/fv5/rrr+ePf/xjSlTGxG6389e//pVt27ZRVlaGx+PhlVde4ZxzzsFqtfZ77O4XHiFEvxejm2++mba2tsSf/fv3D+W/kne0+5qxaLKNtrCsmnZhfBl2jP55UalzovpIRRkRGy2HH9JWf5I5n8UGzqL+nzBIwhb5WppftL2xrznAZb99m5+8sI03duTHKI1gNE6RWWPjKqayyEkMG3+puEZue+d/4PDG3C1wAOJGx1LM+P2nk8Rw01E6KkPRN8FInENtIT5hNTIGiy8HoIwOOoJhhMie3YEjKq8ZwpUacawochBUIxX6ZEjC5t1336WhoYElS5Zgs9mw2Wy8+uqr3HfffdhsNuLxOEuWLGHDhg34fD4OHz7Mc889R3NzM1OnTu3zuDU1NT2iMw0NDT2iOMk4nU6Ki4tT/oxm/G2yBiWEg6KCAtoML5tOX0Mul5UW2oPJ5nwDCJt47gzgfN1bvdN0lz+QmVskpvPVx9bTEZZ3gy3+SFrOO1JC0XhKxMb0z3hNPxKO+BQIHf5xQ96mS/Ww/H3HzELfNGJxyYjNaB1uquib3U1+JmgNLLFsBzRYehUAdi2OO96RmHifDcxOWa2XiE1IqHbvvhiSsDnttNPYtGkTGzZsSPw5+uijufTSS9mwYUNKVMbr9VJZWcn27dtZu3Yt5513Xp/HPe6443jxxRdTtr3wwgscf/zxQ/zvjF6C7fIuPWApwmLRaNdktCDU1tzf00YF7aEYZf2Y8wFgl8LGmsuITSDaNScqTfU1AFFD2PRl5vbTFz5k435f4md/OD86jlIiNs7ihLBp7AjD2XeCsxgOrYO1v8vhKvvBaMXWbekXNjanvPGw5zB1qsgMu5v8fMKyWv4w9WNQMglhWFRkuzPKFZfXDM1dkrK9oii5K0q9B7tjG8rORUVFzJ8/P2VbQUEB5eXlie1/+ctfqKysZNKkSWzatInrr7+e888/P6U4+PLLL2f8+PGJOp3rr7+e5cuXc/fdd3Peeefx1FNP8dJLL/VbcDzWCHdKl+GQVV4w/dZiiEO4fWykoqYNIGw0wxfEoucwYhOMdKWi0tARZRI3vlhFuKeweXVbI//z2i4AJpS6OdAaxB/JjwhIavFwMVXOJGFTVAOn3QLPfh1evh3mniu35RNGx5LIhLAxprY7R+lwU0Xf7G7q5FwzDTX/QgC0gkoI+ahACpsab89SjEzgNoSNtZuwqSx0JqWiVNSwO2l3Hj58+DCXXXYZc+bM4brrruOyyy7r0eq9b9++lGLi448/nscee4zf//73LFiwgFWrVvH4449z7LHHpnt5eUvM7wMgbLT1hazy72jn6B+EKedEGUXQnt5TUZpdChtrLouH02zOZ2J6nohIqrBpaA9x0+MbALj8uMmcOkd2jOVLxKZnKkpezNtDMULROBx9JYxbDOF2eO7mHK60dzSjsFcYhb7pxOGWNyDO5BlgQsA/74DXf5r28ymyh//A+8y17Ceu2WDeJ+RG44asQstuy7dHyGuGrbAkZXtlcsRGDcHswZAiNr3xyiuvpPx83XXXcd111w3pOQAXXnghF1544UiXM2qJB6SAiRnCJuwogQjogTGQikqZE9W7sLEYqShbDiM2rf5I2lu9AUTC86Srg0b/8AXsj1/Nx0KX8EHNOXz743P575elQ3dnngibYDgpYuMqpthlw2GzEInpNHaEmVjmgXPvhV+fDJv/BkddCjNO7++QWSXRiu1If8TG4Zaf02RhE/3weeyv/QgA/egvYnGnucVckRWm1v0fAE01y6k2b3CM61Z5loVNgfCDBvaC1But8kInYaPGRo8G1GykbqjfR54ggjKioTvlxTDmkH+LwOiP2AT8HRRohmDpKxXlkBGbXAobXzC5xqYkbcc1hY0lahy7o57wE1+iVG/h6/a/8IvPLMRlt1LgkDVqgXB+pKKi4QA2zSiUdBahaRpVRp1Ngznlu3YhHPsVAA4+8p/sqcuPji4AqyFszA6mdOIqkMd0m8JGCNqevS3xeEND3/YWivxF6DrHBV4BIDbvU10PGP5b2ayxiesicWPh6CZsChxW1RXVD0rY5AmWsJGqMe7y4i75RrYYltqjGeGXdUIxzdFnC7XVEDb2XAqbQIRSjJbsNNbYdHmeBEAIgn+7FnfUB8AErZEZ7WsAKHDKAGpnJD8iNhiupwINDHGQUkBs0H7c16kXpYwX9fzjb3/K/jr7wGpYzVuc6U9FOT3yfWzX4ohYmMjmZ6ho35J4vO7wobSfU5F52ne9w0TqCQgn5YuTGl6MG7LKLLoPB8LRhLBxFqUKG5vVkhhDo4qHe6KETZ5gOkxazNCnkQqxhX05WlH60PzyLj7sLOuzhdpqpAvsIoepqEC0awJ5GmtsNKODxhoLINb9AffuFwgLG+ucx8gdjK4iU9jkS42NKWwi1oLE61ZpTBZu7Ox6nV7cEeDf+hEAhA5u4s2d+RG1sRut2DZ3+iM27oKu9tuwv52O525PebypIc+FTdtB2PZ8rleRdwTX/wWAf1uPwZX0GnelorIXsQkE/Dg1eS1wFvS8HulGXaKmIjY9UMImT3AYDpM2TwkAloIyY/vodx62huQXXdTZdxSkS9jkzsOlNRChRDMiNmmssbEYwqYivJ/4/30LgHv1iyj/pKzHYNv/QdsBChxS2ORLKkozhkhGkyYLVxUbwqa96y7xmfcOsU2XE+lnWQ5wx/9uRdezZ2LWFw5drtHqzICwcbkIC/l6Bdf+kfLObXQINx9o0wBob65P+zmHS1sgyv2v7OCgL+kL8MmvwJ8ugl2v9v3EjxpC4NnxvwB8WH5a6mMFXamo9iwJG7NTNi40tF4i3WYnqaZH89ZLKlcoYZMnOAwjJnthqfF3OQDuMSBsEpO9++iIArC55IfUkUNh0xaIUmLM60pnKsqMGFTFDmGLBXhbn4PjxK8yec5imHyiNLpb9wcKnLLGJl+Khy1GxCZm7xIGlYWyyNuM2LT6I7y+vYkPhRxpMtdygM2H2vnb+oNZXm1PzFZsuzs9DtLJJA83db4pu6CesH8CV/UsAAJt+WPT8Oe1+/nRcx/yk+c/lBtiEdj3lvz3gTW5W1i+cWg9xeHD+IWTmiUrUx8zu6KymIoKGx2xnVpBr5Fusy4RUHU23VDCJg8QQuAxJnm7iuQXqrNICpsCfXQNTuwNZ0TeefTV6g1gM+ogbMQhnv0vdiGEUTxsRmzSl4qyubq+WDuFi3sLb+Irp86WG465Uv797kMU2WWUw58nNTbWmOnc2zNi09Auhc3zm+uI6QK9XP5/plsOYSPGT57/kECO/x9mx5IjA8IGIGQMN/XEO2gXHspPvwG3V77HY535kY4DqDOia2/vMjosGzaD6fDdsDVHq8o/fO/KNNQr+lGcduSU1AcNYZPNVJRpAdKp9V4jZrUneekoYZOCEjZ5QCiqUyRkPUCBVwoal1d+kNwiKO+wRjGeqBQ2lqI+XIcBuzOpJTeW/Q9peyhGXNe7IjZpTEXZ3V25+ttjl/HVT52Gy264dM85Vwq+zjqqDr8CgD9PUlFWw3dHT5os3L3G5pn3ZPfP0qMWgqMQq4hxrNdHXXuI37y2O8srTkLXcSO/0J0FmRE25tR2gCec5/PxY+ZQWCrHwFjDrdLrJw9oDcjrx6G2kExHHXy368HGD3K0qjxDCLQtTwGwq/p0SgscqY8bw3uLtCDBQO8O4ukmFvABELD0nkp1OOxdYxVycM3MZ5SwyQN8wQheo2jVjNgUeivQhRF+DLbmamkjRghBUVyu317U9+wvR/JQ1RwYTvkCEQoJYteML6M0Rmyonsc/44v4Xexs4gs+x/EzkiJXNgcsvgyAig8fAfKneNhuRGxEUn4/uSuqqTOcKBReuXA8VM4B4MaF8nf4wKs7qW/PUcdG0oXe5cmMsIlY5Hu2VRRSfcYN2KwWPCXyC7CUTvY254cjbFugK8Kwdk8LHFzX9WDTNohnz5clbzm8EW/oIEHhoHbJuT0fdxajW6TY0QLZicbphrAJWnsXNm67RZn09YESNnmALxCl2IgUaMZMEm+BkzZkCFIER6+XjT8STwzAdHqr+tzPabcnDKdycffhS54TZXOl1dRtXGkhV8W/yS+cX+Q7K+f13GHxFYCGe9+rTNbqCEbjxPOg+NZu2LkLRy/Fwx1hnt10GF3AggleJpcXQJUUNktchzlqUgnBaJyfvvBh9hcOiXEKAG5P+ouHAdqt8ibk765Pcs4SWVujeWTEtZQOdjdl585+IMyIDcCaPS2pEZt4BFrkSI/Xtzfyzu7Re60ZCb53nwDgFbGI0xZM67mDphF3y9fWHsyOaaoe9AEQtvYuzN12qxqr0AdK2OQBbR2duDTjrskwhvO67bQKeUEOt+dPvn6otAejiQGYtuJ+hI3NQghzWm327z5aA5lxHQaoKnbx9/88gX989UTKuoe4AcqmwgzZhfEZ6z+B/KizccVNsd3loFteIC+kMV3wx7f2ArByQa18sEqKNq3xA75nCLi/vHsgZcBntogEZa2UXzgpcPbyO08Dr02+ju9Er2TyuTdjsRjRVaPovFTrZGejv59nZw9fUsRm8+6D0GiIzZLJ8u+GrRz0Bfn879fw2d+8xf6Wj9iXpBCw5UkAdlWe1jMNZe5m1Nk4I1lygw/JxpGwrY+IjcOalIpSEZtklLDJAwKGcNHRwCHVeYHDSjuFKY+PRtpD0cQ4Ba2vyd6YwkZeUPRI9i+smZoTZXLkBC/jS9x973D0fwBwkfUVHETzouXbaQgbS1LxrcNmSYizbfXy97ViwTj5oJGKomEriyeV8qmjxiMEfPfJ97MegQoHpLAJ4MRtODqnm2su+jhX33QHp80f37XREMUlWge7m/JD2CRHbJyNmwAB3klycjVAw1Ze2FxHXBfEdMEDr+7MzUJzRf37lAT3ExJ2qpec1+dumuE+7NV9WamfMrsSo/biXh932axJqaiPmBgdACVs8oBQuwz/Bi2FYJEviaZpdFqMuVGjeMJ3eyCaSEX1NU4BwGm3EhLyQxqNZD8VlRKxSafr8GCZeSYUjaNM6+Q4y5a8aPl26YawcaXOPDILiAEWTyrpEmxGxIaWnRANcfPH51LksrHpYBt/emdfVtZsEg7I91wQFw5bZi5zLruVSeXdUpZmxIZOdjXmPhWl6yLRxeN121moGaJl/OKu16thC89vrks85y9rD1DX9tGJAPjWyjTUa2Ihpy2c3ud+tiKz5Ts7nVGWiIzYxBx9CBuHlVAiFfXReb0GgxI2eYDpVxDqViQWsBkTvttH7yBMf3trwj2zrwGYkBqxiYZyE7HJxJyoQWO1JWpUymnLeQGxEIICo1PP5ukmbIq6hM25C8d1PVBUA64S6cvTvJ3KIidfP1O2gf/4uQ9o6syeq7QZsQkldS5lBSNi49YiHG7Mfb1KeyiKGSw7ZXYlCy2msFkCVXMBiNdvSdTWzKwqJBLXPzpRGyEQm/8OwM6KvtNQ0BWxydYgTNONPt5HxMaddDOouqJSUcImD4j6ZddQuNsbOGw3BmKO4kGYkXbpwBrU3GDvOxVjs2iEDWETC+dC2EQozVCNzaBxyte/SAvmvMYmGhcUIC+Wtm5Tqs1BmJoGHz+ytusBTUt8WZr+KJ9bNpkjxhXTHopx57PZay2OGjU2Ya2f9F8mcBYhLEbdQ7CVVn9urRpajfqaAoeV42dUpAqbSvlaaa27sYsIc2uLufVcORrj0Xf20dDxEYgCNGylNLiXsLBRueT8/vfNspeNIyrfw8LZt7AJJlJRStgko4RNHmC29XVX5lFHCTC6J3zHOhoA6LT1X7eiaRoRzRQ2OeiKSpnsnf4am0FhpHyK8efcyyYYjSdNFi5JecyM2CydUkZ1cbeISELYyIGQVovGD86fD8Bf1x2QXTlZIBqUr6XZkp01NA0tUUDcwa4c19mY9TUlHgfLKqOM15qJC41Q5fxEhM0i4kzTDnPWEdWcMKOcoyaVEI7p/Oa1XTldezbwrZWmfK+LBZy6aEb/Oye7DwcyL2zsprDplgo2cTuSa2yUsElGCZt8wGjr07u9geNG67dlNPvYGA6sQfvAYiGiyS/MeCT7XwbBSLxL2OSixga6hI0WyHkqKhyNU6iZEZtUwX3BkgksnVrGN86e3fOJibqNrujM4kmlXHKMHLnwvSffJxbXM7PoJOJhQ9hY09e2P2jcScImx3U25hdwaYGdiUEZRdsuJrCpMS5bmI2ozSxtP2cdUYOmaVx36kwA/vjWPpqzmD7MKtEQbHkK63vSO2pHxWm9dywmYwqbLEVsXHEpbCzG/MDuJKfvVVdUKkrY5AFa2JgH1U3YCOMCaR3FE761gCx8DjkGFguxHEZswjE9Y+3eg8ZlpKII5DwVJSM28nXQur0vZ1UX8eerj2PJ5F5+T4nOqC0pm7959hxq3XHObvo9T72c+cGL8ZAUxzFrliM2AAkvm868idiUehxohjHfRn16InJ2wD4FgGM8dcypkd1vJ8+u5MjxXoLROA++kUP36HSjx2H36/DUtYifzIQ/X05RuJ4mUTxwGgqynopyGT5S3T9/JrLdW0VsesOW6wUowGYUiVk8qVENixE5cER82V5S2rAGZcQm5hpYLEQtToiDnoMPaTgWz2i796AwInTFWoADOY7YBCNRJhrChl4mC/eJmYry7YWIHxzSZLK0wMGvp77Kkbv+xntvbUWcsRqtl8F+6UKPyLvdeC4iNsbnuETrZHeOvWzMGpsSjyNhzLdRTKduj4wCr/FXMxlYWtCQeD00TeOrp87gqoff5Q+r93LV8mny+aORcAfs/Cd8+BxsfwEM12ANOCjKeTp+PE9Zz+RPi2YNfCxD2JTRTlsgw5EsIfAYXYk2T+/XI7fdik+lonpFCZs8wB41DOy6hRytBVIMuGKjd8K3IywvoHF33x1RJjFT2OSg3Tsc0/Hmst0bUmpsOnNcYxMOdGDRjHaaoQibggooqAJ/g5xDNH6J3B6LML9OzuJZoG/l/c3vMX/+wjSvugthFKDr9t4HCGYUI+JXRgfv5Nh92GdEbMrcVtjbFbHZv6eFcCzO/9aXcCEwKb435XlnzKtmTk0RH9R18NvXd/P1s3pJO+YzHXXw9Fdh579A74qutAkPz8aP5cn4ibxvn8fZC8bzsxOnDpyGgkRXp03TiXZmuFM1FsKOXLe9W42bidtupU4Jm15Rqag8wGmEHB2FqcrcUSQ/SJ746J3w7YoYxaL9eNiYxCyyxkbkwJMhFNVzH7FJ6ooK5DhiE/MbHhpY++1m65WqLqO+BB88k0hLAtS/8dBIl9g/Rp2WbstyVxR0paK0DvY0B3I6HsNMRU3W6iDUhrC52G+fTHsoxsOr97IhVAOAq3M/hLtEmKZp3HC6rLV54NWdOXGPHhHv/VlGaPQolE2DZddwnfN2loQf4O8TvslFF17CO985k59etJB543rvOuqB1U7IJm8+RGeGvcUM1+G40HB4+vCxsVsJqnbvXlHCJseEY3EKdXlBcReVpzzmNiZ8O0RkYEW+5w342Tz48P8yss7hUmBO9i4cWNjEjXoIkYO7j2g0gjcDk72HRHJXVI5rbGJBeWENaB7Zxj0UEgXEScJm7e8A8Hvll+XMumeIZNC9VTOcWIUjBxEbI+JXZvETiekc8uXuS8dMRU2PbANAq13Igknys/jzf+6glWI6bMb7vTF1rtdZR9Sw4shaYrrg+sfW57ygfUiY772PfQ2uW8/+pd/l6bYZYLHzuy8cwwVLJlDgHHrCIuyUvyvNnx1h004Bnj7W6bJb1RDMPlDCJse0BaMUd5vsbVJYVEJUGHbwA7V8v/lzaD8IGx/NxDKHTWKydz8DME3i1txFbGzRzq7US67bvbVAzlNR8aDh3GsZhjDo5mVD4zbY8zpoFlyXPkIAF5OoZ/3qF9K02p5YYlLYaLkQNoYwHmeXn+tcFhCbqagJAaOYe/wSjp4i399mAWy03IiwNW5Nea6mafzXJ49knNfFnuYA3396c3YWnQ7M4vXaRQD8e4esrVk0sYTCYQgak5gxCNMWyvCYG1PYCA8eR+/rdTvUEMy+UMImx7QFoolIQffiYa/Hgc+Y8E1/Ld9mgRz0uOvKNcVCfkAdxdUD7qtbzQ9p9u9w3UYdU9xeALYcFUomdUUFQpnvuugP3biwhizDKL6t7CZsjGgNs87GWjWbHeUnAxBe+8gIV9k3VlPYOHMRsZFffpU2uYZctny3+uX7qKL9fblh/BKOmdJ1A1XotFE8+Uj5Q8PW7k/H67Fzz8WL0DQ50PSZ9w5lfM0jRte7roNG9PANQ9gcP2PgWr/+EB55g+YMZ9iPKRGx8eDpY9aZ224lbA4OVu3eKShhk2N8wSjFmqG2u7X1lXjs+IQs3BSBforVtr8IccPhtHkHxHP7pWgSi8UoFfLOv6i8doC9QbfKeggtBx9St1HHpLtyFK2BxOvv0OJEwrntphEhw7nXOpyIjREB6Dgkizg3/kn+bAz69B57OQAL2v5JW3tmvvRtRs2BJYepqBLk7zCXwzB9gQh2YhS1GqJl/GIWTSzBakwjP3l2JbZq6TbcvUXf5Nhp5VxzsjSv+/Hf3qDlzYdk63S+4tsja06sTiibiq4LVu+U188TRyhsNCOl7s7whO94QN7ItouCPoWNy25R7d59oIRNjvH5wxRjCpuSlMe8bjutxoTvUH8Tvrf+o+vfegxa8sMxtLW5HquR3ikprxn4CTYjYpMDYVMQk8JG5FLYOAoRmvGRDOe4YNy4Y4x0m182KFxeKDYmXv/zh/JYJZNh+qkATD76bJq0Mko0Pxv/9ed0rTgFuy4/U1bXEDq60oWRiiqIy9/hrhy2fPuCUWZr+7DoEZliLZ1KgdPG4kklAKw4srZn6rAXrj99JosmlvCD+H2UvXAd+sbHs7D6YWKaQ1bOAouVD+s7aPZHcNutLJpYMqJD24pkxKYwllnT1IgxZqcdT5/T6eVIBTN9r4RNMkrY5JjO9tau2o5uERuX3Uq7IWyCfQmbaEhW/0Oiq4bG7M3k6Y/25sMA+CjEah84vSNssnjYEs+usNF1QaFuCIk+PCOygqYRt8svYmuOhY1m+MBEbcMQNtD1Zbn+j/Lvo7+QmFyPxcqBiecC4NqSIWETN1yTXcNc/0gwIjb2mB87sZxFbMKxOIFInEXmfKhxixOF4D/99CJ+8dmjOHt+TZepYsfhPlPedquFX55TwnLrJgD2bPp3xtc/bMxaISMlatbXLJ1aNuJJ744SmVIvEW2EY5mLWsX8PgA68eCw9r5mV9JIBSVsUlHCJseEO2SuNqI5wN7TJTVglWIn0tGHsNn9KkQ6oWgczFkht+VJnU2gpQ6AdkvJ4J5gtOZashyxicT1xDgFLVceNga6IU5zLWwsEfn7iNlHKGwQYLHDos+lPDzhpC8AsCj0DgcP7h/uMvvEqcsLvd2dA2HjKgEj8lZCJwd9QYKR7KdufEZH1FyL8futXZB4bFK5h5ULxklTPlcxeOXIi+RRGN0Zv+sviX9bmvLj5qlXzMiTkRI1hc0JM8r7esagcXqlsKnI8ITvuDE/MGgp7NPIUk73ljU2StikooRNjgl3GpO9rb2HzM2J3/HOPorVtj4t/56zouvLJE8iNqE2OdnbbysZ3BPMiI2e3fk04WiXsLHkWNiYk3xtxgC8XGExIjYx+zBTOWYBMcC886Bbu3/F9KPYY5+BQ4uz/Z9/GO4y+8Qp5HvIkQthY7Ek0sqTXPILJxdRG9PDZo7VKPhNfk260214aQ/iUVjfVexd2rkzHUvMDKY4q5pHNK7zzm557TxhhPU1AJZCmYoqp532DAob3ZgfGOonYmq3WrqGvCphk4ISNjkm5jciNvbeTZgixoRvvbd273isy7dm7rldIeU8idjE2uVk78HMiQLQHPJDao1nWdjE4ok5UbkWNprbKCCOdaDn0NjNFpO/D90x0ogNcPSVve7SNusCuevuJxEijf9XIXAZ4yCcfZibZRyjM2puifR+yYmw8UcBwQzNiNiYRd29kZjx1UedzbbnwN9A1PBx8eqtA1tQ5IJ4DJqM61/lHDbu9+GPxCkrcDC3Jg3vhWzNizJq3Pq64U1gM4WN6opKRgmbHGOGHGOO3t/AMWcJAFqwl4vIvtUQaJZFgZNPgErD9rxpO8RjdIZjbK/P3Z2/MEysoq7BhYA1u2wtturZ/ZCGojolhpeQlisPGwOLuwSQXjbBDBrYDYQ9KoWN6ON9OSDV82HyiVJwTz6+111mnPZ5YsLCPH0bH7y/brhL7Uk8gg05QdzpyUHxMCTqbKYXyqhJLlq+fYEI5bTjFR2ABuUz+97ZNFXsK9r7rnSKji+8lANCRj4Ch/LQ16Z1t+wQtXugZDL/3iG7l46bVo7FkobZZIawKdRCdHRkMF1sChtb/+9f01lbiykfm2SUsMkxWsgHgDAETHeE8UXb64Rvsxtq9sfBagPvJFmnEg8Tb9nDZ3/zFmfe+xpbDuWmXsNiDJwTnsGFgDXDut+Wg4iNB+OcuWgPTsJqRGyKCOTU6dVhjPlguF1FNgd84X/h4j/26VxcUDaO7a75AHRsf3N45+kFkTQawFOYo4iN0Rk1xS1Fem5SUVFmWQ7IH0qngKMfTyIzwla3KTGOIoFvP+x4CQDXsV9gr0XW4zTv2pjmFacBM+JUORsslkR9zfFpqK8BwFlExPCOCRup9kxgMWrsYn1E8k3MKLclHoZ0Rj1HOUrY5BjNLBJ19z6a3ixmtXef8C0EfPCM/Pdc2WGCxSJbHIE3Vr/BewfaEALW7MlNyNgekucdzDgFAIvxIbWJbAsbHSeGD9BQ5yKlGS1pwndnLoVNzPhyc/b+vkwXIZu8cOtpLBgPB6WwCQs7bpczbccdEkYqqtpwHz6Yg7EKrYEIM7SD8ofKftJQIIVNQRWEfPDXL6b61Kz/IyBgysegfDqtBdMBCOVjxKahqyMqEImxfr+sYRypf00CTaPTGEERzaCwsUYMYTNAxFRLnoWmTPoSKGGTY2wRGXK09JECsRrCxhntFnU5tE6OULAXwLRTurYbF7D31r+d2LQtR+koV1QKG1vxwOMUAKxGKsqe7eLhWByXZggbW8/OtKySNC8qkINOmsQydCM158psxEPPwBiNcEC+3wM48dh79wDJOIZtgOmPlIu0oi8QYZZmRGz6q68B6SF18R+lqd2Hz8Jz35I3T3q8q2V/yecBiJXLmyd7y7YMrXwEmK3eVXN5Z3cL0bhgfImbSWXDcNDug4BdvrZ6R+bmRdmM673ZJdkXluQonCogTqCETY5xxORFuK/R9IkJ37G21FDjViNaM/OM1DZxo85mfGxfwl00V8KmMOYDwF0y8DgFAKtT3n04RCSrYdVwVMeVJxGbxFgFLZjTiI3LNLhzZ1jYWMzpxOkTNiG//FII4MLWhwdIxjFSUW7jM5ALkepLTkX11xFlMulY+NT/yH+/82tY/UvY8TK0H5B1fHNWAuCslU7FedkZ1dAlbN403IZPmFHeZ8v0cAgZBdQEMids7DEzFVzS734Oh4OIOU9QCZsEStjkkFhcxx2XosNR2Hs3jtMrhY2VuJwJJQR01He1eZtpKIPDjskAzNQO8I2zpMjZVt+Z3q6TQVKi+wAoLBs3qP1thrCxoGd1LEQoFu8SNnkUsclljY3HiNjY+kiRpovEfLBYJG3HjASNcRBaDl/LRKTVmJKeg9eyNRBNSkXNHtyTjvgknPlD+e8XvisjNwALP5O4gaqcJv1w8q4zKhaRI2UAKufwxnbTvyZNaSiDqMu4JgczNFYhGsKmG58HV/+fP3fyhG+VikqghE0OaQ/FEuMUuk/2NikqLEqYMPE/y+GOGvjpLGjegbA6YOaZiX2FEPxsg3xJZ1kPc8Vxk7BocopvY0d20zuhUBCv0WlUXDHwnCgAW/LAwix+SPMrYmMUD2tB/LlKRelx3Mjfvy3D7dIiIWzS93pHjFRUKKfCRtbYOIzauEAOUlF6ZwPlWgcCDSpmDf6Jx10Lx3wJENBiRGUWX554ePr4mkRnVDCf6mxadsqRMo4iWmxVbDksI3fHT0+vsNGNZghHpiZ8Gx1RcaFhHcA522W3EFITvnughE0O8QUiiS9/q6ek1328bjt7hZHKad0NsRBCs3BYlHO/+DQ/e+0wh9tkCPJfHzbwt902wsKOU4Rx+Q8yuVyKhQ+znI5qbZKuwzFhoahkcBcWu8OFLoyQcTaFTUzHpRkRolxHbIycek4jNuGu94rdk9mIjTkfTEtjJ1wsJMP4EUsORaqRirIZ3Yy5SEWVGKmicOHE/juiuqNpcM7dMOsc+fPEY1N8iUoLHInOqKZ86owyzQWr5vDiVlnYO7u6iMqi9BaQawXmIMwMRasMYdOBB7ez/1E0ye7DysumC1uuF/BRxheMUozRfdJHLtXrdvCl6I2c6NzBDy47i3/Wu7n2fxsIxI286j938MtXdnL63Co+qOsgjhVfwWSqAzug8UNmVZezu8nPtvpOPjZzcN1J6aCj+TC1QJtWTLllcAWcTruVMHbcRLKaLw4np6LyJGJTrOWw3dvo1AsLOy53+ooue8WaOWETtebwtTRSUVajMzAS04nF9azW/FSFdgMQLZ/FkOW6xQoX/g42PAIzTuvxcGvBdOhcn1+dUYbjcKBkFv/1rPz3JxYNLg0+FKxF8kazIJqhQZiGsGkXnj4ne5u4k+ZFqYhNFypik0Pq20IUa+Zk797vjL1uO7tFLQ+HPsbDDVP5j380E4hbWbmgll989iiWTSsjrgue31zP3uYAFYVOyiYfKZ/c+AGzqmW7YLaN+gKtxpwoa8mgn+PMUb44pd071xEbs3iYAP5wjlJRRsSmAzeuTHcVGb9vLZ6+Ghvd8LGJ5VTYyFSUFvLJmjGym44SQjAhtleuYaBW775weGDpl6BsWo+HYuWyZievOqOMiM1f9xfRFoyycIKXq5b3XPtIcRhdnkV6hoUNBQMKG1fShG9VY9OFitjkkHf2tHCyGbExHGe743XbE//+3lPy7uhzyyZx2yfmY7VorFwwjm31HTzy1l5e39HEt8+Zi71xHmz9u4zYTP00kP1UVLhNjlMI2Abv5Ou0WZLuPrIYsYlEcGpGdMSe4QjFQBiRuwItTDCUmwtVPNiGFegQbryZFjZ2eVG2pDFiEw/Lz1ROhY1h36AhKNH8tIgiAuE4xS77AE9MD+2hWKJw2DHuiLQf31k7D/bmWWeU4Zr8fGMpLruFn128CHsGImSukhoASvS2tB8bkF5CmBGb/r+iU4qHVVdUAiVscsi7O+txa/1XvztsFjwOayJHf92pM7jxjFkp7Yuzqou47bz5XU/aYnRANH7ArGVmxEZ2RqWz7bE/4h0yxx129l4U3RtOm4WwsINGVu8+4uGkC0IvE9azSpJvRcwYhJdton4pbDpxU5NhYaMZNTYWPX0RG9M5V7fn0EXaapevZbid8Y4gLeEiApHspRZ9gQgzDQ8bR828tB+/ctpCeCupMyrHM9aIhhAtu9CAD/UJfOfcuUyvzMwA1IIyKWxKaScSieJwpFmsDjFiExJK2HRHpaJyhC8Q4XC9TNcItH4dXs0P6C0r53HTmbMHFidJwzCnlnuwWTQ6wzEOtWUxAuCXHQOxQc6Jgm6pqCx+SOORpHPZclxjY7UlakNEMEN3hAMQMc7bKTw4bZm9RGiGkLSm05TRFDa2HEffjC/7KptcTzYLiDua6yjXOtCH2hE1SKaPr86rzqho/YdoQscnCjhi1kw+t2xyxs5VaAgbqyZob21I/wmCMsXVLjy4B6qxSUnfK2FjooRNjnh7dwtFRhpKcxbLcQh98NCVS3nxxuVceeLUwR28bBpYbBDpxBE4zNQKeee6a/8B+M1p8ETv05bTicXweBAFg2+1lKkoefcjsihs9Iisc4pp9n5fh2wRNQffhXIz4yvm9wHg1zzpGRzYD5aEsEljxCZqpHeH0gmUCYzOqGq7fH9lU9jE6mS9Sb2lOiO/h3zrjHrulX8BsEubyI8uXJjRyLTFZqeREgB8e99P/wkMYdNK4YDO2a6U4mFVY2OS+6v4R5S3djXjTdTX9N9SW1bgYGb1EIYRWu1QPkP+O1FALBj3+s1wcC28/9eMG2s5w1LYWAsHN04BjFSU8SGNRrIobAwRFbPkaK5QN2IOmY7SwrmJ2OiGoApaMi8MrBkQNlazOySXqShIRGwqLbKYOZupKK3pQ6DLsDMT5MvMqIaOEAc+eBeAimmLqCrOfDp5Z+ESAFo3PpP+gxvCxieKBlVjExSqK6o7StjkiLd2tVCtGVX1A9hmDwvTabTxQ2ZWF/Ipy+tMb3ix6/G6Tek/ZxJuoxXS4R2KsOnKF8fC2YzYyHPFrfkhbHRD2JiD8LJ+/qA8b8iSeWFgRmxsaRQ2FiMkrzkzU2MxaIzOqHKrKWyyF7FxtEhh0+ROf1eQSb50Rr27p5UZRj3RpNlLsnPSmWcBUHn4lfQf27jp9FGAxzlQjY1FOQ/3ghI2OcAXiPBBXTsrrG/JDZNPSP9JEnU2H3BUQSu321fJn8272PoMhFCTKErMiaoZ9HPsVi3xIY2Fs3j3YVwQ4pYcFw4bCKPl2xbNzYwvM2ITtmZe2FiNie52kT5hY4sbc66c+ZGKKtWyX2NT2C5HC7QVZk7YOGtlUXKuO6PW7WtNGvY5iJlYaWD2CecTExam6Ps4tOfD9B7cTEWJooF9bFRXVK8oYZMD3t7dQqEIcKZ1ndyw8OL0n8SM2NRv5tgN36JQC7FWzEEs+0+5PYMRGyEEJcIHQGH54IWNpmlENfkhjWdT2BgRGz3XHjYGmhHBs0c7c7MAw8cmYst8xMNmCBubSN9sMFtcvp4W1xDSt5nASEWVCmMoZxZTUaWBXQCEStJfOGxSOW0hkPuZUZt3H2KyxSjizZKwKa2oZptTCru9q/+W1mOLRMSmEI9dtXsPByVscsBbu5o52/qONIWrmA21i9J/EjNic/BdXPXraBcerg9/haZio/WzLnMRm87Odgo02eVSUjE058+oUeeiZ7HGxozY6HmSitKMidrOeG4iNprhPByxZSNiIzvA0hmxcRjCJmX2WC4whE0x8nXMWsTG30RhzIcuNGLlMzN2mnzojArH4lTVvQpAtHgSDKFZYaR0TpKOzK49L6f1uCJoCBtROGBXlMuR1O6tUlEJlLDJAW/tauGTljfkDwsukrNZ0k35DNC6Xt5fFlzDQSr5EKOYsPGDtE5UTsbXKNvYw8KOu7BkSM81C3jjWbz70OLygiDyJGJjM+aGuWIdOZnKbonIL+K4PfMRG7sx0d1B+iI2TiHfO/YMD/AcECMVVaRnWdgYRnUHRAXFRZmb9ZUPnVGbD7WzUnsdANvCi7J67olLPwnAvNAGfL40uRALgWakojq0IhwD2C24bEnOw6p4OIESNlnGF4jQVrebZZatcsOCDH0YbU4om26c4xIOT1wBwHudxdIzR49CU5pzwwadLYcB8Fm8QxZtprARWYzYaIaIErn2sDEwhU2RFiQU1bN+fqshbGK2zKdybHazxiaNwkaXQtXhznXxsBQ2hYZDbSBbs78a5LVlm5hAaUFmnY5z3Rm1ZftOTrK8B4CWqWtpH9TOXES9VoVTi7LlzTR1R4Xb0YQUwGHHwKLUrdq9e0UJmyzz9u4WzrP8G4smYPKJUDIpcyc76w5YejV8/MfMrpFfUtvqO6HasFjPUDoq6JMRm44hzIkyiZvCJosRm4Sdf54IG3uBtOMvxk9nDgZhWmOytifuyLywsbvMiE36oocu5AXe6c51jY3sivLEDGGTrVlRRsRmh5iA193/dOiREi2TNTy56oyyf/Akdi1OQ+HcrrrCbKFpHKo+CYDYB8+n55hGtCYoHNgG4T+UMt1bGfQlUMImy7y1s4lPWmXoNGPRGpNZZ8HHfwSuYmZWybvXbfWdUGMMycxQZ1SkTY5TCNoHPyfKJG6Vd/Aii3cfVqMmI+fjFAws7q4J39ksODWxGwZ3wpl5YeAwOpccWhw9lob/azyG00hrOT05FjZGKsodawdE9iI2jTISu00fT6knsxEblzGHKhedUUIIjmh6DoDAnAuyfn6AkoUrAZjZ9iahdHxWjcLhVgoH7IgCs3jYrEtUqSgTJWyyTOP2tcyyHCRuccC887J2XnPK947GTvRExOa9jJwr3tkIQMQ5+HEKiecaBbwii3cfCTt/e35EbMzxGkUEchKxccRlxEZkI2Lj7BKTkTSkH0Wkq5PMVZjriI0UNhYRo4hg1mpshJGK2i4mUOrJbMQml51R9Xu2MF9sJy40qo7/bFbPbTLl6LMI4qRWa+a9d98Y+QGNwuG2QRQOAziTfGyyGeXOd5SwySK+QIRFrfIOIzrj7D4nemeCiWUeXHYLkZjOIZfhSlz3PmSgOFUz50S5hy5shBGx0bIasZHnsuSLsDEGohYTwB/OnvcJALEINkPoaVlol3a6usLt4TRMMw8FpLCJCQsF7hx3RdndifRmidZBMBupKH8TWkB+/nYyjmJ3ZiM208dX0yhkkXawcU9Gz9WdtrcfAWCDYzGesvFZPbeJZnezt/hoAFo3pKHOxhh82yoGF7FJdmvPZpQ731HCJou8s7OB86xvAuBa/Jmsnttq0ZhhpKO2xMaDZpV3Bx2H034uW0iOU6CgcsjPTXjJZLF10fwitzjyTNhofvzZTkWFu1rMLa7MdxXZbHbiQhaYR0MjD6UH/YZnDE7cA9jRZwWjzqaUTvzZiL759gFQJ0pxuIuwZnjWV2mBgwZNtlg3H96V0XOlIASVu58EYPe4Fdk7by9Y55wDQE39q8T1Ed4oJqWiBvP+1TSt65qpuqISKGGTRRo2Pk+l1obf6oUZp2f9/GY66oOmKFQY/hYZKCA250TZigY/TsHEbLm2fKSFjRQUhQTxhzLTkt8nhodNQDhxODLv66NZLESMwaeRNIzRCAekMAviyvgAz0HhkXVmZVpHdlJRxhdjsyjOeBrKpM1RDUBn/d6snA+AA2spCx/EL5y4jvxE9s7bC5OXnQ/AkWI7m7aPsNYoKRU10ADMBGbTg4rYJFDCJouM2/c0AI2TV4AtOxedZExhs62+A6rny40ZqLMpiMnKfucQ5kQlMD6kprdMphFCYDfmFFlzPQ3axIjYWDVB2J/leVFGxKYTN+7BXlhHSESTwiYaGflrbgqbkJYfheBmAXEJnVkSNvKmokUUUZLhwmGTkLsWgGjr/qycDyC24TEAntePYeG03KShTBxlEzngnI5FE+x/++mRHSxpsrd3kGlEkeVr5mhACZssEY5GOTa8GoDipZfmZA2zqmUqant9J9QYwqaXzqi6phZeePl5GjvCwzpPYVy2t3pKBz9OIYFNRgmsWfqQRuI6Ls0UNnkSsbG5iBpf9rFAlid8GxGbDuEeVPFiOoiQvsGn0aAUNmEtT15LMxWVtYiNrK9ppShrERu9SLqLa+0Hs3I+4lHE+38F4F+Ok5lQmvvXOjBFRuCL9v9zhAcyUlGiiEnlg7zRsptR7mBGaiZHI0rYZImGg/so0MLEhIXSmcfmZA1zamSKY0djJ40FxgyZbqkoIQR7f30JZ75+Ed+5+yfc+PgG1u8bvKumHtcpFfLLuLi8dshr1MwPaXx4omqohGM6LsNDJedDE000jbAxWTse8GX33EbEpiOLERtzPlgsDe2q0ZAsHo7kyUDTxLworSM7rftGxKZZFFOS4cJhE1updB92BtJfr9crO17GHm6lUXiJTV6Olgnn9iFSME9O+54bGaFRoRGxaaOASWWDux6ZKXRN6BBPn9HlaGZEwubOO+9E0zRuuOGGxLbOzk6uvfZaJkyYgNvtZu7cufzqV7/q9zirVq1C07Qef0Jp6JLIF5oPyWm7jZYKNGt2LjjdGVfi5sQZFcR1we92GK6szTsg4k/ss+6tf3Fs5G0AztJW8/f1B/nk/W9y3i/e4IXNdQOeo62tBacmL+DeiqELG+zyw5ytiE042iVsbPkSsQHChutv3OiSyBohM2Iju+iyQSI6FRm5mI0FDWFjzZPX0pzwTSfBaDzzIzIMYdMqiijJUsTGUynHtBRH6rNyPt57HICn48dz1JTszYbqD+8k6Q1WrbXg7xxB+jjYFbGZPMiIjSU5ha4KiIERCJs1a9bw61//mgULFqRsv/HGG3nuuef44x//yNatW7nxxhv56le/ylNPPdXv8YqLizl8+HDKH5crT+660kCgXnYM+BzD+LJPI9ecIlu9H9wYQPdUACJhwS6EIPbKjxP7fqJgCxccVYvDamHjgTa+8si6AX1V2hplOLoTN3bX0NttLUbExqZnp2g2FI0nUlFavtTYAFG70WodynaNjTxfJ25cWYrYxIyITToGn+pho907X4RNIhXViRBkfkSGYbXQQlHGzflMSmunAlCmN4Oe4XSbEIjdrwHwv/FjWTxp6CagmaCwtIo2Ia93rQeG78Ks+80BmAVMLhvc9dNmdyY6C9UgTMmwhE1nZyeXXnopv/nNbygtTX1jrV69miuuuIKTTz6ZKVOmcNVVV7Fw4ULWrl3b7zE1TaOmpiblT3+Ew2Ha29tT/uQzsRbZMRAsyG2h27JpZSyeVEIkprPHZsySqtsEwHvr3+LY8JvoQkPY3NhDzfz0RFh986kUOKzEdUFdW/8fnM5WGdVp14Y3fM+8+7CJ7KWinKadf56MVACIGcLGEs5NjU2nyH4qKp6Grg5T2MTzRtiYxcMyxZfx9n2jRqNFFFFSkJ2ITfX4ycSFhp04gZZDIzpWKBonGu9H/Pn2ogWaiAgrH1qmMn985oZ8DpXDVllr5K8bvrAxJ3vHXaV4BylMXQ5b0rwoZdIHwxQ211xzDStWrOD003u2LJ944ok8/fTTHDx4ECEE//rXv9i2bRtnnXVWv8fs7Oxk8uTJTJgwgZUrV7J+/fp+97/zzjvxer2JPxMnThzOfyVr2Nplx4BenNt1aprGtafKqM0rbUbXkiFsOl+8G4CtJSehzTRe2+0vUl7opLpYRlKaOvsXHCFfgzyWrWRY6zPzxTY9nJVCuHAsjtsUNnkyUgEg7pD1UJZwbrqiOvBkLWITt8gLeDqEjTDSqnF7js35TFwlAJRY5BdOMNMFxGYqKosRm2KPmwZNCrimQ8P3smnqDHPWHX/n+of6cfA9+C4AW8VkZoyrzNp7dDA0OScAEGvcMbwD6PHE572odPAdpW578oRvJWxgGMLmscceY926ddx55529Pn7fffcxb948JkyYgMPh4Oyzz+b+++/nxBNP7POYc+bMYdWqVTz99NM8+uijuFwuTjjhBLZv397nc26++Wba2toSf/bvz16r4XAoCMoUjb1iSm4XApwyu4p5tcVsjBoiq/59Nr+/nmWBVwCo+Pi3YcYZ8rEdL8pthfKDM1CnVLTdmBPlKBvW2szOJAsC4plPRyUXD+dTxEY4pbCxRjsG2DPNJBcPZ6krqmvwaRrC6IawEfY8SSsarfteTdY+ZD5iY6SiRPa6ogBarfKLuK1+z7CPsfmDD/hfcQ2X7fkmHaE+imAPrgNgoz6dJXmShjLp9MiBxpbW3cM7QNCHhryZK6uoHvTT5Lwo47VWgzABGJI15/79+7n++ut54YUX+qx/ue+++3jrrbd4+umnmTx5Mq+99hr/+Z//SW1tba8RHoBly5axbNmyxM8nnHACixcv5uc//zn33Xdfr89xOp04nZk3EEsXZRHZMVBYPS3HK5FRm2tOmcF/PyqL/kT9+zQ/dzdWTbC1cBlzZx8L7YboObAW/M1UFsnf9UARG92YExUdxpwoAJsz6U47Gky0f2eKcLSr3TufIjbmF6I928ImqXg4W6mouMWosUmDsNGM4kmRNxEbczyGTJFltOVbjye6alpEcdZ8bAD8rmrwbyXctG/YxwjufptCLcRx2hbe2b6DpUfO7bmTEbHZoE/n1Mklwz5XJoh4p0AzuDv2DO8AxmvXLtxMrBi867fbYSUs7KChTPoMhhSxeffdd2loaGDJkiXYbDZsNhuvvvoq9913HzabDb/fz7e//W1+9rOfce6557JgwQKuvfZaLr74Yn7yk58MflEWC8ccc0y/EZvRRDgapVrIL/zyCbNyvBrJ2fNrEOUzCAs7WsTPiR1yhpX3rJvlDsXjDBM/ATv/SUWh/PIZKGJjzqmJe4bXrWC3O9CzWAgXisXzMmJjTvh2xrIrbERS8bAzS11RuiFsRGzkdVUWsyskzyI2hQQAkdlUVNAHQtantFKY1YhNtFDWl+i+A8M+hjCmkgO0bn655w7xGOLQBgA2iukcM2V4UeGMUS5rFr3BYWYPgmbhcOGgW71BDsJUqahUhnTlOu2009i0aRMbNmxI/Dn66KO59NJL2bBhA/F4nGg0isWSelir1YquD74bQAjBhg0bqK3NbQdRuqg/uBeHFicqrHir8qMWyGrRuOrk2WwTspjZogm2exYx7siTu3Yyxz5sf2HQERuHMSfKUjA8YeN0WLNaCCfbvY2wdx5FbKymsIl3DrBnehGh7BcP69b0paIsMSlsNGfhiI+VFkwXaXQKCGV2XpRRX9MuPMSwZTVio3llfYndP/ziYU9b1zgC54E3e+7QuBUtFqRduImVTE/U/eULzipZu1gaa4TheDIZERsfhUwuH3zEUaWiejIkYVNUVMT8+fNT/hQUFFBeXs78+fMpLi7mpJNO4v/9v//HK6+8wu7du1m1ahV/+MMf+OQnP5k4zuWXX87NN9+c+Pm2227j+eefZ9euXWzYsIH/+I//YMOGDXz5y19O3/80h7QclMVkTTn0sOmN848az157V2rMfdo3U3eYeab8e+fLVBTIdQ8UsXFG5IfTVjyMcQqA02YlZMwOykbEJhyN4tQMYZNHERtbQQkAHj27wkY3U1FZLB42hQ1piNjY4vILxeLMk1SU3Q1W+aVTTCCzE74T5nxFOGyWrAlTAFeFrC8pCA3sddUbQggqw3sSP0/rfLen54+RhnpPn8bRU/PDvyaZsooa2oQRaWndM+Tnxzrl6+cThUwZrOswRvGwUF1RyaQ91vzYY49xzDHHcOmllzJv3jzuuusu7rjjjhSRsm/fPg4f7nKp9Pl8XHXVVcydO5czzzyTgwcP8tprr7F06dJ0Ly8nJDxsnPkVgbJbLVTPOQGA3a65TFh8TuoOE5eCsxgCzUyPyhbGps7+C3q75kQNvvgtGafNknT3kXlhE0v2TsmjiI29QIbZ3bo/86ZuyRjFw0GLB7s1O6koYXzxkwa3aZth7Gh15UnERtNSp7WHMylsusYpVBY6s+rIW1w9BTCiFcOgpTPEFNEV7ZlEHQf3desuMoTNRjGdpVPzq3AYoLrYzW4hbUpE89A7o9paZONFu1aciJAPBneWo9yjgSEVD/fGK6+8kvJzTU0Nv//974f0nHvuuYd77rlnpEvJW2KthoeNJ7ceNr1x9Ce/yoEiK7XHXCgvwslY7TD9FNjyFJOa/w0sHTBi49V9ABSWDWNOFIawEY6sFcKlzCfKo4iNs7AEgCICsnMrS3ffmiFsotbsRTxEGiM2Dl2+nrZ8ETYghY2/ES/+zI5VSBqAOa0yuxGryvGyvqRc+AgGg7jdQ/ss7d+zg0VamCg29tkmMz22k/qNLzFh8szEPvqBtViQHVHfyrf6GqCq2Ml6UcMidhFq2IF73tCe39HaQDmgu0qGJEpdNivhLN4MjgbUrKgskPCw8eZHfU0yms3JhLNvxFXex9qMtu/SQ68C0OwPo+u9RxAikSglQqYyisvHDWs9TnvyhzTzdx/xsExdxLGCdcQ6P204C+UdabHmz87wRAAhsEQMYWM6H2cDa/rmg5nCxu7K4voHIhGxCWT2tUwIm2KmDKFGIx0Ul9cSFnYsmqD+4NDbnX375My6evt46sqPA0Db83rXDuFOtMYPANjrnsfUijxJNSbhslsTJn2R+qE3voTbZcRN8wxNtLkcyakoNVIBlLAZFs+9f5h/fdgw6P0LAoaHTfmUDK0ogxgFxPb6DZTRTjQuaAv27jHha6nHqknRU1yWhlRUFiI2po1/NF+GJhrYPIawIZjZgtNkYmE0Xb62cXsWIx42+XprabjbdOryGA5PnkVsgGIyLFL9hrChKPtf/JpGs1XWvbQcHrqwidTJsS7tBVOxTl8OwHhfklv94Y1oQueQKGPa1Ol5MfiyN9rc8gZRtAzdqNCssXEWDc0qI6V4WLV7A0rYDJmDrX7aHv8yex65gUhscJ1epVFZUFdYPT2TS8sMxbVQcyQagrNdcnJtX51RHU2ybspHIRb78FpNE6koyErERje6F6KWPPNEckkfC6cWxe/PUgFxuKu1PKsGdzYpKrU0GDK6MYSNO5+ETQkgIzbBLKSiWkURU7OcigJod8iGgUDj3iE/19Eqa1LiZTOZuPBUosJKtV5PuNEQSWZ9jZ6Hbd5JBIumAOBo2zPk52pGV5RnCK7DoLqiekMJmyGy5d3Xudj6Cl+w/C9NLc0D7h8KR6hJeNjMyPDqMoTRHXW6bSPQd2eU32fMibKUDPtUMhVldEVl4e5DGMV28XwTNo4idORdabizNTvnTMyJcuF0ZM8DRbPL371lpINPdR2XMWPMVZA/M4S6IjYB/BmM2IhAV8RmWg5SNWGPbI6ItQ7dx6U0IAWMs3Yu46oq2GqR18pDG14AQE8SNkun5q+w0UvlQFBPqG7Ihbz2iA8A7xCj3S67haAqHk5BCZshEtr2r8S/fXV7Bty/7tCeJA+bSRlcWQYx6myOia/Hgk5jHxGbkE9W9fttw+9YSE5FiSx8SBPCxppfqSgsFgKajJqE/b7snNOI2GRzsjeAZnSjWUdYYxOPBLAYqVBXHqaivJo/owZ90XaZHm/Xihlfkv1CeFFseGK1HxzS88KxOOPj0tivfMqRaJrGAe8SAGI7ZW1fbJ9MS31om8Xc2sG78mabotKqrpbvlsGn5HRd4I7LG4vKyqE1Xrjs1q4otxI2gBI2Q0IIQUXj24mf/Q17BnxO60FpOtVorUTLo+LUITHhGLA6KRIdjNOa+4zYxIwLa2iYc6LAFDYyYhMbjsnVEBERU9jkWcQGCGjyrjvqz1bExhA2IsvCxkhFjTRiEwp0pdIKCvPoyy+pxiaTs6J0o8bGUVyJLUut+snYy2R9iSs4NC+bAwcPUqHJL/WySbKVKD75YwBUNL0DnQ04Og+gCw37xMVYLflZXwNQ7XWzx2j5Zgh1NvUdIUqMsRsVVUMTNrLd2+wsVDU2oITNkNjT4GOhviXxc7hlYPvwQIN8c7c58svDZkhYbVAiL1oTtYY+vWyEX6bcYu6RCJuuu494JAsfUuNCoOdbxAYIWWXUIZ6DiE02zd2sDvm7t4mRCZtgZxsAAeHEac+jm4gsdUVZQ9KSv6gsN9eagsopAHgjg2+sAGjc/R4ADZbKhGN0zRHLiQir9MXZ9BcAtovxLJg2IX0LzgBVRa4kYbOz/52T2Nfgo1CT1yJb4UiKh1XEBpSwGRI71r2KR0uKVgwi5BpvyV8PmyFRItNoE7TGPiM2FsMgTLiH7wpqt2qENVPYZD5ioxnFdrot/4RN2CYv8nrQl6UTSmHTLjxZm+wNYLHLtIl1hBGbcEDe8QZx5VfXjLsEkDU2GfOxiYawG67L5VW5ETZl42R9SaVoJDQEh+XgIXmz2Oyemth2xJQaNgjpYRP/9y+A/C8cBqgudrJHGDUyQ4nY1Mkol44FnEOrD5OpKBnlzkb6fjSghM0QCO/4V8rPto6B56LYOvLXw2ZIlMhJ4BO0xj67otwB+fuwlw5fxGmaRlSTYdV4OPMfUrPFWORhxCZik14s5vymjJM0ADObqSirUTw80ohNOCjXH9LyLK2Y5DycsYiNUTgcFVbGVQ/PamGkFFXJa0SZ1smhxpZBP09rkp4vIW9X16jHYWOH5ygArJ3yuvK+NoOFE0vStNrMUF3sYo9uug8PPmLT2Cg7SoPWIrAM7WvZbbcmhmDqWbgZHA0oYTNIhBDUNr8DwD6PzAO7BzEXpcD8sh+NHjbJlMqL1sR+IjZlERnB8tSObIJ5zOhQ0rNw95HwTsmjcQomUbusE9HCbdk5YUqNTfYuDWYqyj7CiE00KCM2IUv+OEgDiXbvjBYPm63eFDG1MjeF05qrhADyd998aPDRiqJOua+tenbK9uD441J/rlyUVcE9HCqLnOw1Ijb6EIRNuzFOIeYYejdfSsNFREVsQAmbQbPjYCPzhZyXFJp3EQDe6MC55DLDw6aoZtoAe+Y5Jaawaei1KyoUDlOry99H1aS5IzqVKWyy8SG1GLOFyMNUlO6QERtLOFsRm1zV2MgvQzu9Gz8OloghbCJanr2Wye3eGTJbjHbI+rYWkQNzPhNNw2evBKC9fs+gniKEoDoi0/XFE49Ieaxs9omEjRRLWNipmrE4fWvNEHarhTaPTNtbOw4NuuYl0Gak8YfoOgxgsWiJQbJC+dgAStgMml3rXsapxWi2VuCadQoA5fGmfp8TCkeoTnjYzOx337wnkYpqosUfId5trELd/h3YtThhYaekZvKITpX4kGYhYpOw8bfn2V0+oBu5dmsku6mojiwLG5sZsREjEzZ6SAqbqDXPXktD2BQSJBQd2f+xL5qNVEabVkTVEAYoppuAS6Zhws37BrV/U6uPcUJeR6unLUx5bOHUat7V5XXzfTGFo6cNzbguVziLKmkfQsu3EIJwh/wdDLVw2EQ359ypGhtACZtBo++SfgqNFcdSViuL3Iq0IP72vnPJhw92edgUV47yGhsjFVVNK1Y9QmsgNW3Quv9DAOqtNWiWkX0pJjxlsmDQZzUiNlo2nXYHi+E+bItm13k42+3edqcZsRlZKioWNoVNnr2WhrCxaAJXPDBox/Kh4DNcvyPOspwWTkcLjfq6tsF52RzeuQmLJvBRhKsktTZoakUBr1uPBeA1fQGLJ+ffRO/eqPa6ElO+B1NA7AtEcUVlutldPMzGCzPirIQNoITNoNB1wQTfGgBcs06lsMhLm5Dh3pbDe/p8nu+gLIob1R42Jp5ysBdg0QTjtaYeBcSBemmJ3uYeefeXnpj2nPkPqU2X/w8tD2tsNOML0RHrGGDPNJFs0JfFrihT2DhHGrExhE08j6a0A2BzIow1FWeozsbfKms0xAisFtKBtVS2Y9sDhwe1f8cB2RFV7+wZ5dU0jQ8mXcIF4Vv5Z8Xn8Lrt6VtoBqkudiXqbAbT8r23JUCpJt+71oLhRWw0I+KcjnlrYwElbAbBB3sPME/IN+iEo+R4gSZj4FtHQ99zUQINMgw5qj1sTDQt0fI9UWvoWUBs3JmEjFkpIyHhKRMb+bTngbAbQxMtjjy7ywcsRpuwK55lYSOym4qyG6kopxZF6MOPZghzUnu+RWzoEqleAgSi6a+zMWtsHEWVaT/2UHBXyGtE4SAaKwDiDXJit7+o9xrEE2dV866YzcfmjEvPArNAdfHQIjZ7m/0Jcz6GKUzN2W6JmsGPOKM8jJAd9q9/iXmaoM42npoy+cFtt1dDeG+/ueR4q+FhU5DfplKDpnQyNG5lQi8RG1eH/D1Yyqb29swhIRJDETP/ITUjNmYBaz5hKygBwBX3Z+eEOaqxsbu6hEgsGk5EcIaKFpFfDlkd4DlYXF7orKNY8+MPpz9iIwzXYXdpblq9TbzV8vNfEW8kHIvjtPX/PnK3yUivqOi9k/Lzx09hdnURx0wdHWkokMJmrW68DoPojNrbHGCmZgqbkmGd04w4W/QoxGPSVPUjjIrYDAJtz2sAtFYtS2wLuuUbN+7r233Y1j5GPGxMkjujukVsSsPy9+CuGXmRtClsLFkIq9qNoYn5GLGxG8LGI7JbY9MhPFlt93Y6u373kdAI0o9RGbERjhx1BfVHUmdUJlJR9rCs9fOWD82OP92YXja1WjOHWwd+LcuD8uavYPy8Xh+3WjROnFkxoEDKJ6RJnxmxGbh4eG9zVyqKYXRFAViSPkNqwrcSNgMSi+tMbpOTZQvmnJrYHi2U6SVrPyZ9hcEx4mFjkuRlkzxWIRaLUxuXOfWyiXNGfh4jX5zpsKoQArthCmcbZpQgkzgL5V1qochWxCY37d4OZ1d9UyQ8fIMxS8z4PeVjxMZ0H9bSPy/KH45RaA5QrMptykbzyuh0oRbicEP/dhihcJgJuiwyrph6ZMbXli2qi5PGKrQfGLCgd19LcipqeJEpW3JXZxaaLvIdJWwGYOuOXczW5F3F+KPOSmy3eGWRrLOfgW9jxsPGpI+xCvWH9uLWIsSEhaoJM0Z8Gs2WHWET0wUuoxPHlocRG6fRIVFAKDvdDsldUVksHrZaLQm/ksgI3KYt5u/IkUeTvU2SBmGmO2Kzu8lPmSZfu4Icp6JweOi0SP8l3+H+60sO7f4AhxYniIPycdP73Xc0UVXspIWirpbv1j397r+3OUCJNrIaG5fDlhiroCI2StgMyKENLwCw3zENa1JhnsOotSkK1/f6POlhI70JKiaMzIk3b+hjrEKz0erdYKnEYk+Dh4aRL7bGM1s8HI7pXcLGlX/CxltSSZtxceys257Zk8UiiYGgHbjxZFHYAITNie4jEDY2Y1aS1ZXHqSgtkPaIze7GTkoxCsw9w+uqSSftThmtCDT172XTsvd9AA7bJozYIiKfKC9wYrVYumZG9VNnE4jEaOgIUcrIUlHJYxVUy7cSNgNi2/cGAO01qfbeBYYvTVkfJn11B3ZjNzxsiipG+QBMEyMVVaG109HuS2z210lH5hZneoqkLYawMQt7M0UoGsdpCBu7M/+ETYHLzn6LfO807N6c2ZNFuup4/LipKMyuyVtUk8ImOgK3aVtcPtfizOeITfonfB+sr8euGcfMA2ET8cg0fX/1hwCROtnq7fOMvOEgn7BaNCoLk+ts+o5cba/vxEUEp2ZYHQwzFeVSE75TUMJmAOZHNgLgnXd6yvbSGvlhLCCI6GX6csshWe3faK0a/R42Ji4vccMN19nZddHSm41W78JJaTmNWchr08MgxAB7D5/kiI2Wh87DAK0u+Tv1H/ogsycKSYOwgHAiNCulHkdmz9eNiBGxiUeGL2Yduryg2115LGwy4GPT3CBr+SJWT37MPDPS9JaO/k36bC3yGhktG+Wu7L1QXexkp27UO9W/3+d+G/b7uqI1Fvuw06huhzUpFaVqbJSwGYDqKx4ifsr3GL/w1JTtlWVl+AyTvvaGniFX08PGNxY8bJLQvaZPxSFicek54mjfA4AonZKWc1iSL84Z9LIJR+O4zDulPBU2Ea9xN9uc4VRUUuFweaETqyW77rVRTQqp2AiKhx2GJ1F+CpsSwJgXleZUVFuzrOWLOfOjJdpeKqPZBf3UHwJ4/fIa6ahJQ8NBnlFV7GK9MOoN97/T534b9vso1cw0Ypn0CxsGMmJjpqLUhG8lbAZiwtFYT/o6Wjd/AYfNQoMmizvb6nq29OktewAIFYyRNJSBrWwKABNopMUYq+ANyuiNqzo9d17W5HbdDBbCJUds8lXYWCvl79RtiMeMkWj1zn4aCiBmpKJikeHfbTpNYeMpSsua0opp0JfmiI0QgqBP1vlpw3StTTeeSpmyLo019Jgpl0xVTEaaSib23uo9mqkudrJBn4FAg9bd0NnY637r97Xi1YxuvmGmoUDW2HSlolTERgmbEdBql0PZeiuSs3WMMQ8bA6001ctGCEF1TLZ6l6SpSNrudBATxlszgx/SFGGTh9O9AbwT5EW/IrI/sydKithU5mCIYsyI2MRH8Hq7kM91uvNX2MgJ3+kTNi3+CK6oDwBHcX4MiSyungJANc09jDxNOjs7EgXPFePHSNdoEtVFLtopoN45RW440DNq0+qPsKc50FX4PYJxGC67haAwhI3qilLCZiQEnPJCEuulSK68UxbUesaNsTBr0pTvps4ITY11iTuO6knp+b86bZauu48MfkhDkRhuLb8jNrXTpbApEe1EO5szd6KUiE1262sAYhYppuLDjNj4wzGchtlieVlJupaVPpJrbNI4UmF3k58y44vRWjDMAYppxmrU2NRqLRz29f75bToko9wBnBR582Pd6aTaK2+UPrDNlRv2v91jnw37fQDMLDKuQWmL2Chho4TNCIgUyOIwrT21SK7N18KMuPzgjpt/craXlVm6RWya9m4FoIlSHGlKATht2QmrRpNbi/M0YlNdXs5hIVMMdbv6LkIcMcY4hU48VOYiFWWRr7cY5ut9sKUTpyYFQ1FBfkds0tkVtavJn1SjkR+pKIrlddGlRWlq6H0Ypq9eeoO1WCqGXVeSz1QXy+vJu7qRnu+lzma9IWxmFRtC1zMCYeNQwiYZJWxGgDA+wA5/apHcgU2vYdUEh7RqSmp6Tq0d1ZQkuw+HaT8si1qbHOmrJcpWxCYaTnL0zdOIjaZpNNhlG71v/9bMnSjHqSjdMrJU1OGmlq4f8qEzqDvG3XihFiIYigyw8+DZkxSxIU9qbLA5abfK/29HY+9eNoEmmVrtsOd2aGemqC6Wn6HXw4bx4MF10isqifX7WgGYWmBGbEaSirISVMImgRI2I8Cs/i/sZtIX3Cm9b/YXjh2b8ASG+3CxFqCjtZFYozSf6ixIT6s3gNPe5URLJHPjBOJGxCaOBaz2jJ1npHQWSjEZrt+WuZPkuHg4nojYDK8Lrq7F1/WDLQ9FqrM48U8t7EvbYaXrsIy25U3EBvAbafpIS++1YbFWmb4PeXLslJwhqoukuN4QKEe4yyAehrr3Eo/rumCjEbGpdRhCZASpKJfdSjhRY6OKh5WwGQHuCilsSqINKX4rRfVrAQjVLs3JujKKw0PALu8shG8ftrY9AOglU9J2CqfNyn5hFEI2Za7NORaRbZERLftf5ENBL5N3fbbWHZk7SY4jNnGrcc5hXpQbDWET0xxgycPLmtVGzCb9mTQj7ZcOkscp5JOwMU369LbevWwsHTJFpRfmdrZVpijx2HFYLYBGqOZouTGpzmZXk5/2UAyX3YJXJLV7DxNVY5NKHl4BRg9eo/rfTShhcEY8ysSgTBkUzfpYjlaWWUKFMjVib99HYUDeeTmq0jfrxWmzsEUYKby6TWk7bnfihmdKNM+Fjdvw+fAG+reoHxFJc6JyEbERZsRmmL5FzT75+Ytb8zANZRB3yKiNLU3CRgjB/pakrhpPHhXhGml6u7/3GhtHUEa5raVjyw7DRNM0qox0VHPZIrkxSdiYhcNHjvdiCcmU1EhSUW6VikpBCZsRUFVWSquQZmCxViNnvGc9HkL4RAHT5y7J5fIyRrxYpp08gQNUxeQdWfG42Wk7vtNmYbM+Rf6QFL5NN3HDvt/syMlXyqfIzqia2EGEnl7XWhPdEOY5q7ExIjZimBGb1jYpFkSeFoED6IZrty2aHmHTHorhj8Qpz8OIjd0QLO5g77P0iiJy8renbGzZYSRjFhAfKDBKEva/k4jsm/U1R00qhaApbEZSPGwhpNq9EyhhMwLKCxyJjpX2Blnl37DlFQC22ObiLcjvL8zhYikzZkYFdlGJD4CqyXPTdnyn3doVsanfDPH0OrWaiOjoEDbjpswmIqy4tQjNh/f0eDwYibNuXytiBOMnogEpbPyahxJ39uuNhE2+BtowIzZt7VIsWBx5WF9jIIzOKEesIy3HO+QLYiNGsWY4zeaRsCkwTPpKYo09TPp0XSRm7BWPteaKJMwC4u22GWCxQcdhaJM3wGbEZtHEEggYhe8jSEU5bclDMFWNjRI2I8Bi0Wixyap+v1H9L/a+BUBj6VE5W1emcVZIm/9lmmw/9lFIYUn6wuAOq5yMG8Alay5a+p6OOxJ0M2KTx+kLAKfDyWGLrFmo76Xl+86/vsHvHvgpr37Qv4V9f8SDhjBwFmHJ8jgFAMwam/jQO4YCkRiRkN84TB5O9jbQjLEKrjQKm8ScIc0C3dzRc0lhpYzq1tBMY0eqWG1u9yduiMprx9YAzGTMiM37DTGo6YraBCNxPqiT74GjJnrTFLHpqrERaqSCEjYjpcMhi1zDLftBCCpb18sHJi3L4aoyi8eopxmvScO4Rlt6CwCddgsCCx9i3M0dzlA6yrizied5xAagxS1D9p3dhmGGonFO+uB2fuH4OfqWfwz/BEaNjc3jHf4xRoAZsbHEhx6xOdgaxG04SFvzOGKjueXv1hXvQO9n1MBgOeQLdnVEuUvBYh3xMdOFtUTW4dVoLRzypX7RNtXtw6IJotiwF+WHW3ImOGOe7Ph6Yt0BfOXGje7+d9h0sI24LqgudlLrioIw0ssjrLEJG4NkzRu2jzJK2IyQsMcYTd92AFp34423EBY2quccn9uFZRAzFWXS4Ulvntxpk2/LLbpZQJwZYWPe2eh5XJdhEi6WtvOiKbUzas2H+ziRDQA4/IeGfXwtIoWNo6Bk2McYCZrxGmjxoYfRD7QGk2Z+edK5rLRiMwzYirUAwejIa6UO+kJ52REFJIqHC7QwjY2pc5La6vcA0Gopy88OtjRx/PQKTp9bTVwXPHrYGIa8/+1Efc2iiSWw90253VE4Iv8ll91KuzGUWfh7n0v1UWLsvquyhNmuaPPX0bntdQA2iWkcMWlsGk8B4J2ATle6IuqdktbDO23yznNT3PDGyVRnlBGx0fM8FQVgM4dhdqQOXN2/9pmE4y6RzuEfPyqf6ykqGfYxRoJmk2F0yzBSUQdaA7g1I9KTxyLV6kmaF5WGCd+HfMEuc7586ogCsLvptMgusM7GvSkP+ZtkJ6UZ7R7LfGfFXOxWjT8cMG6A6zaxZa/sFDuhOgZPXSu3L7p0ROexWjQOaPK7yNKyK8V+5KOIEjYjxGKEXD3BOtq3S2O+7c75FLny1/BtxNictFq7LqT2ivQOsXPa5dsypTMqAx9ULS5DtqNB2BRNkMXZFaGulm8hBKX7Xkz8bBmusInHsBuTsT1Fww+HjwQzYmPRhyNsgjiJyh/y1EEaQHObEZv0TPg+6AsmjVPIzevWH36XTMWEu5n0xY3ZemH32DTnS2ZqRQFXHDeFw5TToFWAiBPd9y4WdM7b9X0INEH1kXDG7SM+V4O9Fl1oWCLt4G8a+eJHMUrYjBB3ueHEG23AdUjOA+moOjqXS8oKbc7axL8La9Mz1dvETEVtExMQmhUCzbKjIM1oRmuxWd+Rz9RMPUL+LRrw+2Wh7OYDLSyLv5vYxxodpktzpKuYtdhbMuw1jgTNCMNbh1Fjk5qKyl9hY86L8uJPy7yolIhNngzATMZM04u21BSppUP+rBeNTXO+7nz1tJmUeuy8E5sBwJTg+1xjewpv3WqwF8Cnf5+WMSAWh5tDGCnJ5gyaeY4ClLAZIUVVsg7EJUKUBfcA4J5+XA5XlB0Cni5jrcrJ6Z1gLh07IYyDeJm8GGQiHWUxWotFPlrwd8NbOYFO3Fg1wcFd0gBy6zsvUKp1RWmssWEKG6NwOCTslHsLR7zW4WAxLuy2YUVsArjJ/1RU14TvAIERpqKicZ369lBejlNIYNTZ2DpThY0jKD1szALjsY7XbeemM2cnBmJ+2voqN9j+Kh9c8VOomJmW87jtVnbpxg2nEjaKkVBZ5qVFdH0ZbNfHM3vqlNwtKEtEimTBcEA4KalM7wVK07RE1CZcMV9uzEBnlMUoVNXycWhidzSNemMYZsv+LQDYtj8HSAEIYIsPs83TnBOFOyeTvaFL2FjF8FJRLi3/i4fTOeG7vj2ELqDCkqfFw4DdEC7uUKpJX7FhzldQ8dEQNgCfOWYijSWLAJhqqceKDgs/A4s+k7ZzuOxWdgklbEAJmxFT7XUlTPoA1opZHDGuuJ9njA200ikA1Ntq0TLQ2WAKm2C5dN3NRGeU1ezAyef0RRIdBVMAiNR/yGFfgKMCqwE4VH0SAPYRRmw6RW5ch2H4EZtAJEazP5Jo987Lyd4miYiNH394ZMLmkE++d6ttpjlf/qWiCgwvm5JYA7G4DkAkplOuG+Z81WPXnK87NquFSz7xcYKGO3B7wVT4+E/Seg6X3cpuJWwAJWxGTJHTRr3WdVHZV7CAAqcthyvKDrNO/BQ73UcSPOpLGTm+0y47ozpLMy9stFEibMxhmNbWXaxZs5oplnoi2OmY+nEAnPrwIjbRgA+Q4xRyMScKwOYwhM0QIzYHW2UBeJHNSO3kdcSmBJARm2B0ZKmoQz75/67M44hNoeE+XEMLDYZJX31bgCpku7P3IyRsAE6cPY73y86glWJin3oQnOlN+7rtVnYLo/vqIy5sxv43cIbRNI0OexUY16nY+GNzu6AsUVBWy/RvvpGx45sRmzavUb/TukcOGnWlz0DOpsuLrWWUCBtX9SzYBcX+PRx6/xkADpcdi1YkL2ZOfXjGXB1trZQBfjx4czBOAbqM9WwiOqTnHTC+4EsdcYgwKmps3FqEYHBkJmoHfUFAUCmM7pfC/GudtnhlHV6N1sL2thDjStzU1x1kohZDR8NSVDvAEcYex1z/J9DjGTFTdDusbDMjNi27Mnae0YCK2KSBgNG22CBKGDc1fTOTPsqY7fJbfTYoNnLx9ZvTeg5T2GjOPL7LT6JsstEZFTvIzNbXAHAfuRKbp0j+WwwvYhPokHfQYWthbsYpAFYjYuMYYsTmgBGxKbWbEZs8FqnO4oT/k25EyYbLIV+QSnwUxNvlOIU0FaCmFaN4uFgL0tgkTePa66WnTZulFKxj2BKjPzIkNtx2K4dEBXGLQ44mads/8JPGKErYpIGmkoUAvBBfwpETSnK7mDHC+YvkRfFXr+5EN+espLmA2C6ksLE5RoewqZos03KVWhuLLDLUXLnkPBxuWdPlEsMbfhfs8AEQt+duzpLdKQWJnSFGbFqlmCuyjgJhY7EQtsjfcTwNwma2RfrBUDYtP//fzkICFplu6WiQ/kvBZrnmzo+AOV+2cdot/P/2zjxKjuq+99+qrqX37tlHM6ONVcgjyUYsEmYXRvgJGwc7xkSWldgOIUEEiI8Tg3MeJMc5IidgO3KMfYwdsOM8K8kBDHnEtkSMJHgIm0gIJBYhg0CjkUaz9r533ffHrarunpmemZ6Znu6q/n3OmaOZqtulW7eru771WzWICOvtVxrZHUXCZh6ILlqPa9MP4Rv5LVjZAIHDC8GW9UvR7FHwwUgCbxmdvuc55VvWLTb13F+oGNHlx4hQKMTW71kJwd8FVa9o62apWRUyzMRDAACm+uZlnrPBiLGRK3VF6RYbr6i/rp5dUQDSkh5XkQrN6TinQimcL+jFGttXzm1SVSSmGkX6uKDJGcX53PYvzrfQuPS4xFGnXrF9pDrNg60ACZt5oN2n4j3WhaXtLXArFLY0H7gVCX9yJa9o/LMTQb5xngOIFd1i41CtIWwAYNRZ6MuVP/fjAADVy8W0KDBo6cqrD2f1zt5QayfKZSe3mqnIVCTODGHjNoRNPQcPA8jI+hqnwnM6zqlQEucLusWmjoVNRi/Sp4X5XAvF+RovvqbaGMJmSCGLDQmbeeCys1vhd0r4vQu7px9MzBjDarMnon8JDr4F5Cqvc1IOI55DUmvngqmUVKDQvqLr0psBAG63D3nGYzdSicpvmCzFhY3DVTtho+ji0iEwsPzMrTb9uitKtUK6N4CsLmyE9OyFTSSVRTSdw/miHkPRUb/ChplF+njlcDXJa9rIDVKcbyFxKVzYDOr1rjB8DGciKfzqjYF56SZvJUjYzAMru/w49L+vx+1XnV3rqdgKtyLhtivPwknWiijcgJYFho/Oy7Fzec28GcoWstg0L+Y3sTG1C1InDyZ2KRLi4Df0VCxS+UH1OjaKe/4yzipFVguCJJ2eWcZQMpPHcIy/h4re6wp1XkU6r3B3n5SZxfukcyqUhAAN54n1b7GRm/ReeqkBAIA/w4OI3a2Ly76GmB1O3WLT7+AP2PHTR3Hdw3vxJ/9yAI++8F4tp7bgkLCZJ2qVTWJ3tqxbimaPije0+Y2zyeQ1s1qt7LSOxab78j9ArnMNAh+/HxD4NSeKApK6sMkkKr9hGs0znd7gvM2zUtQicZmdobDpD+mBw6oE0egxVY9BtEVoKhePUlF/rko5FUpiiTDIixI6VKBp+XxNb94xBEwgO4SxeMZMT2+0GjYLgSFsXkvyumquxGlk0vwz8pP9HyDfQFYbEjZEXeNRudXG6PStnXptXo6bympm40TZIuneAIBAD6Tb90H88OdKNicFfkOfjbCRc1zYuH1Nc5/fLFFkGRnGv5gzqZkJmz49vqa7yQUhq6e617mwYbqwUXKzt9j0h1I4X9DdUG3nA476jevztupF+oRRHDoxhk5hFADg1JsHE/OHEWPzq/dzCDM3RIHhby53IeiW0R9K4vm3B2s8w4WDhA1R92xZtxQfyNzNN/LugWlGz4x0Lg+nnlpslayoqUgKXJxlZyFsnBpvxeCrUWdvgBe6zIDXNclmZpa2blQd7mlyAzmLtMdwcWGj5uZmsTnPAoHDACAGuVtkkTCKw++dhEfQLWsUPDzveFSjPk6hr9znzsrglou41exfXv6gRjNbeEjYEHWPR5XQe9HlAADXyJuzSmkeTzqTNy02dX8znAFpkZ9DPlmZsEll83Drhf38wdr2G8oIurBJzazQoJERtTioFoRNncfYiK4gAMCZn5uwWWGBwGEAgJ8Lm6AQx8Bx7kaOiz7AIrWjrMQV57bhhg914q83XYBzLvgw3zjyO/zBpUsgCMDed4bwwcgs+8lZDBI2hCXYeMUVyDMBXsQRGZp7Rc1MJgVR0AWSHYSNQy/8lqrshjkcS8MLvRaMv3auKADI6F3Kc5mZuaKM4nxLAkVfY3WeFeXQhY0rX3lavgG32OifgTq32MDpR0rkIsY1eAgAEKXifFWh2aPg+1vW4stXnAXRqEQ98jssbfHgqvPaAAA/bRCrDQkbwhIE/F6cFLj5euDdudezyaSKnlzq/Cl/JmQd/OaRr7COzXAkCa+gNwN11ra4ZE632Mxc2PBxS3xFgft1/l5KHi4e3drsn5yHxqI4S+Dp03UvbFCoMtzL3gFQqG1DVJGWc/i/ei2bLet4rNO//89JpLJz6yxvBUjYEJZhyLUMABDtm3tmVFYPUM1DtEXPmpwubFiFwmZsbKzwRw0rDwNAVjAsNukZjTeETY9XFzaiXNeBtACgeLmw8SIONguXai6vwR17D5KgQVP9Zj+meibj4ULmIwK/yVJxvgVgnLC5+vx2dAddCCez+M/XTtVwYgsDCRvCMqQC/MPKhuZeyyanC4AMFDNt2srkjD5PFQqbSJhnqWQhA5I639OqCEPYaDOw2KSyeQzHuABaZGTr13nVYQBQvLwlhh9xZPJaxa8fjKZxLuOtFIT2D1ni2mU+Lr6WiXpxviYqzld1mvVCnokRIDEKhyjg87rVphHcUXMSNtu3b4cgCLj77rvNbbFYDNu2bUNPTw9cLhcuuOACfO9735v2WE888QRWrlwJVVWxcuVKPPXUU3OZGmFDHB0rAADeyNxLhef0WikZ/WZqdTRd2IjZyoRNTBc2aUftRUFed0Xls9MLG7NHlCrB57BG1WEAcOoWGz/iSKRyFb++uPml0HHBvM6tWkhNpcX4PK2U6l11VC+gC0qM8uJ8n72oB4pDxGsnw3itL1S7uS0AsxY2r7zyCn7wgx9g9erVJdvvuece/PKXv8RPf/pTvPXWW7jnnntw55134umnny57rP379+OWW27Bli1b8Nprr2HLli347Gc/i9/85jeznR5hQwJLeJfvjvTcnzjyurDJCrW1UswXmsybKwqZyoRNIhYCAOSM5ow1JCdykZnPTu+KMgKHe5pcEHLWKM4HFGJsFCGPZLLyAOL+ULJQw8YC8TXAxCrDvjaqOrwgtOiV8HV3VItXxabVi9CECP5z329rOLHqMythE4vFsHnzZjz66KNoairNpNi/fz+2bt2Kq6++GsuWLcNtt92GNWvW4H/+53/KHu/b3/42Pvaxj+Hee+/FihUrcO+992LDhg349re/PZvpETal+5w10JiAJkQQGz09p2Pl9YJuWdEewoapXJg4cmVSpRkD3n4WCJ0o2ZzShU1eqb2wyevvBZtBHRszvqbJBRgWnjoPHAYAKF7koff1io5W/PL+ULLQI8oiwma8hcZBfaIWhqLMKIM/6pWwS/1L3PPOFkTHztRoYtVnVsLmjjvuwKZNm3DddddN2Hf55ZfjmWeeQX9/PxhjeP755/HOO+9g48aNZY+3f/9+XH/99SXbNm7ciJdeeqnsa9LpNCKRSMkPYW8CgQAGBJ62eOp3c6tArOkWm5xNhI2ocFeUlCuTbdP3G2DnHwBP/knJ5qzRNLPGgcMAkNctNlpuemHTHyoqzmcIGwu4oiAIiIG/V5nY2DSDJzIyMowegbclQLs1XFFiYFxzYAsEPNsCI4B4+Bj/N5vCqhfvQJsQgUdI4eQb+2s3typTsbDZuXMnDh48iO3bt0+6f8eOHVi5ciV6enqgKApuuOEGPPLII7j88svLHnNgYAAdHR0l2zo6OjAwMFD2Ndu3b0cgEDB/Fi8m82YjcMa5DAAQ7Tsyp+NousXGLsJGcHJhIpcRNtoYd99pJ18BcgVXj1HQT3DWrgGmgaYLGzYDV9SZMBc/nQEnkDOETe3jhGZCTODWsWy8couNqAfOJ9R2wN08r/OqGkXCJi04gTq41hoCMzPqXW6xffYrEE4fMnfHTsxPe5p6pCJh09fXh7vuugs//elP4XRO/nS0Y8cOvPzyy3jmmWdw4MABPPzww/izP/szPPfcc1MeWxgX3c8Ym7CtmHvvvRfhcNj86eube9E2ov5J+PmHVRucW2YUy/Ibo12EjagLG0Wb3BX13gkecCpqWQwXt6VIcWEj17Czt4Hm0F1RM7DYxDM88NajSkWuKAtYbAAkHVzY5BOhil/rCfNaMKmm8+ZzStXFGeSCBkBMbbdEJpctMITN6LvAKz8EDv0UEES8H7gEACAOvVnDyVWXioo+HDhwAIODg1i7dq25LZ/PY9++ffinf/onhMNh3HfffXjqqaewadMmAMDq1atx6NAhPPTQQ5O6rgCgs7NzgnVmcHBwghWnGFVVoar2uCkRM0dsPx8YBFzhuWVGMT2lOO+wxs1wOiRd2Kj5yYVNNjZi/v7LXb/A5vPWI53TIOdjgAQonnoQNnqGWm56i00iw4uMuWVHkSvKAjE2AJIiFzZaMlzxa9uS/LoXOj40r3OqKoIAzbsIiB6H2kzxNQtGcAkgSkA2AfziL/m2DfdjKBLEst/+FsHo3LNL65WKLDYbNmzA4cOHcejQIfPnoosuwubNm3Ho0CHk83lks1mIYulhHQ4HNK18zYb169dj9+7dJdt27dqFyy67rJLpEQ2A38iMSr0/twPp7gvNJsLG4eJVg1U2eao0SxbiOZQzr+H/vn4aQ9FCOwWlDiw2TLfYzETYJHVh41Ed1mmAqZOW9HimRGWuqEgqi+V5Hvzt7umd72lVFZeeGeWlVO+FwyEDTcv470wDVt4EfPQutJ79EQBAd/YDaLnKSw5YgYosNj6fD729pR8oj8eDlpYWc/tVV12Fr371q3C5XFi6dCn27t2Ln/zkJ/jmN79pvuYLX/gCuru7zTidu+66C1deeSX+/u//HjfddBOefvppPPfcc3jxxRfnen6Ezeg6Zw0AoA2jiIdH4QnMMs5Ad0VpFnFfTIeiCxsnS3F/+jhzv5gqCJvV4nvY/MwbeOj315jCRqiD4GFT2ORnbrFxKfoTKWAZV1RY7QISgByprGzB6bFCRpTavaoaU6seft1SQ4HDC0vreTwrqm0FcNN3AUFAz1krkWQKXEIGpz94E4vOXj39cSzGvFce3rlzJy6++GJs3rwZK1euxIMPPoi/+7u/w+23326OOXHiBE6fLqTrXnbZZdi5cycee+wxrF69Go8//jj+7d/+DZdeeul8T4+wOE3NrRgEFzOn3z006+MIeS5smEVuhtOhuLmwEcGAzMQAYildcHucK/YjEY/gr39+BF5Bt/Cote0TBRTeC2FGrij+pOlWHKZItUrwcL6Z1xdRQ+9W9LqhMyfRIkShQQBaz6/G1KrHRV8Ezt0IrLm11jNpLC6/B1h9C3Drz8zMR1mW0SfxKsSDvztYy9lVjTk3VtmzZ0/J352dnXjssccqeg0AfOYzn8FnPvOZuU6HaAAGlKVoz4wi9MER4MJrZ3UMQXdFMZu4opxuL/JMgENgQCbGK48WoWQLwsYBDb3i+3gl5IRPNoRN7S02gm6xESqx2JTE2FjjvXQuugB4Fwgm36/odal+3iNtSOpCh2INEWey+GJg87/XehaNx+JL+M84xrznAOFjSPfPLbu0XqFeUYTliOs9o/Jn3pr1MUTDYmORuIzpcKsS4tBv7JP0i1KzPPspJQcBAF8+KwQARRab2gsbyFzYiPnMtEONGBu34rBcunfrUu62b80PAZkyBRUnQRjkWSwjnrOrMi+icci38RpIyujbNZ5JdSBhQ1gOoY2b4eeSGeXQA04Fm7iiXIoDcXCRxjLRifvzXNgMta0HAGzw96OnyWXG2NSFsNHfC4c2tcWGMYZE1ggetl6695LFizHGvBDBED0187IFrhiPr4n7SNgQc8O9mMfVtCbsmRlFwoawHF49I6R1DplRou7uEGxisfEoEhKMWzwy8XHCRtPg0fi2UNcVAABp4FV8//Nr0a7o1hFn7WNsRNNiM7WwSec05DUGgAs6q6V7e50y+kRetG7o/Zm7Agx3Yt7VNM1IgpiarnN5yZau/ABScftV7SdhQ1iORXpmVBcbRCJWeS0QAJA03WKjWONmOB0u2YGYbrFJJ8atSSYKB3i5hczSq/m20ffQ26yhyaGLiDoIHjasZ6I2tSvKcEMBeh0bi6V7A8CoiwdvJk/P3J1quBPhDFZhRkQj0bZoMUbhhygw9B87VOvpzDskbAjL0dLehVHwG/Gp370+q2MY7g67WGxEUUBS4OeSTYx7AtNr2CSZwgukGbUtTr0KpPWxdeCKEhUubKRphI3hhlIcIiSHWJTubZ33MhPQ3UnDM3cFqDn+XolustgQc0MQBPQrZwEAxo4fmtFr+kYTGIqmTWtpPUPChrAkAwp/4g2dmN6UPxhNYTBaWqZf1oWNaBOLDQCkDGGTnFzYhOCF3ykDXbxAF46/AED/kqoDYePQs5okNp3FRk/1Vh18Q9Z6FhupnXde9kbfm/Fr3HkeFC55SNgQcycW4NegNvDGjMa//8jv4fg/XIEjB/ZVc1rzAgkbwpJE9QDK7MDUpvxMTsMnvvMibtzxoln7BCgIG8lqabNTkBb5ueRSpVlROb3ZYoh54HdJQNeFfMfxvfxfUaqLwFtRFzaOaYRNSTsFwHLp3gDgX8xbIrRn+nhBxRngZjxOioQNMR8IHTxW0R2aWQD7ebl3cIl4FAG1/mVD/c+QICaB6QXKnKFjU44bjqVxJpLGYDSNF48Nm9tl/ebpUO0jbLIOfi5aqjR4OBXhfaLC8MKrSkC3LmxOvcr/VX110ZhQ0l1R8jSuqHjaqDqsCxuLpXsDwKLlFyDHRLiQQjZ0cvoXMAYf44UXVa9FunoTdU1gGY9VXJSe3mqo5TUEdWHtaSrfw7FeIGFDWBJfD3/ibZmmyNlovHCT/O+3Bs3fFcYtNg7VOu6L6chK/MbOxgmbdJQLm6jg5TEpi9YAEHj/GKAu3FBAkbBBdspxyaxRdVivL2q4ourA6jRTFjX50Qd+gxg6PoPMqGwCMvh5q76Wak6NaBAWn38hNCagBWGEBvunHBuNhqEK/HPpbyFhQxBVoUPPjOrWTiOVLF/kbKRY2Lw9CE0PfFN0i42keqo4y4UlK/FzYZlxrqgYd0UlHHrmk+rjPWQM6iAjCgAkPd5JZlMLm0KfKMMVpb//FoqxEUUBgwpvDBnpe3Pa8UYT0xwT4fbWx/tFWBuvL4BTIhcp/e8cmHJsdPQMACDNZKiu+r/+SNgQlqSlYwkicMMhMJx893DZcaPxQk2U4Vgar50MAQCc4NtlG1ls8rqwEcYV6MvFucUmJRV9IRnuKKBuLDbGe6FgZjE2HtMVZb3gYQCIenlWSnbwnWnHJiNcnIbhgdcpV3VeROMw6OKxivETr005LhbiwiYs+OvCbT0dJGwISyKIIk7LPDNq7P2phE3p0/9/v8WtNqp+81Sc9rHYaArvDyWMK9NvPO1n5EBhY1exsKmPJzBJ5a4kZRqLTaGdgsQDby2Y7g0ArIW3BlFD06d8p6Nc2ESYh/fHIoh5IN20AgAgDE2dhJEKczd+zFEf3xXTQcKGsCwR44l3iswow2LT4lEAAM+9dQbpbB5OU9hYJ+B0WmQu0sRsqStKSIYAADk1WNhYlxYb/l7IQh7Q8mXHxfXsNpfiAIo7gVvMYuPq4v16mpInph2b1t2JUcELwQJPzIQ1kLt5ZlQgOnUSRibCEy8SUrDaU5oXSNgQlkVr4XUY5HD5qH4jePgTa7ogCsDbA1G8d2aMd8GGvSw2RkdvKRcv2exIhwAAWnHF2o5enuYN1I2wUYrcgvlsquy4SRtgApYTNq3LeAB8mzYIZOJTjs3qwiYueqccRxCV0HY2r2nVk/0AWr78w0Q+xoVNWgkuxLTmDAkbwrJIQd5vx5UeKTtmJMaFzdltHly0jKfJ7nr9g8IxbJTubQgUKVfqipIyIQAAcxbVP5GdQPvKktfVmmJhk0kly45LFLuijBo2ggNwWCv2ZGnPEowyLlTCJ6fuspxP6NWjHfXxXhH2oPusDyHNZLiFNAben+IaTPDv2LzTGqUGSNgQlsUZ5BH93lx5YTOW4MKm2aPiugvaAQD73uCmf40JgKRWeZYLh6hbbJR86dO/0WNIGF+Kf+ll/F9/V9XnNhNURUGecTdLJl0+0y1RbLHJWq+GjYFLceCk2AMAGJ6mGaamx0mlJBI2xPwhyQr6pCUAgDPvHiw7Tkxyi6FGwoYgqouvhd+Qg1qo7Bgj3bvZo+C6C/S6ISHeJDItyJaI8J8pDie/6SlakbWDMTj1HkOSZ9yX0tVfA27+IXDh1oWa4pRIkgNp8FioTHoqi41Rx8ZhyarDxYy5jWaYU1tsoMdJpSVrBG8S1mHMy4PY0yfLJ2HIaS6sBY81aiiRsCEsS6CNP+0GEUOqjOvCiLFp8So4q82Ls1o9ZuBwGvax1gCApNeXULRkoUx/NmlWWVa8476UXE3A6t8H6qitRAbcnZSbUtgU1bGxaKq3gdEMUxiZOnhTSHExnlUCU44jiErJt/EgdmW0vLh2ZrmwcfhaF2ROc4WEDWFZ/M3tyDF+CY9NUjkzl9cQSvDU4WY9K2rDBe2msMkIygLNdGGQXdxiI4IVglGNVG/mgMtT/0/7GYELm2xmhsHDFk31NpA6eWsQb+z4lOMcaS5s8kr9v4eEtXB3rwIANCfKX4PuHL/+VF/bgsxprpCwISyLIDowJgQBAJGR0xP2j+mi5myhH8EQTwm/7oKOImFjL4uN4vKaMSowqg/rwiYML3yu+g+uzeoWm+yUwcNFLRXMzt7WdEUFunkAd3umD9C0suOkDL+xaMUp+wQxD/g6lgEAmrTysYo+jbuzXcH2hZjSnCFhQ1iaiIMHxCZGJlpsxhIZqMjgafV/Q/rhVcAPP4aL4vvQpvIbY9ZmwsatSohDv8GnS4VNiHnht4Kw0a1o+exMsqIclmyAWUz3WRcgyxxwIY3MWPlmmLIeAA5XcGEmRjQMTe28tUcAcWRSE4P2maYhwPj1Z4UGmAAJG8LiJBQeEJsJn5mwbySWQacwCi/0m9/J38LxxB/iW+K3AABZ0WbCRnEgDt0lM85iE4IHPqdUo5nNnKzuisqny7uiJs2KslADzGLag95CM8wpKmirOb1NRnEtIoKYB/xNbUgz/rkbO9M3YX88OgZJ4NbEQDNZbAii6qRVHsyWj04UNqPxDDrBb+zw9wBXfhVwNcFpZA1ZNOC0HG5FQoLpYm28sGFe+CzQYyhnWGxy0wsblyxZOt0bAARBwKDK023DfeUraLvy/InZ4Q4uxLSIBkJ0iBg1XPpDE62GkRH+3RpnKlxuaxSIJGFDWJq8mwezCfHBCftG42l0CLz+ApqXA9f+NXDPm8CmbyLacTEWX/enCznVquNWHIgZFhvdFZWNG80TvfBbwGKT061o+SmDh7kr0aNaP90bAOK+5QCA3ODRyQdkk2bHc9nTNPkYgpgDYYlnTCZGJ7r0EyH+3RoR/JZp51H/33QEMQWCl5tG5eTwhH0j8QwWGcLGKEKnuIGLvwTfxV9aqCkuGC7FgX7Gb/AsHYUAIBsdgQwgxDzwKPX/cc+L3KqklRE2jDEkssXp3jawvrWcA4wAznKtQfQaNnkmQPFQujcx/ySUViAHZEITkzCSegPMqCOARQs9sVlCFhvC0kiBTgCAMzMxon8szmNsAAA+q3wkZ49bkcwYm2ySuy5yusUmKfkhivX/tJXXLTasjCsqndPMEj0lWVEWTfcGCs0wm5PvTz4gFQIAROCBxwLuRMJ6pF3c8s2iAxP2ZaNDAICkZB1RTcKGsDSuIBc2vuzohH0j8Qw6BCPGpj7aBlQTl+xATM+Kyia4sNESfF3SFvlSyos8xkbLpifdH0/nzN9dclEdGwu7otqW8ToirdowWDo6cYBusQlbxOpGWI+8hwewO+IThU0+ajTAtI4blIQNYWk8rbwRZlAbm7BvtMEsNg5RQErglotcqjR42CoVazWHXjQxN3m6txE4rEoiHKJQVHnYmsHDALC4uxshxrvMj516d+IAvepwGB54VBI2xPwj+vgDopocmrCP6Q0wsyoJG4JYEJraubAJCHGkkqU1GEZLLDbdCz21mpARubDJp/iTv6C7MfIWKezGTFdUZtL9ST2+xrzBWzzdGwCcsgPDAg/ejA1PTLc1XFFh5oGXhA1RBZQgt2h7shNd+mKKPxwylzUaYAIkbAiL4w20IsMcAIDRcW0VxmJJdJjp3va32ABAVuJP/poubKR0iP9tkfonmkNPVy8TY1NI9ebvudXTvQ3GHPymkR2bmJWiJfg1HCGLDVEl3C36A2JuorApNMC0Rp8ogIQNYXEE0YGQ2VbhlLmdMQYxMQxJ0MAEEfBYo7DUXMk6uLAxYjWULHdjiG6LmJF1YSPkJo+xSaSLOnsDRa4o61psACAi8+DNfHhiVko2rrfFYB6e4k4Q80xArz4cZBGwfLZknzMTAgA4xjfRrWNI2BCWJyIZbRUKwiaSyqGV6fE1ng7A0RhPujmZCxtkYkAuDTnPLRpWETZM0i02+TLCprjqMGD5JpgGSZULGzE6Udjk9cy2CDwFSxVBzCMt7V3IMRGiwBAd13fPnQsBAFS/dR4OSdgQlich8yeJTLgQ0V8cOCwE7J8RZaDpwkbIxM1sGo0JkD3B2k2qAgxhI5QTNllD2BgxNobFxtrCJuXiWSlSYpKsFN0VlXT4LFMgjbAWTkXGiMATDMKDpXFeXqMBZoCEDUEsGBknFzZatFB9eDSebqiMKAOmCxsxGyvq7O2Bz2WNvliCLmzEMsImmRnnijLTva0tbHIefo2qyYkVtJkuUNOSfyGnRDQYIZHHecWHi+K8tDz8jLu1PU0kbAhiwZisrYLRABNAQ9SwMWAK7+XiyMaL+kR54LdKYTfTYjN5VpQZPDwhxsbawgZ6uq0nPVHYGFlRGdm3gBMiGo2YzIOD06GCSz8VHYVD4BUxAy3W6OwNkLAhbIDg4x+44rYKY4kMOo1U7way2EAXNlIuXmSx8VqiszcACHratkObWtgULDZGure1hY1DT7f15saAfK5kn6jXsclZpBYRYU1STr2hcLgoVnGUu0YjzA2f2zqfMRI2hOWR/VzYFLdVGIln0AHDYtMYNWwAQHTyp3o5lxjX2dsawkbUs5scZYOHDVfUuDo2Fs+KUgIdPHgTGjCuoasjQ8KGqD45N/8eFeJnzG3xMX4thi3UABMgYUPYAFcTf9r15QptFUZjxQ0wG8diIxjCRksCesXQEDzwu6zhihJkPcaGzdBiYxNXVNDjwhCC/I9xmVFyhgdvMovUIiIsiu4OlRMFYZ3SG2DGHNaK7yJhQ1gebwsXNkEtZG4rqTrsa5wYG0kXNgIYEOEm5RDzwm8Ri41DFyhSGVdUsljYMGabdO+gS8YZpqfkR4qETS4NSePiTXCRxYaoHlKAPwC60wWXfkZvgJmQgrWY0qwhYUNYnmBRW4VEIg4AiEfH4BX0p/kGstgoTg/yjJuMWfgEACPGxhoWG4fCXUrlhE0heFgC8lmAaXyH1S027iJhU2yxKUrZd5CwIaqIq5l/j/qKqg/nY/z3tBysxZRmDQkbwvJ4/C1mW4Uxva2CI8ZvDlnZByiems1toXGpEuJ6h29tjNejCDGP5WJspLKuqKJ072xRbzCrCxuXggFd2BQHbxoZUVG44HEqNZgZ0Sj4WnsAAE0sBGj6A0OcC5uc0zp9ogASNoQNEEQRY2IQABDRazAocR7Nb9QHaRTcigNx8Ju8EObCJir4LFOxVtItNjLLTrq/JMbGiK8RRMBh7Zu+zylhEFzYZENFdUR0iw1vp2ANcUpYk6b2bmhMgIQ8sjHughJSXNhoFmqACZCwIWxCRG8imBjllhqXUQ+kgdxQAM8WijMuDsQUjzFKy9bJaHAoXJTJ0wYPS6Wp3hY5v3KIomD2i9KK+0UZnb3hKQRME0QVaPZ5MAYeoxcePAkAUIwGmG7r9IkCSNgQNiGpcGGTCQ8gmcmjOc+fNKRA46R6A4bFpjT1OacEazOZWSDrwkbB5BabkuBhm6R6GyRUXtlViBULG57qHWYeeMliQ1QRURQwKnKrYXSICxvVaIDps05nb4CEDWETMnpxKS06iJGidgpSsHEyogBd2LDSG73mCtZmMrNAduoWmzLCJq7H2LgUB5AzhI17QeZWbdJOLmykojoipisK5Ioiqk9E4paZ1Bh3h7pzXFirvraazWk2kLAhbEHezW8KYnwQY/FsUQPMxrLYuGTJjLExYKo1OnsDgKRbbFRkeTr3OAyLjafEFWUPi03Wy92mcjYCZPTAaMMVRRYbYgFIKPwBMau3VfBpXNhYqQEmQMKGsAmil3/w5NRwicWmkWrYANxiExvnihLd1hE2irMwd2YEBxdREjxsk87eBqo7gATTm5UaKd+6xSYCL1lsiKqTdekCJjoA5LPwgZfP8DRbp08UQMKGsAlygFfNdGVGMBov6hPVcMHDpa6oCHPB67aORUNRCm6lbCpZsk/TGJLZoiaYpivKHsIm6CmkfJvCpshiQ8HDRLVhXi5gHIlBZPUaNhoTEGgiVxRBLDiuZi5sfLlRhKJxtICXoW80i41rXPBw2EJ9ogBAdblMq0VGTzk1SOXy5u8lwcM2cUXx6sN6Wm2k1GITBrmiiOojBvj3pTM9hOgoj/UKwYOAx1qfMRI2hC3w6VUzg1oI6dApiAJDTpABi6UpzhWe7l2wYITgsUzVYQBQJRHD4BV2k2MDJfvi6YKwcUrFWVH2CB4OuBWcGdcvihkd2qmODbEAqE3cwu3NjCAR4sImLPjhEK1VToGEDWELgh28aqZfSCA79C4AIK60AWJjXeIOUUBKLBI2FuoTBQCCICCsp5zGh/tL9hWneouiYLt076BLxoBhsdGFjUYWG2IB8bTw79GgNoqkLmxiovVaeTTWtz5hW9y+ZmQY/+JXh48AANIuawW8zRdZqdBCIgwv/Bay2ABATOI391So1GKTyBa1UwBsl+4ddMsYHB9jowubKDxwyvR1TVSXYDsXNiqy0EaOAwASEgkbgqgNQqG41KL4UQBA3ttZyxnVjHyRsLFSnyiDpMrdh7nImZLthQaYurCxW4yNWy4ED+sxNoIePJxRrFM9mrAurcEAwow/KDiG3gQApBXrZFUakLAhbEPUwT+AHxL4kwZ8jZURZZCXChaMkIU6exvkXXqV0/hgyXbTFSXrQi1rr6yogEspckWdAvJZiDlezyYvW++pmbAeHlXCsN6zzBPiD4g5C9XBMiBhQ9gGo63C2SJ/2pWaemo5nZqhKV7z9xDzwu+ylsWGeXgtDUeiNCsqntZdUarhirJXHZugWzYbYbLoAKAHDgMAU321mhbRYIT0vnut6RMArNcAEyBhQ9iIjLO01oKzuUGFjVwcY2OtrCgAkPw8NkpNj5RsN2rYmDE2Wb06r01cUQGXjEEWBAAI+Qwwyi2PEeaC26nWcGZEI2FUH5bBHyQEj/UyS0nYELZB85QKG3fr4hrNpMaMs9hYLcZGCXIXoic7WrLdjLExXVGGxcYewcOyQ4SiujDCdOvMII9xiFCfKGIBMXqWGTi81irOB5CwIWyE4B33gQw0VnE+A6HIbWFFYeNu5sLGnx8r6RdV0k4BsF26N8CtNmaRvqG3AVANG2JhyXtKv0dVv7U6ewMkbAgboQTGZUE1aPCw6CxYbBIOH1TJWqX4A2282KILaSATM7cn0mXSvSV7xNgA4zKjdIsNNcAkFpRxbWicFmuACcxR2Gzfvh2CIODuu+82twmCMOnPP/zDP5Q9zuOPPz7pa1KpiU3wCKIcrubCBzIsBgCpMeMSJNWDGHNCYwJSqvWetlqbms22CumiWjYJM8ZmvCvKXsLmjClsdIsNqE8UsXAYrmADb5P16oHN+jHglVdewQ9+8AOsXr26ZPvp06dL/v7FL36BL33pS/j0pz895fH8fj+OHj1ass3ptI+Jmag+vpZu8/ew1IZGTZB1qRK2Zf8cfiTAfNbLaPC7JPQhgCUYRHi4H+0d5wIorTwMoBA8bCdh41JwRs+MMtLdyWJDLCSu5sL3aI6JCDRZL3h4Vp+WWCyGzZs349FHH8U3vvGNkn2dnaXugKeffhrXXHMNzjrrrCmPKQjChNdORTqdRjqdNv+ORCIzfi1hT5raC1lQCdV65tP5wi1L2KN9GACwxmWtjCigqK0CG0R85JS5PZHhriizQJ/N0r0BIOAuaqugE6bgYWIB8bcWvkfH4EPQbT3L96xcUXfccQc2bdqE6667bspxZ86cwbPPPosvfelL0x4zFoth6dKl6OnpwY033ohXX311yvHbt29HIBAwfxYvbtAMGMLE6Q0izfiNPO22nvl0vvCoBbeFlfpEFROTJ7ZVmBg8bKR720fY8A7fpQXRKHiYWEhaW1sQ113BIfihSNYLxa14xjt37sTBgwexffv2acf++Mc/hs/nw8033zzluBUrVuDxxx/HM888g5/97GdwOp346Ec/imPHjpV9zb333otwOGz+9PX1VXoqhN0QBIzpbRVynsYMHAaKLBqA5TKiDFLKxLYKhrDxNEqMjU4EHnhVirEhFoYWj4ohvZ5SzOGv7WRmSUXfen19fbjrrruwa9euGcW//PM//zM2b9487dh169Zh3bp15t8f/ehHceGFF+I73/kOduzYMelrVFWFqlrPREZUl5jUBGQHIQW7px9sU4oDTa3WANMg52oDYgBihbYKjeCKCrqUSS02ZsA0QVQZhyhgVGzGMpxBQgrWejqzoiKLzYEDBzA4OIi1a9dCkiRIkoS9e/dix44dkCQJ+XzeHPvCCy/g6NGj+PKXv1z5pEQRF1988ZQWG4KYDOXirRhwn4/zL/9UradSM8wCdrCuxQZ6UTApWWirUDZ42CaVhwEeYzMKH7JFz5xhUPAwsbBEZZ5NmVaCtZ3ILKno07JhwwYcPny4ZNsf/dEfYcWKFfirv/orOByFJ8Uf/ehHWLt2LdasWVPxpBhjOHToEFatWlXxa4nGZsn1dwDX31HradQUd4krypoWG4dvYluFku7e+SygcQuOvSw2MhhEjAjN6GSFrCiKsSEWkjHXEiALxFzWbEtT0afF5/Oht7e3ZJvH40FLS0vJ9kgkgv/4j//Aww8/POlxvvCFL6C7u9uM0/mbv/kbrFu3Dueeey4ikQh27NiBQ4cO4bvf/W6l50MQDY/bBjE2ahPPkCxuq1AIHpYKVYcBewkbtwIAOMOC6IQubOApCQgniGrzStfnsXu4BV1dN+GTtZ7MLKjKt97OnTvBGMOtt9466f4TJ05AFAtesFAohNtuuw0DAwMIBAL4yEc+gn379uGSSy6pxvQIwta4bBBj427m7TDMtgqCYMbYeBRHoeowYCtXVNDN36/+fBBr9LeR6tgQC80lK5biq0cuw45zG8BiMxl79uyZsO22227DbbfdNuPXfOtb38K3vvWtuU6FIAigJNDUqhYbf+u4tgqqr9QVVZzqLQi1mua8E9DrDhUHEEfhJlcUsaDc9OFu/K9ViyA7rJfqDVCvKIKwHXaIsWltLm2rkNcY0jkNgOGKMjKi7GOtAQCn7IBTFs0ifTHmRA5SIcWdIBYIq4oagIQNQdiOEleUy5o3RL9TwojeFCM01I9ktpBx6S52RcnuWkyvqgRditkIMwwPRAFwyvRVTRAzhT4tBGEz3LL1Y2wEQUBIL7YYHz1tdvYWBUCVxELwsI3iawyCbhm/Y9wV18fa4VElCDZytxFEtbHm4xxBEGWRHCIuWdaM4VganQHr3vjjcjOQAdJjpyAWZUQJglAQNja02ARcMn7DluP/nL8D33lNgDdAX9MEUQn0iSEIG7LztnXQGINkYT95SmkBMkAuOlgaOAwUCRvrCrdyGJlR/09bhdM4jXMocJggKsK633oEQZRFFAVLixoAyLl59VPEBpHMcleUe3w7BTu6oly8lk1/iIs3yogiiMqw9jcfQRC2hXnaAQCO5HDBYiOPt9jYzxVl1rIxhI1CxfkIohJI2BAEUZcYbRWcqWHE03pnb8N6YWNXlF+vZTMUTQMgiw1BVAoJG4Ig6hJn0yIAgCc3Ookryv4WGwOqOkwQlUHChiCIusSt94vy58fMdO8Jrigbx9gYUJ8ogqgMEjYEQdQl/jbep8aFNHLJKIAii00DxNgYkCuKICqDhA1BEHVJa0uhrUIqPAAAcBs3+Zw9WyoAhX5RBtROgSAqg4QNQRB1iU8ttFWIjZwCUFRVubgJps0giw1BzA0SNgRB1CWCICAsBgEA6THdYmO4ogaO8H/9XTWYWXUJuktjbLwUY0MQFUHChiCIuiUmtwAAWGwQAOBSJCA2BJx6lQ84+9paTa1qeBQHJLHQG4osNgRRGSRsCIKoW1IqFzbe3CgA3WLz7n8DYEDnKsC/qIazqw6CIJS4o0jYEERlkLAhCKJuybl4W4VWhAHowubYbr7z3OtrNa2qUxxATHVsCKIySNgQBFG/eHlbhVZBFzayoFtsAJzzsVrNquoUx9m4qaUCQVQECRuCIOoWo61CmxACAHRG3wCSY4AzAPRcXMOZVZcgWWwIYtaQsCEIom5xBnkMjeGK6jizj+84+1rAYd8bfoBibAhi1pCwIQiibnE368JGiAAAmk/v5TtsHF8DlLZVIIsNQVQGCRuCIOoWf2s3AMAtpLFUGIB7+DDfcc51NZxV9TGyohyiAFWir2mCqAT6xBAEUbcUt1X4tEN3Qy36sBlUbFcMYeNWHBAEYZrRBEEUQ8KGIIi6xVvUVuHTjhf4Rpu7oYBCuje5oQiickjYEARRtxS3VegWRvjGc+2b5m3Q4uFWqvENMQmCmB56HCAIoq6JyS1ARv/D1QR0r63pfBaCS89qxufXLcGV57bVeioEYTlI2BAEUdek1CJhc851gGj/gnWyQ8Q3PrWq1tMgCEtCriiCIOqavN5WAYCtqw0TBDE/kLAhCKK+8fAMKA0CcM6GGk+GIIh6h4QNQRB1Tb7lHADAm9IFgKd1mtEEQTQ6JGwIgqhr5LOvxp9k7sYP279e66kQBGEBKHiYIIi65srz23Hqpi/i0uUttZ4KQRAWgIQNQRB1jUMUsPnSpbWeBkEQFoFcUQRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AYSNgRBEARB2AbbdPdmjAEAIpFIjWdCEARBEMRMMe7bxn18rthG2ESjUQDA4sWLazwTgiAIgiAqJRqNIhAIzPk4ApsviVRjNE3DqVOn4PP5IAjCvB03Eolg8eLF6Ovrg9/vn7fjWg1aB1oDgNYAoDUAaA0MaB3mZw0YY4hGo+jq6oIozj1CxjYWG1EU0dPTU7Xj+/3+hr1wi6F1oDUAaA0AWgOA1sCA1mHuazAflhoDCh4mCIIgCMI2kLAhCIIgCMI2kLCZBlVVcf/990NV1VpPpabQOtAaALQGAK0BQGtgQOtQn2tgm+BhgiAIgiAIstgQBEEQBGEbSNgQBEEQBGEbSNgQBEEQBGEbSNgQBEEQBGEbSNgQBEEQBGEbSNhMwyOPPILly5fD6XRi7dq1eOGFF2o9pWl54IEHIAhCyU9nZ6e5nzGGBx54AF1dXXC5XLj66qvxxhtvlBwjnU7jzjvvRGtrKzweDz75yU/i5MmTJWPGxsawZcsWBAIBBAIBbNmyBaFQqGTMiRMn8IlPfAIejwetra348z//c2Qymaqc9759+/CJT3wCXV1dEAQBP//5z0v219t5Hz58GFdddRVcLhe6u7vxt3/7t3NuAjfdGvzhH/7hhGtj3bp1tlqD7du34+KLL4bP50N7ezs+9alP4ejRoyVj7H4tzGQN7H4tfO9738Pq1avNirjr16/HL37xC3O/3a+BmayBba8BRpRl586dTJZl9uijj7I333yT3XXXXczj8bAPPvig1lObkvvvv5996EMfYqdPnzZ/BgcHzf0PPvgg8/l87IknnmCHDx9mt9xyC1u0aBGLRCLmmNtvv511d3ez3bt3s4MHD7JrrrmGrVmzhuVyOXPMDTfcwHp7e9lLL73EXnrpJdbb28tuvPFGc38ul2O9vb3smmuuYQcPHmS7d+9mXV1dbNu2bVU57//6r/9iX//619kTTzzBALCnnnqqZH89nXc4HGYdHR3sc5/7HDt8+DB74oknmM/nYw899FBV12Dr1q3shhtuKLk2RkZGSsZYfQ02btzIHnvsMXbkyBF26NAhtmnTJrZkyRIWi8XMMXa/FmayBna/Fp555hn27LPPsqNHj7KjR4+y++67j8myzI4cOcIYs/81MJM1sOs1QMJmCi655BJ2++23l2xbsWIF+9rXvlajGc2M+++/n61Zs2bSfZqmsc7OTvbggw+a21KpFAsEAuz73/8+Y4yxUCjEZFlmO3fuNMf09/czURTZL3/5S8YYY2+++SYDwF5++WVzzP79+xkA9vbbbzPG+E1WFEXW399vjvnZz37GVFVl4XB43s53Msbf1OvtvB955BEWCARYKpUyx2zfvp11dXUxTdOqsgaM8S+ym266qexr7LYGjDE2ODjIALC9e/cyxhrzWhi/Bow15rXQ1NTEfvjDHzbkNTB+DRiz7zVArqgyZDIZHDhwANdff33J9uuvvx4vvfRSjWY1c44dO4auri4sX74cn/vc5/Dee+8BAI4fP46BgYGS81JVFVdddZV5XgcOHEA2my0Z09XVhd7eXnPM/v37EQgEcOmll5pj1q1bh0AgUDKmt7cXXV1d5piNGzcinU7jwIED1Tv5Sai3896/fz+uuuqqkmqdGzduxKlTp/D+++/P/wIUsWfPHrS3t+O8887DH//xH2NwcNDcZ8c1CIfDAIDm5mYAjXktjF8Dg0a5FvL5PHbu3Il4PI7169c35DUwfg0M7HgNkLApw/DwMPL5PDo6Okq2d3R0YGBgoEazmhmXXnopfvKTn+BXv/oVHn30UQwMDOCyyy7DyMiIOfepzmtgYACKoqCpqWnKMe3t7RP+7/b29pIx4/+fpqYmKIqy4GtYb+c92Rjj72quzcc//nH867/+K37961/j4YcfxiuvvIJrr70W6XTa/L/ttAaMMfzFX/wFLr/8cvT29pYcu1GuhcnWAGiMa+Hw4cPwer1QVRW33347nnrqKaxcubKhroFyawDY9xqQKhrdgAiCUPI3Y2zCtnrj4x//uPn7qlWrsH79epx99tn48Y9/bAaGzea8xo+ZbPxsxiwk9XTek82l3Gvni1tuucX8vbe3FxdddBGWLl2KZ599FjfffHPZ11l1DbZt24bXX38dL7744oR9jXItlFuDRrgWzj//fBw6dAihUAhPPPEEtm7dir179075f9rtGii3BitXrrTtNUAWmzK0trbC4XBMUIqDg4MTVGW94/F4sGrVKhw7dszMjprqvDo7O5HJZDA2NjblmDNnzkz4v4aGhkrGjP9/xsbGkM1mF3wN6+28JxtjmIAXcm0WLVqEpUuX4tixY+a87LIGd955J5555hk8//zz6OnpMbc30rVQbg0mw47XgqIoOOecc3DRRRdh+/btWLNmDf7xH/+xoa6BcmswGXa5BkjYlEFRFKxduxa7d+8u2b57925cdtllNZrV7Ein03jrrbewaNEiLF++HJ2dnSXnlclksHfvXvO81q5dC1mWS8acPn0aR44cMcesX78e4XAYv/3tb80xv/nNbxAOh0vGHDlyBKdPnzbH7Nq1C6qqYu3atVU95/HU23mvX78e+/btK0l33LVrF7q6urBs2bL5X4AyjIyMoK+vD4sWLQJgjzVgjGHbtm148skn8etf/xrLly8v2d8I18J0azAZdrwWxsMYQzqdbohrYLo1mAzbXAMVhRo3GEa6949+9CP25ptvsrvvvpt5PB72/vvv13pqU/KVr3yF7dmzh7333nvs5ZdfZjfeeCPz+XzmvB988EEWCATYk08+yQ4fPsxuvfXWSdMce3p62HPPPccOHjzIrr322klT/FavXs3279/P9u/fz1atWjVpit+GDRvYwYMH2XPPPcd6enqqlu4djUbZq6++yl599VUGgH3zm99kr776qpmeX0/nHQqFWEdHB7v11lvZ4cOH2ZNPPsn8fv+c0zunWoNoNMq+8pWvsJdeeokdP36cPf/882z9+vWsu7vbVmvwp3/6pywQCLA9e/aUpLEmEglzjN2vhenWoBGuhXvvvZft27ePHT9+nL3++uvsvvvuY6Iosl27djHG7H8NTLcGdr4GSNhMw3e/+122dOlSpigKu/DCC0vSJesVox6DLMusq6uL3XzzzeyNN94w92uaxu6//37W2dnJVFVlV155JTt8+HDJMZLJJNu2bRtrbm5mLpeL3XjjjezEiRMlY0ZGRtjmzZuZz+djPp+Pbd68mY2NjZWM+eCDD9imTZuYy+Vizc3NbNu2bSXpfPPJ888/zwBM+Nm6dWtdnvfrr7/OrrjiCqaqKuvs7GQPPPDAnFM7p1qDRCLBrr/+etbW1sZkWWZLlixhW7dunXB+Vl+Dyc4fAHvsscfMMXa/FqZbg0a4Fr74xS+a391tbW1sw4YNpqhhzP7XwHRrYOdrQGBsjqUNCYIgCIIg6gSKsSEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjaQsCEIgiAIwjb8fwdVbxlHlCUsAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot average pressure at junctions that exist in both the original and skeletonized model\n",
     "skel_junctions = skel_wn.junction_name_list\n",
@@ -2315,6 +1547,8 @@
     "ax = pressure_orig.mean(axis=1).plot(label='Original')\n",
     "ax = pressure_skel.mean(axis=1).plot(ax=ax, label='Skeletonized')\n",
     "plt.title('Average pressure')\n",
+    "ax.set_xlabel('Time (s)')\n",
+    "ax.set_ylabel('Pressure (m)')\n",
     "plt.legend()"
    ]
   },
@@ -2329,121 +1563,41 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Valve segmentation groups links and nodes into segments based on the location of isolation valves. "
+    "Valve segmentation groups links and nodes into segments based on the location of isolation valves. Unlike valves that are part of a WaterNetworkModel and used in hydraulic simulations (i.e. PRV, FCV), isolation valves are not included in the WaterNetworkModel and are defined as a separate data layer."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 273,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Create water network model from an INP file\n",
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
     "wn = wntr.network.WaterNetworkModel('../networks/Net3.inp')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 274,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Create a N-2 strategic valve layer\n",
+    "# Create a N-2 strategic valve layer.  Note that the user can create strategic or random valve placements, or use real valve data.\n",
     "valve_layer = wntr.network.generate_valve_layer(wn, 'strategic', 2)\n",
     "ax = wntr.graphics.plot_valve_layer(wn, valve_layer, add_colorbar=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>link</th>\n",
-       "      <th>node</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>10</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>7</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>7</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>7</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    link  node\n",
-       "17    10     9\n",
-       "30     8     8\n",
-       "15     7     6\n",
-       "26     7     6\n",
-       "23     7     6"
-      ]
-     },
-     "execution_count": 275,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "# Identify nodes and links that are in each valve segment\n",
     "G = wn.to_graph()\n",
@@ -2453,22 +1607,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 276,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot segments\n",
     "N = seg_sizes.shape[0] # number of segments\n",
@@ -2483,159 +1626,28 @@
    },
    "source": [
     "# Geospatial Capabilities\n",
-    "Geospatial data can be used within WNTR to build a WaterNetworkModel, associate geospatial data with nodes and links, and save simualtion results to GIS compatible files.\n",
+    "Geospatial data can be used within WNTR to build a WaterNetworkModel, associate geospatial data with nodes and links, and save simulation results to GIS compatible files.\n",
     "\n",
     "**Note,  this example assumes the coordinate reference system (CRS) for the WaterNetworkModel and GIS data are in EPSG:4326 (lat/long). Geographic CRS are not suitable for measuring distance and the example will result in UserWarnings.  The example will be updated to use a projected CRS at a later date.**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 277,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# Create water network model from an INP file\n",
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
     "wn = wntr.network.WaterNetworkModel('../networks/Net1.inp')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>link_type</th>\n",
-       "      <th>start_node_name</th>\n",
-       "      <th>end_node_name</th>\n",
-       "      <th>check_valve</th>\n",
-       "      <th>diameter</th>\n",
-       "      <th>initial_status</th>\n",
-       "      <th>length</th>\n",
-       "      <th>minor_loss</th>\n",
-       "      <th>roughness</th>\n",
-       "      <th>geometry</th>\n",
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-       "  <tbody>\n",
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-       "      <th>10</th>\n",
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-       "      <td>0.4572</td>\n",
-       "      <td>Open</td>\n",
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-       "      <td>12</td>\n",
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-       "      <td>0.3556</td>\n",
-       "      <td>Open</td>\n",
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-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Pipe</td>\n",
-       "      <td>12</td>\n",
-       "      <td>13</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.2540</td>\n",
-       "      <td>Open</td>\n",
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-       "    <tr>\n",
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-       "      <td>21</td>\n",
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-       "      <td>False</td>\n",
-       "      <td>0.2540</td>\n",
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-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Pipe</td>\n",
-       "      <td>22</td>\n",
-       "      <td>23</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.3048</td>\n",
-       "      <td>Open</td>\n",
-       "      <td>1609.344</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>100.0</td>\n",
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-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   link_type start_node_name end_node_name check_valve  diameter  \\\n",
-       "10      Pipe              10            11       False    0.4572   \n",
-       "11      Pipe              11            12       False    0.3556   \n",
-       "12      Pipe              12            13       False    0.2540   \n",
-       "21      Pipe              21            22       False    0.2540   \n",
-       "22      Pipe              22            23       False    0.3048   \n",
-       "\n",
-       "   initial_status    length  minor_loss  roughness  \\\n",
-       "10           Open  3209.544         0.0      100.0   \n",
-       "11           Open  1609.344         0.0      100.0   \n",
-       "12           Open  1609.344         0.0      100.0   \n",
-       "21           Open  1609.344         0.0      100.0   \n",
-       "22           Open  1609.344         0.0      100.0   \n",
-       "\n",
-       "                                             geometry  \n",
-       "10  LINESTRING (20.00000 70.00000, 30.00000 70.00000)  \n",
-       "11  LINESTRING (30.00000 70.00000, 50.00000 70.00000)  \n",
-       "12  LINESTRING (50.00000 70.00000, 70.00000 70.00000)  \n",
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-       "22  LINESTRING (50.00000 40.00000, 70.00000 40.00000)  "
-      ]
-     },
-     "execution_count": 278,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Convert a WaterNetworkModel to a collection of GeoDataFrames in EPSG:4326 coordinates\n",
     "wn_gis = wntr.network.to_gis(wn, crs='EPSG:4326')\n",
@@ -2644,86 +1656,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Build a WaterNetworkModel from a collection of GeoDataFrames. \n",
-    "# Note that models built from GeoDataFrames will not contain patterns, curves, sources, controls, and options\n",
-    "wn2 = wntr.network.from_gis(wn_gis)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "EPSG:4326\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>demand</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5000</td>\n",
-       "      <td>POINT (48.20000 37.20000)</td>\n",
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-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1500</td>\n",
-       "      <td>POINT (71.80000 68.30000)</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>8000</td>\n",
-       "      <td>POINT (51.20000 71.10000)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   demand                   geometry\n",
-       "0    5000  POINT (48.20000 37.20000)\n",
-       "1    1500  POINT (71.80000 68.30000)\n",
-       "2    8000  POINT (51.20000 71.10000)"
-      ]
-     },
-     "execution_count": 280,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Load hydrant data\n",
     "hydrant_data = gpd.read_file('../data/Net1_hydrant_data.geojson') \n",
@@ -2741,79 +1678,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:94: UserWarning: Geometry is in a geographic CRS. Results from 'distance' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  closest[\"snap_distance\"] = closest.geometry.distance(gpd.GeoSeries(closest.points, crs=crs))\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node</th>\n",
-       "      <th>snap_distance</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>13</td>\n",
-       "      <td>2.475884</td>\n",
-       "      <td>POINT (70.00000 70.00000)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12</td>\n",
-       "      <td>1.627882</td>\n",
-       "      <td>POINT (50.00000 70.00000)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  node  snap_distance                   geometry\n",
-       "0   22       3.328663  POINT (50.00000 40.00000)\n",
-       "1   13       2.475884  POINT (70.00000 70.00000)\n",
-       "2   12       1.627882  POINT (50.00000 70.00000)"
-      ]
-     },
-     "execution_count": 281,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Snap hydrants to junctions\n",
     "snapped_to_junctions = wntr.gis.snap(hydrant_data, wn_gis.junctions, tolerance=5.0)\n",
@@ -2822,25 +1689,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 282,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot results\n",
     "ax = hydrant_data.plot()\n",
+    "ax.set_aspect('equal', adjustable='box')\n",
     "ax = wntr.graphics.plot_network(wn, node_attribute=snapped_to_junctions['node'].to_list(), ax=ax)"
    ]
   },
@@ -2854,99 +1711,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
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-       "      <th></th>\n",
-       "      <th>mean_income</th>\n",
-       "      <th>mean_age</th>\n",
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-       "      <td>5618.0</td>\n",
-       "      <td>POLYGON ((23.21084 40.19160, 22.99063 27.71777...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>91452.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>5650.0</td>\n",
-       "      <td>POLYGON ((22.99063 27.71777, 61.93720 16.36165...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>5546.0</td>\n",
-       "      <td>POLYGON ((61.93720 16.36165, 22.99063 27.71777...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>26135.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>5968.0</td>\n",
-       "      <td>POLYGON ((61.93720 16.36165, 64.04456 22.10119...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   mean_income  mean_age  population  \\\n",
-       "0      63326.0      35.0      3362.0   \n",
-       "1      78245.0      31.0      5618.0   \n",
-       "2      91452.0      40.0      5650.0   \n",
-       "3      54040.0      39.0      5546.0   \n",
-       "4      26135.0      38.0      5968.0   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((41.67813 82.75023, 41.98596 60.85779...  \n",
-       "1  POLYGON ((23.21084 40.19160, 22.99063 27.71777...  \n",
-       "2  POLYGON ((22.99063 27.71777, 61.93720 16.36165...  \n",
-       "3  POLYGON ((61.93720 16.36165, 22.99063 27.71777...  \n",
-       "4  POLYGON ((61.93720 16.36165, 64.04456 22.10119...  "
-      ]
-     },
-     "execution_count": 283,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "# Load demographic data associated with census block groups\n",
     "demographic_data = gpd.read_file('../data/Net1_demographic_data.geojson') \n",
@@ -2955,110 +1724,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>intersections</th>\n",
-       "      <th>values</th>\n",
-       "      <th>n</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>min</th>\n",
-       "      <th>max</th>\n",
-       "      <th>mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>[0]</td>\n",
-       "      <td>[63326.0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>[0]</td>\n",
-       "      <td>[63326.0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>[5]</td>\n",
-       "      <td>[57620.0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>[5]</td>\n",
-       "      <td>[57620.0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>[3]</td>\n",
-       "      <td>[54040.0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>54040.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   intersections     values  n      sum      min      max     mean\n",
-       "10           [0]  [63326.0]  1  63326.0  63326.0  63326.0  63326.0\n",
-       "11           [0]  [63326.0]  1  63326.0  63326.0  63326.0  63326.0\n",
-       "12           [5]  [57620.0]  1  57620.0  57620.0  57620.0  57620.0\n",
-       "13           [5]  [57620.0]  1  57620.0  57620.0  57620.0  57620.0\n",
-       "21           [3]  [54040.0]  1  54040.0  54040.0  54040.0  54040.0"
-      ]
-     },
-     "execution_count": 284,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "# Intersect junctions with census block groups, extract mean income\n",
     "junction_demographics = wntr.gis.intersect(wn_gis.junctions, demographic_data, 'mean_income')\n",
@@ -3067,170 +1737,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:254: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_length = A.length\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:259: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
-      "  val = float(B_geom[B_value])\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:263: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_clip_length = A_clip.length\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:259: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
-      "  val = float(B_geom[B_value])\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:263: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_clip_length = A_clip.length\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:259: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
-      "  val = float(B_geom[B_value])\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:263: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_clip_length = A_clip.length\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:259: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
-      "  val = float(B_geom[B_value])\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:263: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_clip_length = A_clip.length\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:259: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
-      "  val = float(B_geom[B_value])\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:263: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_clip_length = A_clip.length\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:259: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
-      "  val = float(B_geom[B_value])\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:263: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_clip_length = A_clip.length\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:259: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
-      "  val = float(B_geom[B_value])\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:263: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_clip_length = A_clip.length\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:259: FutureWarning: Calling float on a single element Series is deprecated and will raise a TypeError in the future. Use float(ser.iloc[0]) instead\n",
-      "  val = float(B_geom[B_value])\n",
-      "C:\\Users\\kaklise\\Projects\\src\\EPA\\WNTR\\wntr\\gis\\geospatial.py:263: UserWarning: Geometry is in a geographic CRS. Results from 'length' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  A_clip_length = A_clip.length\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>intersections</th>\n",
-       "      <th>values</th>\n",
-       "      <th>n</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>min</th>\n",
-       "      <th>max</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>weighted_mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>[0]</td>\n",
-       "      <td>[63326.0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>63326.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>[0, 5]</td>\n",
-       "      <td>[63326.0, 57620.0]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>120946.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>63326.0</td>\n",
-       "      <td>60473.0</td>\n",
-       "      <td>61002.920197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>[5]</td>\n",
-       "      <td>[57620.0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.0</td>\n",
-       "      <td>57620.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>[3]</td>\n",
-       "      <td>[54040.0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>54040.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>[3, 6]</td>\n",
-       "      <td>[54040.0, 44871.0]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>98911.0</td>\n",
-       "      <td>44871.0</td>\n",
-       "      <td>54040.0</td>\n",
-       "      <td>49455.5</td>\n",
-       "      <td>47067.894876</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   intersections              values  n       sum      min      max     mean  \\\n",
-       "10           [0]           [63326.0]  1   63326.0  63326.0  63326.0  63326.0   \n",
-       "11        [0, 5]  [63326.0, 57620.0]  2  120946.0  57620.0  63326.0  60473.0   \n",
-       "12           [5]           [57620.0]  1   57620.0  57620.0  57620.0  57620.0   \n",
-       "21           [3]           [54040.0]  1   54040.0  54040.0  54040.0  54040.0   \n",
-       "22        [3, 6]  [54040.0, 44871.0]  2   98911.0  44871.0  54040.0  49455.5   \n",
-       "\n",
-       "    weighted_mean  \n",
-       "10   63326.000000  \n",
-       "11   61002.920197  \n",
-       "12   57620.000000  \n",
-       "21   54040.000000  \n",
-       "22   47067.894876  "
-      ]
-     },
-     "execution_count": 285,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
     "# Intersect pipes with census block groups, extract mean income\n",
     "pipe_demographics = wntr.gis.intersect(wn_gis.pipes, demographic_data, 'mean_income')\n",
@@ -3239,25 +1750,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
     "# Plot results\n",
     "ax = demographic_data.plot(column='mean_income', alpha=0.5, cmap='bone', vmin=10000, vmax=100000)\n",
-    "ax = wntr.graphics.plot_network(wn, node_attribute=junction_demographics['mean'], link_attribute=pipe_demographics['weighted_mean'], link_width=1.5, node_range=[40000,80000], \n",
-    "                                link_range=[40000,80000], ax=ax)"
+    "ax.set_aspect('equal', adjustable='box')\n",
+    "ax = wntr.graphics.plot_network(wn, \n",
+    "                                node_attribute=junction_demographics['mean'], \n",
+    "                                link_attribute=pipe_demographics['weighted_mean'], \n",
+    "                                link_width=1.5, ax=ax, title='Mean income ($)')"
    ]
   },
   {
@@ -3270,111 +1775,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>node_type</th>\n",
-       "      <th>elevation</th>\n",
-       "      <th>initial_quality</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>betweenness_centrality</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Junction</td>\n",
-       "      <td>216.408</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>POINT (20.00000 70.00000)</td>\n",
-       "      <td>0.200000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Junction</td>\n",
-       "      <td>216.408</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>POINT (30.00000 70.00000)</td>\n",
-       "      <td>0.404444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Junction</td>\n",
-       "      <td>213.360</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>POINT (50.00000 70.00000)</td>\n",
-       "      <td>0.417037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Junction</td>\n",
-       "      <td>211.836</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>POINT (70.00000 70.00000)</td>\n",
-       "      <td>0.044444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Junction</td>\n",
-       "      <td>213.360</td>\n",
-       "      <td>0.0005</td>\n",
-       "      <td>POINT (30.00000 40.00000)</td>\n",
-       "      <td>0.231852</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   node_type  elevation  initial_quality                   geometry  \\\n",
-       "10  Junction    216.408           0.0005  POINT (20.00000 70.00000)   \n",
-       "11  Junction    216.408           0.0005  POINT (30.00000 70.00000)   \n",
-       "12  Junction    213.360           0.0005  POINT (50.00000 70.00000)   \n",
-       "13  Junction    211.836           0.0005  POINT (70.00000 70.00000)   \n",
-       "21  Junction    213.360           0.0005  POINT (30.00000 40.00000)   \n",
-       "\n",
-       "    betweenness_centrality  \n",
-       "10                0.200000  \n",
-       "11                0.404444  \n",
-       "12                0.417037  \n",
-       "13                0.044444  \n",
-       "21                0.231852  "
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "# Compute betweenness centrality and add it to the WaterNetworkGIS object\n",
     "G = wn.to_graph() # directed multigraph\n",
-    "uG = G.to_undirected() # undirected multigraph\n",
-    "sG = nx.Graph(uG) # undirected simple graph (single edge between two nodes)\n",
-    "betweenness_centrality = nx.betweenness_centrality(sG)\n",
+    "betweenness_centrality = nx.betweenness_centrality(G)\n",
     "\n",
     "wn_gis.add_node_attributes(betweenness_centrality, 'betweenness_centrality')\n",
     "wn_gis.junctions.head()"
@@ -3382,148 +1791,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>link_type</th>\n",
-       "      <th>start_node_name</th>\n",
-       "      <th>end_node_name</th>\n",
-       "      <th>check_valve</th>\n",
-       "      <th>diameter</th>\n",
-       "      <th>initial_status</th>\n",
-       "      <th>length</th>\n",
-       "      <th>minor_loss</th>\n",
-       "      <th>roughness</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>flowrate</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Pipe</td>\n",
-       "      <td>10</td>\n",
-       "      <td>11</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.4572</td>\n",
-       "      <td>Open</td>\n",
-       "      <td>3209.544</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>LINESTRING (20.00000 70.00000, 30.00000 70.00000)</td>\n",
-       "      <td>0.069037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>Pipe</td>\n",
-       "      <td>11</td>\n",
-       "      <td>12</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.3556</td>\n",
-       "      <td>Open</td>\n",
-       "      <td>1609.344</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>LINESTRING (30.00000 70.00000, 50.00000 70.00000)</td>\n",
-       "      <td>0.037369</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>Pipe</td>\n",
-       "      <td>12</td>\n",
-       "      <td>13</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.2540</td>\n",
-       "      <td>Open</td>\n",
-       "      <td>1609.344</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>LINESTRING (50.00000 70.00000, 70.00000 70.00000)</td>\n",
-       "      <td>0.009919</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Pipe</td>\n",
-       "      <td>21</td>\n",
-       "      <td>22</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.2540</td>\n",
-       "      <td>Open</td>\n",
-       "      <td>1609.344</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>LINESTRING (30.00000 40.00000, 50.00000 40.00000)</td>\n",
-       "      <td>0.004307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>Pipe</td>\n",
-       "      <td>22</td>\n",
-       "      <td>23</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0.3048</td>\n",
-       "      <td>Open</td>\n",
-       "      <td>1609.344</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>LINESTRING (50.00000 40.00000, 70.00000 40.00000)</td>\n",
-       "      <td>0.005854</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   link_type start_node_name end_node_name check_valve  diameter  \\\n",
-       "10      Pipe              10            11       False    0.4572   \n",
-       "11      Pipe              11            12       False    0.3556   \n",
-       "12      Pipe              12            13       False    0.2540   \n",
-       "21      Pipe              21            22       False    0.2540   \n",
-       "22      Pipe              22            23       False    0.3048   \n",
-       "\n",
-       "   initial_status    length  minor_loss  roughness  \\\n",
-       "10           Open  3209.544         0.0      100.0   \n",
-       "11           Open  1609.344         0.0      100.0   \n",
-       "12           Open  1609.344         0.0      100.0   \n",
-       "21           Open  1609.344         0.0      100.0   \n",
-       "22           Open  1609.344         0.0      100.0   \n",
-       "\n",
-       "                                             geometry  flowrate  \n",
-       "10  LINESTRING (20.00000 70.00000, 30.00000 70.00000)  0.069037  \n",
-       "11  LINESTRING (30.00000 70.00000, 50.00000 70.00000)  0.037369  \n",
-       "12  LINESTRING (50.00000 70.00000, 70.00000 70.00000)  0.009919  \n",
-       "21  LINESTRING (30.00000 40.00000, 50.00000 40.00000)  0.004307  \n",
-       "22  LINESTRING (50.00000 40.00000, 70.00000 40.00000)  0.005854  "
-      ]
-     },
-     "execution_count": 288,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "# Run a hydraulic simulation and store average flowrate to the WaterNetworkGIS object\n",
     "sim = wntr.sim.EpanetSimulator(wn)\n",
@@ -3536,21 +1808,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 289,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
+    "# Write the model and analysis results to GIS compatible files.  These files can be loaded into GIS platforms for further analysis.\n",
     "wn_gis.write_geojson('Net3_analysis')"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -3569,7 +1835,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.7"
   },
   "toc-autonumbering": true,
   "vscode": {
diff --git a/examples/demos/geospatial_demo.ipynb b/examples/demos/geospatial_demo.ipynb
index c3ecc4a5d..2cdcc81ac 100644
--- a/examples/demos/geospatial_demo.ipynb
+++ b/examples/demos/geospatial_demo.ipynb
@@ -1,13 +1,18 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "tags": []
    },
    "source": [
     "# WNTR Geospatial Tutorial\n",
-    "The following tutorial illustrates the use of the `wntr.gis` module to use geospatial data in resilience analysis.  The tutorial uses a water network model from Kentucky coupled with GIS data to quantify potential water service disruptions from pipes damaged in a landslide."
+    "The following tutorial illustrates the use of the `wntr.gis` module to use geospatial data in resilience analysis.  The objective of this tutorial is to 1) quantify water service disruptions that could occur from pipes damaged in landslides and 2) identify the social vulnerability of populations impacted by the service disruptions.\n",
+    "\n",
+    "To simplify the tutorials, it is assumed that pipes within a 1000 ft buffer of each landslide susceptible region are damaged in that landslide.\n",
+    "This assumption could be replaced with detailed landslide analysis that includes slope, soil type, weather conditions, and pipe material.\n",
+    "Social vulnerability data could also be replaced by datasets that describe other attributes of the population and critical services."
    ]
   },
   {
@@ -19,7 +24,7 @@
     "## Imports\n",
     "Import WNTR and additional Python packages that are needed for the tutorial\n",
     "- Geopandas is used to load geospatial data\n",
-    "- Shapely is used to define the region of interest\n",
+    "- Shapely is used to define a region of interest to crop data\n",
     "- Matplotlib is used to create subplots"
    ]
   },
@@ -35,22 +40,40 @@
     "import wntr"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Units\n",
+    "WNTR uses SI (International System) units (length in meters, time in seconds, mass in kilograms), **with the exception of the landslide buffer which is in feet to match the coordinate reference system of the geospatial data**.  See https://usepa.github.io/WNTR/units.html for more details on WNTR units."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The following line defines coordinates used to zoom in on network graphics\n",
+    "zoom_coords = [(5.75e6, 5.79e6), (3.82e6, 3.85e6)] "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "# Water Network Model\n",
-    "Water network models can be created from EPANET INP files, from GIS data in GeoJSON or Shapefile format, or from scratch using methods such as `add_junction` and `add_pipe`. The following section creates a water network model from an EPANET INP file and illustrates how models are created from GIS data."
+    "The following section creates a `WaterNetworkModel` object from an EPANET INP file and converts the model to GeoDataFrames for use in geospatial analysis."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create a Water Network Model from an EPANET INP file\n",
-    "The water distribution network model used in this tutorial was downloaded from the [UKnowledge Water Distribution Systems Research Database](https://uknowledge.uky.edu/wdsrd/). KY10 was selected for the analysis.\n",
+    "## Create a WaterNetworkModel from an EPANET INP file\n",
+    "The water distribution network model used in this tutorial was downloaded from the [UKnowledge Water Distribution Systems Research Database](https://uknowledge.uky.edu/wdsrd/). KY10 was selected for the analysis. The following section creates a `WaterNetworkModel` from an EPANET INP file and computes some general attributes of the model.\n",
     "\n",
-    "Citation: Hoagland, Steven, \"10 KY 10\" (2016). Kentucky Dataset. 12. https://uknowledge.uky.edu/wdst/12. Accessed on 4/4/2024."
+    "*Citation: Hoagland, Steven, \"10 KY 10\" (2016). Kentucky Dataset. 12. https://uknowledge.uky.edu/wdst/12. Accessed on 4/4/2024.*"
    ]
   },
   {
@@ -59,7 +82,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Create a water network model from an INP file\n",
+    "# Create a WaterNetworkModel from an EPANET INP file\n",
     "inp_file = '../networks/ky10.inp'\n",
     "wn = wntr.network.WaterNetworkModel(inp_file)"
    ]
@@ -70,42 +93,46 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Print a basic description of the model\n",
+    "# Print a basic description of the model. \n",
+    "# The level can be 0, 1, or 2 and defines the level of detail included in the description.\n",
     "wn.describe(level=1)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Create a basic network graphic, showing junction elevation\n",
-    "# Note, the remaining graphics in this tutorial are created from the geospatial data directly, rather than the plot_network function\n",
-    "ax = wntr.graphics.plot_network(wn, node_attribute='elevation', node_range=(175, 300), title='ky10 elevation')"
+    "# Compute total pipe length\n",
+    "length = wn.query_link_attribute('length')\n",
+    "total_length = length.sum() # m\n",
+    "print('Total pipe length =', total_length, 'm, =', total_length*3.28084, 'ft')"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Convert the Water Network Model to GIS data\n",
-    "The Water Network Model is converted to a collection of GeoDataFrames and set the coordinate reference system (CRS) is set to EPSG:3089.  Data for junctions, tanks, reservoirs, pipes, pumps, and valves are stored in separate GeoDataFrames."
+    "# Compute average expected demand per day \n",
+    "average_expected_demand = wntr.metrics.average_expected_demand(wn) # m^3/s\n",
+    "average_volume_per_day = average_expected_demand*(24*3600) # m^3\n",
+    "total_water_use = average_volume_per_day.sum() # m^3\n",
+    "print('Total water use =', total_water_use, 'm^3, =', total_water_use*264.172/1e6, 'million gallons')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Convert the Water Network Model to GIS data and set the CRS\n",
-    "wn_gis = wntr.network.to_gis(wn)\n",
-    "wn_gis.pipes.head()"
+    "# Estimate population using the default average volume of water consumed per capita per day of 200 gallons/day\n",
+    "population = wntr.metrics.population(wn) \n",
+    "total_population = population.sum()\n",
+    "print('Total population =', total_population)"
    ]
   },
   {
@@ -116,17 +143,18 @@
    },
    "outputs": [],
    "source": [
-    "# Set the CRS to EPSG:3089, NAD83 / Kentucky Single Zone (ftUS)\n",
-    "crs = 'EPSG:3089'\n",
-    "wn_gis.set_crs(crs)"
+    "# Create a basic network graphic, showing junction elevation\n",
+    "# Note, the remaining graphics in this tutorial are created from the geospatial data directly, rather than the `plot_network` function.\n",
+    "# The `plot_network` function currently does not include vertices.\n",
+    "ax = wntr.graphics.plot_network(wn, node_attribute='elevation', node_range=(175, 300), title='ky10 elevation')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Save the GIS data to a GeoJSON or Shape file\n",
-    "The dictionary of GeoDataFrames are written to GeoJSON files or Shapefiles.  One file is created for junctions, tanks, reservoirs, pipes, pumps, and valves."
+    "## Convert the WaterNetworkModel to GIS data\n",
+    "The `WaterNetworkModel` is converted to a collection of GIS compatible GeoDataFrames and the coordinate reference system (CRS) is set to **EPSG:3089 (NAD83 / Kentucky Single Zone (ftUS)**, see https://epsg.io/3089 for more details).  Data for junctions, tanks, reservoirs, pipes, pumps, and valves are stored in separate GeoDataFrames."
    ]
   },
   {
@@ -137,15 +165,14 @@
    },
    "outputs": [],
    "source": [
-    "wn_gis.write_geojson('ky10')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Generate a Water Network Model from GIS data\n",
-    "Water network models can be created from GeoJSON files or Shapefiles. A specific set of column names are required to define junctions, tanks, reservoirs, pipes, pumps, and valves.  Model attributes like patterns, curves, and options need to be added separately."
+    "# Convert the WaterNetworkModel to GIS data and set the CRS\n",
+    "wn_gis = wn.to_gis()\n",
+    "wn_gis.junctions.head()\n",
+    "#wn_gis.tanks.head()\n",
+    "#wn_gis.reservoirs.head()\n",
+    "#wn_gis.pipes.head()\n",
+    "#wn_gis.pumps.head()\n",
+    "#wn_gis.tanks.head()\n"
    ]
   },
   {
@@ -156,32 +183,37 @@
    },
    "outputs": [],
    "source": [
-    "# Print valid GeoJSON or Shapefiles column names required to build a model\n",
-    "column_names = wntr.network.io.valid_gis_names()\n",
-    "print(\"Junction column names\", column_names['junctions'])\n",
-    "print(\"Tank column names\", column_names['tanks'])\n",
-    "print(\"Reservoir column names\", column_names['reservoirs'])\n",
-    "print(\"Pipe column names\", column_names['pipes'])\n",
-    "print(\"Pump column names\", column_names['pumps'])\n",
-    "print(\"Valve column names\", column_names['valves'])"
+    "# Set the CRS to EPSG:3089 (NAD83 / Kentucky Single Zone (ftUS))\n",
+    "crs = 'EPSG:3089'\n",
+    "wn_gis.set_crs(crs)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Build a water network model from a set of GeoJSON files\n",
-    "geojson_files = {'junctions': 'ky10_junctions.geojson',\n",
-    "                 'tanks': 'ky10_tanks.geojson',\n",
-    "                 'reservoirs': 'ky10_reservoirs.geojson',\n",
-    "                 'pipes': 'ky10_pipes.geojson',\n",
-    "                 'pumps': 'ky10_pumps.geojson',\n",
-    "                 'valves': 'ky10_valves.geojson'}\n",
-    "wn2 = wntr.network.read_geojson(geojson_files)"
+    "# Use the GIS data to create a figure of the network\n",
+    "fig, ax = plt.subplots(figsize=(5,5))\n",
+    "ax = wn_gis.pipes.plot(column='diameter', linewidth=1, label='pipes', alpha=0.8, ax=ax, zorder=1)\n",
+    "ax = wn_gis.reservoirs.plot(color='k', marker='s', markersize=60, label='reservoirs', ax=ax)\n",
+    "ax = wn_gis.tanks.plot(color='r', markersize=20, label='tanks', ax=ax)\n",
+    "ax = wn_gis.pumps.centroid.plot(color='b', markersize=20, label='pumps', ax=ax)\n",
+    "ax = wn_gis.valves.centroid.plot(color='c', markersize=20, label='valves', ax=ax)\n",
+    "tmp = ax.axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "#tmp = ax.set_xlim(zoom_coords[0])\n",
+    "#tmp = ax.set_ylim(zoom_coords[1])\n",
+    "tmp = plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save the GIS data to GeoJSON or Shapefile files\n",
+    "The GIS data can be written to GeoJSON files or Shapefile files.  One file is created for junctions, tanks, reservoirs, pipes, pumps, and valves (ky10_junctions.geojson, ky10_tanks.geojson, etc.).  The GeoJSON or Shapefile files can be loaded into GIS software platforms for further analysis. **Note that controls, patterns, curves, and options are not included in the GIS formatted data files.** "
    ]
   },
   {
@@ -192,9 +224,8 @@
    },
    "outputs": [],
    "source": [
-    "# Compare model attributes of the original model with the model built from GeoJSON files (note the absence of patterns and controls)\n",
-    "print(wn.describe(level=1))\n",
-    "print(wn2.describe(level=1))"
+    "# Store the WaterNetworkModel as a collection of GeoJSON files\n",
+    "wn_gis.write_geojson('ky10')"
    ]
   },
   {
@@ -202,15 +233,18 @@
    "metadata": {},
    "source": [
     "# External GIS Data\n",
-    "The external data used in this tutorial includes landslide footprint data and social vulnerability data."
+    "The external data used in this tutorial includes landslide inventory and social vulnerability data."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Create a region of interest (ROI) for the analysis\n",
-    "The region of interest (ROI) is defined by a bounding box around all pipes, with a 5000 ft buffer. The ROI is used to clip external data to only include the area included in the analysis."
+    "## Load landslide GIS data\n",
+    "The landslide data used in this tutorial was downloaded from the [UKnowledge Kentucky Geological Survey Research Data](https://uknowledge.uky.edu/kgs_data/).  The Kentucky Geological Survey Landslide Inventory from March 2023 was selected for the analysis.  The data contains locations of known landslides and areas susceptible to debris flows, derived from aerial photography. \n",
+    "\n",
+    "*Citation: Crawford, M.M., 2023. Kentucky Geological Survey landslide inventory [2023-03]: Kentucky Geological Survey Research Data, https://uknowledge.uky.edu/kgs_data/7/, Accessed on 4/4/2024.*"
    ]
   },
   {
@@ -221,55 +255,84 @@
    },
    "outputs": [],
    "source": [
-    "# Region of interest\n",
-    "bounds = wn_gis.pipes.total_bounds\n",
+    "# To reduce the file size checked into the WNTR repository, the following code was run on the raw data file.\n",
+    "# The region of interest (ROI) includes a 5000 ft buffer surrounding all pipes. The function `box` was imported from shapely.\n",
+    "\"\"\"\n",
+    "bounds = wn_gis.pipes.total_bounds # total_bounds returns the upper and lower bounds on x and y\n",
     "geom = box(*bounds)\n",
-    "ROI = geom.buffer(5000) # feet"
+    "ROI = geom.buffer(5000) # feet\n",
+    "\n",
+    "landslide_file = '../data/KGS_Landslide_Inventory_exp.gdb'\n",
+    "landslide_data = gpd.read_file(landslide_file, driver=\"FileGDB\", layer='Areas_derived_from_aerial_photography')\n",
+    "print(landslide_data.crs)\n",
+    "landslide_data = landslide_data.clip(ROI)\n",
+    "landslide_data.to_file(\"../data/ky10_landslide_data.geojson\", index=True, driver='GeoJSON')\n",
+    "\"\"\""
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
-    "## Load landslide GIS data\n",
-    "The landslide data used in this tutorial was downloaded from the [UKnowledge Kentucky Geological Survey Research Data](https://uknowledge.uky.edu/kgs_data/).  The Kentucky Geological Survey Landslide Inventory from March 2023 was selected for the analysis.  The data contains landslide areas derived from aerial photography. \n",
+    "# Load the landslide data from file and print the CRS to ensure it is in EPSG:3089.  \n",
+    "# The methods `to_crs` and `set_crs` can be used to change coordinate reference systems if needed.\n",
+    "landslide_file = '../data/ky10_landslide_data.geojson'\n",
+    "landslide_data = gpd.read_file(landslide_file).set_index('index') \n",
+    "print(landslide_data.crs)\n",
     "\n",
-    "Citation: Crawford, M.M., 2023. Kentucky Geological Survey landslide inventory [2023-03]: Kentucky Geological Survey Research Data, https://uknowledge.uky.edu/kgs_data/7/, Accessed on 4/4/2024."
+    "landslide_data.head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# To reduce the file size checked into the WNTR repository, the following code was run on the raw data file\n",
+    "# Plot the landslide data along with pipes\n",
+    "ax = landslide_data.plot(color='red', label='Landslide data')\n",
+    "ax = wn_gis.pipes.plot(color='black', linewidth=1, ax=ax)\n",
+    "ax.set_title('Landslide and pipe data')\n",
+    "tmp = ax.axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "tmp = ax.set_xlim(zoom_coords[0])\n",
+    "tmp = ax.set_ylim(zoom_coords[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Social Vulnerability Index (SVI) GIS data\n",
+    "The social vulnerability data used in this tutorial was downloaded from the [Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry](https://www.atsdr.cdc.gov/placeandhealth/svi/index.html). The data contains census and social vulnerability metrics for each census tract. \n",
+    "\n",
+    "The quantity of interest used in this analysis is `RPL_THEMES` which ranks vulnerability across socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation.  The value ranges between 0 and 1, where higher values are associated with higher vulnerability.\n",
     "\n",
-    "#landslide_file = '../data/KGS_Landslide_Inventory_exp.gdb'\n",
-    "#landslide_data = gpd.read_file(landslide_file, driver=\"FileGDB\", layer='Areas_derived_from_aerial_photography')\n",
-    "#print(landslide_data.crs)\n",
-    "#landslide_data = landslide_data.clip(ROI)\n",
-    "#landslide_data.to_file(\"../data/ky10_landslide_data.geojson\", index=True, driver='GeoJSON')"
+    "*Citation: Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry/Geospatial Research, Analysis, and Services Program. CDC/ATSDR Social Vulnerability Index 2020 Database Kentucky. https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html. Accessed on 4/4/2024.*"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "scrolled": true
+    "tags": []
    },
    "outputs": [],
    "source": [
-    "# Load the landslide data from file and print the CRS (which is already in EPSG:3089)\n",
-    "landslide_file = '../data/ky10_landslide_data.geojson'\n",
-    "landslide_data = gpd.read_file(landslide_file) \n",
-    "landslide_data.set_index('index', inplace=True)\n",
-    "landslide_data.index.name = None\n",
-    "print(landslide_data.crs)\n",
-    "\n",
-    "landslide_data.head()"
+    "# To reduce the file size checked into the WNTR repository, the following code was run on the raw data file. \n",
+    "# The region of interest (ROI) was defined above.\n",
+    "\"\"\"\n",
+    "svi_file = '../data/SVI2020_KENTUCKY_tract.gdb'\n",
+    "svi_data = gpd.read_file(svi_file, driver=\"FileGDB\", layer='SVI2020_KENTUCKY_tract')\n",
+    "print(svi_data.crs)\n",
+    "svi_data.to_crs(crs, inplace=True)\n",
+    "svi_data = svi_data.clip(ROI)\n",
+    "svi_data.to_file(\"../data/ky10_svi_data.geojson\", index=True, driver='GeoJSON')\n",
+    "\"\"\""
    ]
   },
   {
@@ -280,10 +343,13 @@
    },
    "outputs": [],
    "source": [
-    "# Each landslide is extended to include the surrounding 1000 ft, to create a region that might be impacted by an individual landslide.  \n",
-    "# Other datasets or methods could be used to define landslide susceptibility or vulnerability.\n",
-    "landslide_regions = landslide_data.copy()\n",
-    "landslide_regions['geometry'] = landslide_data.buffer(1000)"
+    "# Load the SVI data from file and print the CRS to ensure it is in EPSG:3089.  \n",
+    "# The methods `to_crs` and `set_crs` can be used to change coordinate reference systems if needed.\n",
+    "svi_file = '../data/ky10_svi_data.geojson'\n",
+    "svi_data = gpd.read_file(svi_file).set_index('index') \n",
+    "print(svi_data.crs)\n",
+    "\n",
+    "svi_data.head()"
    ]
   },
   {
@@ -294,26 +360,24 @@
    },
    "outputs": [],
    "source": [
-    "# Plot the landslide data and landslide regions along with pipes\n",
-    "ax = landslide_regions.plot(color='gray', alpha=0.5)\n",
-    "ax = landslide_data.plot(color='red', label='Landslide data', ax=ax)\n",
-    "ax = wn_gis.pipes.plot(color='black', ax=ax)\n",
-    "ax.set_title('Landslide and pipe data')\n",
-    "# Uncomment the following 2 lines to zoom in on a specific area\n",
-    "#ax.set_xlim(5.74e6, 5.76e6)\n",
-    "#ax.set_ylim(3.82e6, 3.84e6)"
+    "# Plot SVI data and pipes (higher values of SVI are associated with higher vulnerability)\n",
+    "ax = svi_data.plot(column='RPL_THEMES', label='SVI data', cmap='RdYlGn_r', vmin=0, vmax=1, legend=True)\n",
+    "ax = wn_gis.pipes.plot(color='black', linewidth=1, ax=ax)\n",
+    "ax.set_title('SVI and pipe data')\n",
+    "tmp = ax.axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "tmp = ax.set_xlim(zoom_coords[0])\n",
+    "tmp = ax.set_ylim(zoom_coords[1])"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Load Social Vulnerability Index (SVI) GIS data\n",
-    "The social vulnerability data used in this tutorial was downloaded from the [Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry](https://www.atsdr.cdc.gov/placeandhealth/svi/index.html). The data contains census and social vulnerability metrics for each census tract. \n",
-    "\n",
-    "The quantity of interest used in this analysis is \"RPL_THEMES\" which ranks vulnerability across socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation.  The value ranges between 0 and 1, where higher values are associated with higher vulnerability.\n",
-    "\n",
-    "Citation: Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry/Geospatial Research, Analysis, and Services Program. CDC/ATSDR Social Vulnerability Index 2020 Database Kentucky. https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html. Accessed on 4/4/2024."
+    "## Expand the size of each landslide using a buffer\n",
+    "Each landslide is extended to include the surrounding 1000 ft, to create a region that might be impacted by an individual landslide.  The distance unit for buffering matches the distance unit of the CRS (ft).\n",
+    "This assumption could be replaced with detailed landslide analysis that includes slope, soil type, weather conditions, and pipe material. "
    ]
   },
   {
@@ -324,32 +388,20 @@
    },
    "outputs": [],
    "source": [
-    "# To reduce the file size checked into the WNTR repository, the following code was run on the raw data file\n",
-    "\n",
-    "#svi_file = '../data/SVI2020_KENTUCKY_tract.gdb'\n",
-    "#svi_data = gpd.read_file(svi_file, driver=\"FileGDB\", layer='SVI2020_KENTUCKY_tract')\n",
-    "#print(svi_data.crs)\n",
-    "#svi_data.to_crs(crs, inplace=True)\n",
-    "#svi_data = svi_data.clip(ROI)\n",
-    "#svi_data.to_file(\"../data/ky10_svi_data.geojson\", index=True, driver='GeoJSON')"
+    "# Create a GeoDataFrame to hold information used in landslide scenarios (initially copied from landslide_data)\n",
+    "# Buffer each landslide polygon by 1000 ft\n",
+    "landslide_scenarios = landslide_data.copy()\n",
+    "landslide_scenarios['geometry'] = landslide_data.buffer(1000)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Load the SVI data from file and print the CRS (which is already in EPSG:3089)\n",
-    "svi_file = '../data/ky10_svi_data.geojson'\n",
-    "svi_data = gpd.read_file(svi_file)\n",
-    "print(svi_data.crs)\n",
-    "svi_data.set_index('index', inplace=True)\n",
-    "svi_data.index.name = None\n",
-    "\n",
-    "svi_data.head()"
+    "# Add a prefix to the landslide scenario index to indicate the scenario name\n",
+    "landslide_scenarios.index = 'LS-' + landslide_scenarios.index.astype(str)"
    ]
   },
   {
@@ -360,26 +412,32 @@
    },
    "outputs": [],
    "source": [
-    "# Plot SVI data and pipes (higher values of SVI are associated with higher vulnerability)\n",
-    "ax = svi_data.plot(column='RPL_THEMES', label='SVI data', cmap='RdYlGn_r', vmin=0, vmax=1, legend=True)\n",
-    "ax = wn_gis.pipes.plot(color='black', ax=ax)\n",
-    "ax.set_title('SVI and pipe data')"
+    "# Plot the landslide data, region included in each landslide scenario, and pipes\n",
+    "ax = landslide_scenarios.plot(color='gray', alpha=0.5)\n",
+    "ax = landslide_data.plot(color='red', label='Landslide data', ax=ax)\n",
+    "ax = wn_gis.pipes.plot(color='black', linewidth=1, ax=ax)\n",
+    "ax.set_title('Landslide scenario and pipe data')\n",
+    "tmp = ax.axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "tmp = ax.set_xlim(zoom_coords[0])\n",
+    "tmp = ax.set_ylim(zoom_coords[1])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Intersect Water Network Model with GIS data\n",
-    "In this section, landslide and SVI data are interested with the water network model."
+    "# Geospatial Intersects\n",
+    "In this section, landslide scenario and SVI data are interested with pipes and junctions in the `WaterNetworkModel`."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Intersect pipes with landslide regions\n",
-    "Pipes are intersected with landslide regions to determine the landslides that could impact each pipe. This information could be used to compute the likelihood that a pipe will be impacted by landslides."
+    "## Identify pipes that intersect each landslide\n",
+    "Landslide polygons are intersected with pipes to obtain a list of pipes that intersect each landslide.  <font color='red'>This information is used to to define the pipes that are closed in each landslide scenario.</font> The pipe attribute `length` is also included in the intersection to gather statistics on the pipe length that intersects each landslide.  "
    ]
   },
   {
@@ -390,10 +448,14 @@
    },
    "outputs": [],
    "source": [
-    "# Determine landslide regions that intersect each pipe and print in order of descending number of intersections.\n",
-    "pipe_intersect = wntr.gis.intersect(wn_gis.pipes, landslide_regions)\n",
+    "# Use the intersect function to determine pipes and pipe length that intersects each landslide\n",
+    "A = landslide_scenarios\n",
+    "B = wn_gis.pipes\n",
+    "B_value = 'length'\n",
+    "landslide_intersect = wntr.gis.intersect(A, B, B_value)\n",
     "\n",
-    "pipe_intersect.sort_values('n', ascending=False).head()"
+    "# Print results in order of descending total pipe length\n",
+    "landslide_intersect.sort_values('sum', ascending=False).head()"
    ]
   },
   {
@@ -402,17 +464,49 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Add the intersection data to the water network pipe data\n",
-    "wn_gis.pipes[['intersections', 'n']] = pipe_intersect\n",
-    "wn_gis.pipes.sort_values('n', ascending=False).head()"
+    "# Add the intersection results to the landslide scenario data\n",
+    "landslide_scenarios[['intersections', 'n', 'total pipe length']] = landslide_intersect[['intersections', 'n', 'sum']]\n",
+    "\n",
+    "# Print results in order of descending total pipe length\n",
+    "landslide_scenarios.sort_values('total pipe length', ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Plot intersection results\n",
+    "fig, axes = plt.subplots(1,2, figsize=(15,5))\n",
+    "\n",
+    "wn_gis.pipes.plot(color='gray', linewidth=1, ax=axes[0])\n",
+    "landslide_scenarios.plot(column='n', vmax=10, legend=True, ax=axes[0])\n",
+    "tmp = axes[0].set_title('Number of pipes that intersect each landslide')\n",
+    "tmp = axes[0].axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "tmp = axes[0].set_xlim(zoom_coords[0])\n",
+    "tmp = axes[0].set_ylim(zoom_coords[1])\n",
+    "\n",
+    "wn_gis.pipes.plot(color='gray', linewidth=1, ax=axes[1])\n",
+    "landslide_scenarios.plot(column='total pipe length', vmax=10000, legend=True, ax=axes[1])\n",
+    "tmp = axes[1].set_title('Length of pipe that intersect each landslide')\n",
+    "tmp = axes[1].axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "tmp = axes[1].set_xlim(zoom_coords[0])\n",
+    "tmp = axes[1].set_ylim(zoom_coords[1])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Intersect landslide regions with pipes\n",
-    "Landslide regions are intersected with pipes to determine the pipes that could be impacted by each landslide.  This information is used to build landslide scenarios."
+    "## Identify landslides that intersect each pipe\n",
+    "Pipes are intersected with landslides to obtain a list of landslides that intersect each pipe. The landslide attribute `Confidence_Ranking` is also included in the intersection to gather statistics on landslide confidence for each pipe.  <font color='red'>While this information is not used in the analysis below, this type of information could be used to inform uncertainty or probability of damage.</font>\n",
+    "\n",
+    "**Note that `Confidence_Ranking` has a value of 3 (\"Landslide likely at or near the specified location\") for each landslide in region of interest. Since the values are uniform in this dataset, the intersected sum, min, max, and mean are all the same value.** More information on Confidence ranking can be found at https://kgs.uky.edu/kgsmap/helpfiles/landslide_help.shtm.  "
    ]
   },
   {
@@ -423,11 +517,14 @@
    },
    "outputs": [],
    "source": [
-    "# Determine pipes that intersect each landslide region, remove landslides that intersect no pipes, and print in order of descending number of intersections.\n",
-    "landslide_intersect = wntr.gis.intersect(landslide_regions, wn_gis.pipes)\n",
-    "landslide_intersect = landslide_intersect[landslide_intersect['n'] > 0]\n",
+    "# Use the intersect function to determine landslides and landslide confidence ranking that intersects each pipe\n",
+    "A = wn_gis.pipes\n",
+    "B = landslide_scenarios\n",
+    "B_value = 'Confidence_Ranking'\n",
+    "pipe_intersect = wntr.gis.intersect(A, B, B_value)\n",
     "\n",
-    "landslide_intersect.sort_values('n', ascending=False).head()"
+    "# Print results in order of descending number of intersections.\n",
+    "pipe_intersect.sort_values('n', ascending=False).head()"
    ]
   },
   {
@@ -436,38 +533,39 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Add the intersection data to the landslide regions data\n",
-    "landslide_regions[['intersections', 'n']] = landslide_intersect\n",
+    "# Add the intersection results to the GIS pipe data\n",
+    "wn_gis.pipes[['intersections', 'n', 'Confidence_Ranking']] = pipe_intersect[['intersections', 'n', 'mean']]\n",
     "\n",
-    "landslide_regions.sort_values('n', ascending=False).head()"
+    "# Print results in order of descending number of intersections\n",
+    "wn_gis.pipes.sort_values('n', ascending=False).head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Plot intersection results\n",
-    "fig, axes = plt.subplots(1,2, figsize=(15,5))\n",
-    "\n",
-    "wn_gis.pipes.plot(color='gray', alpha=0.5, ax=axes[0])\n",
-    "wn_gis.pipes[wn_gis.pipes['n'] > 0].plot(column='n', legend=True, ax=axes[0])\n",
-    "axes[0].set_title('Number of landslide regions that intersect each pipe')\n",
-    "\n",
-    "wn_gis.pipes.plot(color='gray', alpha=0.5, ax=axes[1])\n",
-    "landslide_regions.plot(column='n', vmax=10, legend=True, ax=axes[1])\n",
-    "axes[1].set_title('Number of pipes that intersect each landslide region')"
+    "ax = wn_gis.pipes.plot(color='gray', linewidth=1, zorder=1)\n",
+    "wn_gis.pipes[wn_gis.pipes['n'] > 0].plot(column='n', vmax=20, legend=True, ax=ax)\n",
+    "tmp = ax.set_title('Number of landslide scenarios that intersect each pipe')\n",
+    "tmp = ax.axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "tmp = ax.set_xlim(zoom_coords[0])\n",
+    "tmp = ax.set_ylim(zoom_coords[1])"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Intersect junctions with SVI data\n",
-    "Junctions are intersected with SVI to determine the social vulnerability of the population at each junction.  This information is used to determine the social vulnerability of individuals that experience water service disruptions."
+    "## Assign social vulnerability to each junction\n",
+    "Junctions are intersected with SVI to determine the social vulnerability of the population at each junction.  The SVI data attribute `RPL_THEMES` is included in the intersection. <font color='red'>This information is used to determine the social vulnerability of individuals that experience water service disruptions.</font>\n",
+    "\n",
+    "The SVI data column `RPL_THEMES` ranks vulnerability across socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation. The value ranges between 0 and 1, where higher values are associated with higher vulnerability.\n",
+    "\n",
+    "**Note that since each junction only intersects one census tract, the SVI sum, min, max, and mean are all the same value.**"
    ]
   },
   {
@@ -478,9 +576,12 @@
    },
    "outputs": [],
    "source": [
-    "# Determine the SVI of each junction using \"RPL_THEMES\", which ranks vulnerability across socioeconomic status, household characteristics, \n",
-    "# racial and ethnic minority status, and housing type and transportation. The value ranges between 0 and 1, where higher values are associated with higher vulnerability.\n",
-    "junction_svi = wntr.gis.intersect(wn_gis.junctions, svi_data, 'RPL_THEMES')\n",
+    "# Use the intersect function to determine SVI of each junction.  \n",
+    "A = wn_gis.junctions\n",
+    "B = svi_data\n",
+    "B_value = 'RPL_THEMES'\n",
+    "junction_svi = wntr.gis.intersect(A, B, B_value)\n",
+    "\n",
     "junction_svi.head()"
    ]
   },
@@ -490,7 +591,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Select the mean value to use in the analysis\n",
+    "# Add the intersection results (SVI value) to the GIS junction data\n",
     "wn_gis.junctions['RPL_THEMES'] = junction_svi['mean']"
    ]
   },
@@ -502,16 +603,24 @@
    },
    "outputs": [],
    "source": [
-    "# Plot SVI for each census track and SVI assigned to each junction\n",
+    "# Plot SVI for each census tract and SVI assigned to each junction\n",
     "fig, axes = plt.subplots(1,2, figsize=(15,5))\n",
     "\n",
     "svi_data.plot(column='RPL_THEMES', label='SVI data', vmin=0, vmax=1, legend=True, ax=axes[0])\n",
-    "wn_gis.pipes.plot(color='black', ax=axes[0])\n",
-    "axes[0].set_title('SVI and pipe data')\n",
+    "wn_gis.pipes.plot(color='black', linewidth=1, ax=axes[0])\n",
+    "tmp = axes[0].set_title('Census tract SVI and pipe data')\n",
+    "tmp = axes[0].axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "tmp = axes[0].set_xlim(zoom_coords[0])\n",
+    "tmp = axes[0].set_ylim(zoom_coords[1])\n",
     "\n",
-    "wn_gis.pipes.plot(color='gray', alpha=0.5, ax=axes[1])\n",
+    "wn_gis.pipes.plot(color='gray', linewidth=1, ax=axes[1])\n",
     "wn_gis.junctions.plot(column='RPL_THEMES', vmin=0, vmax=1, legend=True, ax=axes[1])\n",
-    "axes[1].set_title('SVI value at each junction')"
+    "tmp = axes[1].set_title('SVI value at each junction')\n",
+    "tmp = axes[1].axis('off')\n",
+    "# Comment/uncomment the following 2 lines to change the zoom on the network graphic\n",
+    "tmp = axes[1].set_xlim(zoom_coords[0])\n",
+    "tmp = axes[1].set_ylim(zoom_coords[1])"
    ]
   },
   {
@@ -521,7 +630,7 @@
    },
    "source": [
     "# Hydraulic Simulations\n",
-    "The following section runs hydraulic simulations for the baseline (no landslide) and landslide scenarios. A subset of landslide scenarios is run to simply the tutorial. For each simulation, the water service availability (WSA) at each junction is computed.  WSA is defined as the ratio of delivered demand to the expected demand. A value below 1 indicates that expected demand is not met."
+    "The following section runs hydraulic simulations for the baseline (no landslide) and landslide scenarios. A subset of landslide scenarios is run to simply the tutorial.  Simulation results are stored for later analysis."
    ]
   },
   {
@@ -532,13 +641,13 @@
    },
    "outputs": [],
    "source": [
-    "# Create a function to setup the model for hydraulic simulations\n",
+    "# Create a function to setup the WaterNetworkModel for hydraulic simulations\n",
     "def model_setup(inp_file):\n",
     "    wn = wntr.network.WaterNetworkModel(inp_file)\n",
     "    wn.options.hydraulic.demand_model = 'PDD'\n",
     "    wn.options.hydraulic.required_pressure = 20 # m\n",
     "    wn.options.hydraulic.minimum_pressure  = 0 # m\n",
-    "    wn.options.time.duration = 48*3600 # 48 hour simulation\n",
+    "    wn.options.time.duration = 48*3600 # s (48 hour simulation)\n",
     "    return wn"
    ]
   },
@@ -557,28 +666,10 @@
    },
    "outputs": [],
    "source": [
-    "# Run a baseline simulation, with no landslides or damage.  Compute water service availability (WSA) for each junction.\n",
+    "# Run a baseline simulation, with no landslide or damage.  \n",
     "wn = model_setup(inp_file)\n",
     "sim = wntr.sim.EpanetSimulator(wn)\n",
-    "baseline_results = sim.run_sim()\n",
-    "\n",
-    "expected_demand = wntr.metrics.expected_demand(wn)\n",
-    "demand = baseline_results.node['demand'].loc[:,wn.junction_name_list]\n",
-    "wsa = wntr.metrics.water_service_availability(expected_demand.sum(axis=0), demand.sum(axis=0))\n",
-    "\n",
-    "wsa.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Add WSA from the base simulation to the junction GIS data\n",
-    "wn_gis.junctions['wsa_base'] = wsa"
+    "baseline_results = sim.run_sim()"
    ]
   },
   {
@@ -587,10 +678,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Plot WSA from the base simulation\n",
-    "ax = wn_gis.pipes.plot(color='black', alpha=0.5)\n",
-    "ax = wn_gis.junctions.plot(column='wsa_base', cmap='RdYlGn', vmin=0, vmax=1, legend=True, ax=ax)\n",
-    "ax.set_title('Baseline WSA')"
+    "# View a subset of the simulation results\n",
+    "baseline_results.node['pressure'].head()"
    ]
   },
   {
@@ -600,7 +689,7 @@
    },
    "source": [
     "## Run landslide scenarios\n",
-    "Landslide scenarios are down selected by identifying the set of landslides that impact a unique set of pipes.  Scenarios are further down selected to 6 scenarios to simplify the tutorial."
+    "Landslide scenarios are downselected by identifying the set of landslides that impact a unique set of pipes.  Scenarios are further downselected to 6 scenarios to simplify the tutorial.  A hydraulic simulation is run for each landslide scenario, where pipes that intersect the landslide are closed for 48 hours.  Results from each scenario are stored for later analysis."
    ]
   },
   {
@@ -611,11 +700,15 @@
    },
    "outputs": [],
    "source": [
-    "# Down select landslide regions that impact a unique set of pipes\n",
-    "duplicated_intersections = landslide_regions['intersections'].astype(str).duplicated()\n",
-    "landslide_scenarios = landslide_regions.loc[~duplicated_intersections, :]\n",
-    "landslide_scenarios = landslide_scenarios.sort_values('n', ascending=False)\n",
+    "# Remove scenarios with no intersecting pipes\n",
+    "landslide_scenarios = landslide_scenarios[landslide_scenarios['n'] > 0]\n",
+    "landslide_scenarios = landslide_scenarios[~landslide_scenarios['n'].isna()]\n",
     "\n",
+    "# Downselect landslide scenarios that impact a unique set of pipes\n",
+    "duplicated_intersections = landslide_scenarios['intersections'].astype(str).duplicated()\n",
+    "landslide_scenarios = landslide_scenarios.loc[~duplicated_intersections, :]\n",
+    "\n",
+    "print('Number of unique landslide scenarios', landslide_scenarios.shape[0])\n",
     "landslide_scenarios.head()"
    ]
   },
@@ -627,10 +720,20 @@
    },
    "outputs": [],
    "source": [
-    "# Further down select the landslide scenarios to a small set for demonstration purposes. Comment out the following line to run a full analysis.\n",
-    "landslide_scenarios = landslide_scenarios.loc[[6980, 7003, 7202, 7028,6966, 7058],:]\n",
+    "# Further downselect the landslide scenarios for demonstration purposes. Choose one of the following 4 options.\n",
+    "# Option 1. 6 scenarios that illustrate a wide range of impact\n",
+    "landslide_scenarios_downselect = landslide_scenarios.loc[['LS-4495', 'LS-7003', 'LS-7111', 'LS-5086', 'LS-6966', 'LS-7058'],:] \n",
+    "\n",
+    "# Option 2. 6 scenarios with the highest intersecting pipe length\n",
+    "#landslide_scenarios_downselect = landslide_scenarios.sort_values('total pipe length', ascending=False).iloc[0:6,:]\n",
     "\n",
-    "landslide_scenarios"
+    "# Option 3. 6 scenarios with the highest number of intersecting pipes\n",
+    "#landslide_scenarios_downselect = landslide_scenarios.sort_values('n', ascending=False).iloc[0:6,:]\n",
+    "\n",
+    "# Option 4. Random selection of 6 scenarios\n",
+    "#landslide_scenarios_downselect = landslide_scenarios.sample(n=6, random_state=1234)\n",
+    "\n",
+    "landslide_scenarios_downselect"
    ]
   },
   {
@@ -639,10 +742,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Plot the location of landslide regions used in the analysis\n",
-    "ax = landslide_scenarios.plot(color='blue')\n",
-    "wn_gis.pipes.plot(color='black', alpha=0.5, ax=ax)\n",
-    "ax.set_title('Landslide scenarios')"
+    "# Plot the location of landslides used in the analysis\n",
+    "ax = landslide_scenarios_downselect.plot(color='blue')\n",
+    "wn_gis.pipes.plot(color='gray', linewidth=1, ax=ax)\n",
+    "tmp = ax.set_title('Landslide scenarios')\n",
+    "tmp = ax.axis('off')"
    ]
   },
   {
@@ -651,25 +755,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Run hydraulic simulations and extract water service availability for each landslide scenario.  \n",
-    "# Print the landslide number, the number of pipes that intersect the landslide, and the average WSA\n",
+    "# Run a hydraulic simulation for each landslide scenario, store results in a dictionary\n",
+    "# Each scenario closes all pipes that intersect the landslide for the 48 hour simulation\n",
     "results = {}\n",
-    "for i, scenario in landslide_scenarios.iterrows():\n",
+    "for i, scenario in landslide_scenarios_downselect.iterrows():\n",
     "    wn = model_setup(inp_file)\n",
     "    for pipe_i in scenario['intersections']:\n",
-    "        pipe_object =wn.get_link(pipe_i)\n",
+    "        pipe_object = wn.get_link(pipe_i)\n",
     "        pipe_object.initial_status = 'CLOSED'\n",
     "    sim = wntr.sim.EpanetSimulator(wn)\n",
-    "    results[i] = sim.run_sim()\n",
-    "    \n",
-    "    # Compute WSA\n",
-    "    demand = results[i].node['demand'].loc[:,wn.junction_name_list]\n",
-    "    wsa = wntr.metrics.water_service_availability(expected_demand.sum(axis=0), demand.sum(axis=0))\n",
-    "    \n",
-    "    # Store WSA in the junctions GeoDataFrame\n",
-    "    column_name = 'wsa_'+str(i)\n",
-    "    wn_gis.junctions[column_name] = wsa\n",
-    "    print(i, len(scenario['intersections']), wsa.mean())"
+    "    results[i] = sim.run_sim()"
    ]
   },
   {
@@ -679,15 +774,56 @@
    },
    "source": [
     "# Analysis Results\n",
-    "The following section plots analysis results, including water service availability for the landslide scenarios and SVI of impacted junctions."
+    "The following section computes and plots analysis results, including water service availability (WSA) and the social vulnerability index (SVI) of impacted junctions for each scenario."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Water Service Availability\n",
-    "Each scenario includes WSA for each junction.  Note that WSA can be > 1 and < 0 due to numerical differences in expected and actual demand. For certain types of analysis, the WSA should be truncated to values between 0 and 1."
+    "## Water Service Availability (WSA)\n",
+    "Water service availability (WSA) is the ratio of delivered demand to the expected demand.  WSA is computed for each junction (alternatively, WSA can be computed for each timestep, or for each junction and timestep).  A value below 1 indicates that expected demand it me, while a value of 0 indicates that the expected demand is not met. \n",
+    "\n",
+    "**Note that WSA can be > 1 and < 0 due to numerical differences in expected and actual demand. For certain types of analysis, WSA should be truncated to values between 0 and 1.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute expected demand for each junction and timestep\n",
+    "expected_demand = wntr.metrics.expected_demand(wn)\n",
+    "\n",
+    "expected_demand.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute total expected demand at each junction (axis 0 is the time index)\n",
+    "expected_demand_j = expected_demand.sum(axis=0)\n",
+    "\n",
+    "expected_demand_j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute baseline WSA for each junction\n",
+    "demand_baseline = baseline_results.node['demand'].loc[:,wn.junction_name_list]\n",
+    "demand_baseline_j = demand_baseline.sum(axis=0) # total demand at each junction\n",
+    "wsa_baseline_j = wntr.metrics.water_service_availability(expected_demand_j, demand_baseline_j)\n",
+    "\n",
+    "wsa_baseline_j.head()"
    ]
   },
   {
@@ -698,12 +834,66 @@
    },
    "outputs": [],
    "source": [
-    "# Extract and plot WSA for 6 scenarios. \n",
-    "column_names = ['wsa_'+str(i) for i in landslide_scenarios.iloc[0:6,:].index]\n",
-    "wsa_results = wn_gis.junctions[column_names]\n",
+    "# Add WSA from the base simulation to the junction GIS data\n",
+    "wn_gis.junctions['baseline'] = wsa_baseline_j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot WSA from the base simulation\n",
+    "ax = wn_gis.pipes.plot(color='black', linewidth=1)\n",
+    "ax = wn_gis.junctions.plot(column='baseline', cmap='RdYlGn', vmin=0, vmax=1, legend=True, ax=ax)\n",
+    "tmp = ax.set_title('Baseline WSA')\n",
+    "tmp = ax.axis('off')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute WSA associated with each landslide scenarios\n",
+    "for i, scenario in landslide_scenarios_downselect.iterrows():\n",
+    "    demand = results[i].node['demand'].loc[:,wn.junction_name_list]\n",
+    "    demand_j = demand.sum(axis=0) # total demand at each junction\n",
+    "    wsa_j = wntr.metrics.water_service_availability(expected_demand_j, demand_j)\n",
+    "    \n",
+    "    # Add WSA to the junction GIS data\n",
+    "    wn_gis.junctions[i] = wsa_j\n",
+    "    print(i, len(scenario['intersections']), wsa_j.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Extract WSA for each scenario \n",
+    "wsa_results = wn_gis.junctions[landslide_scenarios_downselect.index]\n",
     "\n",
-    "ax = wsa_results.plot()\n",
-    "ax.set_ylim(-0.1, 1.1)"
+    "wsa_results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot distribution of WSA for each scenario. Note that WSA can be > 1 and < 0 due to numerical differences in expected and actual demand. \n",
+    "# For certain types of analysis, the WSA should be truncated to values between 0 and 1.\n",
+    "ax = wsa_results.boxplot()\n",
+    "tmp = ax.set_ylim(-0.25, 1.25)\n",
+    "tmp = ax.set_ylabel('WSA')\n",
+    "tmp = ax.set_title('Distribution of WSA for each scenario')"
    ]
   },
   {
@@ -717,16 +907,14 @@
     "# Plot WSA for each scenario\n",
     "fig, axes = plt.subplots(2,3, figsize=(15,10))\n",
     "axes = axes.flatten()\n",
-    "axes_counter = 0\n",
     "\n",
-    "for i, scenario in landslide_scenarios.iterrows():\n",
-    "    wsa_column_name = 'wsa_'+str(i)\n",
-    "    ax = axes[axes_counter]\n",
-    "    ax = wn_gis.pipes.plot(color='black', alpha=0.5, ax=ax)\n",
-    "    wn_gis.junctions.plot(column=wsa_column_name, cmap='RdYlGn', vmin=0, vmax=1, legend=True, ax=ax)\n",
-    "    ax = landslide_scenarios.loc[[i],:].boundary.plot(color='blue', ax=ax)\n",
-    "    ax.set_title('Landslide ' + str(i))\n",
-    "    axes_counter = axes_counter + 1"
+    "for i, scenario in enumerate(wsa_results.columns):\n",
+    "    wn_gis.pipes.plot(color='gray', linewidth=1, ax=axes[i]) # pipes\n",
+    "    wn_gis.junctions.plot(column=scenario, cmap='RdYlGn', vmin=0, vmax=1, legend=True, ax=axes[i]) # junction wsa\n",
+    "    tmp = axes[i].set_title('WSA '+scenario)\n",
+    "    tmp = axes[i].axis('off')\n",
+    "    if i >= 6: # axes is defined to have 6 subplots\n",
+    "        break"
    ]
   },
   {
@@ -734,20 +922,20 @@
    "metadata": {},
    "source": [
     "## SVI of impacted junctions\n",
-    "In this analysis, impacted junctions are defined as junctions where WSA falls below 0.5 (50% of the water expected was received) at any time during the simulation. Other criteria could also be used to defined impact."
+    "In this analysis, impacted junctions are defined as junctions where WSA falls below 0.5 (50% of the expected water was received) at any time during the simulation. Other criteria could also be used to defined impact."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Identify and print junctions that have WSA < 0.5\n",
-    "impacted = (wsa_results < 0.5).any(axis=1)\n",
-    "impacted_junctions = impacted[impacted == True].index\n",
+    "# Extract junctions that are impacted by WSA < 0.5 for each scenario\n",
+    "impacted_junctions = {}\n",
+    "for scenario in wsa_results.columns:\n",
+    "    filter = wsa_results[scenario] < 0.5\n",
+    "    impacted_junctions[scenario] = wsa_results.index[filter]\n",
     "\n",
     "impacted_junctions"
    ]
@@ -755,15 +943,22 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# Plot the SVI of impacted junctions\n",
-    "ax = wn_gis.pipes.plot(color='black', alpha=0.5)\n",
-    "wn_gis.junctions.loc[impacted_junctions,:].plot(column='RPL_THEMES', cmap='RdYlGn_r',vmin=0, vmax=1, legend=True, ax=ax)\n",
-    "ax.set_title('SVI of impacted junctions')"
+    "# Plot SVI of impacted junctions for each scenario\n",
+    "fig, axes = plt.subplots(2,3, figsize=(15,10))\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, scenario in enumerate(wsa_results.columns):\n",
+    "    j = impacted_junctions[scenario]\n",
+    "    wn_gis.pipes.plot(color='gray', linewidth=1, alpha=0.5, ax=axes[i]) # pipes\n",
+    "    if len(j) > 0:\n",
+    "        wn_gis.junctions.loc[j,:].plot(column='RPL_THEMES', cmap='RdYlGn_r', vmin=0, vmax=1, legend=True, ax=axes[i]) # junction wsa\n",
+    "    tmp = axes[i].set_title('SVI '+scenario)\n",
+    "    tmp = axes[i].axis('off')\n",
+    "    if i >= 6: # axes is defined to have 6 subplots\n",
+    "        break"
    ]
   },
   {
@@ -771,7 +966,7 @@
    "metadata": {},
    "source": [
     "## Save analysis results to GIS files\n",
-    "Results that are added to the `wn_gis` object can be saved to GIS formatted files.  Note that lists (such as the information stored in 'intersections') is not JSON serializable and must first be removed.  The resulting GIS files contain WSA per scenario and can be loaded into GIS platforms for further analysis."
+    "The analysis above stored WSA results for each scenario to the `wn_gis` object, which can be saved to GIS formatted files and loaded into GIS software platforms for further analysis.  **Note that lists (such as the information stored in 'intersections') is not JSON serializable and must first be removed.**"
    ]
   },
   {
@@ -810,7 +1005,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.11.7"
   },
   "toc-autonumbering": true,
   "vscode": {
diff --git a/examples/getting_started.py b/examples/getting_started.py
index ec0facbf7..1924845f1 100644
--- a/examples/getting_started.py
+++ b/examples/getting_started.py
@@ -1,21 +1,20 @@
 """
-The following example demonstrates how to import WNTR, generate a water network 
-model from an INP file, simulate hydraulics, and plot simulation results on the network.
+The following example demonstrates how to import WNTR, create a water 
+network model from an EPANET INP file, simulate hydraulics, and plot 
+simulation results on the network.
 """
+# Import WNTR
 import wntr
 
 # Create a water network model
 inp_file = 'networks/Net3.inp'
 wn = wntr.network.WaterNetworkModel(inp_file)
 
-# Graph the network
-wntr.graphics.plot_network(wn, title=wn.name)
-
 # Simulate hydraulics
 sim = wntr.sim.EpanetSimulator(wn)
 results = sim.run_sim()
 
 # Plot results on the network
 pressure_at_5hr = results.node['pressure'].loc[5*3600, :]
-wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, node_size=30, 
-                        title='Pressure at 5 hours')
+wntr.graphics.plot_network(wn, node_attribute=pressure_at_5hr, 
+                           node_size=30, title='Pressure at 5 hours')
diff --git a/pyproject.toml b/pyproject.toml
index d7fa01ee0..ffac3e2fc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,5 +1,5 @@
 [build-system]
-requires = ["setuptools", "numpy>=1.21"]
+requires = ["setuptools", "numpy>=1.21,<2.0"]
 build-backend = "setuptools.build_meta"
 
 [tool.pytest.ini_options]
diff --git a/requirements.txt b/requirements.txt
index 0ffe76ca7..d3e56fd1f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 # Required
-numpy>=1.21
+numpy>=1.21,<2.0
 scipy<1.13.0
 networkx
 pandas
@@ -11,7 +11,8 @@ plotly
 folium
 utm
 openpyxl
-geopandas
+geopandas<1.0
+fiona<1.10
 rtree
 pyswmm
 
@@ -27,4 +28,4 @@ pytest
 nbformat
 nbconvert
 ipykernel
-coverage
\ No newline at end of file
+coverage
diff --git a/setup.py b/setup.py
index 0ae9e1510..25d730df7 100644
--- a/setup.py
+++ b/setup.py
@@ -72,7 +72,7 @@
 MAINTAINER_EMAIL = 'kaklise@sandia.gov'
 LICENSE = 'Revised BSD'
 URL = 'https://github.com/USEPA/WNTR'
-DEPENDENCIES = ['numpy>=1.21', 'scipy', 'networkx', 'pandas', 'matplotlib', 'setuptools']
+DEPENDENCIES = ['numpy>=1.21,<2.0', 'scipy', 'networkx', 'pandas', 'matplotlib', 'setuptools']
 
 # use README file as the long description
 file_dir = os.path.abspath(os.path.dirname(__file__))
diff --git a/wntr/gis/geospatial.py b/wntr/gis/geospatial.py
index d73fbd8cc..332b7af37 100644
--- a/wntr/gis/geospatial.py
+++ b/wntr/gis/geospatial.py
@@ -64,8 +64,7 @@ def snap(A, B, tolerance):
     assert A.crs == B.crs
     
     # Modify B to include "indexB" as a separate column
-    B = B.reset_index()
-    B.rename(columns={'index':'indexB'}, inplace=True)
+    B = B.reset_index(names='indexB')
     
     # Define the coordinate reference system, based on B
     crs = B.crs
@@ -228,7 +227,7 @@ def intersect(A, B, B_value=None, include_background=False, background_value=0):
     
     n = intersects.groupby('_tmp_index_name')['geometry'].count()
     B_indices = intersects.groupby('_tmp_index_name')['index_right'].apply(list)
-    stats = pd.DataFrame(index=A.index, data={'intersections': B_indices,
+    stats = pd.DataFrame(index=A.index.copy(), data={'intersections': B_indices,
                                               'n': n,})
     stats['n'] = stats['n'].fillna(0)
     stats['n'] = stats['n'].apply(int)
@@ -250,9 +249,9 @@ def intersect(A, B, B_value=None, include_background=False, background_value=0):
             weighted_mean = True
             
     if weighted_mean and B_value is not None:
-        stats['weighted_mean'] = 0
+        stats['weighted_mean'] = 0.0
         A_length = A.length
-        covered_length = pd.Series(0, index = A.index)
+        covered_length = pd.Series(0.0, index = A.index)
         
         for i in B.index:
             B_geom = gpd.GeoDataFrame(B.loc[[i],:], crs=B.crs)
diff --git a/wntr/gis/network.py b/wntr/gis/network.py
index 86f399e30..d9c4e9d6b 100644
--- a/wntr/gis/network.py
+++ b/wntr/gis/network.py
@@ -133,7 +133,6 @@ def _extract_geodataframe(df, crs=None, links_as_points=False):
                 # Set index
                 if len(df) > 0:
                     df.set_index('name', inplace=True)
-                    df.index.name = None
                 
                 df = gpd.GeoDataFrame(df, crs=crs, geometry=geom)
             else:
@@ -191,8 +190,8 @@ def _create_wn(self, append=None):
         for element in [self.junctions, self.tanks, self.reservoirs]:
             if element.shape[0] > 0:
                 assert (element['geometry'].geom_type).isin(['Point']).all()
-                df = element.reset_index()
-                df.rename(columns={'index':'name', 'geometry':'coordinates'}, inplace=True)
+                df = element.reset_index(names="name")
+                df.rename(columns={'geometry':'coordinates'}, inplace=True)
                 df['coordinates'] = [[x,y] for x,y in zip(df['coordinates'].x, 
                                                           df['coordinates'].y)]
                 wn_dict['nodes'].extend(df.to_dict('records'))
@@ -201,8 +200,7 @@ def _create_wn(self, append=None):
             if element.shape[0] > 0:
                 assert 'start_node_name' in element.columns
                 assert 'end_node_name' in element.columns
-                df = element.reset_index()
-                df.rename(columns={'index':'name'}, inplace=True)
+                df = element.reset_index(names="name")
                 df['vertices'] = df.apply(lambda row: list(row.geometry.coords)[1:-1], axis=1)
                 df.drop(columns=['geometry'], inplace=True)
                 wn_dict['links'].extend(df.to_dict('records'))
@@ -301,7 +299,7 @@ def add_link_attributes(self, values, name):
                     self.pumps[name] = np.nan
                 self.pumps.loc[link_name, name] = value
     
-    def _read(self, files, index_col='index'):
+    def _read(self, files, index_col='name'):
         
         if 'junctions' in files.keys():
             data = gpd.read_file(files['junctions']).set_index(index_col)
@@ -322,7 +320,7 @@ def _read(self, files, index_col='index'):
             data = gpd.read_file(files['valves']).set_index(index_col)
             self.valves = pd.concat([self.valves, data])
 
-    def read_geojson(self, files, index_col='index'):
+    def read_geojson(self, files, index_col='name'):
         """
         Append information from GeoJSON files to a WaterNetworkGIS object
 
@@ -337,7 +335,7 @@ def read_geojson(self, files, index_col='index'):
         """
         self._read(files, index_col)
 
-    def read_shapefile(self, files, index_col='index'):
+    def read_shapefile(self, files, index_col='name'):
         """
         Append information from Esri Shapefiles to a WaterNetworkGIS object
 
diff --git a/wntr/graphics/network.py b/wntr/graphics/network.py
index 45b420b13..1b270bba4 100644
--- a/wntr/graphics/network.py
+++ b/wntr/graphics/network.py
@@ -47,7 +47,7 @@ def plot_network(wn, node_attribute=None, link_attribute=None, title=None,
                node_size=20, node_range=[None,None], node_alpha=1, node_cmap=None, node_labels=False,
                link_width=1, link_range=[None,None], link_alpha=1, link_cmap=None, link_labels=False,
                add_colorbar=True, node_colorbar_label='Node', link_colorbar_label='Link', 
-               directed=False, ax=None, filename=None):
+               directed=False, ax=None, show_plot=True, filename=None):
     """
     Plot network graphic
 	
@@ -127,6 +127,9 @@ def plot_network(wn, node_attribute=None, link_attribute=None, title=None,
         Axes for plotting (None indicates that a new figure with a single 
         axes will be used)
     
+    show_plot: bool, optional
+        If True, show plot with plt.show()
+    
     filename : str, optional
         Filename used to save the figure
         
@@ -228,8 +231,14 @@ def plot_network(wn, node_attribute=None, link_attribute=None, title=None,
         clb = plt.colorbar(nodes, shrink=0.5, pad=0, ax=ax)
         clb.ax.set_title(node_colorbar_label, fontsize=10)
     if add_link_colorbar and link_attribute:
-        vmin = min(map(abs,link_attribute.values()))
-        vmax = max(map(abs,link_attribute.values())) 
+        if link_range[0] is None:
+            vmin = min(link_attribute.values())
+        else:
+            vmin = link_range[0]
+        if link_range[1] is None:
+            vmax = max(link_attribute.values())
+        else:
+            vmax = link_range[1]
         sm = plt.cm.ScalarMappable(cmap=link_cmap, norm=plt.Normalize(vmin=vmin, vmax=vmax))
         sm.set_array([])
         clb = plt.colorbar(sm, shrink=0.5, pad=0.05, ax=ax)
@@ -240,7 +249,8 @@ def plot_network(wn, node_attribute=None, link_attribute=None, title=None,
     if filename:
         plt.savefig(filename)
     
-    plt.show(block=False)
+    if show_plot is True:
+        plt.show(block=False)
     
     return ax
 
@@ -766,7 +776,7 @@ def network_animation(wn, node_attribute=None, link_attribute=None, title=None,
     ax = plot_network(wn, node_attribute=initial_node_values, link_attribute=initial_link_values, title=title_name,
                node_size=node_size, node_range=node_range, node_alpha=node_alpha, node_cmap=node_cmap, node_labels=node_labels,
                link_width=link_width, link_range=link_range, link_alpha=link_alpha, link_cmap=link_cmap, link_labels=link_labels,
-               add_colorbar=add_colorbar, directed=directed, ax=ax)
+               add_colorbar=add_colorbar, directed=directed, ax=ax, show_plot=False)
         
     def update(n):
         if node_attribute is not None:
@@ -790,7 +800,7 @@ def update(n):
         ax = plot_network(wn, node_attribute=node_values, link_attribute=link_values, title=title_name,
                node_size=node_size, node_range=node_range, node_alpha=node_alpha, node_cmap=node_cmap, node_labels=node_labels,
                link_width=link_width, link_range=link_range, link_alpha=link_alpha, link_cmap=link_cmap, link_labels=link_labels,
-               add_colorbar=add_colorbar, directed=directed, ax=ax)
+               add_colorbar=add_colorbar, directed=directed, ax=ax, show_plot=False)
         
         return ax
     
diff --git a/wntr/morph/skel.py b/wntr/morph/skel.py
index ee80f1316..77a4cb818 100644
--- a/wntr/morph/skel.py
+++ b/wntr/morph/skel.py
@@ -14,7 +14,8 @@
 logger = logging.getLogger(__name__)
 
 def skeletonize(wn, pipe_diameter_threshold, branch_trim=True, series_pipe_merge=True, 
-                parallel_pipe_merge=True, max_cycles=None, use_epanet=True, 
+                parallel_pipe_merge=True, max_cycles=None, use_epanet=True,
+                pipes_to_exclude:list=[], junctions_to_exclude:list=[],
                 return_map=False, return_copy=True):
     """
     Perform network skeletonization using branch trimming, series pipe merge, 
@@ -43,6 +44,10 @@ def skeletonize(wn, pipe_diameter_threshold, branch_trim=True, series_pipe_merge
     use_epanet: bool, optional
         If True, use the EpanetSimulator to compute headloss in pipes.  
         If False, use the WNTRSimulator to compute headloss in pipes.
+    pipes_to_exclude: list, optional
+        List of pipe names to exclude from skeletonization
+    junctions_to_exclude: list, optional
+        List of junction names to exclude from skeletonization
     return_map: bool, optional
         If True, return a skeletonization map. The map is a dictionary 
         that includes original nodes as keys and a list of skeletonized nodes 
@@ -60,7 +65,12 @@ def skeletonize(wn, pipe_diameter_threshold, branch_trim=True, series_pipe_merge
         nodes as keys and a list of skeletonized nodes that were merged into 
         each original node as values.
     """
-    skel = _Skeletonize(wn, use_epanet, return_copy)
+    if len(pipes_to_exclude) > 0:
+        assert len(set(pipes_to_exclude) - set(wn.pipe_name_list)) == 0
+    if len(junctions_to_exclude) > 0:
+        assert len(set(junctions_to_exclude) - set(wn.junction_name_list)) == 0
+    
+    skel = _Skeletonize(wn, use_epanet, return_copy, pipes_to_exclude, junctions_to_exclude)
     
     skel.run(pipe_diameter_threshold, branch_trim, series_pipe_merge, 
              parallel_pipe_merge, max_cycles)
@@ -73,7 +83,7 @@ def skeletonize(wn, pipe_diameter_threshold, branch_trim=True, series_pipe_merge
 		
 class _Skeletonize(object):
     
-    def __init__(self, wn, use_epanet, return_copy):
+    def __init__(self, wn, use_epanet, return_copy, pipes_to_exclude, junctions_to_exclude):
         
         if return_copy:
             # Get a copy of the WaterNetworkModel
@@ -93,6 +103,7 @@ def __init__(self, wn, use_epanet, return_copy):
         self.skeleton_map = skel_map
 
         # Get a list of junction and pipe names that are associated with controls
+        # Add them to junctions and pipes to exclude
         junc_with_controls = []
         pipe_with_controls = []
         for name, control in self.wn.controls():
@@ -101,8 +112,10 @@ def __init__(self, wn, use_epanet, return_copy):
                     junc_with_controls.append(req.name)
                 elif isinstance(req, Pipe):
                     pipe_with_controls.append(req.name)
-        self.junc_with_controls = list(set(junc_with_controls))
-        self.pipe_with_controls = list(set(pipe_with_controls))
+        self.junc_to_exclude = list(set(junc_with_controls))
+        self.junc_to_exclude.extend(junctions_to_exclude)
+        self.pipe_to_exclude = list(set(pipe_with_controls))
+        self.pipe_to_exclude.extend(pipes_to_exclude)
         
         # Calculate pipe headloss using a single period EPANET simulation
         duration = self.wn.options.time.duration
@@ -163,7 +176,7 @@ def branch_trim(self, pipe_threshold):
         patterns) to the neighboring junction.
         """
         for junc_name in self.wn.junction_name_list:
-            if junc_name in self.junc_with_controls:
+            if junc_name in self.junc_to_exclude:
                 continue
             neighbors = list(nx.neighbors(self.G,junc_name))
             if len(neighbors) > 1:
@@ -181,7 +194,7 @@ def branch_trim(self, pipe_threshold):
             pipe = self.wn.get_link(pipe_name)
             if not ((isinstance(pipe, Pipe)) and \
                 (pipe.diameter <= pipe_threshold) and \
-                pipe_name not in self.pipe_with_controls):
+                pipe_name not in self.pipe_to_exclude):
                 continue
             
             logger.info('Branch trim: '+ str(junc_name) + str(neighbors))
@@ -215,7 +228,7 @@ def series_pipe_merge(self, pipe_threshold):
         to the nearest junction.
         """
         for junc_name in self.wn.junction_name_list:
-            if junc_name in self.junc_with_controls:
+            if junc_name in self.junc_to_exclude:
                 continue
             neighbors = list(nx.neighbors(self.G,junc_name))
             if not (len(neighbors) == 2):
@@ -239,8 +252,8 @@ def series_pipe_merge(self, pipe_threshold):
                 (isinstance(pipe1, Pipe)) and \
                 ((pipe0.diameter <= pipe_threshold) and \
                 (pipe1.diameter <= pipe_threshold)) and \
-                pipe_name0 not in self.pipe_with_controls and \
-                pipe_name1 not in self.pipe_with_controls):
+                pipe_name0 not in self.pipe_to_exclude and \
+                pipe_name1 not in self.pipe_to_exclude):
                 continue
             # Find closest neighbor junction
             if (isinstance(neigh_junc0, Junction)) and \
@@ -305,7 +318,7 @@ def parallel_pipe_merge(self, pipe_threshold):
         """
         
         for junc_name in self.wn.junction_name_list:
-            if junc_name in self.junc_with_controls:
+            if junc_name in self.junc_to_exclude:
                 continue
             neighbors = nx.neighbors(self.G,junc_name)
             for neighbor in [n for n in neighbors]:
@@ -322,8 +335,8 @@ def parallel_pipe_merge(self, pipe_threshold):
                        (isinstance(pipe1, Pipe)) and \
                         ((pipe0.diameter <= pipe_threshold) and \
                         (pipe1.diameter <= pipe_threshold)) and \
-                        pipe_name0 not in self.pipe_with_controls and \
-                        pipe_name1 not in self.pipe_with_controls):
+                        pipe_name0 not in self.pipe_to_exclude and \
+                        pipe_name1 not in self.pipe_to_exclude):
                         continue
                     
                     logger.info('Parallel pipe merge: '+ str(junc_name) + str((pipe_name0, pipe_name1)))
diff --git a/wntr/network/io.py b/wntr/network/io.py
index 83bca2cf0..e4ceac8de 100644
--- a/wntr/network/io.py
+++ b/wntr/network/io.py
@@ -486,7 +486,6 @@ def write_inpfile(wn, filename: str, units=None, version: float = 2.2,
 
     """
     if wn._inpfile is None:
-        logger.warning("Writing a minimal INP file without saved non-WNTR options (energy, etc.)")
         wn._inpfile = wntr.epanet.InpFile()
     if units is None:
         units = wn._options.hydraulic.inpfile_units
@@ -551,7 +550,7 @@ def write_geojson(wn, prefix: str, crs=None, pumps_as_points=True,
     wn_gis.write_geojson(prefix=prefix)
 
 
-def read_geojson(files, index_col='index', append=None):
+def read_geojson(files, index_col='name', append=None):
     """
     Create or append a WaterNetworkModel from GeoJSON files
 
@@ -612,7 +611,7 @@ def write_shapefile(wn, prefix: str, crs=None, pumps_as_points=True,
                        valves_as_points=valves_as_points)
     wn_gis.write_shapefile(prefix=prefix)
 
-def read_shapefile(files, index_col='index', append=None):
+def read_shapefile(files, index_col='name', append=None):
     """
 
     Create or append a WaterNetworkModel from Esri Shapefiles
@@ -635,7 +634,7 @@ def read_shapefile(files, index_col='index', append=None):
 
     """
     gis_data = WaterNetworkGIS()
-    gis_data.read_shapefile(files, index_col='index')
+    gis_data.read_shapefile(files,index_col=index_col)
     wn = gis_data._create_wn(append=append)
 
     return wn
diff --git a/wntr/tests/test_gis.py b/wntr/tests/test_gis.py
index 9772ceaec..31513f265 100644
--- a/wntr/tests/test_gis.py
+++ b/wntr/tests/test_gis.py
@@ -74,6 +74,39 @@ def setUpClass(self):
     def tearDownClass(self):
         pass
     
+    def test_gis_index(self):
+        # Tests that WN can be made using dataframes with customized index names
+        wn_gis = self.wn.to_gis()
+        
+        # check that index name of geodataframes is "name"
+        assert wn_gis.junctions.index.name == "name"
+        assert wn_gis.tanks.index.name == "name"
+        assert wn_gis.reservoirs.index.name == "name"
+        assert wn_gis.pipes.index.name == "name"
+        assert wn_gis.pumps.index.name == "name"
+        
+        # check that index names can be changed and still be read back into a wn
+        wn_gis.junctions.index.name = "my_index"
+        wn_gis.pipes.index.name = "my_index"
+        wn2 = wntr.network.from_gis(wn_gis)
+        
+        assert self.wn.pipe_name_list == wn2.pipe_name_list
+        assert self.wn.junction_name_list == wn2.junction_name_list
+        
+        # test snap and intersect functionality with alternate index names
+        result = wntr.gis.snap(self.points, wn_gis.junctions, tolerance=5.0)
+        assert len(result) > 0
+        result = wntr.gis.snap(wn_gis.junctions, self.points, tolerance=5.0)
+        assert len(result) > 0
+        result = wntr.gis.intersect(wn_gis.junctions, self.polygons)
+        assert len(result) > 0
+        result = wntr.gis.intersect(self.polygons, wn_gis.pipes)
+        assert len(result) > 0
+        
+        # check that custom index name persists after running snap/intersect
+        assert wn_gis.junctions.index.name == "my_index"
+        assert wn_gis.pipes.index.name == "my_index"
+
     def test_wn_to_gis(self):
         # Check type
         isinstance(self.gis_data.junctions, gpd.GeoDataFrame)
@@ -248,8 +281,13 @@ def test_write_geojson(self):
         for component in components:
             if component == 'valves':
                 continue # Net1 has no valves
+            # check file exists
             filename = abspath(join(testdir, prefix+'_'+component+'.geojson'))
             self.assertTrue(isfile(filename))
+            
+            # check for "name" column
+            gdf = gpd.read_file(filename)
+            assert "name" in gdf.columns
 
     def test_snap_points_to_points(self):
         
diff --git a/wntr/tests/test_morph.py b/wntr/tests/test_morph.py
index 8953b5b2e..cc267b802 100644
--- a/wntr/tests/test_morph.py
+++ b/wntr/tests/test_morph.py
@@ -295,7 +295,13 @@ def test_skeletonize_with_controls(self):
 
         inp_file = join(datadir, "skeletonize.inp")
         wn = wntr.network.WaterNetworkModel(inp_file)
-
+        
+        # Run skeletonization without excluding junctions or pipes
+        skel_wn = wntr.morph.skeletonize(wn, 12.0 * 0.0254, use_epanet=False)
+        # Junction 13 and Pipe 60 are not in the skeletonized model
+        assert "13" not in skel_wn.junction_name_list
+        assert "60" not in skel_wn.pipe_name_list
+        
         # add control to a link
         action = wntr.network.ControlAction(
             wn.get_link("60"), "status", wntr.network.LinkStatus.Closed
@@ -310,21 +316,46 @@ def test_skeletonize_with_controls(self):
         control = wntr.network.Control(condition=condition, then_action=action)
         wn.add_control("raise_node", control)
 
+        # Rerun skeletonize
         skel_wn = wntr.morph.skeletonize(wn, 12.0 * 0.0254, use_epanet=False)
-
+        assert "13" in skel_wn.junction_name_list
+        assert "60" in skel_wn.pipe_name_list
         self.assertEqual(skel_wn.num_nodes, wn.num_nodes - 17)
         self.assertEqual(skel_wn.num_links, wn.num_links - 22)
 
+    def test_skeletonize_with_excluding_nodes_and_pipes(self):
+
+        inp_file = join(datadir, "skeletonize.inp")
         wn = wntr.network.WaterNetworkModel(inp_file)
+        
+        # Run skeletonization without excluding junctions or pipes
+        skel_wn = wntr.morph.skeletonize(wn, 12.0 * 0.0254, use_epanet=False)
+        # Junction 13 and Pipe 60 are not in the skeletonized model
+        assert "13" not in skel_wn.junction_name_list
+        assert "60" not in skel_wn.pipe_name_list
+        
+        # Run skeletonization excluding Junction 13 and Pipe 60
+        skel_wn = wntr.morph.skeletonize(wn, 12.0 * 0.0254, use_epanet=False, 
+                                         junctions_to_exclude=["13"], 
+                                         pipes_to_exclude=["60"])
+        # Junction 13 and Pipe 60 are in the skeletonized model
+        assert "13" in skel_wn.junction_name_list
+        assert "60" in skel_wn.pipe_name_list
+        self.assertEqual(skel_wn.num_nodes, wn.num_nodes - 17)
+        self.assertEqual(skel_wn.num_links, wn.num_links - 22)
 
-        # Change link 60 and 11 diameter to > 12, should get some results as above
+        # Change diameter of link 60 one link connected to Junction 13 to be 
+        # greater than 12, should get some results as above
+        # Note, link 11 is connected to Junction 13
         link = wn.get_link("60")
         link.diameter = 16 * 0.0254
-        link = wn.get_link("11")
+        link_connected_to_13 = wn.get_links_for_node('13')[0]
+        link = wn.get_link(link_connected_to_13)
         link.diameter = 16 * 0.0254
 
         skel_wn = wntr.morph.skeletonize(wn, 12.0 * 0.0254, use_epanet=False)
-
+        assert "13" in skel_wn.junction_name_list
+        assert "60" in skel_wn.pipe_name_list
         self.assertEqual(skel_wn.num_nodes, wn.num_nodes - 17)
         self.assertEqual(skel_wn.num_links, wn.num_links - 22)
 
diff --git a/wntr/tests/test_network.py b/wntr/tests/test_network.py
index d7ec9a1ce..5e30e1204 100644
--- a/wntr/tests/test_network.py
+++ b/wntr/tests/test_network.py
@@ -142,7 +142,7 @@ def test_add_pipe(self):
         self.assertEqual(l.name, "p1")
         self.assertEqual(l.start_node_name, "j1")
         self.assertEqual(l.end_node_name, "j2")
-        self.assertEqual(l.initial_status, self.wntr.network.LinkStatus.opened)
+        self.assertEqual(l.initial_status, self.wntr.network.LinkStatus.Opened)
         self.assertEqual(l.length, 1000.0)
         self.assertEqual(l.diameter, 1.0)
         self.assertEqual(l.roughness, 100.0)
@@ -255,7 +255,7 @@ def test_remove_controls_for_removing_link(self):
         wn = self.wntr.network.WaterNetworkModel(inp_file)
 
         control_action = self.wntr.network.ControlAction(
-            wn.get_link("21"), "status", self.wntr.network.LinkStatus.opened
+            wn.get_link("21"), "status", self.wntr.network.LinkStatus.Opened
         )
         control = self.wntr.network.controls.Control._conditional_control(
             wn.get_node("2"), "head", np.greater_equal, 10.0, control_action
diff --git a/wntr/tests/test_network_controls.py b/wntr/tests/test_network_controls.py
index 6650d6ea1..9b6222a68 100644
--- a/wntr/tests/test_network_controls.py
+++ b/wntr/tests/test_network_controls.py
@@ -102,13 +102,13 @@ def test_time_control_open_vs_closed(self):
                     link_res["flowrate"].at[t, "pipe2"], 150 / 3600.0
                 )
                 self.assertEqual(
-                    link_res["status"].at[t, "pipe2"], self.wntr.network.LinkStatus.open
+                    link_res["status"].at[t, "pipe2"], self.wntr.network.LinkStatus.Open
                 )
             else:
                 self.assertAlmostEqual(link_res["flowrate"].at[t, "pipe2"], 0.0)
                 self.assertEqual(
                     link_res["status"].at[t, "pipe2"],
-                    self.wntr.network.LinkStatus.closed,
+                    self.wntr.network.LinkStatus.Closed,
                 )
 
 
@@ -147,14 +147,14 @@ def test_close_link_by_tank_level(self):
                 self.assertAlmostEqual(link_res["flowrate"].at[t, "pump1"], 0.0)
                 self.assertEqual(
                     link_res["status"].at[t, "pump1"],
-                    self.wntr.network.LinkStatus.closed,
+                    self.wntr.network.LinkStatus.Closed,
                 )
                 count += 1
             else:
                 self.assertGreaterEqual(link_res["flowrate"].at[t, "pump1"], 0.0001)
                 self.assertEqual(
                     link_res["status"].loc[t, "pump1"],
-                    self.wntr.network.LinkStatus.open,
+                    self.wntr.network.LinkStatus.Open,
                 )
         self.assertEqual(activated_flag, True)
         self.assertGreaterEqual(count, 2)
@@ -229,20 +229,20 @@ def test_open_link_by_tank_level(self):
                 self.assertGreaterEqual(results.link["flowrate"].at[t, "pipe1"], 0.002)
                 self.assertEqual(
                     results.link["status"].at[t, "pipe1"],
-                    self.wntr.network.LinkStatus.open,
+                    self.wntr.network.LinkStatus.Open,
                 )
                 count += 1
             else:
                 self.assertAlmostEqual(results.link["flowrate"].at[t, "pipe1"], 0.0)
                 self.assertEqual(
                     results.link["status"].at[t, "pipe1"],
-                    self.wntr.network.LinkStatus.closed,
+                    self.wntr.network.LinkStatus.Closed,
                 )
         self.assertEqual(activated_flag, True)
         self.assertGreaterEqual(count, 2)
         self.assertEqual(
             results.link["status"].at[results.link["status"].index[0], "pipe1"],
-            self.wntr.network.LinkStatus.closed,
+            self.wntr.network.LinkStatus.Closed,
         )  # make sure the pipe starts closed
         self.assertLessEqual(
             results.node["pressure"].at[results.node["pressure"].index[0], "tank1"],
@@ -277,7 +277,7 @@ def test_pipe_closed_for_low_level(self):
                 self.assertLessEqual(results.link["flowrate"].at[t, "pipe1"], 0.0)
                 self.assertEqual(
                     results.link["status"].at[t, "pipe1"],
-                    self.wntr.network.LinkStatus.closed,
+                    self.wntr.network.LinkStatus.Closed,
                 )
                 tank_level_dropped_flag = True
         self.assertEqual(tank_level_dropped_flag, True)
@@ -384,7 +384,7 @@ def test_open_by_time_close_by_condition(self):
         inp_file = join(test_datadir, "control_comb.inp")
         wn = self.wntr.network.WaterNetworkModel(inp_file)
         control_action = self.wntr.network.ControlAction(
-            wn.get_link("pipe1"), "status", self.wntr.network.LinkStatus.opened
+            wn.get_link("pipe1"), "status", self.wntr.network.LinkStatus.Opened
         )
         control = self.wntr.network.controls.Control._time_control(
             wn, 6 * 3600, "SIM_TIME", False, control_action
@@ -432,9 +432,9 @@ def test_close_by_condition_open_by_time_stay(self):
         tank1.init_level = 40.0
         tank1._head = tank1.elevation + 40.0
         pipe1 = wn.get_link("pipe1")
-        pipe1._user_status = self.wntr.network.LinkStatus.opened
+        pipe1._user_status = self.wntr.network.LinkStatus.Opened
         control_action = self.wntr.network.ControlAction(
-            wn.get_link("pipe1"), "status", self.wntr.network.LinkStatus.opened
+            wn.get_link("pipe1"), "status", self.wntr.network.LinkStatus.Opened
         )
         control = self.wntr.network.controls.Control._time_control(
             wn, 19 * 3600, "SIM_TIME", False, control_action
@@ -482,9 +482,9 @@ def test_close_by_condition_open_by_time_reclose(self):
         tank1.init_level = 40.0
         tank1._head = tank1.elevation + 40.0
         pipe1 = wn.get_link("pipe1")
-        pipe1._user_status = self.wntr.network.LinkStatus.opened
+        pipe1._user_status = self.wntr.network.LinkStatus.Opened
         control_action = self.wntr.network.ControlAction(
-            wn.get_link("pipe1"), "status", self.wntr.network.LinkStatus.opened
+            wn.get_link("pipe1"), "status", self.wntr.network.LinkStatus.Opened
         )
         control = self.wntr.network.controls.Control._time_control(
             wn, 5 * 3600, "SIM_TIME", False, control_action