diff --git a/__init__.py b/__init__.py
index 15fb04108..724c0ddb3 100644
--- a/__init__.py
+++ b/__init__.py
@@ -91,6 +91,8 @@
     to_smiles,
     writepdb,
     yield_coords,
+    to_image,
+    get_reaction_image,
 )
 from scm.plams.interfaces.thirdparty.cp2k import Cp2kJob, Cp2kResults, Cp2kSettings2Mol
 from scm.plams.interfaces.thirdparty.crystal import CrystalJob, mol2CrystalConf
diff --git a/doc/source/examples/MoleculeFormats/MoleculeFormats.ipynb.rst b/doc/source/examples/MoleculeFormats/MoleculeFormats.ipynb.rst
index d68aa79f2..1e5fd8b9d 100644
--- a/doc/source/examples/MoleculeFormats/MoleculeFormats.ipynb.rst
+++ b/doc/source/examples/MoleculeFormats/MoleculeFormats.ipynb.rst
@@ -1,8 +1,5 @@
-Worked Example
---------------
-
 Complete guide to storing and converting PLAMS Molecules between Python libraries and file formats
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+--------------------------------------------------------------------------------------------------
 
 .. code:: ipython3
 
@@ -16,6 +13,7 @@ Complete guide to storing and converting PLAMS Molecules between Python librarie
     AMSHOME = os.environ["AMSHOME"]
     cif_file = f"{AMSHOME}/atomicdata/Molecules/IZA-Zeolites/ABW.cif"
     xyz_file = f"{AMSHOME}/scripting/scm/params/examples/benchmark/ISOL6/e_13.xyz"
+    badxyz_file = f"{AMSHOME}/scripting/scm/plams/unit_tests/xyz/reactant2.xyz"
     
     assert Path(cif_file).exists(), f"{cif_file} does not exist."
     assert Path(xyz_file).exists(), f"{xyz_file} does not exist."
@@ -140,12 +138,6 @@ library, for example ASE or pymatgen.
     type(mol)=<class 'scm.plams.mol.molecule.Molecule'>
 
 
-.. parsed-literal::
-
-    /home/user/adfhome/bin/python3.8/lib/python3.8/site-packages/ase/io/cif.py:401: UserWarning: crystal system 'orthorhombic' is not interpreted for space group Spacegroup(74, setting=1). This may result in wrong setting!
-      warnings.warn(
-
-
 
 .. image:: MoleculeFormats_files/MoleculeFormats_15_2.png
 
@@ -381,6 +373,78 @@ Convert RDKit Mol to PLAMS Molecule
 .. image:: MoleculeFormats_files/MoleculeFormats_36_1.png
 
 
+Convert problematic PLAMS Molecule to RDKit Mol
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code:: ipython3
+
+    mol = Molecule(badxyz_file)
+    mol.guess_bonds()
+    plot_molecule(mol)
+
+
+
+
+.. parsed-literal::
+
+    <AxesSubplot: >
+
+
+
+
+.. image:: MoleculeFormats_files/MoleculeFormats_38_1.png
+
+
+This molecule will fail to convert to an RDKit Mol object, because RDKit
+does not like the AMS assignment of double bonds.
+
+.. code:: ipython3
+
+    try:
+        rdkit_mol = to_rdmol(mol)
+    except ValueError as exc:
+        print ("Failed to convert")
+
+
+.. parsed-literal::
+
+    [13.12|17:47:23] RDKit Sanitization Error.
+    [13.12|17:47:23] Most likely this is a problem with the assigned bond orders: Use chemical insight to adjust them.
+    [13.12|17:47:23] Note that the atom indices below start at zero, while the AMS-GUI indices start at 1.
+    Failed to convert
+
+
+.. parsed-literal::
+
+    RDKit ERROR: [17:47:23] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14
+    [17:47:23] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14
+    
+    RDKit ERROR: 
+
+
+The problem can be fixed by passing the argument ``presanitize`` to the
+``to_rdmol`` function.
+
+.. code:: ipython3
+
+    rdkit_mol = to_rdmol(mol, presanitize=True)
+    rdkit_mol
+
+
+.. parsed-literal::
+
+    RDKit ERROR: [17:47:06] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14
+    [17:47:06] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14
+    
+    RDKit ERROR: 
+
+
+
+
+.. image:: MoleculeFormats_files/MoleculeFormats_42_1.svg
+
+
+
 SCM libbase UnifiedChemicalSystem Python class
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
diff --git a/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_38_1.png b/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_38_1.png
new file mode 100644
index 000000000..69d6cc59e
Binary files /dev/null and b/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_38_1.png differ
diff --git a/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_42_1.png b/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_42_1.png
new file mode 100644
index 000000000..2499c7b76
Binary files /dev/null and b/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_42_1.png differ
diff --git a/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_42_1.svg b/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_42_1.svg
new file mode 100644
index 000000000..8e31d1ee9
--- /dev/null
+++ b/doc/source/examples/MoleculeFormats/MoleculeFormats_files/MoleculeFormats_42_1.svg
@@ -0,0 +1,43 @@
+<?xml version='1.0' encoding='iso-8859-1'?>
+<svg version='1.1' baseProfile='full'
+              xmlns:svg='http://www.w3.org/2000/svg'
+                      xmlns:rdkit='http://www.rdkit.org/xml'
+                      xmlns:xlink='http://www.w3.org/1999/xlink'
+                  xml:space='preserve'
+width='250px' height='250px' >
+<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='250' height='250' x='0' y='0'> </rect>
+<path d='M 185.034,136.645 192.405,121.229' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 192.405,121.229 199.775,105.812' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 185.034,136.645 150.732,131.396' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 180.366,138.771 187.005,153.346' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 187.005,153.346 193.643,167.92' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 189.702,134.519 196.341,149.093' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 196.341,149.093 202.979,163.668' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
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+<path d='M 133.066,72.5499 126.639,86.772' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 132.621,193.785 120.807,169.3' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 67.8752,190.072 79.5165,167.358' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 121.964,84.6594 137.538,120.597' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
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+<path d='M 58.4518,124.99 79.5165,167.358' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 79.5165,167.358 120.807,169.3' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 86.1922,157.401 115.096,158.761' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<path d='M 120.807,169.3 136.634,139.405' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />
+<text x='195.878' y='105.812' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>
+<text x='194.221' y='182.892' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>
+<text x='55.7416' y='60.7117' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>H</tspan></text>
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+<text x='129.335' y='210.883' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>H</tspan></text>
+<text x='56.0838' y='207.17' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>H</tspan></text>
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+<text x='132.494' y='139.405' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>C</tspan><tspan style='baseline-shift:super;font-size:12.75px;'>-</tspan><tspan></tspan></text>
+</svg>
diff --git a/doc/source/examples/PlotReaction2D/PlotReaction2D.common_footer.rst b/doc/source/examples/PlotReaction2D/PlotReaction2D.common_footer.rst
new file mode 100644
index 000000000..dca217012
--- /dev/null
+++ b/doc/source/examples/PlotReaction2D/PlotReaction2D.common_footer.rst
@@ -0,0 +1,5 @@
+Complete Python code
+--------------------
+
+.. literalinclude:: ../../../../examples/PlotReaction2D/PlotReaction2D.py
+    :language: python
\ No newline at end of file
diff --git a/doc/source/examples/PlotReaction2D/PlotReaction2D.common_header.rst b/doc/source/examples/PlotReaction2D/PlotReaction2D.common_header.rst
new file mode 100644
index 000000000..c569e9132
--- /dev/null
+++ b/doc/source/examples/PlotReaction2D/PlotReaction2D.common_header.rst
@@ -0,0 +1,4 @@
+To follow along, either
+
+* Download :download:`PlotReaction2D.py <../../../../examples/PlotReaction2D/PlotReaction2D.py>` (run as ``$AMSBIN/amspython PlotReaction2D.py``).
+* Download :download:`PlotReaction2D.ipynb <../../../../examples/PlotReaction2D/PlotReaction2D.ipynb>` (see also: how to install `Jupyterlab <../../../Scripting/Python_Stack/Python_Stack.html#install-and-run-jupyter-lab-jupyter-notebooks>`__ in AMS)
diff --git a/doc/source/examples/PlotReaction2D/PlotReaction2D.ipynb.rst b/doc/source/examples/PlotReaction2D/PlotReaction2D.ipynb.rst
new file mode 100644
index 000000000..c864872e2
--- /dev/null
+++ b/doc/source/examples/PlotReaction2D/PlotReaction2D.ipynb.rst
@@ -0,0 +1,79 @@
+Worked Example
+--------------
+
+Creating 2D images of molecules
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: ipython3
+
+    from IPython.display import SVG
+    from scm.plams import ReactionEquation
+    from scm.plams import from_smiles, to_image, get_reaction_image
+    
+    aspirin = from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")
+    text = to_image(aspirin)
+    SVG(text)
+
+
+
+
+.. image:: PlotReaction2D_files/PlotReaction2D_1_0.svg
+
+
+
+It is also possible to write the image to file in a range of different
+formats (SVG, PNG, EPS, PDF, JPEG)
+
+.. code:: ipython3
+
+    text = to_image(aspirin, filename="aspirin.svg")
+    text = to_image(aspirin, filename="aspiring.png")
+
+Creating 2D imageas of reactions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+We can have aspirin react with water to form acetic acid and salicylic
+acid
+
+.. code:: ipython3
+
+    acetic_acid = from_smiles("CC(O)=O")
+    text = to_image(acetic_acid)
+    SVG(text)
+
+
+
+
+.. image:: PlotReaction2D_files/PlotReaction2D_6_0.svg
+
+
+
+.. code:: ipython3
+
+    salicylic_acid = from_smiles("O=C(O)c1ccccc1O")
+    text = to_image(salicylic_acid)
+    SVG(text)
+
+
+
+
+.. image:: PlotReaction2D_files/PlotReaction2D_7_0.svg
+
+
+
+We can create a 2D image of the reaction as well, and optionally store
+it to file
+
+.. code:: ipython3
+
+    reactants = [aspirin, from_smiles("O")]
+    products = [acetic_acid, salicylic_acid]
+    text = get_reaction_image(reactants, products, filename="reaction.svg")
+    SVG(text)
+
+
+
+
+.. image:: PlotReaction2D_files/PlotReaction2D_9_0.svg
+
+
diff --git a/doc/source/examples/PlotReaction2D/PlotReaction2D.rst b/doc/source/examples/PlotReaction2D/PlotReaction2D.rst
new file mode 100644
index 000000000..2b875965c
--- /dev/null
+++ b/doc/source/examples/PlotReaction2D/PlotReaction2D.rst
@@ -0,0 +1,11 @@
+.. _PlotReaction2DExample:
+
+PlotReaction2D
+==============================
+
+..
+   Feel free to modify the above label, header, and add any custom information here
+
+.. include:: PlotReaction2D.common_header.rst
+.. include:: PlotReaction2D.ipynb.rst
+.. include:: PlotReaction2D.common_footer.rst
\ No newline at end of file
diff --git a/doc/source/examples/PlotReaction2D/PlotReaction2D_files/PlotReaction2D_1_0.svg b/doc/source/examples/PlotReaction2D/PlotReaction2D_files/PlotReaction2D_1_0.svg
new file mode 100644
index 000000000..c3610edb0
--- /dev/null
+++ b/doc/source/examples/PlotReaction2D/PlotReaction2D_files/PlotReaction2D_1_0.svg
@@ -0,0 +1,32 @@
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+<text x="942.883" y="39.4025" style="font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000"><tspan>OH</tspan></text>
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+<text x="180" y="55" font-size="16">+</text>
+<text x="780" y="55" font-size="16">+</text>
+<defs> <marker id="arrow" viewBox="0 0 10 10" refX="5" refY="5" markerWidth="6" markerHeight="6" orient="auto-start-reverse"> <path d="M 0 0 L 10 5 L 0 10 z"/></marker></defs>
+<line x1="460" y1="50" x2="540" y2="50" stroke="black" marker-end="url(#arrow)"/>
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diff --git a/doc/source/examples/examples.rst b/doc/source/examples/examples.rst
index 351f43872..7dff22cc9 100644
--- a/doc/source/examples/examples.rst
+++ b/doc/source/examples/examples.rst
@@ -36,6 +36,7 @@ Molecule analysis
    PlotCorrelation/PlotCorrelation
    MapMoleculesAndConvertToDCD
    HydrogenBondsFromMD
+   PlotReaction2D/PlotReaction2D
 
 MD trajectory analysis
 ----------------------
diff --git a/examples/MoleculeFormats/MoleculeFormats.ipynb b/examples/MoleculeFormats/MoleculeFormats.ipynb
index 64e0143c6..cd5991943 100644
--- a/examples/MoleculeFormats/MoleculeFormats.ipynb
+++ b/examples/MoleculeFormats/MoleculeFormats.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 20,
    "id": "f609f4c1-5dab-4349-9250-d4bb89ddb656",
    "metadata": {},
    "outputs": [],
@@ -25,6 +25,7 @@
     "AMSHOME = os.environ[\"AMSHOME\"]\n",
     "cif_file = f\"{AMSHOME}/atomicdata/Molecules/IZA-Zeolites/ABW.cif\"\n",
     "xyz_file = f\"{AMSHOME}/scripting/scm/params/examples/benchmark/ISOL6/e_13.xyz\"\n",
+    "badxyz_file = f\"{AMSHOME}/scripting/scm/plams/unit_tests/xyz/reactant2.xyz\"\n",
     "\n",
     "assert Path(cif_file).exists(), f\"{cif_file} does not exist.\"\n",
     "assert Path(xyz_file).exists(), f\"{xyz_file} does not exist.\"\n",
@@ -56,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 21,
    "id": "9ad1c2c4-29e9-4320-bf8b-1df2df7b7831",
    "metadata": {},
    "outputs": [
@@ -69,14 +70,12 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -100,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 22,
    "id": "8f21e769-031a-414a-ac65-cb5cfe5a193b",
    "metadata": {},
    "outputs": [
@@ -137,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "id": "c50ddd01-c601-4bb1-855c-5c57370123b7",
    "metadata": {},
    "outputs": [
@@ -150,14 +149,12 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -181,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "id": "01e5758f-cefa-4d80-8c75-2ec08fc0ff33",
    "metadata": {},
    "outputs": [],
@@ -192,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "id": "e6299460-fde0-4f9f-9df2-5d9cdf61a3fa",
    "metadata": {},
    "outputs": [
@@ -232,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "id": "bd212e28-b10d-46c3-b1e8-e5022d19efee",
    "metadata": {},
    "outputs": [
@@ -243,24 +240,14 @@
       "type(mol)=<class 'scm.plams.mol.molecule.Molecule'>\n"
      ]
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/user/adfhome/bin/python3.8/lib/python3.8/site-packages/ase/io/cif.py:401: UserWarning: crystal system 'orthorhombic' is not interpreted for space group Spacegroup(74, setting=1). This may result in wrong setting!\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -287,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "id": "4de4e588-9f26-48b8-a306-c7ad498ef511",
    "metadata": {},
    "outputs": [
@@ -322,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 28,
    "id": "2026498d-f78e-4337-993a-109a05094a8d",
    "metadata": {},
    "outputs": [
@@ -353,20 +340,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 29,
    "id": "1846a459-afdc-48d6-98c6-3741c41d1453",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -397,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 30,
    "id": "13ba6b79-71e3-4cdb-bd9c-cebbfb25c9e6",
    "metadata": {},
    "outputs": [
@@ -436,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 31,
    "id": "9d4538fe-ebf2-483e-9cb9-2bb79e74265d",
    "metadata": {},
    "outputs": [
@@ -449,14 +434,12 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -488,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "id": "f475f91d-b65c-4566-a35d-dba3fd11c041",
    "metadata": {},
    "outputs": [
@@ -504,14 +487,12 @@
     },
     {
      "data": {
-      "image/png": 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GWIPLFcfVW7fI70ATrFFK9ieRyDW/2cz1mPsOG8vRzF29g27du7tNctz/GqGhoaRNn5HtBy1P792BuWt38fEnvZI8njt37leS87yM2Wxm7969No/tco8Xo8mEI0si3ZOSt+PjuZWIj75O4ra75g8exbPy730c+XGOq0X5zyKEoGfvvsxeOpu6lUq6WpxkuXT9NvsiT/PrH+2TbDNo0CCL/Tx+/JiGDRtalfDnRVw+4wjw8+ORA/tLzKryjMdC4O+msQgr/tpLrVo1yZPHs4KsXjfef/999kWe5pKb74UtWLuTDh06EhgYmKJ+PNaqUiQ4mBMOXFNKIKng4OMaQaH8OR02lqN4FK9n8tJN9O3n3nku/wsEBgbSqVNnxi/Y4GpRkuTWnVgW/r6Lnr16J9vObDZbtCJKKW0Kp3+GyxVHaI0ahDnQRJpTUTiTyDLFLCWHH8VTpkh+h43lKL6bsYqqNWpRp04dV4viBRg+4lu2h51k2/5jrhblFaSUfP6/JXTt9hFFihRJtu3HH3/M7Nmzk22TIUMGuzLOuVxxVKtVi7+tCMRJKXvNZrQSHrmZW/E/h0/x2+YDvNeqDTExMa4W5z/P48ePOXHiBF0/+pheo+YR98i9IpV/27Sfc9fuM3zEtxbbOjMDmMsVR6NGjTgvBEesKGmXEqbrdFSpX49PxyxyGyewR/F6Pvp6Ovn1Rv734YcUzJOHfFmy0LlNG/bt2+c2cr7uXL58ma+//JJSQUFkyZCBno0bs3/iRPwfJTBo/CJXi/ecm3fu89XE5cxfuNiqMPjX2hyr0+no3qsXExz0BS+rKjVeqsN6RVVZbzCwcNFiHpl1DJn8q8sfSoPRRKfPf6RxvJ6DQrDLZOKejw9bEhIo+fvvvN+gARWKFWPPnj0ulfN15vr167Rt1owyhQsTO3Uqc2JiuOfryyGzmfVGI4cQhG8PZ9riP10tKrEPH9Om/2R69u5DBSs9i0eOHGkxBWVMTIzV/b2IyxUHQN/PP+dvHx+2O8BUagCuvbDZKqWku6Lw2RdfkC1bNtZv2MjOyAuMmr3WZcrDYDTRbdAkAk9fYob4f+c3RQgKKQr9tVrOAAMvXuTdBg34onfvJJO1eLGPxYsWUaZwYQpt3colrZZJQlBRo8H3BR+adEKwSdEyY/Ya5q/a6jJZHzyKp93AKVSpVZ9vhg23+rzcuXOTMWPGZNsYjUauXrU9+NMtFEfGjBmZsWABHyoKcSl8mF82x841m7meIwdfDRv2fKzNW7axcf9pl8w8Hifoeb/veGTkGZYJDdoknL0UIWit03FUCM4vXEijmjWJi4tLVVlfR6SUDBkwgO8++YSNqsp3ikKaZBzu8ikKWzRafpq0nMkL16eipE+4G/uQFp/+SPGylZkydZrDnQM91hz7jKZNm9K0fXveFoJHKXiYcwrB9KebrRtMJr7Salm8ejW6FwLfsmfPzo6du4k4f5d3P5/I5Rt3Uiy/NUScvED99kPJfvoivwkNflbcBFkUhZVSUuTkSd6uV88780ghI4YMYf2MGewGylkZ6lBQUdil0fLLvN/pOnAid2MtVxN0BDsOnqD2h99Rr3FzZs6abfMDPmnSJItL3cyZMzNnju1Oh26jOAAmzJhBcNOm1BeCK3b6dqQRgnoaDfONRjprNKz9+29KlCjxSruMGTOyfccu6jR5hzoffsfCtTucNvvQG4yMnLKc1j1GMfjuAxYKDTob3hyKEMwAspw6xZD+/Z0i43+Bv/76i7mTJvGXlGS18SHMqyiEabTkCTtJ1fcG8vv2cCdJ+WRp8vm4xfT6YREzZs1jzNhxds00du/ebdHU6u/vz5tvvmlz326lOBRFYfbixbz12WeUVVXmmUw2P8w3VJWWisL/cuZky549VK5cOcm2Op2OoUO/Zuv2ncz7M5x3PpvAnohTDlMgJpOZP3aEU6ftV5xavZ0IjY4OOp1dN4EiBD+rKsvmz+eff/5xiHz/JWJjY/moQwdmqyrZ7dyIDxCCCRotK0ySESNm0vmLn4g8ddFhMiboDSz78x+qfTAcmTYXR4+foHHjxnb358xKbimqVi+E+AzoxhOHzaNAFyllkjHytlSrj4iI4MM2bTDduMEnBgMdtFrSJvEjSCmJUFWmazQsNxioXL0669avtylzs9FoZMaMGUyZPBEtZrq2qEnrRlVIG+hvdR/PuHH7PgtXb2PBii3kV1U+N6m8o9U65CKuMBr5IW9ewk+d8gbD2cCQgQO5Pn06cx30mz2Wkp9MJn4Wkhw5s9C1YxNa1K1gV3mFC1dvMW/NTn7ZsJuyZcsxYNBg6tevn2IZlyxZQunSpSlVqlSSbe7cucPChQv57LPPXjmWXLV6uxWHECI3sBsoLqWMF0L8CmyQUs5P6hxbFAc8UQjbtm1j2tix/Ll1K4UCAgiVkrwJCWiBh4rCUX9/wvV6fAMC6N6nD1WqV+ezzz4jMjLSru/1bMwpkyawbdt2QksWpEyhPJQpko+QokHky5nlXw+s2awSfekGEScvcPjUJXaFHefKpRu01unoJaGMg1MGqFJSGFj09992Zaf+L6LX68mXLRs7zWaKONivwSQlG8xmpmgEe8xmqpYrTrli+Qkpkp+QokHkzPpvq4bRZOLk+WtEnLxA5OnLRJy8xPkrN/mgUyc+6dmLQoUKOVQ+S5w+fZqmTZty+vTpV44lpzhSGh2rBfyFEEYgAHBo3jUhBHXr1qVu3bokJCRw5MgRwsPDuXnjBvEGA+nSpqVbsWJMDw0lb968CCE4fPhwipxeXhzz1q1b7N+/n7CwMH7dtZ8vJ64g5s4d/P18n6SlNxhJ0BvImycXoeXKoWjTEXD9Lpd9fJOcHaUURQg+MRqZ/r//UWXlSqeM8bqxevVqSisKRZywh6UVgmZaLc2AEaqZzXoNIksR5m7cz6EfFvDg4SP8fH3QabUk6A3oDUaC38hPaGh5yldpTMfeoVSoUAF/f9tnto7AXquK3YpDSnlVCDEeuATEA39LKV9JNySE6A50B8iXL5+9w+Hn54eqqrRq1YosWZKuzpY+fXoaNGhg9zgvki1bNt5++23efvvt55/p9XoSEhIwGAz4+vri7+//3GLTpU0buppMpPVxbgRuC42GCdu2OXWM14ktf/xBi/h4cPJ16aLVMuPMGXYeOIAQAinl8/vFaDTi5+eHv78/Wm3qZLM4ePAg+fLlI3v27Em2SZMmDY0aNbK575QsVTICvwFtgPvACmCllDLJ0lG2LlVepm7dugwdOpS6deva3YczKRUUxLyYGKdnNJNSkslo5NSlSxYTtXiBcgULMu36dSqnwnXJZjYTceYMuXPndupY1vDWW2/Rs2dP3nrrLbvOT26pkpIFX33gvJQyRkppBFYBVVPQn0Wc6XufUsxmM6euXKFUKsgnhKB0QABHjx51+liejpSSYxcuUDqVrksZf3+3uS7uGqtyCagshAgQT3YL6wFRjhErcbJkyWKxpF18fDwXLzrORGYt8fHx6BTlXy7LziSDEDZnbfovotfrQUoCUuu6SOk21yVz5swW904SEhK4cOGCzX3brTiklPuBlcAhnphiFWCmvf1Zw8qVK5P1ywA4dOgQ7dsnnU7NWQghUFPRfV0Ft519uROKopCaQQXudF0WL15M7dq1k21z4sQJ3nnnHZv7TtEujZRyGDAsJX04GlctZ/z9/VEUhVgpSZ8Kb7ebqkrmzJmdPo6no9Pp8NPpuCOlw2r3JMdN8Kjr4vGxKo7Cmd5yyaEoCmUKFeKQk/OKwJNM7scePqRMmTJOH8vTEUJQtmjRVLkuZimJfPiQsmXLOn0sR2Hv8+LyLOe2UK9ePcaMGUP58olu9AJPamNs3rw5FaX6f0KrVuVgdDTOTgB4VFXJnyMHadKk4f79+xw9epS4uDgSEhJQFAU/Pz+yZctGyZIlbfKe9UQeP37MkSNHuHv37vMAQH9/fzJmzEjp0qUJDAwktFo1Dp48iWOM9ElzQlXJmTkz6dOnd/JI1vHWW28xePBgqlevnmSb8uXL2xXC4FGKIy4uzmIciaIo+DjZXp8UzVq1ov/y5Qxw8qxnoaqi+PkR/EZ+bsXcpnjB/GRIF4ivTotEkqA3cj3mHucuXadI4YKEli9PpcpVeffdd8mUKZPD5Lh79y47d+4k/MABIv75h/v37gGQIWNGQqpVI7RiRWrWrOnQMa9cucLq1as5uH8v4eHhnL9wkSIF8pE1Y9rn7t4JegO37z/g5NlLBOXPR+48ebkoBIOldOp1WSwEzd57z2n924oznxePUhzubI6FJzOihLRp2XP3LtWc5OTzWErmGQ0MbBJK/SqlKZQvJxpN4r/J4wQ9x85cJuLkBTaunM+A/p/TokULevf5NNlZmyXCw8OZMm4ca9aupbK/PxUSEvjwhT2EO1Jy+OBBZvr50SU+nhbNm9N7wAC7S1tKKdm6dStTJ09k+/YdvFWrHBWKB9G1QXuKB+fBR5f4b20wmog6d5WIqPN8v2cPO81majnpuiRIyTxVZc+nnzqlf3tw5vOSoiA3W0mpA1h4eDiFCxdONp37vn37WLx4MVOmTLF7nJQwbcoUVnz1FVukRHHC222kycj+UsEsnjTA5nNv34tj8R+7mbd2J1mz5WD8jxOoVauW1ec/ePCAgX378vuKFfRVVbooisXw9BhVZZ6qMklReLtVK8ZOmmRTOv6VK1cy5Ksv0WKmW8uatHrTvsDD+au2smLKCnYqGqdclzFmM7srVeL3ra7LFPYyhw8fpkCBAskunQ4fPsy0adOYNWvWK8ec5QCW6oSGhlq86e7fv090dHQqSfQqH3/yCQlBQcxwwmbcUbOZiUhGD/nQrvOzZExHv45NOLR8FH3eq0b7Nq3o3asnjx5ZLokVGRlJ6UKF0P/2G8eEYKBWa1VOi6yKwkCtlmNCoP/tN0oXKkRERITF82JiYmj13jt8NfAzxn36Lv8sHMaH79S1S2kAdGxeG3OerExywnU5YTYzXgimzJvn8L5TQtmyZS3ut8TFxSUa4GYJj1Ic1uDq5YxGo2Her78yTFE46MCb9J6UtEPlm37tyJM9ZeY+jUahed0K7Fk0nDsXj1G6ZHF27NiRZPuwsDAa1qjB6Lg45vLE+cxWMgjBXGB0XBxv1qxJcjPPlStXUqpEcXL6G9g57xtqlS+e4r0JjUZh6siejJRm9jnwutyXknaKwshx48if3/1q9ljCbDa//ubYkSNHcv369WTblCpVin79+qWOQElQtGhR5vzyC28DYQ64Se9ISUPVTK3G1ejYzPqlhSUypAtkxtdd+b5XS9q0epc5c14t3nPp0iXerl+fmUYjbRywP9BGq2Wm0UjT+vW5dOnSv45JKfl66BAGffEpi0b14LverfD3c9xGd3C+HEz7vifNVVOShclt4Z6UNBaCWu3b071HDwdI6FjGjBnzym/8MkWLFmXAANuXvR6lOBYvXsz9+/eTbZM3b14aNmyYOgIlQ7NmzZi5dCmNhWCayWS3V+lOk4mKZiPVmtXg+/4dnGIVaFyjLOunDuDbYUOZOHHC88+llHRr357eRiPNdY4rDd5cp6Ov0Ui39u2f7/pLKfn8s36s+20Zf834koqlCjpsvBd5s1oIk7/vSVPVzCST0e7rsttkoiJQ9YMPmDhjhlsmVVq2bBm3bydfAzdXrlw0adLE5r49SnE4szKVM2jWrBk7Dx5kYXAw9cWTRC/WbkafV1V6mk201SqMHNGDEf3aO/XmLJgvB+unDuTHcaOfzzzmzZnD3SNHGOSE33ygonD3yBHmPi1R+PXQIWz/ez3rJn9B1kzpHD7ei7xZLYQ/FwxnSe6s1JZmdtuQovKiqtJHVWnt68u4RYv435Qpbqk0wLnPi0eZY1u1akWGDBmSbXPmzBlOnTpF06ZNU0coCxQrVox/IiKYPnUqnUaPJuDRIzrq9VRQFMpqNKR7etMZpOS4qhJmNrNGp2G/aqbtW9XZ81FLMqZ/tRauM8ibIzOrfvqMpr2/JF++/Iz6+msWms1onRCOrhWCSWYzH3zzDVIIfl26iD+nDSRD2pRVX7eWQvlz8ueCEcxfvY0PF/yB7+MEOhpVKioK5V64LkYpOaaqhJvNrPPz4x9V5YMPPuDod9+5vWt5y5YtLcp4/vx5IiMjLRZuehmPMsdaw8qVK1m2bBkr3TA7lqqqbN68mXW//kr43r0ciY5GqiqqyYSqCIKzZiSkeAGqVyvDO/UrEuDnGq/PTXuO0HfMIrI+TiDCiU5TUkrKCcE5KflzxiBKFMzrlHGskWPHwROs33KAyCPRHL18A7PJ/CTLm6pSKFcuypUvT52mTWnTpg2Bgamj3FKD9evXM23aNNavf7VmjDNTB7odrraqJIeiKDRs2PD5HsyjR48oG1Kaz9rWpVWjyuhSKTOUJRpULU2NkELc3BmO0DrPC1cIQTeDgTk5M7tMaTyTo3bFEtSu+KSMhqqqjJv3O/9E3WLTlq0u80RODf4TQW6HDx9+kl8hGbJmzUrJkiVTSaKUMXzYNxQPykb7ptXdRmk8Y2z/9znto2OLA8pyJkcNjYYHDx9bbpiKKIrCF52a8jg2JlFLk6cQGRlpsYBXpkyZKF26tM19e9RSJW/evOzZs4e8eV33dnIUly5domyZ0uz/5Tunbwbay4adh/lhxEwihcZpyxWTlKRPiOfkX1NIZ6dzl7OIOneVpr3HcenyVQICAlwtjs0ULlyYP/74g8KFC9t1/mvjOerOyxBb+XnGDFq9WdltlQZAo+plSAjwY48TQ9K1QpDH14drt+46bQx7KVYgN+VLBLN8+XJXi2IX7po6MNXx9fW1+EMkJCTw8GHq1Pa0F4PBwJzZs+ja0nHOXM5AURS6tHuTqRrnmht9hMBocn6+DHvo2rImUydPdLUYduHj44PGgkVMr9fblerQoxTHuXPnyJkzZ7Jtli1bRu/evVNJIvtYtWoVRd/IReGgXK4WxSLtm9bgT6ORm3bW8rWGBCnxTSLC1dXUq1SKOzG3OHDggKtFsZkTJ07wxhtvJNtmzZo1dOvWzea+PUpxWIOrMoDZwoJ5c/jgbacmhHcYGdIF0qR6WZY7aZM0QUqu6g3ky5l0rRxXotEodHy7Ggvmu1cAm6P4T1hVrMHdvUullBwMC6NqSBFXi2I1VSqXZL+TZgRHVJWC2TLZVXM1tahapjAH9u91tRhOwd7nxX2fsEQIDQ0lNjY22TZt27Zl/PjxqSSR7Vy6dAmdVvNKTVF3JqRoEOHSOUuVLaqZ8iH27fqnFqUK5+NE1CmMRqOrRbGJqlWrcvPmzWTbtGjRgsmTJ9vct0cpDmvybAQGBpIxo/s+lGFhYYQULeBqMWyi6Bu5uKw38MDBpnuzlMwSgg7v1nNov44mTYAfeXNl5/jx464WxSbOnTtnMQYnICDArtSOHqU4XgdzbHh4GGUKu748oC3otFqK585GhIPNsuvNZjJkzUC54slv4LkDIUXyER4e7moxbMJrjn3K4sWLLVamWrduHdOmTUsliWznxrVr5M7mvjOipMidPRM3HDjjeCAln6LylZOjfh1F7qwZuHHjhqvFsIm5c+dazAC2ceNGfvrpJ5v7dk8bWBI0b97cYpsLFy64NHWgJRIS4vHz8TzF4evnQ4ID++uvmqlePYQGVW13d3YFvj5ai+7b7oY1EeKXL1/mxIkTNvftUYrDGtx9OWNvqjZXoyoKjjLIjjUa2ZY+gE0DP3BQj85HowiMTo7bcQX/CXPsd999h9nCOrtatWq8/fbbqSSR7fj5+aE3eNbuPECcwcgfqoo5BcsVs5QMNRoZL2D1zCGplnvDESQYTB4Xr/LDDz+QkJD8PLFSpUp21Y71KMUxYsQIi7vEFSpUoF49992lT5suPbFuFg1qDUZV4XS+fNQQglN2eJGeUlVqCMGqbNmoX7dCihMupzaxjxJsKuvgDowaNcqiCTkkJIQ333zT5r49SnG4+zLEGkqXCeHY2WuuFsMmpJQcj77EH5s38/7IkVSVkk5CsN9CKkQpJfvNZjoJQVUpeX/kSKbMm8fZq8nnwXRHjp+9Zlf4uSvxWlWe0rx5c4s78MePH+fw4cOpJJHthIaGEnEq+czT7sa1mHtICfny5aNX376cvnSJUkOG0D5tWoKAd7VaRhoMTH/6Z6TBwLtaLUFA+7RpKTVkCKcvXaJX376EhoZy/PQFTG4a1JYYZrPKkZPnKFeunKtFsYmmTZuitZDn5eTJk8mWqkgKj9ocXb16tcU269atIy4uzm0rhpcsWZLzl67zOEHvstSAthJx8gLlyoU8V9qZM2em/8CBfN6/P2fPniU8PJyIsDCuxMQAkCFrVlqVL8/o0FCCg4P/9dZLnz49OXNk5/TF6xQPzuOS72Mr0ZdukDVrFrd2LEwMa9IBbNy4kQsXLthcEtSjFIc1uPtyxtfXl2JFC3Hk1CUqlylk07m37sRy+OR5IqMucPXyTYxGE75+PhQIzkNI0SDKFA1ySjKcw1EXCK1Q6ZXPFUWhUKFCFCpUiLZt21rdX/ny5TkUdT5VFIfJZCbq/FUiTl7geNR5HsY+AiHImDkdpYq9QUjRIArmy5HsPXMo6jyhHjbbsBZ7n5cUKQ4hRAZgNlASkMCHUkqnRANJKTl37hzBwcHJtsuSJQsGg8EZIjiMps1asHLzPqsUh8lkZuM/EcxZtIHDpy9Rzs+H8kYzVVSJTgjipSRqxyG+1yicSDDQqGppurZvRMVSBR3iWKWqKr9tPsiS5V+muK9nvN28JbMnj6VD0xoO6/Nlrt66y/zftrJo1VbSq5JQRaGswURGntyot4C/Nuzhe5MZbaAfnds2pMPbNRPNKL9ycxgdu7tPMWlriY6OJjg4ONn7IHPmzKh2bHanKHWgEGIBsEtKOVsI4QMESCnvJ9U+JakDExISyJAhg0Xzkidw9epVSpUozpFVY5KthRpx8gK9hkwjTexD+phU3tNq8U3mJrgrJfNNJqYpEFw0iAnDPyZXCr1UN+89wsh5f3Mo4ojDPDz1ej358uRm/dT+FMqffH4VWzGaTPw4Zx0//7KRthoNPRGUSCaZjZSSfarKVAU2ms183acNnVrWef5dz12+ScOPR3P56jX8/PwcKqszeRb1mpI0E05JHSiESAfUBOY8FdSQnNJIKe6+BLGF3LlzU6dOHX7dmPjkTErJmBm/0brHKL68+4C9KLyv0yWrNAAyCcHnOh1RipYqJy9Sq+1gVm/anyJZ56zZRa8+nzrULdzX15cPu3Zj7pqk69Xaw7nLN6nXbiiHf91MpFbHVI02WaUBTzKcV9FoWCw07BAaFk1dwTsfj+LO/SdZseau2UHnLl08SmnAk3tICOE0d/6UPIkFgBhgnhDisBBithDiFY8eIUR3IUSYECIs5unmmT28TooDoFefvsxevfOVaaKUks+/m82WXzcTodHRQau1+eLrhGC4RstfKAz9fg7zfttil4wXr8Vw4MgZ2rdvb9f5ydHjk09Y/uceHj52zAzy5PmrvPXht3x0+z4bhEIeO+6VEhoN+4SG0OgrNOk8nIvXYlj25x4+6dnLITKmJs5OaGX3UkUIUR7YB1STUu4XQkwE4qSUXyd1TkqWKlJKHj9+bLEYzpAhQwgICGDIkCF2jZNaSCmpUrkirWoVp9u7dZ9/PmLiUvas3ckmRUNaCxf+qqqywWQiXFU5bDZzW0pUIK0QlFIUQjUaCgnBJ6iM+robzetVtEm+doOmULFWE0Z8+629XzNZOnZoTxrzPUb3s35jNTGux9yjfoevGWUw8YE2+Rq3qpRsN5v5x2wmXFU5parES4kOyK0olFMUKmg0RApYpNVQtUFDVvy2KkXyuQJrn5eRI0eSkJDAyJEjXznmrIJMV4ArUspnc+GVgON20F5CCGFVBS1PmZkIIZi/YBHVq1amfpVSBOXKyu5DJ1m+ejtHtLpklcYuk4kJRiPbTCaaaLVU1GjooNWSQ1EQQKyURD4tWzjRZCKLEPQeMYvyJQuSO7t1uReWb9zDlTuPWTt0qIO+8atMmDiZUiWK06x2Wbszokkp6Td8Jl2M5mSVxgMp+dloZLrBQDohaKjV0l6rpbiiECgERp7UhQ0zm/nFaOQfs5msQqBJveohDsXZz4vdT5iU8gZwWQjx7IrXA2wPs3MwnqI4AIoWLcrALwfTZ9QCHj6Op+83M5ghFDInoTRipaRrQgIdExKor9FwMU0aFvv709fHh+paLQUVheCntU+76HRM8fPjXGAg3/r6UsRkpl67rzhz8bpFua7H3OPrKStZsHCxU6uYZc6cmek/z6T3qAU8ik++0FZSLNvwD9ejzvO1kvRexlaTidKPHj1RCv7+HAoIYIyvL611OkpqNLyhKBRWFBpotQz29eX3gACOBgbSVqvlj9Wr6fHRR5hewwA3sP95SalVJYQn5lgf4BzQRUp5L6n2KVmq3L9/n7Zt27Jx48Zk2127dg2tVku2bNnsGie1MZvNVKtamWz+EsKjWCcSfwCOmM28HR9PY62Wcb6+FpcxL6NKySSDgW+RjP2qC60aJZ4s2WQy02bgZKrUeYvvEpm+OoP327dFlxDDhIEdbVqXm80qoW/345cEE1US2QSVUjLYYGCJ0chMPz8a21Et76Kq8r7BgFqsGOu2bCFLFvdMqvwy8fHxNGnShG3btiXb7lmOkRw5crxyLLmlisdUcrt16xYlS5bk1q1bDpbK9Zw/f55yhQqx0seHeonc3JFmMw3j45no60tbXfJreEscN5tppJoZ/Pn7vN+s5r+OqapKz+/ncc/oy7rf16dazdTY2Fhq1axOo4qFGNzNcs6VZ/y9J5Ix3/xMWCLKVkpJH72eMLOZDQEBZErBRqGUki+lZH22bOw4eNDtq9QDPHjwgFy5ctlVM+UZr0UlN09agtjK3bt3yeTnR91E3pq3VJUm8fFMcYDSgCeWg80aLd/9uJgdYf+/sjSbVfqOXsjle0ZWrV6bqoWW06dPz9+btrBmxxHGzP3dYgT0M375dTM9jInHvIw3GtlnNvN3CpUGPNkvGKMoNI2JoXn9+hZTO7gDzn5ePOZJTJcuHePGjbPYbtGiRfz111+pIJHj2Lt3L/UU5ZVpupSSnno9HXQ6WjlAaTyjiKIwV2jo+/UMHjyK5+HjBLp88zPXH8LGvza5JO9EtmzZ2L5zN3/sieLLCcswGC3vKRw8cY66L8zQDFJyyGxmREIC3+v1/ObvTzoHmiRHAT7nzvHj2LEO69NZ+Pn5MWHCBIvtli9fzu+//25z/x6jOAICAujYsaPFdgcOHOD06dOpIJHjCN+1i9BE3OTXmEycUFVGOOHt31irpWa8no++nEy1jsPJnLcI6zdsJE2aV12uU4scOXKwY+duLsdBvW6jOHL6YpJtb92J5XGCgQQp+cxsojwqGRPieT+NH7N8tPzg50d+B79xFSGYYzYzZuRIt05PCU+c7Lp06WKxXXh4OFFRUTb37zGKw1o8oZLby0RHRVEkkZt8rMHAaF9f/Jz0fX7UaNl+8AR5g97gw64fperyJCkyZszIH+v/5PMvv+bdzybyw+y1r8w+jCYTC9ftwMdHR10FtO/UYdT/+nFm0zTGD+9OWinpYcdGqDW8oSh8LAST3bh2jy3Y+7x4jOKIi4tj4cKFFtvVr1/fbUPqk0Kv1+P/0sU7ZDZzTUresuAynRKyKArvBvqTXkngw47tqFKpol2Jax2NEILOnTtzOPIIR648ovL73zDll7+4F/uQzXuPUO69Qfy95wijBn3A0T8nMbR3ayqVLkSgvy9zlvxJTxWnvjw+BhYvWsSjR4+cNkZKefz4MXPnzrXYrlatWlSsaL1j4DM8xqpy/vx56taty/nz5x0sleupXqoUo86epeYLb8lBej2+wLe+zs3Zsd1kon+2DGxa+j0L1u7k+1lr+GLAQPr3H2AxCUxqIKVk3759/PS/8Wzc+CcBfj78PKw7tSuWeKVtgt5AgXqfcM3Pn/ROnnU20mr5aNYs3n33XaeOYy/Xr1+nbNmyKSrp4LWquDn5CxTg7EsKPMxspqoTZxvPqKjREHX1FiazSpeWtdkyewh/rVlG1SqVOHPmjNPHt4QQgsePH7Nv317ebViFsF9HJ6o0AE6cvUKwn6/TlQZAlfh4wvbtc/o49qKqKhon3j8e8yT6+vpSoUIFi+2OHTvGlStXUkEixxFaqxbhL7zdpZSEm82EpoKiDBCCID9fTpx98pvlz5WV1RM+472axalVo7rL0zAuXbqU9m1aMWng+0wY9IHFNAS25bGyn1AhCN+1K5VGsx2dTkelSq8mX3qZEydOcOmS7aksPUZx5MmTh2XLllls9+OPP/L333+ngkSOo0KFCuzSaJ77L8QDeiBrKs2w8ikKN+/cf/5/IQQft67PmH6tebNBfbtyUjqCBQsW8MVnfVk98XPqVCppsf31mHvkN6SOa3g+Ibh+3bL7vqvIli0bq1ZZDs6bOnWqXeZY1y9iHYwnLmmqVavGAz8/Dj54QEWNBiOpq9F1gDGR5MHN6pRHo1F4+60mbNm2neLFi6eaTGvWrGHQgC/4ffIXFA7KZdU5JqM51W7oJ7+Z58ev2FsgzGOeMLPZbJX7bLp06TyucI6iKPTo148pT9ek+00m9JCi4ke28AiSTJz8Vs1yfN29OS2av51qJRCjo6P5qOuHLB/Xx2qlAeAf4MtDJXVM8Y8AfzdO7mPt85I2bVq7fHc8RnEcP36cqlUTD8x6kUmTJtG6detUkMixdOvenc0aDZuMRj4WkhxpAzhjRy5IW5FScsJgSDaF3/tNq1OqQA6GfDXY6fKoqkrnDzryRacmhBQNsuncwvlzctzHcR62yXFcVSmaijMwWzl37pxVbgnjxo2zyrHyZTxGcXjiEsQWMmXKxNS5c3lfEVStVoaKJYMJSwXFcU1KTIpCHgt5OsZ93o5flixmz549TpVn0qRJqAlxfNzK9mp8IUWDOGShcpmjCNNqCa1VK1XGsgdvrMpTniVffZ3JmDEj+PowalAnKlUuxUaN87/v32YzlYoXsOgwlTlDWsZ+1pZOH3Rw2pIlOjqakd+OYMrgD+y61vlzZcWs1XDMyUFoUko2CUH16tWdOk5KcPbz4jFPYokSJdiwYYPFdl27dmXt2rWpIJHjGfb1EH74rB0Z0gbSulFV1puM3HbyrGOaVuGDdg2tatu8bgWCcmSwyrplD2NG/8BH79UhON+ruSGsQQhBx5Z1mI5z94a2mc3oMme2ytzpKoKDg9myxXKu2V69etl1PT1Gcfj4+JAzp+VU+rGxsRYL7bojx48f5/TpUzSv+8QTIVP6NLxVoyzTVOe9PXebTMTotNSvbH1N1O7v1mbq5IkOl+X+/fusXLmCLi1SNv3v/F49lppMTlW4I40GPurXz61jonQ6Hblz57bYLi4uzq7nxWMUh7V46l7ItKlT+KBZDXQvOIIN+OQ9JkmVk06YeidIyUdCMuzz99HYsCSqV6kUd2JuceDAAYfKs2DBAupVLk32zBlS1E/ubJlo36wmvZ0061hhNBKpUUjjYZXrkyLVc46mNlFRUfTv399iu+HDh7v12jMxHjx4wNKlv9DppYxcQbmzMeiT9+gsJAYHm2aHqmYKlylMy/q2BThpNApdWtRg6pRJDpNFSsm0qZPp2rKm5cZWMKR3a8IDfFnh4JnnDVWlLyp9u7Zg1s/THdq3ozl37hx9+/a12G7w4MHUq2f7RrTHKI67d++yd6/l6pKlS5f2mHyjz9iyZQuhJQqSO9urlo1ureqTtWQw70szRgcpj0kmE6sDfPnxm252TbfbNa7GmjVr7SodmBhRUVEY4h9TpUxhh/QX4OfLrLF96SUk2x3kpHVHShpIMx91aELv9o04ffq0W3uO3r9/n927d1tsV7JkyUTzjVrCYxSHpy5BrOHgwQOUK5ov0WOKojB7bF9ii+SnpTRzJwXKwyQl35iM/BTgw6qZQ8iSMZ1d/WTLnJ4M6dIkmszGbDYTFRXF4sWLGTt2LN9//z0//vgjGzZs4ObNm4n2FxYWRmgJy5YdWyhb7A3mjO9HK6myPIUzj1OqSk3VRIPmtfi8a3O0Wg0hxYIJDw93kLSOx2uOfUr+/Pn56KOPLLb7+eef3SKnhC2EH9xPSJH8SR738/VhycT+5H2zMqVNRtba8SAcN5uprJrZXTAPf84fTlCurCkRmZCiQf96cMLDw+navj0Z06ShaaVKrOvTh5gRI3g4ciQXhw3jxw4dKJo/PwVz5mTMqFG8WNUv7OABQgo7vnJ9zdBirJwxmK/TBdBKNXPLxhmSWUrGm0xUN5vo1rMVwz5t91y5lSmU22UxPNaQK1cuevbsabHdvHnziIyMtLl/j1Ec+fLl44MPPrDYbvXq1Vy8mHTKOXdDSkl4+GGLXpI+Oi2jB3Zi9oTPGZDOnwrSzDyjkcfJzEDMUvK3yURzaaamaqZj71asmjmEnFlTVogaoEyhPIQdPMi5c+eoX6UK79SqRcE1azit0XAW+NVsZpxGww86HROFYLPJxF2djqVxcZwaM4bC+fIxdOBA9Ho9YQf3U8ZGL1FrCSkaxM5fR5PzraoUMRrorpqJMJuTTYh8S1UZbTISbDbyR4GcbF7yHR++9+99gJCi+Qk/mLK6vM4kV65cfPjhhxbbrVu3jrNnz9rc/2sX5OZpjmLXr19HCEnOrBmsal81pAj7Vv+PzfuOMHvRBnodO0thf19CJWQ3mlGQxGo0RGgVIhP0vJE9Ex++35hpb1YhTYDjYivKFMnHiFnrWThzJl9KyUZFsZj4RwhBBY2GClLyvUbDJz//TPmVKzl7/RqlC1vOj2kv/n4+fPd5B3p90JSFq7fR7LetxMcnEOrrSwmjiQCzilFRuKDTEK6q3DAYaVE7lHnt3qRc8QKJ9lmmSH6GTvW80pAvY+/z4jGK48aNG5w8eZLatWsn265BgwZW2a/dhfv375M5Q3qb1vcajcKb1UJ4s1oI8QkGjkdfJvLURe7GPkSVkpwBftQPzkOZIvnJmN45yYczpU/D9ctX2A0UsSNhTE5FYbWqsvD6dT5J0HPy3DUqlynkeEFfIEeWDAz8qCUDurXgWsw9Ik5eIPriDeL1Bny0GmplzcinRYMonD8nWm3y3ylj+jTcj41zqrwpISYmhiNHjli0mNStW5f8+ZNeJieFxyiOI0eOMH78eIuKwxqTrTuRkJCATwoCs/z9fChfMpjyJYMdKJVl/Hx9yCwlRZIpvWgJIQSddDpyCkGHfuNZNXMIpQolvknsSIQQ5M6WKVErlrX4+/oQn2Bf2crUICoqihEjRlhUHNaYbBPDY+b09uYNcHfc2fswOVRVOuyt01Cr5X9S8NHASSToXy0T4Y48sVq477XzWlWeEhAQYNWU6tSpUykqe5fa+Pn5ofeQh+VF9AajQ8s2dNBqKRr3iDE/e8a+QYLBiJ+TE0mnBH9/f4KCgiy2O336NHFxti+5PEZx1KpVi59//tliu27duhEREeF8gRxElixZuHn7ntVlD92Fm3diyeJAxSGEYAYK81Zu+VcaQ3fl1p1YMme2f6njbCpVqsT8+fMttuvVqxf799tuHfIYxWEtnuYoljVrVgIDA7h4LcZyYzci4sQ5yjk4v2d2ReE9nY5Fa7Y7tF9nEHHyAuU8rH5PYrz2sSrWoiiKU9PCO4Ny5coRcfKCq8WwiciIU07JKN5TwoIVW9x+BhZx6hKhFdw3rN5aFEV5vRXH6tWradWqlcV2u3btonLlyqkgkeMoX7EyEadsT1HvSiKiLxPqBAUdoijo4/VcvXXX4X07ksgzVyhfPrWKMdjOxo0badq0qcV2mzZtok6dOjb37zGKw2zB28+TqVChAgeOe06FugvXYjAbTeRzgkVICEE5Xx+3noEl6A0cPXWecuXKuVqUJHH28+IxisPT9i5soW7dukRfvMGZi+4bbfkii1dtpa1G6zRTckmDidMX3Pe3WL3lIJUrVSJz5syuFiVJ3N4cK4TQCCEOCyH+cIRASdGgQQNGjx5tsV3Lli09LsjN19eXD7t2Y+6aHa4WxSIGo4mFq7fTC+f5MKRRVeIT3NdEPWfNTnr37edqMZKlRo0a/PTTTxbbtWvXzq5qfY5QSZ8CUQ7oJ1kyZsxIgQKJxw28yLlz5zwydWCPTz5h+Z97eBTvvt6IAL9vD6OEEBR14ga0UQh0Ovfc4I44eYFbdx/QpEkTV4uSLBkyZKBgwYIW250/fx693vZ7LkWKQwiRB3gLmJ2SfhyJpy5p8ufPT9WqVfllveXkK65CVVVmzP+DnibnJlC+pNOSPVN6p45hL9N/3UL3Hp94nOUuKVxljp0ADASSvJOEEN2FEGFCiLAXczDYyj///MOsWbMsths3bhx58+a1exxXMnLUaMbO+4Nrt+65WpREmfPbVrh+mxYWomBTykHV7LQw+5Sw/cBx9kSepXfvPq4WxSIHDhxg6tSpFtt9//33BAfbHudkt+IQQjQFbkkpk02DJKWcKaUsL6UsnzWr/cljzpw5wz///GOxXaNGjciQIYPd47iSMmXK0LN3H/qNXeR2FqQL12IYMXUFC1DQOjG+5r6UnItPIDhvdqeNYQ9xj+LpO2Yhs+bMJX1695wNvcj58+fZuXOnxXYNGjSwa5M3Ja+OakAzIUQTwA9IJ4RYLKXskII+k0RVVY8NCEuO6Oho1q5dS/iOHRw6dIhb9+5hRGXx77vo2MwxyXtTiqqq9PlhAVKVpNc49xosMJnIkz07Py3awDc93nXqWLYwcPxi7t2NpWu7dpQoUoTQGjWoXb8+9evXd8ulsdtaVaSUg6WUeaSUQUBbYKuzlAY8Kcj05ptvWmw3bdo07t51b+chgL/++otG1atTtXRpzg4bxpubNrHi3j3OKAobpGDYj0s4duayq8UEYPyC9ejx5f22bZnlxJmQlJLpOh0/zviZJev3smnPEaeNZQsr/9rL7m0HOapo+MdgoE9kJD6TJzO4dWsK5crFuDFjePz4savF/BeFCxe2agN35syZSeaCTQ7hiCmxEKI20F9KmayrWvny5aWz8zQWLFiQjRs3WrWj7Aru3LlD348+4sCmTXxjMtFKq000yvRXo5F+Og2/z/magnZWNnMEM1du4effdrLrn73ExsZSo1w5DikKeZ3wNpthMjE/OJi9R46wf/9+mjVtwvyRPahWtojDx7KWv/6JoM+QaWxWtJR6aUNUSslBVWWsRsORdOmYt3w51apVc5Gk9lGyZEmWLVtGyZIlXzkmhAiXUibqHuuQqy+l3G5JaaQW7mxVOXz4MGUKFybbpk1EAh11uiRD01vrdHxnMtOs23ecOHsldQV9ypSlfzFl2VY2bdlGzpw5KVq0KJ8OGMBHiuLwPZgLqsrXQjB3+XKEEFSuXJlflv1KpyHT2bb/mEPHspZ128LoPWQa6xJRGvDEy7WiRsNKYMydO7Rq2JAxI0emvqAp4LUPcrt8+TInT5602K5u3boEBgamgkS2cejQIRrVqsXE+Hh+EoIAK/Zrump1jDOYafHRSDbujnC+kE9J0BsYPGEZCzccYPeevbzxxhvPjw0aOpS7efMywoGKI05K3lMUBg4dSvHixZ9/Xr9+fVatWUf3b+cyf832VNswNptVJi9az8ARM9moaKlkhem1pU5HmBAsGjOGEUOHpoKUyXPt2jWrHCFr1apFWjuq0jlkqWItKVmqTJkyhZMnTzJlyhQHS+V8bt26RUiRIkxJSOAdne1pAneaTHRGpXLV0vwwqDMZ0jlPMR48Fk2vUQsoXbY802fMJEuWLK+0uXnzJjXLl6fdvXsMEyJFm9Z3paSJEIS2asWU2bMT7evEiRN07NCe9L4w6csPyJvDea7eZy/doNfQaWivxrAAhQI2vo1vqio1hOCbadPo0LGjk6S0zJw5c9izZw9z5syxuw+nL1VSA3degliiV5cudDAa7VIaADW1Wo5odGTad4yq7w1k/c5DDn/7PnycwDdTVtBh8AxGjv4fK1auSlRpAGTPnp2dYWGsz5ePporCVTsrum00mQiRktpduyapNACKFy/O/gNh1Gv6HnU+/I75a7ZjMjm2nq7BaGLq4g00/OAb2l6+xQ6hsVlpwJOcIstVlc979uTatWsOldEW3Naqktr4+flZNaW6fPkyZicUabaXtWvXcmznTr5NoSk5jRBMUbQsMaqMHD6TKi37M+vXTcQ9ik9Rv6fOX2PgT0sp/c5AYoyBHD1+wqr0BdmzZ2dPZCSV+valrJR8bzJx0woFIqXkgNlMO6BHmjTMW7uW0T/9ZHHWotVqGTJkKNt27GLFjpOEtBrM+Pl/cOtOrLVfNVGu3rrL99NWUKpxX7bN/4N9Gh2fanUoKbheZTUaekhJn65dUyRbSvD19SVdOsuV+q5cuYLJjjKZHrNUsZbs2bNz5MgRsmd3DweiGiEh9Dt1inftnG0khpSS7WYzUzWCLUYTjetVoGHVMpQtFkT+XFmTfQgT9AZOnL3C4agLrN1+mNMXb9C120d83KOH3R63x48fZ8Lo0axYuZK6vr5UTUggVFHIryjogEdSclxVCReCv3x8uOfnx8effkrP3r3tWl/Dkz2jaVMn89tvv1GvcmkaVC5OSNEgCuXLiUaT9PvQZDJz8vw1Ik5dYP3f+9kXcYr2Oi09paC4A93I46Ukn9nMvqNH7fLMTC2CgoLYvn17ovlJk1uqvHaKI2vWrBw/ftwtCk8fPXqUxlWqcEEIp3lbXlNVWpuMxL4RxP24OB49ekyZYm+QL0dm/H11aDUKeqOJh4/1nDh3lTPnr1KoYAFCy5fnzUZNaNmyJT4+Pg6R5d69e/z++++E/fMP4Xv2cO3mTYwmE/6+vhQtXJjQWrWoVqMGdevWdVisx/3791m8eDG7dmwjPDycm7diKFkkiIJ5sxPgq0On1WAwmonXGzl98QbHz1wge7as3L99m5FGMx11OtI46doMUFVEt26MnTDBKf07gnz58rFr165EE4H/pxRH5syZOXXqVJLr89Rk2NChJPz4I2OcHNsRaTbTKn16Tl+7xs2bNwkPD+fKlSvEx8djNBrx8/MjMDCQ4sWLU7p0afz9/Z0qjyu5f/8+hw4d4uzZs8THx2MwGPD19cXf35/ChQtTtmxZJvz4I/fHjuV/Tg5UO2420zQwkPO3bjl1nJSQJ08e9u7dm+hs87VQHFOmTOH+/fsMtWDqiouLI02aNG6xkfpWjRp0CwujpQOXKYlhlpL0ej1XY2I8Io7C1TSvW5cOe/fSysnXRZWSDAYD569dS/WkP7Nnz+bChQuMtOBX8uDBAwIDAxN9Xl4Lq8qDBw949OiRxXbp0qVzC6UBcOjoUafk5XwZjRCUSZOGQ4cOOX2s14FDkZGpcl0UISgbGOiS6/Lw4UMePnxosV3atGlfbwcwTzPHSim5ERtL7lQKzMvDE38RL5a5GRtLntS6LlLaFQuSUpz9vHhM7dh27dpZZWZt0KABa9euJSAgIBWkShr1qWkytVSdRkqPzHzmCkyqSmql4dGAXebOlPLOO++QkJBgsV3Tpk1ZvHixzakoPEZxWJM2ELAqZ0dqoNFo8NVqeQykhgP8QyHc0tXeHQn08eEhkBq7QQ+FcMlLzJryjwB79+61y+/Jc+b+VuJOS5ri+fNz1E6vSls5ajT+K87DS9IUCwriaCo5CR41mShRokSqjGUPr32Q28aNG9m8ebPFdhMmTEDn5N1yawmtXJnwVLhB70jJHZOJQoUKOX2s14HQKlUITwWFHisl1/V6ihYt6vSxXmbz5s38+eefFtuNGzfOrhmRxyxVdu3aRUBAAPXr1weebD6eO3eOiIgI7t+/j5SS9OnTU7duXadlCouNjeXQoUMcPHiQ8IP7OXz4MHfv3iNBr0dK8PfzJUOG9ISEhBBaoRKZcudmva8vvZxs8t5oMlG9QgW3mWm5OzUbNmT+mjV86mSlvtFkoliBAowePZqwA/uIPHKE2NhYEhIMCAH+fn5kzpyJcuXKUb5iZUJDQylbtqxVruKW2Lt3L3q9nsaNGyfbrlu3bnb17zGK41nqwJ07dzJt/Hg2bt5MWkWhrI8PWZ++Pe4oCgOMRu6ZTNSvVYueAwakWJGYzWb+/PNPpk6eyO5/9lCySBBlCuWlTvF89GvZlWyZ0uHn+8TzUm8wcvveAyJPXSDi6C4OnbxIRFwc5wMDecOJD/VUX18Gfv650/p/3WjZsiWffvwxZ1SVQk68LmOMBhJMj7l5cg9vV8jHkPerkSl9Gnx9dEgp0RuM3Lgd++R+2fcXyxfO5kT0RerWqU2vPp+mKC2hlNKpLxKPcQAbOXIkC6ZNQ/PwIT2NRtpoNGRP5IeZYTDwjkbDb6rKNB8f1IwZmb5wITVr2pa/MzY2lmnTpvHz9KlkzRDIhy1q0rJeRfz9bHPP/nLsApQNe5iqc4xb98vsNZtp4+/PuRs30DrZQ/V14svPPyd25kymO+nhOmg200wDR/+chM6G6/IoXs/Kv/Yye/VO4o0qPXr2pkePHjZvfK9evRpVVXn33eTzts6aNYsuXbokeu94tOeowWDg60GDWDBzJhNUlTbapEsPqlKiefgQ+TRwSkrJWpOJXorCux06MHbiRPz8/CyO+ddff/FR1y5UKR3MJ63rUbbYGxbPSYrb9+Ko+t4g1khBFQc7HSVISTlg2KxZtGnb1qF9v+7cuXOHUgULslyvp4aDFa5BSkJVE30GdKRNY/tSCUopOXjsLFOXb+Zo9DXmzFtA7dq1HSonPImijYuLw9fX95VjHus5mpCQQMs33+To7NkcEYK2Ol2yyw4V/mWfF0LQQqfjqKJwdelSmtSunaw3XWxsLF0/7Ez3rp2YPKgDM4d1S5HSAMiSMR1jv+pCJ6kS52AlPVRKilWvTus2bRza73+BzJkzM23ePD5UFGIdfF2+MZvIW6IArRtVtbsPIQQVSxVkwcgejOr9Du+3bU3vXj2t8ga1hdfOqmI2m2ndtClpwsJYC2Sz4stJSPStnkkIfpWSoOPHadGwIQbDq3VJIyMjKV2yBOr9K+xeOJzaFR1nQmtRryI161XgbdXMIwfdpD+ZzazLkIEZCxe+lmUjUoMWLVrQ+P33aSoEDxx0XSaZTKwI9GPi8I8ddl0aVQ9hz6Lh3L14nJDSJTl16pTFc65fv26Vx2qlSpVeL8Xx49ixxB44wGJAZ+UF0AnBriRMSxohmAX4HDvG98OH/+vY3r17aVi/HsM/bsaEQR1JF+j46NGxg7uQr1oZ6qomLqTAFGiQki9VlWkZMrB5zx5SUuTKC0yYPp1iLVpQTwjOp+C6GKXkK7OJCQE+rJ45hGyZHeteliFdINO//pDP3q9P7Zo1LBaKnjhxIvPmzbPY7+7du+1KceCWiuPkyZOMGTmS+apqtdKwBo0QzFZVpk+c+PyHDwsLo/nbTZn6VSfeaVDJYWO9MrZGYdKI7jTp2IQKZiMzjEZUG99yh8xmKgjBsYoV2X3oEPny5XOStP8dFEXh5wULaP3VV1SQkmkmE2Ybr0uE2UwF1URYkXxsXDiC/Lmcp8w7NK3BmH6tafxmQ44dSzr7+38ydeCXffowREqbTZhSSotLgVyKwihVZcAnn3DmzBmaNmnMhEEdaFC1dEpEtgpFUejXpRl/zBvGgrzZCDabGGM0ciOZN12ClKw2GmmgKDTR6fhi8mR+37LFbTKcvQ4IIej/5ZfsCgtjWbFiBEvJaJOJaxauy1qjkYbSTEMh+ahfO36d9iXZM2dwurzN61ZgZO/3aNSwAZcvJ160S0ppcakkpbR7z8TtrCqXLl0ipEgRLmm1NmdmipOS3A8f8sBCOjr907RuOQsF065+GXq0bmDTOI7i0IlzzF6ykQ27I0irCEoJQXaTir+vLw8UhSNCcPrRI8qXKEGPAQN49913E9399uJYwsLCmP7jj6xau5Y0QhDq40MOkwlTQgKPdRpOaBROx+spE5SLLu83pkW9Cvj6pL638rh5v7M/+i5/b9pi136K0WjE398/ySA8jzLHDhsyhLuTJjHZjh8iVkryPXxIrBV5LAcZjSxPG8DhteNd7nEppeTC1RgOR51n/IL15A4qTNt27ShVqhSlSpWyyoTsxfE8804+dOgQU6dOIfbWFbq+U5viBfJQomCe545/rsJkMtPw49F07/M5H3/cw+bzDQYDadKkSdRYAMkrDrfzGNrx558MNpvBDtu6ivVrr2ZCsEGruFxpwJOp8ht5svFGnmxULVuEGp1G8MPo0YSGhrpatP80QgiCg4O5evUqp6KOs3fRCDKmT+NqsZ6j1WqYOqQzb/f+ikaNGieaNzQ5UrIP4vqn5gVUVeVwVBShdn6ZdMAhKz3sQjQazsbcw2BM/VwJyZEjSwZG9m5F1y6dU61ymZekMZvNdP2wM//r/75bKY1nFCuQm55t6tO7579nHKNGjWLp0qXJnuvj42NVtbfEcCvFceXKFQIVhSx2Kg6NEFZvqAYKQW5fH85dSf3sTJZo3agK+vgH7Nixw9Wi/OdZv349GQJ9eKtmOVeLkiQ92zZg//59nDlz5vlnV65c4d69e8mepyiK1XluXjnXrrOcxKNHj0iXivEW6RSFx/H6VBvPWoQQdG1Rk6mTJ7lalP88UydPpGsL2+KcUhs/Xx/aN6nO9GlTn3/2nzLHajQaUhLofFdKusRbX9nMhHRYfQ9H06ZxVTZv3uzSMoL/daKjozl8+BAt61VwtSgW6dKyFgsXLODx48dP/t+lC/Xq1Uv2nEePHvH+++/bNZ5bKY5s2bJxIyHBZgecZ8RLyd9W5liQUnLdYCJrxpTnPnAG6QL9eadBJWbPnuVqUf6zzPx5Bu0aV3O59cQagnJlpXzJYFasWAE8cSW3lNhJr9dblewnMdxKcWTIkIFsGTNy2k7XX1usKlekBK2GnFkz2DVWatC4Wmm2b7Wc9cyLc9i2dTNNaoS4WgyraVytFFs3b7K6vUusKkKIvEKIbUKIKCHEcSHEp/b29SKh5cqx307FkVYIvrCynOF+s5mQgnncOkAspGgQhyOOeK0rLsBgMHAi6hSlCnuOW39I0SDCw5/4Sa1fv56jR48m297f358vv/zSrrFSMuMwAV9IKYsBlYFeQogUZ8t9t1MnFtjpHZlBCPpZqTjmaxXefqu6XeOkFlkzpSPQ349z5865WpT/HMePHydf7uwE+nuOp26xArk5d/4ijx8/5tdff7VYCCowMJD+/fvbNZbdikNKeV1Keejpvx8AUUBue/t7RsuWLTkJnHBiPsgLqspek5n3GlZ22hiOIqRYEOHh4a4W4z9HeHg4IUVsc6hyNb4+OooUyEtkZKRnWFWEEEFAWWB/Ise6CyHChBBhMTExFvvy8fGhz2efMUijsXmK/kBKNlpR/OZLJJ1a1ibAz/3fJkXyZbMq/4IXx3Lq1EmK5M/majFspsgbOTl16hSlSpUid+7k3+OPHz9m/fr1do2TYsUhhEgD/Ab0k1LGvXxcSjlTSlleSlne2twR/QcP5lK2bCyysQLWNSn51EL1qlVGI+EBvgz4+B2b+nYVAX4+PH5suWauF8fy+NFDj3ixvEyArw/x8fEMHDiQunXrJtv29u3bfPLJJ3aNkyLFIYTQ8URpLJFSrkpJXy/i4+PD/BUr+EJRiLBhyaJKiZLMZmeU2UxPVKaM7OkxN4Wvjw59gvs5qb3u6PV6l0S8phQfnQa93rr7xVVWFQHMAaKklD/a209SlC1blhkLFtCYJwlsrEErBG8koTiOm83UNugZPqAjlct4TuEig9GEjwf4Ebxu+Pj4ul0ckzUYTWZ8fHy4c+cO8RacITUajUtczqsBHYG6QoiIp3+apKC/V3j3vfeYtngxDYGfDAaLGbMKKQobXkodKKVkpslEdZOJkuWK0e6tGo4U0enE6434+7u2gPZ/EX//AOITEg83d2cSDEb8/Pzo3r07GzZsSLZt3rx52bp1q13jpMSqsltKKaSUpaWUIU//JC+pHbRs2ZI9hw8z1seH6qrK3yaTVSn3pJRsM5looCjMCgpi5I8/IrSeN/U8d/U2wcHBrhbjP0dwwYJEX7G8me9uRF++RXBwsGdYVZxN4cKFKVW5MpW6d2dAzpwUAYaYTKw2GrmoqjySksdScllVWWcyMcxopIQQ9M6alXdHjWLvkSO0bNmSiKhzHudMdTjqgjcvhwsIDQ0l8vQlV4thEyaTmWOnzlO2bFmnKw63S+STFCtXrsTPzw/djz+yd+9e/vzjD2bt3EnE8ePcf/QICQT4+GDWaOjVsyfTmzShZs2azz1Dc+XKhU7nw+Ubd8iXM4trv4yV3I97RMzd+xQuXNjVovznKF26NGfOXyVBb/CIWBWAUxeukTtXTtKlS8fixYstppmMioqiXbt2RERE2DyWxyiOFwvxVq1alapVXy12ExYWRo8ePfh+zJhE+wgNLUfEyQupqjgS9AZOXbhO3MPHCCHIkDaQwkE58dFZ/ukjT12kTOmSbhvB+zrj7+9PoYIFOHH2CuWK276BKKXkWsw9Ll27jcFkws9HR1DurE5NZhxx8v9np2mtSJ9pNpuTzDdqCY9RHNZgaXpWvWZtNu/fRLM6iaZRdBjnr9xi3orNbN8dwZmbdyjg50ump3LFqGYuJhgolisrDepWoNM7dcmVLWOi/Wzef5zqNWo5VVYvSVO9Rk027T1mteIwmkys33GIZb9tJfzURTCZCfbV4YsgAcnpBAP+vj5UKFGAdu/Vo0GV0mg0jltObDkQRf0WHaxun5LljNslK06Kvn370rVrV8qUKZNkm4cPH3Lx4kVKlEi8CtuNGzcoVqQwEb+NJkNa24r4WsPZSzf4avR8wo+fpZNGy3sIyigK/i+ZiB9JySGzmaWKYJnRSJ2KJRg5sNO/FEh8goFS7wxk34Ew7+aoi4iMjKRRw3ocWzUWrTbpWZ/ZrDJz+d9MnLuOwkB3k0pNjYY8QvwriFJKyfmnm/YzdBpidFoG9nyP9k1rpDjY8uad+1Rq/zUXLl4mffr0DBgwgNatW1OhQtK5RB4/fszZs2cpVapUosc9tnbsixw6dIgHDx4k2yZNmjRJKg2AHDly8Oabb7Jswx6HyqaqKlOX/EnDD76h4YkLXNL5Ml6jpbJG84rSgCdpC2totUxTNFz08aVk+ElqtR3Mkt93Pt+8XbP1IKGhoV6l4UJKly6N2Wjiz90RSbY5e+kGb3UZzvo5a/lbhR1Cw/s6HXkV5RVlIISggKLQ1ceHg0LDMr2JmT/9QpteY7h2K/k0f5ZYuG4XrVq1Jn36JxXkIiIiuH//frLnBAQEJKk0LOExisNRu8S9+vRlzpqdDrOuGE0mPv5qKuvmrGOfRsfnOl2iyiIp0grBCK2OTShM++kXBo9biJSSOWt20quPQzIVeLGT3bt3o43XM3dx4l4G+4+coXHn4bS5fIsdQkMpG/eiKms0HFS0VI66QP0OQzlx9opdcppMZuav3UnPXr2ff+Y1xz6ldu3aTJo0CfF0+jf8hfqvw4cPf/55gwb/Lq704rHhw4dTvXp1AtKk57dNr8Tj2YyqqvQcOp24gyfYpmgomIILVUajYbei5dDGvXQaMJHjp8/TrFmzJL9ncr+B9xzrPrd0TqOaNfncbOZU9GX2RPw70PBw1Hk6fDqe+VLQT6tLNtQhOXRPXxxjjCrv9BhF9KUbNvex+I9dvFEgmJCQkOffZ+vWrUyfPj3Z886dO8fIkSPtkttj9jjMZjNz586lcOHC1KqV+Ibhtm3b+Pbbb9m2bVuyfR04cIC332rM7gXDUlQcePovG1k9ey3blcSXJPZwT0rKPH5Mp6++4rvvvnNIn15sx2QykSEwkEs+Puw0meifxo9dK8cQ4OdL3KN4qr07kAkJRt7ROc6pcJrRyPQs6di6dJRVVjeAyzfuUOfD79i2YxclS5Z8/rnZbGb+/PkEBQVRt27dRPdQ9u3bR79+/di3b1+ifb8WexwajYa2bdty5syZJN1kVVW1apOpYsWKdO7yIV/87xe7lyxnL91g/MxVLBavbn6mhIxCsMLPj1mTJnHr1i2H9evFNk6ePEkuPz8yCUELnY6K8Xq+m7QcgG/GL6aB0eRQpQHwiVZL/nsPGD97jVXtpZT0Hb2Qfp9/8S+lAU+el9atW3P58mW2bNmS6H1u7fOSGB6jOOCJbbp58+acP3+e3bt3v3I8T548vPOOdeHyI779juird1m12b4ly9djF/KlUCjkhHVkJY2GDmYzXw8Y4PC+vVjHoUOH/lUYbLLQsGbDbhau28Hf28L4UTjet0YIwUwUZi/7i8s37lhsv3DtTmL1MGhQ4un/0qZNS7Nmzbh48WKiNXpy5MhB69at7ZLVoxQHQNasWWnfvj3Hjx9nz55/W0eKFClC7969kzjz3/j5+bFw8S98OWE5B45G2yTDxWsx7D96hp4a57nB9Ad+XbHC4s64F+cQExNDTqPx+f8zC8FsoeGbsQvpBaR3Uq7aXIrC+xotC1ZuSbbdjoMnGDlrLQsXLUGbTC2iTJky0aFDB86cOcPOnTv/daxAgQJ89tlndsnpcYoDnnj1de3alWPHjr3yY9hC+fLlWbj4FzoMnsrhqPNWn7dg5RY6arQEODHRcQ5FoZFOx8IFC5w2hpekMZvNaF6a3lfVaDCZzHRz4gsDoCeChau3YUzCq3NvxGm6DZ/FylWrk3U/eIavry9dunR5vsx3xL6mRyoOAK1WS+vWrTl9+vTzzdA7d+5w5MgRm/pp1KgRM2fPo3X/Sew+dNKqc7bvjuDdVNhTftdgYPPq1c4fyMsrpEmThriX3uT/mM1U1GjI5uRC5UU1GrIIwfHoV82zm/ceoeNX01mydDk1a1pfYU6r1fLuu+9y8eLF53se9+7dsytOBTxYccCTOizNmzcnOjqaPXv2sHfvXr766iub+2nevDlLl6+g89CfGT//jyQ1PTxJrBN1LYayqRA/EqrREG6jIvTiGEqUKMGRl65xuNlMhVSKGwpFEHnywvP/6w1GRs5cTc/vF7D29z9ecTuwhmfPy8WLF9m1axeHDh3iiy++sEs+j1Yc8GTPo2PHjkRGRhIVFWW300u9evUIPxzB/ui7NOj+A8ejLyfaLvrSDfL6+hCYCvVYgoTg0ePH3L592+ljefk3ZcuW5ejDh5hemNYfUVVCnDzbeEY5o4ljJ56UxYg4eYE6Xb/n1C0jEUeOJhrgaS2ZMmWiY8eOnDx5kiNHjqR+6kB3ws/Pj65du3Lp0iWr8y0mRr58+fjr78307DeQ5n1/ZMT0la+4Aj94FE9GBwYmJYcQgow+PsTFvZID2ouTSZcuHcUKFGDzC2kr46QkYyoV8MooBDdv3WPIpOW07j+Zwd98x9p1f5AzZ84U9+3j40OXLl24evUqBoN9Wc5eC8UBT36MYcOGUblyZbu8BJ99PmLECD76qDvhhyNI8M9NuVaDyFC5Mxkqd+aHWasRQpCaqYAS9HqCg4PdwuvSnc+x51pbOufj/v2Z7uPDcL0e8eABm8xmFr1gaXEmKrAr/CSazMFEHDlKdHQ0iqI47Dfw8fHhf//7X5LOlJbwGM9RV/HgwQM6tG/Pvm1b8PfRUiQoFyeOn+Oyr5/Tx1alJL3BwOWbN8mQIYPTx/Pybx49ekT+7NnZISUlNBraxcfTRKulo4MdvxJjjMHA5Q8+YMrMmU4fKyleC89RV5E2bVp+WbaMwDTpGPDYQNsTF7htMHI/FRTuaVUla4YMXqXhIgIDA/l+3Di6aDSYpCREUazOuJ9SDvv7U7G6+5Yo9SoOKwgMDGTe8uWM0mhopNUSqtEQlgo3UJiqElq2rNPH8ZI03Xv0IH2pUnyvqoRqNBxIJcURZja7da5Zr+Kwklq1avFJ//68KQS1FYWldqZcs4Wlvr681bat08fxkjRCCOb/+ivz06ThBHBKSs6rqlPHPGg2owYEUKxYMaeOkxK8isMGhgwfTotevViq0/Gb0cg9Jy5XzqkqB8xm2rRp47QxvFhH7ty52bxnDz+lS0dhjYaf7bREWMs0jYYe/fo5NZ9GSnFfydwQIQTfjh7NsKlTEVot36fA9GuJEYpCt+7d8ff3d9oYXqwnODiYPYcP41OuHNNMJq45adYRZTbzu6ryYbduTunfUXgVhx180Lkz2w4eZJaU7HfCmvd3k4ndgYEMGTHC4X17sZ+cOXOybd8+6jVpQseEBIfX6DFLSReNhu/GjCFLFvcu4eFVHHYSEhLCzMWL6QDEOPDtc0FV+VgI5ixdSpo0aRzWrxfHIIRg+apVxAQFMdHBL42vpSSgeHE+trOCfGriVRwpoE2bNrTu2ZOGQjhEeVxQVeoDg7//ntq1a6e4Py/OwcfHh3WbN/O/gABmOkB5SCkZqaqsypSJ5b//7tZ7G89wfwndnJFjx9K0Z08qATtSYGn53WSiqpR8OnIkffr1c5h8XpxDUFAQ2/bt44e0aekjJY/sXLbck5IPhGB59uxs27+frFmzOlhS5+BVHClECMF3Y8Yw4ZdfaO/nR6+nNWyt5Yyq0hHolz49Szds8CoND6JgwYKEnzhBbKNGlJaSNUYjZisViEFKFhuNlFRVMrZrx74jRxwSh5JaeBWHg2jWrBlHo6PRtm9PGSlpqSgsMRo5aTajvnAzmaTkqNnMPKORNxWFaopCvo8/5kh0tN1xA15cR6ZMmVi4YgWTli9nTHAwBaRkhMnENpOJ2JeUyB0p+dtkYrDZTD6zmXmlS7P0zz+Z9PPPBAY6vkCYM/HGqjiBhw8f8suSJWxavZrww4e5ee8e6XU6EIJYg4E8WbMSGhpKk9atee+99/Dzc37ci5fU4dChQyyaM4cDO3cSefo0ARoNPopCgtmMEShbrBiV69ShU7dubu3gBcnHqngVRyoQFxdHXFwciqKQLl06r7XkP4LZbCYmJgaj0Yivry9ZsmTxiI3PZySnOF6rotPuSrp06UiXLp2rxfCSymg0GnLkyOFqMZxCitSfEKKREOKUECJaCJF4jnYvXry8dtitOIQQGmAq0BgoDrQTQhR3lGBevHhxX1Iy46gIREspz0kpDcAyoLljxPLixYs7k5I9jtzAixl9rwCVXm4khOgOdH/6X70Q4lgKxkwNsgCekB3YE+T0BBnBM+R0hYz5kzqQEsWRWNbWV0w0UsqZwEwAIURYUru07oInyAieIacnyAieIae7yZiSpcoVIO8L/88DXEuZOF68ePEEUqI4DgKFhBBvCCF8gLbAOseI5cWLF3fG7qWKlNIkhOgN/AVogLlSyuMWTnNdymbr8QQZwTPk9AQZwTPkdCsZU9Vz1IsXL68HnuP/6sWLF7fBqzi8ePFiM6miODzBNV0IkVcIsU0IESWEOC6E+NTVMiWFEEIjhDgshPjD1bIkhRAigxBipRDi5NPftIqrZXoZIcRnT6/1MSHEUiGEW4QpCyHmCiFuvejzJITIJITYJIQ48/TvjK6U0emKw4Nc003AF1LKYkBloJebygnwKRDlaiEsMBHYKKUsCpTBzeQVQuQG+gLlpZQlebLB7y5FbOYDjV767Etgi5SyELDl6f9dRmrMODzCNV1KeV1Keejpvx/w5EbP7VqpXkUIkQd4C5jtalmSQgiRDqgJzAGQUhqklPddKlTiaAF/IYQWCMBN/JCklDuBuy993BxY8PTfC4AWqSnTy6SG4kjMNd3tHsgXEUIEAWWB/S4WJTEmAAN5UtDcXSkAxADzni6pZgsh3CrFlZTyKjAeuARcB2KllH+7VqpkyS6lvA5PXnJANlcKkxqKwyrXdHdBCJEG+A3oJ6WMc7U8LyKEaArcklKGu1oWC2iBcsB0KWVZ4BEunlq/zNM9gubAG0AuIFAI0cG1UnkOqaE4PMY1XQih44nSWCKlXOVqeRKhGtBMCHGBJ0u+ukKIxa4VKVGuAFeklM9mbCt5okjcifrAeSlljJTSCKwCqrpYpuS4KYTICfD071uuFCY1FIdHuKYLIQRP1uRRUsofXS1PYkgpB0sp80gpg3jyO26VUrrdW1JKeQO4LIQo8vSjesAJF4qUGJeAykKIgKfXvh5utoH7EuuATk//3QlY60JZnJ860E7XdFdQDegIHBVCRDz97Csp5QbXieTR9AGWPH1ZnAO6uFiefyGl3C+EWAkc4olF7TBu4tYthFgK1AayCCGuAMOA0cCvQoiuPFF6rVwnodfl3IsXL3bg9Rz14sWLzXgVhxcvXmzGqzi8ePFiM17F4cWLF5vxKg4vXrzYjFdxePHixWa8isOLFy8283/ZVxX6R6y76QAAAABJRU5ErkJggg==",
+      "image/png": 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mpiR1BPgcf39/KleujF6vZ+DAgfTv3/+VKDh+fn5ERkYyfvx4Bnf8+MXU590H0dx9EP3Ssdkzpyd75vSvtJPaju3f5iM27IqgQ/v2rFu//pW1V7lF+VeOTZvHrl27ln/27GHiN62JfZKAp+HV+J7u8Lw569jOzWvRbsiMp24gU6e+dGxiz6/dbkcURQ4fPvzOY/9dr1ar5ebNm2TNmpUBAwYwbty4V451CNdkY3IcKfIBRkVFiYcOHRLNZrND5Q8ePCj6+fmJbdq0eW2erBYtmosF8/iJ9/fOeZGba2CHj185dmCHj1+bxys1Hrtl+mAREFu2bOk2ed6UY9PusXJ8hqQ4tkZQcVGr1Yp37txx6PzeunVL7NKlS7KuhY+PjxgZGfna45+TIvkAXYFc/QDfxo0bN8idOzfLli2jSJEiL30nCAIVKlRgVO9P6fJJnRefy/ENLyWPFUWR99qNIJNfXsaNH//Sse4wOlCOTd3Hrlixgv79+zNzWGeK5POX5TMkxbGeeh012g3nu++H8N133734PKWu2z///EO/fv24e/fuK1mH/o3iCJ+CXLt2jbx58/LHH39Qp06dl74bMWIEY37+iTObfsHX21MihfJk0cbd9Bm9gIsXL5I/v3tF0VdI3dSvV4/7Ny+yc95QqaXIjl6j5vH3kctcvnIFjSZlV9E2bNhA48aNuXfvHlmyZHnjcYojfArypjVEi8XCzBkz+KReJcX4vYbmdYNJ5+3JzJkzpZaioPCCixcvsv2PP+iQRndrv4sOzWpx4+bNZO8IdQRH92u8CcUAOoE3XZRNmzZx+84dOjRzfaZld8TToOfzD6swd84cxS9QQTbMmDGDDL4+NK1dUWopsqRM0bwElShIyLRpKd62YgBdwMWLF5k9e7bDaTh8fX0ZMGAAuXPnfunzab/+SnDpwpQslPsNJRU6NK3Fw0ePWLVqldRSFBRISEhg3ry5fPFRFTwMOqnlyJaOzWryx59/cuHChSSVCwsLS9azXrhwYQYMGIDBYHC4jn+jGECehkLr3LmzwwYwc+bMjB07lgIFCrz47MyZM+zctUuZRnkHBXNnp2bFEpK8TSoo/JeVK1cSFRVN+ybKrM3baFyrPBnT+zBjxowklVu1ahVDhgxxuN2SJUsyduxYPDycE5FHMYA4f1gNTxdrvT09aFQzyGl1plZafViV0LCwV3aGKSikNOvWraVKuaLky5lVaimyxqDX0bxORdauWZ2kckmNBONq5KNEQlxhAMPCQilXPB96ndZpdaZWgksXAp5OjygoSIUoioQeCKVSqUJSS3ELKpUuzNVr19+ZRODfyM0AKpFg4EX24S1btvDgwQOMRiM2mw2DwYCPjw8lSpSgWLFiid7y+/xBalW/vCtlpxpyZM2IX9aMhIWF0bhxY6nlKKRRrl27xr379wkqUeDdBysQGPDUdSksLIyGDRsmqoxiAGXC2bNnWbFiBeGHDrFv926AF52vTqVCLQgYbTaeO0l66PWULlmSwIoVqVevHg0aNECtfprLz2azERcXh5eXF2q1mhs3bnA3MpLAAOVBSgyCIBBYLB+hBw5ILUUhDRMaGgpAUIDik5oYcmXPRLbMGQgNDU0xA2g2mzEajfj4+DglS718THEKYLFYWL16NbVq1KBYsWJMGTUK259/0sNiYa3BwDUvL2ze3pi8vIj39MTm7U20tze7PTwYBRQ4epQ/Zs+mUaNG5M+dm59++onIyEiOHz+Or6/vi0SPyoOUdMqXKMCh8EMvRuMKCilNWFgYeXNmI3MG9wjAITWCIBBYPB9hz/q7xDBlyhTCw8MdbnPp0qX4+vq+WLZKLmliBCiKIrNmzWLE0KHcvnePKjodSw0Gmmk06N/yFiEIAr5AdY2G6v/6PFyjIeT+fX4cMoQfhg2jfv36wP/XEA8ePEge/6xkzeTrwl+VuggMyE9cXDynT5+mZMmSUstRSIOEhYYSVDxtpjxylKCA/PyyZFuiR3YqlSpZI8DngcsUP8BEcu3aNerUqkXXrl15PyqKo56e7NXr+VyrfavxextBajXzDAZueXryk1rNrt9/B+Dvv/8G4MqVKxTM/eY4dQqvUihPduDpuVNQkIIrVy5TMHd2qWW4FQVzZ+fx4yc8evQoRdp7PvJzxvQnpOIR4PNR39f9+pHBamW7hwd1nRy3LqMg8LVOR1FBoKHNRv/+/Qk/dIhrV69QxE8Z/SWFzOnToVaruH37ttRSFNIgVquVyHv38XtNUGiFN+OXJT0At2/fJnPmzC5vz9mbaFLlCNBms9G5Uye6du3KZ1YrJ/R6pxu/f5Pp2QX5Sadj66pVnDx2HE8likSSUKtVZM2UXvEFVJCEe/fuIYoi2RQDmCSyZUoPkGLPrWIA34HVaqXV558zf948FhgMzDYY8HXScPlNFBQElhoMdNfpiNDryWa3s2bbfi5dv+vSdlMb2TKl5+5d5ZwppDzP77tsyrp9knh+vhL73C5atIiJEyc63N7777/P4sWLHS7/X1KVAbTb7XTs0IE1q1axSq+njTZlnNCzqFR8rtXiKwjkV6kI9fQkq9FC4x4/czPyYYpoSA14GHRKUGwFSUhISAB4bdZ3hTej12lRq1Uvzt+72LFjB+vXr3e4vUKFCvHZZ585XP6/pCoDOHz4cBYtWsQivZ4mKWT8XoefSsUOgwFV1GOa9xpHgtEsmRZ3Qq0SHI7HqqCQHJ7fd2p1quoSUwS1Sp3o51YURVk5wstHSTIJDw9n1MiRDNPpaCmh8XtOTpWKrTo9V25GMmrWWqnluAV2u/giuICCQkry/L5zln9ZWsIu2hP93MotEox8lCQDk8lE2y++oJRGw7c6+Ww+Ka5WM0KjZdrybYQdT1rakLSI2WJFK4OXF4W0h17/dOrTaLZIrMS9sNnsWK02dInsdxUD6AJGjBjB+QsXWKDRoHXxhpfX8UgU2W21YhTFV77rr9NRXqOhx/BZylToO7j/KJasWZUo/AopT5YsWQB4EPVYYiXuxf2oWIBEP7e5cuUif37HI2Rdv36dffv2OVz+v7i9Abx69Spjfv6Z7zUaSkk0fRZus/FeQgL3X2MANYLAAq2O67cfMH3lHxKocw9EUeTugyj8/PyklqKQBsme/akD/N0H0dIKcTOen6/EPrdjx45l1qxZDre3YsWKRMcdTQxubwBnzpyJt0rFVxJOfT5fNXjTySymVvO5RsO8VTuw2ZQ1htcRHRuHyWzB399faikKaRCDwUDGDBkUA5hEnp+vlHpuFT/Af2EymZgzcyZtBQEvCaY+n/PcpL1NQQ+tlpsPotm+71hKSHI7rt15AEDOnDklVqKQVsmVKyfXbt+XWoZbce32fbRabYotXSgG8F+sXr2aB1FRdJN448u7RoDwNH5oea2Guat3pIQktyPi1GU0Gg2lS5eWWopCGiUwqDyHz1yVWoZbEXH6MmVKl0p0rtTkohjAfzFn5kxq6XQUkXhXUWIMIEB3tYa/Dp7i+rPRjsL/CT91idKlSuLh4SG1FIU0SnBwMKcu3iAuwSS1FLch4vRVgitVTrH2nO1H6LYG0Gq1EhoWRkMJpz6fU1Ot5rSnJxnfoeWjZ5t0Dp28mBKy3Irw01dS9EFSUPgvwcHB2O12jpxRMpIkhkcxT7h0/Q7BwcGJLvPVV18xePBgh9vs1q3bi6w7zsBtDeDp06cxms0EysCnxEcQKKZWo3mHAcysUpFbq+Ho2aspI8xNiI6N48LV21SsWFFqKQppmOLFi+Pt7UX4yUtSS3ELIk5dBkjSc3v69GnOnz/vcJuZM2emcOHCDpf/L9JbDwcJDw9HAMq6WeSQIBGOnlbeMP9N+KmnHU5S3iQVFJyNWq2mfFB5DioGMFEcOnWJzJkyJcmvT3GEdxIREREU0evxlsEUaFIIVKk4fvbqi8zGCrD6zzDy5slDwYIFpZaikMapW68eO8NOEhXzRGopssZut7Pmz4PUrVc3SclpFQPoJC5dvEhRm01qGUmmmEpFTIKJh9FKxAmAh9GPWbfjIN26d3dalmcFBUdp3749NrvI0i17pZYia/4JP8Ol63fo1q17ksopwbCdRMKTJ3hJLeIZZ202JpnNWBIxqvN81skrMQefsnTzHhBUtG/fXmopCgpkzZqVFi2aM2/d30pg7LcwZ+1OSpYoQZUqVZJUrnbt2lSrVs3hdvfs2cPChQsdLv9f3NYAmkwm9DIZMUTY7fQzmUjMePR5tjGzWUn7Y7PZmbvubz755BMyZ84stRwFBQC6d+/B5Rt32XXwlNRSZMnNyIds/ecI3Xv0SPKszaBBg+jePWmjxn+zceNGfvrpJ4fL/xe3NYA6nQ6zTNbREusHCPA8HLZWmzKOo3Lmr7ATXLt1L1kPhIKCs6lcuTKlSpZkzpqdUkuRJQvW/42XlyetWrVK8bYVR/hneHh5ES+1iGc8H/kl5mTGPzPaHnol7c/cNTspW6aM4v6gICsEQaB7jx5s33dMCVrxH8wWK4s27qF16zb4+PikePuKAXxGvgIFOCcTF4ikjADP2e34GHRk9PV2pSTZc/XWPf7Yf9yhaRQFBVfTqlUrvLw8mb9ul9RSZMXGXeHcexhNt27dJGlfMYDPCAoK4ozZTJwMpkEFQMfbg2E/J8Jup2ThPLLaCZXSiKLIwIlLyZ4tGy1btpRajoLCK3h7e9OtW3emr/yTS9fvSi1HFjyOS2B4yGo+qF+fgIAASTSo1WoMBoPT6nPbXjgwMBC7KHJMBju12mm1mHx8EjWSCRegTHHHE0KmBtbuCGP7vmOETJ+Ol5dc9vIqKLzM0KFD8fPzp8+YhYrfLvDjjDU8jI1jWkiIw3VUrVqVnj17Olx+4sSJHDp0yOHy/8VtDWBAQAA6rZYIN/IFfCSKXLFYKVM0j9RSJONRzBMGTlxO82bNaNy4sdRyFBTeiJeXF7Nmz2ZvxBkWb/xHajmScvDERWav/ouRI0eRL18+h+tJSEjAapXPDni3NYA6nY7y5crxuxu9mW17duHtdvfR7GwGT1qGVYSpv/4qtRQFhXdSu3Zt2rZty5Bff+PO/Sip5UiCyWyh10/zCQoMpHfv3smqS26RYNx6L36HLl3o0L49l7Va8svopL6JELudjL6+jJ67gfrVypDeJ21N/23be5SVv+/H19ubKhUqkDN3bgIrVCAwMJCgoCAKFSokq4dDIW2RkJDA8ePHiYiIICIigiMHD/Lo0SPi4+OJTzDS9+cFLB/XJ83do2PmbuDSjUgi1m9FncyNh3a7Pdl1OBO3vpKffvop6dOlY4bZ/O6DJeaYzcY+s5nhI0cSE2ei09CZ2GzSr1+mFOev3qbjkOkUU6vpZzLxSWQk2cLC2DBtGl988QVFixYlU/r09OnTh7Nnz0otVyGNYLfb2b59Ox83bEg6Hx+Cg4Pp07MnR5Yupdy5c3zx4AF94uP5FIHt+44xdt5GqSWnKBt2HmLiws2MGDGCUqVKJbs+uY0ABVFmq7uxsbH4+voSExNDunTp3nn8V199xYIpU7hpMOAh0Xb6P6xW1litzHzL7qSuRiMb06fn2q1b7Nq1iw8++ICeLesxotenKahUGqJj46jd7ge0kVGEGgyk+891eiSKHLbZ2GGzMU8UuW+xUKtGDbr36kXjxo1l9caokDqIjo5m7ty5TJ86lUvXrlFap6O9IFBZraakSvXaKFMjTSaGmM0sGt2TRjWDJFCdspy4cJ16nUfRsNHHLF++winuSosWLSJXrlzUrFnTofJz5szh7t27fP/99289LrF2RD6m2EG6du1KlM3GrxbpYmuesttZ9pb2L9ntLLLb6dy9O1qtlrp16zJhwgSmLP2dxZtS9+K6yWyh7eBfeXj3IRv1+leMH0BGQaC2RsPPej039HqWGgyYDhygefPmvFetGhcuXJBAuUJqZcOGDRQtVIhvv/mG4Nu32efpyRGdjt46HUFq9RtDLH6n09FCq6XrsJmpPmnu7XtRtPpmKkWKFGPevPlO89Vt3bq1w8YPnsYC3b59u1O0QCowgIUKFaJv374MtVg4J5FLhJ03n0i7KNLeYiGbnx9ff/31i8/79OlD165d6TN6Aav/CE0RnSmN2WKl3bfTOHDkLGt0egomYupDLwh8rtWyV6/nbw8PboeHU7pkSSZNmqQEJ1ZIFg8fPqTV55/TuHFjysfEcNnDgyUGA5XV6kR18IIgsECvp5Qo0rTnWI6fv5YCqlOeew9j+LjXONAY2LBxI56enlJLeoHiCP8aRo4cSa48eWhnsWCTYEb3bQYwxGLhH7OZuQsX4u39/+gvgiAwbdo0WrduTecfZrHi930pojWlMJrMtB44lR37jrFOb6CmJun7rWpoNBzX6+lot9OvXz/eq1aNe/fuuUCtQmpn9+7dBBQtytZVq1hoMLBRryeHAx2ppyDwu95AfrOFj7v9zNGzV50vVkJu34viw+5jiDOL/LVzJ7ly5ZJa0ksoBvA1eHp6Mm/RIkItFn6RYCrULoqvPZGX7HYGWq1069qVWrVqvfK9SqVizpw5tGvXjm4j5jBp0ZZU4XD7ICqWZr3H8XfocTYYDHzggPF7jpcgMMVg4G8PD84fPEj1ypW5ceOGE9UqpHY2b95MvTp1KB4Twym9ntZabbKm9NILAjv0BgqbLTTs+hPb9x11nlgJOXHhOvW7/ITJrmb3P//IMkG1YgDfQNWqVenfvz+DzGY2prCjpZ9KRfn/bNS4Z7fzodlM9hw5GDtu3BvLqtVqZs2axbfffssPIavoPGwWCUb572p9Eycv3KBW62GcP3mZP/UG6ifD+P2bGhoNe/V6jNevU6NKFW7duuWUehVSN5s3b6ZJ48Z8CPyu1+PvpM4zvSDwp95ATZudz76axOTFW9365XXDzkPU6zyKTNlysHffPpcZv4sXL3L//n2HyxcsWJDixYs7TY/b7wL9N1arlc8++YTNGzawQa+nnpM636TyUBSpYzZzO1069h44kOibaeXKlbRr15aief2ZM7wzBXJnd7FS5yGKIqu2H6Df6PkUsols0OvJ7YLtztftdqqazfjky8c/+/eTKVMmp7ehkDrYvXs39evW5QNR5De9Ho0LdonbRZGhZjOjzGaa16nI+G9au5V/r9liZezcDYxfsIlPPmnB/PkLXLrmlzdvXr788kt+/PFHl7UBaWgX6L/RaDQsXb6c2vXq0chkYq0E06G37Haqm83c9Pbmz507k/Qm9emnn7J37z6iE+xU+XIokxZtwWqVf6i3m5EP+az/RDr/MIuP7bDXYHCJ8QPIrVLxp07H3cuX6dali0vaUHB/Hj16xGctWhAsiix3kfEDUAkCI/V6VhgM/LHzEMGfDGLT3xEuacvZhJ+8xHtthzNpye+MGjWKFStWunzDi91ul1X2l1RlAAH0ej1r16+nSfPmNDca+cpoJCGFBrkbLBYCTSYeZ8rEnv37KVmyZJLrKFeuHCdOnqRHz16MmLGGWh1+5Ng5ee42s9vtzF69g+BPBnPi0GnWGwwsNRjwcvENXkSlIkSjYdWaNaxatcqlbSm4J7179SLh0SOW6HRvdGtwJp9qtZw2eFDhiZEvB03ly4FTuPsg2uXtOsKTeCODfllKnU4j8UyflfDwcL799tsUMUxyc4SXjxInotPpWLpsGWPGjmUaUMZkYr8Lg2Y/EkW+MBppbDRSvm5dwiIiKFKkiMP1eXl5MX78eEJDQ0HnQ632IxgydYVsHihRFAk9doEGnUcxYPwSPreLnDZ48LE25ZL8fqLR0FSrpXuXLslaU1BIfWzYsIGly5YxWat1aKeno+RQqViv17PSYCBs3zGCPxnErFU7iEswpZiGt2G12lj/10EqtxrCwo17GDduHAdCQyldunSKaZCbAUxVa4Cv48yZM7Rr3ZqD4eF012rpr9M5LW5onCiyzGJhiM2GycODKc/CejnzTcpisTBu3DhGjRqJ2Wym4XuBdGhaiypli6T4VMKTeCOrth9g7m9/cvLKbYpqNczQaKkh0VprpN1OgMlE7SZNWPHbb5JoUJAX0dHRFCtcmMDoaDbp9ZJNtz0URb42mVhkseDtoadlw+p0aFqTwnn9U1zLnftRLNywm4Ub/uHO/UfUrVOHkOnTKVCgQIpryZ49Oz179nxnJJfkklg74nQDaLPZ+OGHH1iyZAl3797F39+ftm3b8v333yfqZnS2AXyu6ZdffuGnH38kOjaW+jod3dVqPlCrUTvwgJyz25luNrNAFIm12ShdqhQZM2Xir7/+core1xEdHc2iRYsImfYr585foGj+nHRo+h6fflCFdF4eLmsX4OyVW8xds5MVm/cQZzTTUKuhu0ZLbbUalcTz+XPNZjqaTJw/f55ChQpJqkVBeiZOnMigAQO47OFBThmMNK7Z7cyyWJhtt3HfaqN62SJ0aFGbBtXLonXhi6Moiuw5fJa5a3ayeXcEBoOBL774km7duqXoiO+/7N+/H39/f/LmzetQ+Z49e6LVavnll1/eepxkBvCnn35i4sSJLFy4kICAAMLDw2nXrh2jRo1KVCoNVxjA58THx7Ny5UqmTZ5MxLFj5NBqqSKKBKpUBKrVlFOryfCfDt0iipy024mw2Yiw2wkXBMLNZjJnyEDHLl3o3Lkzs2fPZuXKlVy6dMmpel+HKIrs2rWLkJBprF+/AYNOS7M6FagWWIyggALkzZEl2W+9JrOFE+evc+jUJbbsCmfv0fNk1ajppFLTWat12QYXRzCKIjmMRtr27s2ECROklqMgIXa7nSIFClD+1i2WOTFruDMwiSJrrVZCbFb2Wqxky5COzz6sSnDpQgQFFCBLxuT3dTFP4jly+gqHTl5i9Z9hnLtyi2JFi9K9Rw++/PJLfH19nfBLpKVBgwZ4eHiwZs2atx4nmQH86KOPyJYtG3Pnzn3xWbNmzfDw8GDJkiXvLO9KA/hvDh06xPLlywkPC+PwkSPEJSQA4KVWY1CpUPO0c42z2bCJIiqViqIFChAYHEzdunVp3rw5hmcP2aBBg1izZk2Kx6y8desWs2fPZumSxVy8dBmATBnSEVQ8H0EBBShWIAd+mTOQLbMv2TL5vvLG+TgugciHMdy5H8Wte484cuYqEaevcPzcNcwWCypBoLpGTRe1hqYaDToZ7d76N98Yjczx8ODmnTuyCtukkLL88ccf1KtXjz0eHlSVaFo+MRyz2RhtNrNGtGN9lhEmj39WggLyERiQn4K5s5M9c3qyZ0pP5gw+L62ZiaJIdGwcdx5EE/kgmqu37xNx+jLhp65w7sotRFEkXTof6tatS/fuPXjvvfdktesyudSvXx8fH593bn5LrB1x+l1SuXJlZs2axfnz5ylcuDDHjh1j7969TJw48bXHm0wmTKb/LxLHxsY6W9JrKV++POXLlweeTpGeP3+ew4cPc+/ePYxGIzabDYPBgLe3NyVLlqRMmTJ4eb3ev0eqhd0cOXLwww8/8MMPP/DgwQMOHjxIaGgoYaGhTPvtL6KjY1463sOgR6NWIYpP/X/M/3ETKZA/H8GVKtPok9YMGzaMSVotvfT6lPxJDtFVp2N8bOwzP8p2UstRkIiQX3+llE5HFZlnDymtVrPCw4P6RiN3ihbj2yFDCAsLIyw0lOHT177UH6pUKgx6LWqVCrtdxGSxvOQapVKpKFWyBDXqfMjA4GAqVqxIkSJFZLXRxJk4u691ugEcNGgQsbGxFC1aFLVajc1mY9SoUbRq1eq1x48ePZrhw4c7W0aSUKvVFCtWjGLFijlUXg6+LZkzZ6ZBgwY0aNDghaYHDx5w+/Ztbt++zZ07d4iLi8NisaBSqdBoNGTKlAl/f3/8/Pzw9/fHx8cHgJCQENRAW51Owl+UePKrVNTUalmzerViANMoRqORLVu3Mk6jkfxZTCwd1Go+OXWKKlWq8OmnT9OiWSwWIiMjXzyzd+7cwWQyYbVaUavVaLVasmXL9uK59fPzezETlRZwdl/rdAP422+/sXTpUpYtW0ZAQABHjx6lb9+++Pv706ZNm1eOHzx4MP3793/x/9jY2BQPwLpjxw58fHyoWLGiQ+UrVar0wnjIBZVKRdasWcmaNStlypRJUtmIiAhK6nT4uElHAlBJEJh78KDUMhQk4sSJE1htNiq7wYzFcyo9G6lGRESQM2dOALRaLTlz5nzx/9TGqlWrKFWqlMNuYg0bNnwpqUBycfoaYK5cuRg0aBA9evR48dnIkSNZsmRJojJ9p9Qa4L+pUqUKRYoUYd68eSnSntwpHRBAhQsXmO1Gb5brLBaaGo3cunULf/+U32quIC3Tp0+nd48ePPbywuAmL26iKJLdZKLzwIEuDw0mF7y8vBg9enSiNkQmB8lCocXHx78yR6tWq2Wdy01uzplSkpCQwKmzZwlys/MR+K+3aYW0R0REBCV0OrcxfvA0JVogEHHokNRSUgy59bVOV9KwYUNGjRrFli1buHr1KuvWrWPixIk0adLE2U05DbldFCk5deoUNrudcjLfSPBfcgkCmbVaDh8+LLUUBQk4Gh5OORm/ZL+JcoLAkTT00ia3vtbpa4BTp05lyJAhdO/enXv37uHv70+XLl0YOnSos5tyGnK7KFLy6NEjALK50Zs0PH2bzqpSERUVJbUUBQl49PCh292z8PQ5i4qJefeBqQS59bVON4A+Pj5MmjSJSZMmObtql5HcnUXPy7vL7rO3YTQaAXBtbBnX4MH/9SukLYxGo9vesyaLBVEUU0X/8S7k1tfK11s0Bdm9e3ey3kq6du3K0aNHOZgKdiHKLDRsknF3/QqO4e7XPa0YwEePHiXLbSM4OJgyZcowa9Ysp+hRDCAke1ut3Ib1ycHD4+l7tDuOo4z8X79C2sLDwwPj48dSy0gyRkCv1aaa/uNdJDccm7P72rRx1l1MajKAz2/Q+274Rv3Abk8x1xkFeeHr6+ue96woks6Jfm2pHcUAypDUZABLlCiBSqXiiAvzJ7qC23Y7kRZLkp3+FVIHpYOCOOKGU4iHRZEy5cpJLcNtUAygDElNBtDLy4uiBQoQ4WZbyp/rDQwMlFiJghQEBgZy3GrF7GajwAgg8FlMYoV3oxhAFzBu3Di2bt3qcPl27doxYMAAJyqSlsDgYCLc7G06wmYjc/r05M6dW2opChIQGBiI2W7nlBu9uN2127ltsaSZlzaLxcLAgQM5evSow3UMHTr0RdxUZ6BsggFmzZpFs2bNXgSSTio1a9Z0siJpCQwM5LelS0nQ6fBwE0MYJooEli//0k66+Ph47ty5w+3bt7l37x4mkwmbzYZGo0Gv15MtW7YXAYWVzTPyICEh4cU1i4yMxGw2vwgE/d9r9u/UV2XKlEGlUhFqs1HWTYI4hD1bZkgrBtBsNjN27FjKlCnj8FJF8+bNnapJMYCkrilMZ/DRRx/Rt29fVlqttNVqpZbzTm7Z7fxptdKlUCGGDx9OWGgohw4d4sHDh4muI2uWLJQvX57gSpUIDg6mfPnysk8gGhkZyZEjR3j48CEJCQmIoojBYCBdunSUKlWKvHnzynprfXR0NIcOHXqRwuvgwYPcf/Ag0eUzZ8r04ppVrFiRGtWqsfDAAbq5ULMzWWizEVCkiMPZ0d2N5+Ew5dTXKgYQxQD+lwIFClC/Th1Cdu+mrdRiEsFsiwVEkZCQEDKk8yYoID8dGlclf85sTxOLZk5P1ozp0D/Lq2az2zGaLNx7FMPd+9HcfRjNpRuRRJy6woRxu4mOjUMQBIoVLULDRh/TpUsX8uXLJ/XP5OzZs6xevZrwQ4eICAvjZmTkS98LwL9XwDKkS0dgUBCB5cvToEEDqlWrJrlBvHz5MjNmzGDzpo2cOXsOgPTpvAgsnp92H1chf86sL5I4Z83o+yIXns1ux2SycO9RLHcfRHP3QTSXb0YScfoKkyaMIyr2yYs2IjSaF7Fh5cpNu50NViu/9ukj+TVJKeRoAJ2eDSK5SJENIk+ePLRp04YRI0akSHvuwKZNm2jUqBGHPD0JknFnYhFFcsXHU6RcEX75pg35c2VLdqSJi9fvcujkJQ6euMiGneHEPImnwQcf0K17d+rXr486Bc+HxWJh48aNhEydys7du/HVaCgvCAQKAoEqFYFqNX6CgJ6nBtAMPBJFjtjtRNhsRIgih4DbFgsBRYrQvXdvvvjiixR1F7HZbPz++++ETJvGtu3b8fX25ONaQVQsVYjyJQpQIFe2ZHWKoihy6UYkoccv8O34xTS3icyVeSaToSYTv6jV3I6MlF0qNVcRFRVFxowZWb16Nc2aNXNpW4m1I4oBBKpWrUrz5s3p27evQ+UvXrwIQMGCBZ2oSlpsNhv5c+fmvfv3WSjjzmSlxcJnRiP7lvxIQEHn55GMN5pY/Ucoc9bs4vi5q+TNk4eu3brRvn17smTJ4vT2nmO325kxYwajhg/n9r17VNHp6K5S0UyjQZ9EAy+KIjttNkKsVjZYrXgYDHTt0YMffvgBLy8vF/0CuHfvHvPmzWPG9Olcu36dMsXy0bFZLZrWroCnwTV5+8bP38iE2eu47uFJZhmNNP5NgiiS32SiaadOTJs2TWo5KUZ0dDTlypUjJCSE+vXrO1THiRMnyJgxIzly5HjrcYoBTEEaN26M1Wpl8+bNUktxKr/++iu9evVit4cH1TXymy2PFkVKGBMICCrGb5O+dmlboigSceoyc9buZN2Og4gIdOrUiZ9++snp9+nly5dp36YNu/fupa1WSz+tllJOGnXetNuZYbEwwWolR65czF+8mGrVqjml7ufExMQwePBg5s6di4BIszoV6disFuWK53dqO6/j/qNYyjf/hgYWG0tl+uI2wGhkKnDi1CkKFSoktRy3Il++fLRq1YqRI0e+9TjJ8gGmRVLrGmL37t2pWqkS7axW4uT1ngRAf5OJx1oNEwe3c3lbgiAQVKIAM4Z24vTGiQxs35D58+YQULw4W7ZscUobdrudadOmUapECa6FhfGXhwfzDQanGT+AnCoVI/V6jnl4kO3OHWrUqEHfvn2Ji4tzSv0bN24koHhxFi2Yz6AOjTiz6RdChnRMEeMHkCVjOn4e8CXLLBbWWywp0mZSOGCzMdFqZcTIkYrxcwDFD1CGpFYDqFKpmL9oEXdUKgabTFLLeYnfrVbmWyyM7Pc5ObNlStG2M6X34au2DTmwdCSFc2bko48+4vPPW3L//n2H67RYLLRp3ZqePXvS2mrlhF5PLReOugurVPyj0zFep2Pm1Km8V60aD5KwA/O/REZG8umnn/Dxxx9TPE9mQpePon+bj8jom/Jhvj6tX5n6VUrT1WrhoYxe3BJEkXYWC0HlytG/f3+p5bglyc0m8V9SX68tAaIopkoDCE/XNUePGcNUi4XNVqvUcoCn03gdLWberxDAlw2rS6Yjj38W1kzqz4xhndi2dQvFihZlw4YNSa7HbDbTvGlTVi5fzgqDgRCDAe8U2BmoFgT663TsNxi4duIENapU4e7du0muZ926dRQrVpQdf2xj1g+d+W1iP3JlT9mXkn8jCAKTBrfDpNPSwWTEJgMjKIoi/UwmrgILFi9GI8MlBXdAGQHKkNQ6AnxOr169aNyoES1MJv6R2Ajet9upYzKhyZCOX4d0lHwLuSAIfPZBFQ4uH0VwyXw0btyYkSNHJjo9j81m44vPP2fb1q1s0Ov5VAK/y7JqNXv1emKuXKFurVqJTipst9v54YcfaNq0KVVLF+Tg8lF8Ur+y5NcEIHvm9Ewf3oXNNhudTSbJ0yUNNZuZabHwa0gIxYoVk1SLO6MYQBfQtGlTVq1a5XD5yZMnM3r0aCcqkhcqlYrlK1dSpXp1GpjN7JTICN6x26llNvHQy8C6aQPxy5JBEh2vI0vGdCwe3YNBHRszZMgQPv30k0Stqw0YMIC1a9eyUqfjAwlHBYVVKv7Qarl94QIff/QRtncEQ4+Li+OTFi0YPnw433dpyqLRPcicQV6b1j6oVpZpQzoyz2Khk8kkyUhQFEWGmEyMNJsZM2YMHTt2THENcuHevXvUrVuXw4cPO1zH77//Tvv27Z2mSTGAwF9//cX169cdLl+4cOFUv6BtMBjYsHkzVWrWpIHJxNJnWaxTihM2G9VMJh75eLF5xrcUyJ09xdpOLCqVikEdG7NodE+2bt5Mvbp1iY2NfePxu3fv5pdffmGsTkdjGUTcKa5Ws06rZe/+/UyaNOmNx8XExFCndm22b/udpWN783W7RrIY9b2Ozz6owvShnZhvtfKpyUh0Ct6zCaJIt38Zv2+++SbF2pYj8fHx/Pnnn4meYXgdZcqUeacLRFJQDCCpfwrTWXh5ebFx82aaf/opXxiNNDeZiHRx8GGLKDLKZCIwIQFdjiz8Pud7iuTzd2mbyaVRzSDWT/2akyeOUbdOndcawbi4ONq3bk1VnY6+MjB+z6mm0dBHq+X7b7/l3Llzr3z/3PidOXWCjb8O4MPq8k/l07JBFRb93JM/1CpKGBP4PQVmMA7YbJQ1mVgAzJ49O80bP5BnJBj5KJEQxQAmHr1ez5Jly/jtt9/4x9ubAJOJFS4aDZ6w2Qg2GRlmtdC79Yf8vXgEefxd53zuTMqXKMiGqQM4e+YUDT74gPj4+Je+Hzx4MHdu3WKeVotKZqOnUXo9uUSRdq1bvzQVGhcXR4MPPuDCuTNs+PWbFHNtcAYf1Qhk/4rRFC5TmAYJCbQ3umY0mCCKDDAaqZqQgG+pUhw5dixNT3v+m+cGUE6zBUqvT+rexekqWrRowelz56j18ce0NBoJMpuZazYTn8xOxS6K7LBaaWI0UiY+ngT/LOyYO5Qh3Zqj18lnpJQYyhTNy+pf+nHkSATt27d78ZJw4sQJpk6dyk8aDYVkeN95CgLzNRpCDx5k4cKFwNNnpG2bNhw7eoQ1k76idJE8EqtMOrmyZ2Lt1G+YPKgtq9UC+RLi6W80ct4Jsxg37HaGmEzkM5mYCvw8Zgz7QkOVDS//QhkBypRGjRpRoEABh8tv2rSJ3bt3O1GRe5AlSxZ+W72abdu24ff++3Qym8lhNNLPaGSP1cqTRBpDqyhy3GZjotlMMZOROgkJXMiRhXEDWvP34hGULSZ9IGpHKV+iICHfd2Dlyt/4+eefAQgJCcFPq6WHjKY+/0sVjYYPtVp+nTQJURQZOXIkq9esYcawTgQGuM/I778IgkCbxu8R+tvPtGlZn4V6LUXi4qhrTGCFxcI1uz3Rsxl37HbWWyw0MRrJGx/PZI2GFl26cOLUKQYMGKC4OvwHLy8vmjRpQubMmR2uY+HChZw8edJpmpRQaE6gSpUqFC5cmPnz50stRVKuXLnCzJkzmTtrFg+iohCAIno9gTYbJVQqvAUBA2ABEoBLdjsRwDGbFaNdRKtW8XGt8nRo9j7BpQvJaqokufw0ex3j5m1k+fLldGzXjq9sNn7QuyYeprP43WqlQUICY8aMYeDAgXzbuQnftP9YallOxWgys37nIeat2sHB01cAyKxRU46nwcazCQLPM0UagQeiyBFRJAK48yzSTMlixejRpw+tWrXC2zvlHf/TEt7e3owaNYo+ffq89TglFmgKUqlSJYoXL87cuXOlliILrFYrp0+fJiIigvDwcCLCwjh77hwJRiNmqxUV4KnX4Z8lPWVKFKBM0byUKZqXUoXz4O0pz/iNycVut/P5wKnsP3qBJ4+fcN3Lixwymgp6HXZRpKDJxG1BoHalkiz5uWeqein5L3cfRHP07FWOnLnC0TNXOXHmCvdiHmO1P+0iDTodvj4+lClXjsDy5QkMDCQoKIhcuXKl6vMiJzw9PRkzZgy9evV663GJtSPKGN0JKJtoXkaj0VCqVClKlSpFu3b/j9PZtEkT9v6zi4PLR5FBghBZUqJSqZg8qA1BLQaRS6WSvfEDUAkC3VQqBplM/Njzk1TfyWfPnJ76VctQv2qZF589inlC+U+/peb7dVi1erV04hQAxRFeligG8N2sXbuWdevXM+6rVmnO+D0nW6b0jOrbkmt2O3/IJKzcu2ioVmMHbtx9KLUUScjo682Y/p+zes0ah8LcKTgXxQDKEMUAvp2oqCh6dO/OB9XK8nGt8lLLkZQvG1anetkidLGYE71JSEoKq1R4q1QcPXtVaimS0axORepVKU33bt2IiYmRWk6aRgmG7WSsVitZsmRhxYoVDtdx8OBBfv31VyeqSl0MHDiQ+LjHTBjQOtVPo70LQRCY/F0H7goCP8gsw8brUAkCZdVqjp69IrUUyRAEgQnftCY2JprBgwdLLcdtOXr0KFmyZEnWLk6j0UinTp2cpinNG0C73c6DBw8wm80O16FWq1E7MWdbauLu3bssWLCAAe0a4p9VPrE7pSRfzqz0+rIB0222FA3N5SiBwJGTl6SWISk5s2Xiq7YfMnfu3GSlvUrLWCwWHjx48MIf0BE0Go1T+1rFAMrQOTM1MXfuXDRqlaRpi+RI+yY1MQOLZJi09b8UV6m4GvkoWR1XaqDNx+8hIDJv3jyppbglcuxr5aNEIuR4UVILVquVmTNm0LxORdKn85Jajqzwy5KBj2qUI8RmkzxVz7vwejZtbTTL31i7koy+3jStXZEZ06e/M1uGwqvIsa+VjxKJkONFSS1s2bKFGzdv0qFZLamlyJIOzd/nnNXKLpl3ps/d9c1m99i56ko6NqvF1WvX2LZtm9RS3I7nLw1y6mvlo0QitFotI0aMoGTJkg7XMWLECNatW+dEVamDkGnTCCpRkDJF80otRZZULVeUIrmyMV3m06DPV8e1WsVtuFzxfJQplo+QadOkluJ25M6dmxEjRjgcCs1sNtOjRw8OHTrkNE1p3gDq9XqGDBlCQECAw3UsXryY0NBQJ6pyfy5cuMAff/5Jh6Y1pZYiWwRBoMMndVhns3JbxutrCc/+NbhZMHJXIAgCHZrW5Pdt27h8+bLUctyK3LlzM2TIEIcNoMViISQkhIsXLzpNU5o3gM5AySbxKjNmzCCDrw9N3k/bfn/v4tMPKmPQaZkt41HgObudHBl9UauVexye+gX6ensyc+ZMqaWkKZ6vlSuO8DJDcYR/la1bNtO4ViAGvU5qKbLG19uTBjUC2SrjjTARop0yJdw3A4Sz8TToaVQzkK1bNkstJU3hiv0aSq/tBBQD+DJRUVGcPXeeCiULSS3FLShfsiBHrFaMMjSCoihy2C5Spqj7pqRyBeVLFuTU6TPExsZKLSXNoBhAF2Cz2bh58yZGo9HhOgoXLky2bNmcqMq9eb5IXb6E4zkW0xLlSxTAIoockeE64BVRJMpmUzYy/YfyJQogiqJTN2SkdhISErh586bDLiSCIFCiRAl8fX2dpinNG8DIyEhy5crFzp07Ha7jjz/+oGfPnk5U5d6EhoaSIZ03BXIpLwWJIaBgLgxaDWEydIfYZrU+DYdWLK/UUmRF4Tx+pPP2JCwsTGopbsOuXbvIlSsXkZGRDpX39fXlxIkT1K5d22ma0rwBVPwAnU9YaCjliudL83E/E4tOq6F04dyEyswAiqLIdLuNBtXKkjmDe+TmTClUKhXliucjNPSA1FLcBjn2tfJRIhGu2FmU1jl06BCBAcqmiaQQWLIQh2T2vrDXZuOkxUrH5u9LLUWWBBXPz6GDB6WW4TbIsa+VjxKJkONbiTuTkJDA/QcPyJcjq9RS3Ip8ObNww2rDLqONMCFWKwVzZKF6UDGppciSvDmzcjfyHiY3yOohB+TY18pHiUTI8aK4M3fu3AHAL3N6aYW4GdkzZ8AiijyUiQE8ZbOx2mqlfYvayrPxBp7f43fv3pVWiJsgx75WPkokIlu2bGzatIlSpUo5XEf9+vVZunSpE1W5L88NYDbFACaJ7Jmf7my7IwMDaBVF2lrMFMyVjfZNlEg+byJbpvTA/+95hbcTHBzMpk2b8Pb2dqj83bt3qVKlCgedOO2c5g2gp6cnH330kcPheeDprkflIXjK8x1eWTMqmyaSQpaMTw1gpAwM4FizmcNWG9OGdVYCGbyF5/e4o7sa0xp+fn589NFH6HSO3VNGo5H9+/c71fcyzRtAZ6A4wv+f5+sheiVuZJJ4HmfT8bTMzuGkzcYPFgt9vmygbGR6B3r9s2uWjGTaColHcYSXKYoB/D/PnVzVyvlIEs/Pl1XCEeAtu52GZhMFc2dnYIePJdPhLjy/ZkpuwJRBMYAyRQmG/X80mqcpc2wyjGoiZ56fL41EvpORdjt1TSas6X34bfLXytRnInhxzTRKmqiUQAmG7QJiY2PZtGkTUVFRDtfRsmVLihYt6kRV7ote/zR9qtGkTAslhefZ1s9KMJq4ZrcTHB/PfS8D66YNJFf2TCmuwR0xmZ5eM0fXtNIaN27cYNOmTS9GcknF29ubL7/80qlhJ9O8Abxy5QqNGjVKVo6pOXPmULduXSeqcl+e35yRD2MkVuJe3Ht2vgZZLPxsMqXYVOgai4XyJhM3RZGJg9tRKI9firSbGoh89PSaZc+eXWIl7sHOnTtp1KiRw1PGfn5+LFq0iCJFijhNU5o3gHL0TXFn/P39AcUAJpU795/OQHTv1YvvLBYqm82cduFo8L7dzqdGI82NRsrXro0VUKlkFopG5ty9Hw087ZgV3o0c+1r5KJEIOV4Ud+b52/DdB9HSCnEzIh/GoNPpmDRpEvsPHOBx7tyUNRoZZjIR6cT11DhRZJbZTIDJxA5PT5YvX87GzZvRaDREKtcsSdx9GA2gZIJJJHLsa+WjRCLkeFHcGYPBQPZsWbl8Q/GNSgqXbkSSO1dOBEGgYsWKHDlxgn4DBjBOEMiVkMDnRiN7rdYXGwGSynm7nX5GIzmMRrqazdRs3JjT587x2WefoVaryZ0rJ5eUa5YkLt+IxN8vu7IGmEie97VyCpKf5nt9OQZodXfKV6hA+OnLUstwKyJOX6FicKUX/zcYDPz888/cunOHMePHE54zJ9USEihmNtM+IYEQs5kwm42E1xhEqyhy0mZjocVCb6ORYJOJInFxLPH2ptvXX3Pp0iVWrlr10silfIWKRJy+kiK/NbUQfuoKFYODpZbhNshxt7y81EhAmTJluHnzpsO7OG02G56enixevNjJytyX4OBKHD59xeHdXmkNk9nC8XPXqFix4ivfZciQgX79+nH24kX++OMPqrdpw7HixelrsxEcH49PXBxZjEZymUzkMZnIajTiHR9Pyfh42hqN/JErFwWaN2fx4sXcvHOH0aNHky/fq9ndg4ODOXr2KharNSV+sttjs9k5cuYKFSsqBjCxtGrVimvXrjlc/vDhw3h6enLs2DGnaUrzDiw6nY4cOXI4XN5ut5OQkKB09v+iYsWKxD6J5/y1OxTN5/i5TSucOH8ds8VC8FtGEyqVijp16lCnTh3gacSdEydOcPjwYR4+fPjiHvTw8MDHx4fSpUtTtmxZ0qVLXEi6ihUrYjSZOXXxppL9PRGcvXKLJ/EJb71mCi/j5eWFl5eXw+VtNhsJCQlOVKQYwGTzfEuv3Ib2UlK+fHkEQSD81GXFACaCQ6cuodfrKV26dKLL6PV6goKCCAoKcoqGsmXLotVqOXTyomIAE0H4yUuoVCoCAwOllpJmcMV+DcUAJpPnF0WtVkusRD6kS5eO4sWKEnbsAl98VM0lbZgtVs5cvsXRM1c4eu4qp89dJ/ZxHEazFUEAD72OTBnTUapYPsoUzUuZonnJnzOrLF9Uwo5foFzZspJupjAYDJQpXYqw4xfp1Ly2ZDoc5Um8kRPnr3P03FWOnLnChUu3iE8wYrJY0ahVeOh1+PtlpnSxfJQp8vR+8MuS3uENGaHHL1CyRIDDmQ0Uko4r+lrFACYTZRfp62nY6GOmT5vKz/1b4eWhd0qdNpudHaHHmbvqL/4OP43ZakMFFNdqKSOKZBQEDIKAKIokAHdu3WfzyUv8anm6rpXOQ0+j9yvQsfn7shnlRMU8YdveYwwZOkxqKTRs9DE/jx5F9OM40vs4PlWVUsQbTazdcZD5q3Zw+Pw1RBH0gkBpjYZygA9P/299dj9cvnmP+QdPcd/6dNbGP2M6PmtYnbaN3yO3X+KzwTyOS2Dz7sP07tvfJb9L4fW4oq8VREf3VbuI2NhYfH19iYmJSfT6RXK4cuUKS5YsoVevXqRPnz7J5c1mM7/++isffvihUyMUuDtXr14lf/78TB7UltYf10hWXQ+jH7N44z/MW72D6/eiKKfV8KVKTQW1mtIqFV7veIt/KIpE2Gzss9mYb7dxw2ojsGheOrSoTdPaFSSNe/nrsm0Mn76GmzdvkjVrVsl0wNO8drlz52Zkr0/p+mkdSbW8jcs3Ipm7difLNu4mOs5Ifa2WZioVQWo1xVUqtG+5H0RR5Oaz++Evm41FdhuP7XbqVSpNxxbv835wyXeOCuet3cnX45dw9epVcuXK5eyfl2o5cOAAe/bs4ZtvvnGo/K1bt1i5ciVt27YlY8aMbz02sXbEJQbw1q1bDBw4kN9//534+HgKFizI/PnzE7VekdIG8M8//6Ru3bpcvXqVPHnyuLy9tETDjz7i2oVT/LNwmENTTaIosnLbfgaNW0yC0cRnag3dtVrKq1QOT11ZRZGtNhshVivbLRYK5czKtGGdqVCyoEP1JQe73U7Qp98SXK0mS5cuS/H2X8enn37CkbB9HFwxSlb+WvB02nv8gk1MnL+JdIJAB5WKLjodBZIxIogTRZZbLEyz2zhqsVKjXFGmDun4xhGhKIpU/XIYhQLKsm79eofbTYuMHz+ekSNHEh0d7fK2EmtHnD5vFxUVRZUqVdBqtfz++++cPn2aCRMmkCFDBmc35RSUKUzX0b1HD06cv0r4qUtJLnv3QTQtv55E1+GzaWCxccPDkwUGAxXU6mR1zBpBoJFGwzaDgZOenmSMfET9zqMYMnUFCcaUDeD996HTXL5xl+7de6Rou2+je/ceXLh2m38izkgt5SWOn7/G+22GMXHeRr7VaLjp4cFYgyFZxg/ASxDoqNNxWG/gdw8PLh+/QOXPvmX+ul2vDToQeuwCpy5ep3sP+Vwzd8Fut8tur4TTe/0xY8aQK1cu5s+fT4UKFciXLx9169alQIECzm7KKSgG0HXUq1ePfHnzMmfNziSV27w7guBPBnEk7CTrDAaWGgxkccH1CVCr2ac38LNOx6zl26n+xfecvnTT6e28iTlrdlKqZEkqV66cYm2+i+rVq1O8WDHmrtkltRTg6Yhr4sLN1Go7HOFGJAc9PBiu1+Ph5NGpIAjU12g4afCgpV2k35iFNOk5lkcxT146bu7anRQqWJD333/fqe2nBeSYN9XpajZu3EhQUBAtWrQga9aslC1bltmzZ7/xeJPJRGxs7Et/KYliAF2HSqWiW/furNtxiIfRjxNVZvGmf2g96FdqWaycMnjQWOvazPJqQWCATscRgwcekY9o0HkU4SeTPmJNKjfuPmTb3qN079FDVlONgiDQvUcPtvwTwe17jqcIcwZ2u51vJixhxPTVfKPRcEhvoKyLRxDpBIFZBgN/eHhw6th5GnQa+eI83HsYw4Zd4XTr3l3pLxwgTRjAy5cvM336dAoVKsT27dvp1q0bvXv3ZuHCha89fvTo0fj6+r74S+lFZa1WS5YsWRwemlutVs6dO8eTJ0/efXAapF27dggqFfPWvntEsXD93/QaNY9OGg2/6Q1kSkHDUEytZrfeQIDZwsc9fna5EZyz+i+8vDxp1aqVS9txhC+//BIPDw/mrvlLMg2iKNJ/zELmrP6LmXo9o/R6dCl4P9TRaNijN/Dk9n0+7DyKuw+imbduFxqNhrZt26aYjtSEh4cHmTMnfrftf3ny5Annzp3DYrE4TZPTN8HodDqCgoLYv3//i8969+7NoUOHOHDgwCvHm0wmTCbTi//HxsaSK1euFNsEk1zu3LmDv78/W7ZsoUGDBlLLkSV9+/Zl9qwZ7F86krz+WV57zLodB2n/fQjdtFp+1euTPSr6926/CLudYzYbMYBRFBEAgyCQSRAoq1IRqFYTqFKRVaUiThSpZzJySqdl66zvKF4gZ7J0vI6zV25RrfUwvvlmICNHjnR6/c7g22+/ZeKE8exdPEKSHIHDfv2NyUu2Mt9goK0TZgHsosgFUSTcZiPCZuO03U48T+8HDU/vB39BeHEvlFWr8REErtjtVDMZ8ciWiZsPouneoycTJkxIth6FpPP777/ToEEDbt68+c7oXZJtgvHz86N48eIvfVasWDGuX7/+2uP1ej3p0qV76c+dkGOEc7nx448/kilTFvqNWfjajQWXb0TSffgsWmq1TE2m8Yuw2ehgNJI9Lo7ccXE0MRqZbbFgAXIJAiXVagLUanIIAtGiyASzmQYJCWSLiyP3kycMMZmYpNWR22yh7cCpTs9sb7PZ6f3TAvLlzcf333/v1LqdyZAhQ8iVKze9f1qQ4mH+Nu+OYPKSrUzU65Nl/GyiyFarlUbx8aR/8oSicXF8YTSy3mrFIAjkFQRKq9UUVanIJghcstv51mSiRkICvk+eEBAXx0qLhZVaHfdu30et1jBixAgn/lKFpOAWkWCqVKnCuXPnXvrs/PnzqdbFQFlDfDc+Pj7MnDWLBg0asHzrPj7/sOqL7+x2Oz1GzMZPhFkGPSoHjF+CKPKb1UqI2cxBu53cgkBHrZaKz97m/QXhjUZVFEUuPxsp7rfZWGy18ovFQmWVitM3Ixk5Yw0j+7R0+Lf/l7lrd3LwxAV2796NwWBwWr3OxsPDg1mzZ1OrVi0WrP+b9k1rpUi7j2Ke0P+n+TTUaunroPF7YLczz2JhhsXCFVGkrErFtzodQWo15dRqMr7lHrOKImfsdiLsdnZZrQw3m7ED5VUq9sXHs3XrVlq0aOHgr1NIDq7I3OP0Xrtfv36Ehoby008/cfHiRZYtW8asWbPokUq3DSsGMHF88MEHtGr1Od9NXsG9f2WLn7lqBwdOXGSuVvtOh/bXscNqpWhcHG2NRjIKAhs9PLjs5cUovZ5GGg053uEzKAgCBVQqPtFqmWQwcMPLi8UGAyJgBWYs387yrXsd+MWvcuPuQ0ZMX0OXLl2oXr26U+p0JTVr1qRDhw4Mm7aKW/cepUibA8cvwfwknhk6XZJnAkRRZKbZTL64OIaazVRTqwn19CTC05NBej21NZq3Gj946iZTUq2mrVbLQg8Pbnl785Nez91nnW/rVq04efKkw79PwXFc0dc6vdcuX74869atY/ny5ZQoUYIff/yRSZMmyXKx3xkoBjDxTJo0GbVWT5+fn06rXbl5jxG//kZPrZb3NEmbjHgsinQ1GqmTkEABlYqzXl787ulJQ40GdTKmUA2CwBdaLfu9vAj19KSISkWPEXMYOWMNZovjqYKsVhs9R83DN30Gxo4d63A9Kc348ePx9vGl16j52GyunQrd+s8RVv0ZyhStDv8kPk/X7HbqJiTQ1WTiM62Wm15eLPTwoGIy/UYzCgJf6XSc9/LiN4MBL6uV8mXLsmTJEoeTEys4hlsYQICPPvqIEydOYDQaOXPmDJ06dXJFM05hz549NG3aFKPR6FD57Nmzs3PnTsqWLetkZamPzJkzM2/+fLbtPcrPczfw67Jt+IoiP+uTFit0v81Gybg4llgshOj17PDwoIgLXkAqqtUc9vTke52OSQs28X6bYVy4dsehuob++ht7D59l4aJFbrXOnT59ehYuWsTfh04xPGSVy9oRRZExs9byvlbDF0l8GVpksVAiLo5zdjvbPTyYbTCQ2cn3g0oQaKHVcs7Li8Y83SnbuFGjFIlqklpYvHhxsmYCq1Spws6dO536/KT5YcuNGzdYt26dw29zHh4e1KxZ852x6RSe0rBhQ0aOHMnYuRtYtukfOqvUSZr63Gq18n58PDlVKk54edFNp3No3TCx6ASB4Xo9Bz09sVyPpH7HHzl69mqS6li6eQ8hK7bzyy+/uKUDdZ06dZgwYQJTlv7Oyt/3v7uAAxw+fYVjF2/QT6NN0ohtjMlEG6ORZhoNJ7y8qJtE45lUMgkCyz08WGcwsGfbNmpWr05kZKRL20wtnDhxgj///NPh8lmyZKFmzZponegbnOYNoLKLM+UZPHgw5cqVw2Sx0ikJN/Nmq5WPExKop9Gww8ODfCk47VxWrWavwUB+o5lG3UYn2gjuDj9NvzGL6NChAz179nStSBfSp08f2rZtS+/R89njgjBpc9b8RV6NhvpJ8Mf90WRikNnMEJ2O+QYDvin4DDfWavlHp+Pu2bPUqFKFe/fupVjb7kqacIR3N5Q1PGlIePKERhoNORN53v+2WmmekEBDjYZVBgMGCV5YMgkCf+kNFLXaaNZrLOeu3H7r8fuOnKPlgMnUrFmTkJAQt37JEgSBGTNmUL16DT77ejKhxy44re5HMU9Y+0coXVWqRK/fTjWbGWo286NOxwgn+I06Qgm1mn90OqKvXaNe7drExMS8u1AaRhRF2fWz8lIjAYoBTHnOnj3LmfPnEz36i7TbaW40UlWtZrnB8NZ0N67GRxD4XW8ge4KZ1t9MfqOf4O5Dp2nRfyLBwZVZt369pMlunYVer2f9hg2Ur1CRZn0nOC1g9ra9RzFZbbRL5P2wz2qlj8lEf62W7yQ+r4VUKv7U6bh65gxdu3SRVIvcUUaAMsTPz486deo4fGGio6NZsWIFDx8+dLKy1Et4eDgAlRMx3SWKIt1NJgRgmcGAXgajqIyCwEqdjis37/HznPWvfL9k8x5a9P+F6tXfY/OWLXh4eKS8SBfh6enJlq1bqVylKi36TWT51n3JrvPImSsU1mrJmohnMF4UaWc0UlGlYqxEI7//UlKtZppGw4qVK1m7dq3UcmRL4cKFqVKlisPlr1y5wooVK7DZbE7TlOYNYL169fjjjz8cNoDXr1+nZcuWXL582cnKUi8REREU1OlIn4jO6zerlbVWKyF6faI6yJQiQK3mB62WKUt+fxE31Gq1MXjSMnqOnMuXrVuzYePGVGX8nuPp6cmmzZv5vNUXdBsxm++nrEiWi8Sx05cJesMmtFt2O2ssFr4xGqmfkECe+HiuiiLzDYZkubs4m5YaDY21Wrp16sSDBw+kliNLunXrxsyZMx0uv2fPHlq2bKkYQDmhTKEmnYiDBwlMRHite3Y7PUwmmms0tHBxVghHGKDTUVatpsfwWVy8fpemfScwa9VfTJ06ldmzZ6eKac83odfrmTt3LpMnTyZkxR806zeBm5FJnwWxWm2cuHCDwGezAQ/sdiabzTRPSCBnQgI54+JobjSyPJ0nMcXz8dBuZ5ReT1GZ5ZUTBIHpOh3W2Fh6ufFmJznjNn6AaQnFACYNURQ5cvQo5RJxvqZYLJhFkWlJ9BNMKTSCwHydjnM3Iglu+R2Xbkfzxx9/0LNnT1lMzbkaQRDo3bs327dv5/yNRwS3/J7Zq3ckKXboxRt3SbBYMQCtjUZyJsTzjdXK3YB8NP+sLot/7sWZTb9wcstkPHQaSms09JfhyxBAdpWK8c+mQk+fPi21nFSHYgBliGIAk4bZbCYuIeGdkT7Moshsi4XWiVwbkoqSajW11WpEmw29Xs/Ro0d59ChlwobJhdq1a3P6zBk+/+JLBoxfwgddR79zhyxAvNHE8q37UKtU9DCZ+CejD991b8GZLZPYMvM7hvf8hIbvBeKXJQMXr99lV/gZ+iUz0o+raaXRkFWrZfr06VJLSXW4wmVNvj2Lm6D4ESaNhIQEAN4VBnqt1co9UaSbTN/2/00vnQ4bkC+rN4MGDSRHDn/at2/PzZspl11eanx9fZk5cyZ///03j+JFqrUexg/TfuPG3VenRaMfx/H9lBUUa9iPKUt+p1bFEqya2J8j68bT+4sGZErv80qZeWt3kVGt5hMXO7onF50g0EkQWDhvnpIj1MnY7XaEtwS2d4Q0bwAXLFjwSvqmpFCuXDmioqKSVUda4nnEnXfdwiEWCzWepS6SOx+q1eTWaMie2ZfTGybyTbuGbN20nuLFizFjxowUTyckJTVq1ODY8eMM+OYb5q7/h9JNB9Bq4FR2hZ3Ebrez6e8Iglt+x8L1f9Ou8XscWTOWVb/0p07lUm+cRYk3mli2cTcdVCpJ/D+TSmetlriEBJYsWSK1FFnxzTff0KxZM4fLt23b1um77dO8AYyJiXljrsLEoNFoSJ8+PRqZv5nKhecpgN4WefW23c4emy1JUWKkRC0IdFCpWL8jjIy+3vRv8xFhy0fSpGY5unXrxns1arySIiw14+HhwciRI7l16zYhISFcf2iiSZ/x5K7dnS8HTaVssbyErRjNDz0+eWOC5H+zM+wk0fFGOrnJpqLcKhUfaDSsWLpUaimy4v79+9y9e9fh8jqdjgwZMjhRkWIAZemcmZrR6/VoNRoeviX2avizEVN1Nxj9PaeqWk2cycKF60+DZaf38WLKt+3ZOG0gN69eoHTp0owePTpNjQZ9fHzo0qUL/b/6Ch9vb3Q6LfNHdmfZ2D74Z018R3b0zFWya9QUdIPR33OqCgKHDx9OU9f7Xcixr5WXGgmQ40VJzahUKkoWL87Rt/jyRNhsZBEEcrpRh1fumbH+b4zQ6oHF2L/kR7q2eJ/vvvuOxh9/TGxsrAQKUx6LxUK3bt1o164dH1YvTfjK0TSpXSHJazhHz1wh0MlrP64mUK3mcXw8Fy44L2ScuyPHvlZeaiTg+cKqQsoRWLEiEW8Z3UXYbAS+I5Gt3EgvCBTQal4bJNvDoGN4z09YOaEvu//eSeVKlbh06VLKi0xBHjx4QJ3atZk7dw6TB7VlxtBOZPT1TnI9oihy9PQVAgX36qqevxBFRERIrEQ+KAZQhpQvX57evXs7XP7ixYsMHDgwzW19Tw6BgYGcMptJeMM06GG7/YVjtDsRKMKxU2+OCFS3cml2zPke45MoKlQon2o7x6tXr1KxQgVOnTjGhqkDaNP4PYfrun0/igeP49zufsgkCOTV6VLtNXaEjz76iM8++8zh8v/88w/Dhg1zoiLFAPLee+8xfPhwh8tfvXqVsWPHpplpLWcQGBiITRQ5/IZp0Hui6FbTn8/JKQg8ePT2+6BwXn/+mvM9BXJkolatmoSFhaWQupTh8uXLVK9WDbslnr/mfk/lMkWSVd/9Z+fTLe8Hnm78UHhKy5Yt6ZKMgOEHDx5k8uTJTlSkGMBkozjCJ50yZcrgnzUri63WV76ziiI2cIvt7v/FQxAwmS3vPC59Oi/WTvqKgHz+1KtXl8OHD6eAOtdz/fp1ar73HjqVja0hg8iTiB2e7+L5+XyX36gc8RBFjMa37XdWSAqumEJVeu1kohjApKPRaOjcvTtL7HZi/jMN+nxM6F4TXk9RAbZE7vrz8fJg5cS+FMyZhQ/q10+WK44ciI2N5YP69RHsJjb+OiBJuzzfxvPzKefoL29CBU4N3JzWUQygDFEMoGN06tQJoyiy2PLyiEn1zCAa3+ImIVeMoohel3jfxXReHqwc3weDBhp//DHx8fEuVOc6bDYbrT7/nJs3rrFqQl9yZM3otLoNz86nW94PgvDC71Uh+SgG0AUkJCQka/3OYDCQN29exRE+ifj7+9OkSROmiSK2f3VuA8xm1ECUG3Z40YCXZ9I6vMwZ0rF0bC/OnTtD+/btXkTKcSeGDBnClq1bmTOiC4Xz+ju1bu9n59Mt7wdBwNs76TtfUytPnjwhLi7O4fLp0qUjd+7cTlSkGEB++uknSpYs6XD5WrVqceXKFbJmzepEVWmDr77+mnMWC5OfjQI3W61MtljIlT0Tx9zQgfgoULxIniSXK1koN9OHdGTlyt+YM2eO03W5kr/++ovRo0fzQ/cW1K1c2un158uZFb1G7Xb3g0kUOW02J6tvSW20bNmSzz//3OHyPXv2fJFM21mkeQMoR9+UtEJwcDC9e/fmO4uFwzYb3SxmalcIoE7VMoS72ZqPRRQ5brVSpmheh8p/XKs8rT6qxoABX3P79rszKciB+Ph4OnfqRJVyRenVqr5L2tBqNJQokJNwN1tLO2m3YxFFAgMDpZYiG+TY18pLjQTI8aKkJUaNGkWOXLloYDQSpVYxcXA7yhbLx3mLhVg3mvY6ZbdjEkXKFs3ncB2jen+GXqOiR/fubjEVOnToUG7fvsWUQW1d+gyVLVGACJV7vRBF2GyoVSpKl3b+qNhdkWNfKy81EiDHi5KW8PLyot+AAUTa7Qzp/gm5/TK/GEVFuNFb/yGbDZUgULKw42sU6dN5Me6rVqzfsIG1a9c6UZ3zCQ8P55dffmFQh48pkDu7S9sqXTQvZ83u9UJ0yG6neOHCeHp6Si1FNsixr5WXGgmQ40VJSxiNRqZOmUJgQAE6NX8fgCJ5/cmRyZelr/ETlCvL7HYqliiAl0fystc3qhlEg+rl6NG9O1FRUU5S51wsFgsd2rcnoGAuenxez+XtvVc+AARYaXm3j6UcSBBF1ooidT/8UGopskKOfa281EhAq1atmDBhgsPl//77b+rVq/ci0atC0li6dCnnL1xg6rftUKuf3o5qtYq2zd5nmc3mFrv/Ttls/G2x0PGT2smuSxAEJgz4ktjYGKZNm+YEdc5n9erVHD9xgimD26JNgd3PubJnol7l0kyz29xianil1cojq5WuXbtKLUVWfPvtt8kKOzlv3jzatWvnREWKAaRMmTJ89NFHDpe/c+cOf/zxh1s8mHJDFEWm/fordSqVoniBnC991/rj6liAhW7w1j/dYiGrrzcN3wtySn1+WTLQom5FZs6YgVWGo+CQadOoFlicssUcX+9MKh2bv88xi5UDbrAbNMRup36dOhQsWFBqKbKiZs2aVKlSxeHy586dY9++fU5UpBjAZKM4wjvOwYMHOXL0KB2a1Xzlu2yZ0tOoZhAhdttLfoJyI1YUWWS30bppTXRa542G2jetxc1bt9i8ebPT6nQGx48fZ+++fXR8zTVzJbUqliBv9kz8ajGnaLtJ5ZDNxiGzme69ekktJdWhOMLLkOcG0J1S98iFkJAQ8vhnpXZwqdd+371lPS5YrEyR8ShwsMmETa2mXRPnGoQyRfNSvmRBQmQ2DTp9+nSyZ8lAg+plU7RdlUpFjy8+YIXFyh4ZjooBbKJId5MJb4OBBg0aSC0n1eGK1HWKAUwmygjQMR48eMDKlStp17jGi7W//xJUogCdW9TmW4uZCzKc+tpltRJisTCs5ydODf/1nA5Na/Lnjh2cP3/e6XU7QmxsLIsXL6JNo+opsvb3X9o3qUWFgPy0s1iIl+GswESLhQibjSdGIydPnpRaTqpDGQG6gHPnziUrukCuXLlo0qSJYgCTyPz58xHtdr5oWO2txw3r3gK/rBlpZzbJair0iSjS3mKmcqlCL3avOpvGtcqTMb0PM2bMcEn9SWXx4sUYjUbafPyeJO2r1SqmDevMLQG+NZkk0fAmzthsDLGY6fppHfyyZGT69OlSS5IdERERnDt3zuHyJUuWpHbt5G80+zeCKLPdG7Gxsfj6+hITE0O6dOlc3l7Xrl2JiIjg0KFDLm9L4Sl2u51CBQsSVMSPWT90fufx+46c48Nuo/lJp2OwPnluBs5AFEU6mEysFGDfslHky+m6MHjDfv2NhZv3cevWbUl9ykRRpERAAAWze7NodA/JdAD8umwbQ6asYLuHB3VkEIM3QRSpYTISlTUDe5aOYvKSrUxd/ge3b99JkT7MXQgODqZkyZLMnj3b5W0l1o6k+WGLHH1TUjsRERFcvnKF1o2qJ+r4KmWL8HXbhnxrNjNfBuuBQ5/pGPtNa5caP4DWjaoTHR3Dn3/+6dJ23sXp06c5feYMrRu9fcSeEnT7tC61KgTQxGTioMTBEsyiSHOTkVOCwMwR3fAw6GjdqAZxcfFs3bpVUm1yQ459rbzUSIAcL0pqJzQ0FJ1WS/kSBRJd5rsuTWn7cQ06Go2SuUaIosgPJhMjzWZG9PyELz5yvTHInysb2bNkIDQ01OVtvY3Q0FBUKhUVSxWSVAc8nQpdNKY3JYrlpb7JRKhERtAoinxqMvGn3c6ScX0IDMgPgH/WDOTLlV3yayY35NjXSj9/IDGu2Fmk8HbCwsIoVSRPknLnCYLAhG/aANB2w27uiSJfabWoUujamUWRr00mplosDO3WnN5fpMwuP0EQCCyej7AkdqZ2u51Lly5x9epVEhISMJvN6PV6PDw8KFiwIHny5EnSfR8WFkax/Dnx8fJI6k9wCV4een6b/DWf9ZtI7VOX+U2vp0EKToc+EkU+MZnYh8iSsb2pVbHES98HFcub5GuW2pFjX6sYQBm+laR2Qg/sp3ZQ0p2E1WoVvwxqSwZfb75ZtIVtdhtzdXryuvj6HbXZaGsxc9JmY8KA1nRoVsul7f2X8gEFGL9oKzabDbVa/dpjzGYzW7ZsYf/+/UQcPMjhw4eJefLkjXVm8vUlsHx5AsuXp2rVqtStW/etOS1DD+wnKCDlHN8Tg6+3J2umDqDd4F/58MAJOmm1jNfrSefiTnaDxUIXqwWTTsvq8X2pWq7oK8cElSjAhl9XYTKZ0Mtg3VoOyLGvlZcaCRg9ejRLly51uPycOXOcnqQxNfPgwQMuXb6SpOnPfyMIAsO6t2Dt5K857+tNyYQEZpjNLonEY3425Vk+IQFrjqz8NW9Yihs/gMCA/Dx5Esfp06df+e769et899135PLzo2nTpqyaMoUMBw7wjdnMNg8PLnt5cdfLi0fe3tzx8uKilxebPDzolZCAfvduFk6YwIcffkj+3LkZNWoUkZGRr7Tx+PFjTp0+82KKT054GvQsn9CPCQNas0wFJY0J/OkiP8FHosgXRiONjUbKlC/OgZWjX2v8AIICCmA2mzl27JhLtLgj69evZ8iQIQ6X79evn9P9K9O8AfTz8yNPnqQnMX1OfHw8Dx8+dKKi1E1YWBjw9A05OdSqWIL9K0fTrGE1uplMBJmMzLdYSHCCIYwWRSabzZQwGRlptdCvXUN2LR7hcK6/5FK2WD5UKtWLcwdw/vx5GjdqRL68efl17Fg+ffyY056eXNXrWWMw8K1eTz2NhnwqFdlUKjIIAtlVKgqoVHyk0TBMr2ejwcAtvZ4IT0/qPXjAqGHDyJUzJ5+3bMmNGzdetBUeHo7dbqd8CXmG9lKpVHRoVov9y38ib6lC1E1I4EOjka1Wq1NcZ67Z7XxrMlHEmMAWrZrpQzuxYmJ//LJkeGOZkoVzo9dplXXAf5E3b16yZcvmcPmYmBhiYmKcqEgxgMlGjsN6OXPx4kUMeh15/DInu650Xh5MHtyOjdMGkqlcUTqYjORISOAro5FjtqSFUDOLIgdsNjoYjfjHx/O1xUKpGuXYteAHvuvc1KlhzpKKt6eB3H5ZuHjxIjabjYkTJ1K6ZElObtvGdJ2OWwYDUwwGir1hevRdlFOrmW0wcMvDg3FqNbvXrKFEsWLMnTsXURS5ePEigiBQyMVpj5JLHv8srP/1G6YP7cTNPNn5MCGBQkYjY0wmrtjtSZoleCKKbLFaaWQ0kj8ujmlqgWZNa3Fg5WhaNqjyzrUsnVZD/lzZuXjxYnJ/lsIzXNHXpvk1wOSiGMCkcefOHbJnzuDUxfDqgcWoHliMKzfvMX/dLuav/5uJcfF4qlSUUasJ5Gknn0kQ8ADsgBG4a7cTYbcTAZywWjGLIjkzp+erZrX4slF1smVK7zSNySVbJl/OnTtHjapV2R8aSh+tllF6PZ5OPI8ZBIE+Oh1tRJH+JhMdO3Zk9cqVFC9ZkiwZfdFoHDOwKYlKpaJlgyp89kFlIk5dZs6avxj2ZxiD4sxkVKsJVAkECipKqVSkEwQMgJWnvnxXRZFwm40IAc5ZrIhAyQI5+aVFbZrXq5TkVFfZMqXjzp07rviZaRLFAMoQm82mGMAk8NQA+rqk7nw5szKi16cM7tSE8FOXOHr2KkfPXmXryUtMvfPgleNVgkCx3NkpXaIAnxbNS5mieSlXLJ8sO/rsmdOxedMm8qpU7PbwoJoLdzymFwTmGQw012jo/Pff7N69mxzZM7msPVcgCAJBJQoQVKIAo/t+zsGTFzl69hpHz1xh4alL3Il6/EoZg1ZDiYI5qRqQn57F8lGuWD6K5c/h8Mta9szpuXH7VnJ/isIzXNHXpnkDuGnTJqxWK02aNHGofJUqVRg8eLCTVaVe7ty+TdaMro2O4WHQUS2wGNUCi734LN5o4kmckQSTGUEQ8NDr8PEyYNDrXKrFWWTLlB4vlcBRvR6vFNpK3kCj4YRaTYOEBCJu3mVPxJmXzqm7kMHXm3pVylCvSpkXn8XGJRCfYMJoMqPRqPHQ6/D19nTqy0/2TL6EnT7ltPrcnXnz5lG4cGGqVq3qUPlmzZrx5C07mx0hzRvA+fPnYzQaHTaAlStXpnLlyk5WlXqJj48jc/qU3xbuadDjaXDf7eheHnoyQIoZv+dkEAR2eHjQyJjAZ/0nsmHaoGRvYJID6bw8SOdin0ZPD72SKPtfjBo1ik8//dRhA9i0aVMnK1I2wShreCmM1Wp9Y/YHhTejVquQKuiXlyCw0eBBGbtI8z7juXr7vkRK3AuNWo3FIs/UTVIgx75WXmokQI4XJTWjVquRV/h190C0i0i5MuklCGzWG/A1men145wXacAU3ozdblde9v6FHPdLyEuNBCgGMGXRaLTKW7EDmK1WNBKHkUovCMzT6thz5Bzz1u6SVIs7YLba0GoTH+4vtSPHvlZeaiRAq9ViMBgcLh8bG8vdu3edqCh1kzlLFh5Ev7oDT+HtPIh6TBYZjJxraTR00WoZNnWFMhX6Dh5GPSZz5uT7u6YW9Hp9sl4IHj586PSgI2l+E8y6deuSVX7cuHEsWrSIa9euOUlR6sbf359zxx1PQJxWibwfhZ9M5o7H6fVsMyYweMISlk/oJ7Uc2XL3YTT+/jmkliEbLl26lKzy7dq1QxAENmzY4CRFyggw2chxWC9n/Pz8uPMgWmoZbsfdyEf4yySSvo8gMFSjZdv+Y8oo8C3cfRCDn7+/1DJSDa7IJqH03MlEFEXFACaBnDlz8ig6lifxRqmluA12u52b96PIIRMDCPCZRoOvSsWCdcpa4OsQRZEbdx+SI4cyAnQWrhhsKD13MlFGgEmjbNmyABw5c0ViJe7DpRuRxCaYKOdgrE9X4CkItFWpWbz+b4wms9RyZMete4+49zCacuXKSS0l1aAYQBmiGMCkUbx4cby9vQg/lbz1gLRE+KnLAFSQkQEE6KrT8fBxPJv+jpBaiuyIeHbNKlasKLGS1INiAF3AyJEjmTVrlsPl27Zty/Tp052oKHWjVqupUL7Ci05d4d2En7xEUa2G9DKaAgUoolJRQqtl/9FzUkuRHYdOXiJP7lz4+flJLUU2dOnShW3btjlcfsSIEXzzzTdOVKQYQLZt28b+/fsdLl+8eHFq1Ur5JKnuTMXgYMJPXXFJEtvUSPjx81SU6akKFEWOnlReZv5LxOkrVAyuJLUMWbFs2TLOnj3rcPng4GCCgoKcqEgxgMoUpgQEBwcT+SCKG3eVRMLvIt5o4tTl2wTLbPrzOYFqNacu38KsBDd4gcVq5ejZqwQHB0stRVa4YhdncknzPb+yizPlqVKlCjqdjg07D0ktRfZs/ecIVrudmi5Mf5QcAtVqzDYbpy/dlFqKbPhj/3ESjCZlZug/yHGwIS81EiDHi5LayZQpEy1atGDeur+VmJLvYO5vf1JTq6GITO/REs90XbyuREN6ztw1uwiuWJHSpUtLLUVWyLGvlZcaCShRogQFCjie3uXEiRPs2bPHiYrSBt27d+fKzUh2HVTypb2JkxducODkJbqr5Tn6A/B+9m+80SSpDrlw6fpddoadoHuPHlJLkR0VK1ZM1qagvXv3cuzYMScqSgED+PPPPyMIAn379nV1Uw4xd+5cBg4c6HD5adOm0a+fEg4qqVSqVInSpUoyZ81OqaXIlnlrd+KnUfOxTKc/AVSCgFYQMJktUkuRBfPW7SJTxoy0aNFCaimy459//klWTr8BAwYwdepUJypysQE8dOgQM2fOpFSpUq5sRlLkOKx3BwRBoHuPnmzfd4zrdx5ILUd2xMYlsHLrXjqr1GhltnHg34iiiEUU0cnYSKcU8UYTy7bso0PHjskKsK/wetzKD/DJkye0atWK2bNnkyFDBlc1IzmKAXSczz//HG9vLxau/1tqKbJj5e/7MJotdJJ5Op3nE596vbx1pgRrdxwk+nEcXbp0kVpKqsStDGCPHj348MMPqV279luPM5lMxMbGvvTnTigG0HG8vb1p06YtCzfuUabQ/oXdbmfubztootaQQ+b31sVnm5hyZM0osRJpEUWROWt28kH9+uTPn19qOakStwmGvWLFCg4fPszo0aPfeezo0aPx9fV98ZcrVy5XSHIZihtF8ujRowfRj+P4ZdEWqaXIhkUb/+Hs9bv0kfnoDyDimQEsXTSvtEIk5rdtBzh65gp9lf0ALsMtRoA3btygT58+LF26NFHz4IMHDyYmJubF340bN5wt6a00atSI8ePHO1x+woQJrFmzxomK0hZFihRh4MCBTFi4mTOXb0ktR3Lu3I9i6OTltNNqqeoG62oRNhsF/bOQzstDaimS8SAqlsGTl/PZZ59Sp04dqeXIErPZTMmSJdm0aZPDdWzbto2RI0c6UZULDGBERAT37t2jXLlyaDQaNBoNu3fvZsqUKWg0Gmw220vH6/V60qVL99JfSnLu3DkiIyMdLp8xY0ayZcvmREVpj++//578+fLTe/R8bLa06xcoiiJfjV2Ih8XKBL1eajmJ4qDdTqniaXvKb/Avy0GlYfLkKVJLkS02m42TJ08ma4krW7ZsTt9P4nQD+P7773PixAmOHj364i8oKIhWrVpx9OhR1DIL6aSs4UmPwWBg9pw5HDpxkTlr/pJajmRs3BXO1j1H+UWjJYOMd34+55EocthmIz4h7foAbt93lFV/HOCXXyaRNWtWqeXIlucBL+TW1zp9jsXHx4cSJUq89JmXlxeZMmV65XM5oBjAlCchIYHjx48TERFBeHg4l86fx5iQQKYMGfhh2irqVSlN3hxpqzN5EBXL1+OXIAhgc5Mg4QssFuyCwK5Dpzh35TZF8qWt7OfRsXH0Gb0AXx8fZk6bxupVqyhbrhxBQUEEBgbir2SDf0GaMYDuhmIAU4a4uDiWL1/OnBkzCD9yBJvdjlYQKKHTUdRmw0sQKCWKrBZFPu3/CzvmDcUnjawrmS1WWn8bAiotVYIrMT0igi+kFvUO7KLIdFGkWfPmnDhxglaDprJjzvek9/GSWlqKYLXaaPfdNGKiYmkiqNAePswdQSBk+3YeWJ7uaPbLkoXmn31Gt27dKFasmMSKpUWuBlAQZZaTJjY2Fl9fX2JiYlJkPXDo0KEEBQXRqFEjh8qvXr0as9nM559/7mRlqYNz584xffp0FsydS+yTJ3yo1fKRSkWgWk1JlQr9f6b6TtlsVDIZqV65FIvH9JbdA+MK+o9ZyOLNe9m5cyf37t2jWbNmHPH0pIzMlgv+zR9WK/USEti7dy/ZsmWjQoXylCuSi98m9EOtTv3X7LvJy5mxYjvbDR68/6/NSqIockMUibDZ2G+zsQi4Z7FQs3p1uvfqxccff4zWDXb3OpuEhAS+/vprOnToQLly5RyqY9q0aRQrVixRQcYTa0fSvAFMLs2aNSM+Pp7ff/9daimyIj4+nu+++47JkyeTWaOhoyDQWaslbyIM2iarlY8TEujX5iOGdmueAmqlY+ZvfzJw4lJmz55Nx44dsVqt5M2Zk8qPHvGbTKOJ2EWRWiYTUYUKcfTkSQRB4M8//6R+/fp0bvE+o/t+Lru0N85k0cbd9P5pPpP1enrrdG891iyKrLVamWa3s9dspmSxYsxfvJjAwMAUUpt6KFCgAJ988kmi3OsSa0dS/6uai7Hb7bLb2CM1e/fupXRAADOmTGGsTscNvZ6f9PpEGT+AhhoNY/V6Ji7czMSFm12sVjqWbt7DwIlL6d+/Px07dgRAo9EwduJEVlksrLXIMzjATIuF3RYLEyZPfmHo6tSpw9SpU5mx8k9GzVorsULXsebPUPqOXkAXrZZeiRjJ6QSBz7Ra9uj1HPL0RH3xIhUrVGDIkCGYTGl385AjuKKvVQxgMlHWEP+PzWbj66+/pnr16mS5fZujHh58rdO9Ms2ZGL7W6Rim0zFi+mrGz9/oArXSsmjjbnqOmkenTp1e8UNt2bIlHzdsSDebjQcySxd11W5ngNVK506dXony1L17d8aMGcP4+ZsYOWMNMptcSja/bdtPp6EzaaXREKLXJ3mUG6RWc1CvZ6hGw8+jRhFUtixnzpxxkdrUh1s4wqc1FAP4FLPZTMvPPmPSxImM1enYo9MlO4fdMJ2O4TodI2eu5dtJy7Fabe8uJHNEUWTSoi30/mk+Xbp0YcaMGa90pIIgMGPWLKyenvQ0myVS+ip2UaSDxUKmbNkY94bgEd988w1jx45l/IJNfD1uMRar+2eKF0WRkBXb6fLDLFprNMzX61E5OMWrFQSG6vWEe3hgv3iRapUrExER4WTFqRPFALoAq9WarKSsPj4++Pr6OlGR+2GxWGjetCkb1q5ltV7P1zodaiesAQnPOospej0zV27n034TiX4c5wTF0pBgNNN52Cx+CFnF999/T0hIyBsf6OzZs/Pr9OmstFgYK4OpMlEUGWAysdNsZu7ChW9dVxkwYACzZs1i4cZ/aNJnAg+jH6egUudiMlvoOXIu305azldaLXP1eqfc26XVavbo9RSMj6dm9eqEh4c7Qa18EUURq9WarFmBDBky4OXl3F3GaX4TTKZMmfjmm2+SlRMwLWO32/nyiy9YtXIlG/V66rsofNcOq5VPzCYyZsvEorG9CSjoXjFjr96+T7vvpnP26m0WLFjIJ598kqhyQ4cO5ccffyREr6fbOzZcuApRFBlhNvOD2cyvv/5Kj0Qme/3nn39o1rQpXgY1C0d1p4ybxQu9GfmQDt9O4+iZK8zW6fnSBbs3H4sidc1mLnh6smf//lTrLnHjxg1y587Ntm3bqFevnsvbUzbBJBIlmHXymDJlCsuWL2epTucy4wdQW6PhoMEDj/tR1Gg9jFGz1rpFBgmbzc605dup3Op7HsVZ2bdvf6KNH8Dw4cPp26cP3U0mxkswHSqKIoOeGb+ffvop0cYPoHr16hwKDydjFn/e7/AjP0z7jQSjfKZ034Tdbmf26h0EfzKYm+eusdvg4RLjB+AjCGzR6fCLi6Npo0YYjUaXtCM1z8dZcutr5aVGApQ1PMe5cOEC3w4aRC+tlhYp4NtUUKUiXG/gW42GSfM3Ue3z7wk9dsHl7TrKyQs3qNNpJN9PWUGHjp05cfIkZcuWTVIdgiAw8Zdf+P777xlgMtHWaCQ6hSZtIu12mppMjDWbmTx5MoMHD05yHXnz5iXs4EGGDx9OyModVPlyKHsi5Lvx49yV23zQaSQDxi/hc7vIKYMHFV28yzujILBCq+Xy5csMGzbMpW1JhVwd4eWlRgIUA+gYdrud9m3a4G+3MzoFAzfrBYHhej2HPTzIEPmQD7qMYsC4RUQ+jE4xDe8iOjaOkTPW8F67H0iw69i3bx9TpkzBx8fHofoEQeDHH39kwYIFrNPpCDCZ2OrCzSWiKLLcYqG4ycR+Hx/WrVtH7969Ha5Pq9Xy3XffcezYMfxzF6BhjzH0GT2f2/einKg6ecQ8iWfM3A1U+2IIUeevs9vDg5kGA74p5M8YoFbzg0bD+HHjCA0NTZE2UxLFAMoUxQA6xtSpU9l74ADzNBq8JHB6LqFWs09v4Be9nmXr/qZEo/50+D6E/UfPSbb9/ujZq/QcNZdijfozeek2vvvue44cPUqlSpWcUn+bNm04dfYspWrW5MOEBNoajVxyspvEKZuNZmYznxuN1GnalFPnztG4cWOn1F20aFF2//MPISEhrN0ZQckmX9Hm22n8E3FGsmt24sJ1+v68gOIf9mHcnPV8pVZzTG+gugSpqAbodARqtbT78stUNxUqVwOY5jfBLFiwgKCgIIcDdQ8ZMoQsWbIk6w3Z3TCZTOTy86PxkyfMkkG0kihRZKHFQojNxgWrleJ5/OjwSR0+qV/J5fFEE4xm1v11kLlrdxFx6hK5cuakS9eudOjQgezZs7ukTVEUmT9/PgP69+dRTAz1dTq6q9U0UKsd2qFoFkXWWa2E2O38YzbjlzUrU0NCaNasmQvUPyUmJobFixcTMm0aZ86epXDeHHRo+h6fNaiCr7eny9qFpzs7N+4KZ+5vOwg9dQl/jZrOKjWdtFr8Je6gT9hslIqPZ8GCBbRp00ZSLc4kJiaGVatW8eGHH+Ln5+dQHV9++SUtWrRIVNhKJRRaClGtWjXy58/PwoULpZaSYixbtoxWrVpxxtOTojKKgmMXRXbabIRYLWywWPHQ6/jsw6rUqVyKoID8ZM7gnPsp+nEch09fYWfYSZZu2UdUzGPq1qlD9x49+PDDD9Gk0OghISGBlStXEjJlCoeOHCG3VksdIPBZrNVSKhWG1xjEeFHkqN1OhM1GhM3GNkEg0mKhRtWqdO/Vi8aNG6NLoR2noiiye/duQkKmsW7denRaNS3qBlOvShmCAvKTNZNzXIxinsRz5PQVdh08xdKNu3kQG0ctrYbuag2NNBq0MgrdVs9kIrpECcIU/8CX8PHxYcSIEfTr1++dxyoGMIWoUqUKhQsXZv78+VJLSTGqVqqE/vBh/pJx0tYbdjuzLRYm2GzEP0vCnDdnNsoXz0dgQAFKF8mDf9YMZM+cHr3u9Rt4zBYrkQ9juPsgihPnrxN+6jLhp69w/srTzPWZMmakTdu2dO3alUKFCqXYb3sd4eHhzJ07lwN79nDq7FmsNhsaQSCPTocnoANMQDxw1WzGLorotFpKBQRQpUYNOnXqREBAgKS/4fbt28yZM4c5s2dz4+ZNAPL4ZyUoIB+BAfkpUzQv/lkzkj2TLwb96w20xfrsmt2P5uTFG4SfukT4qSucu3ILURTxVatoo1LTTauV1cvbv9lgsdDYaOTQoUMEBQVJLUc2eHl58dNPP9GnT593HqsYwBSicuXKFCtWjLlz50otJUU4duwYZcqUYbXBQDM3iGq/0WLhY6ORkSNHcv/+fcJCQzl85Ajmf7kUZPD1IX06L7QaNQICFquNmCdxPIz6f/ZqtVpN6VIlCa5UmYoVKxIcHEyhQoVkGfTZaDS+yLd45coVjEYjJpMJvV6Ph4cHhQoVIjAwkICAgBQb6SUFURS5fv06YWFhhIaGEhYaSsThwy/FzsyQzpv0vt4vXbPYuHgeRsW+tOW+VMkSBFeqzB/bt5Pt5k32JiOKS0phFUXym0zU+eIL5s6bJ7Uc2eDp6cmYMWPo1avXO49NrB1J8/kAk0ta20SzcuVKsmi1NJJgk4AjfKjR4K/VEhUVxaRJk4Cna5jnz5/nzp07L/6ioqKw2WzY7Xa0Wi3p0qXDz88Pf39//Pz8KFSoEJ6erl2bchYGg4EKFSpQoUIFqaU4hCAI5MmThzx58rzwmTSbzVy4cIHbt2+/uGaPHj166Zr5+Pi8cs28vLy4fv06M2bMYLTBIHvjB6ARBNoLAhNWrmT2nDlpqn95G67oa92jF3MRoigSHh5O3rx5yZIli0N1lC9fnvz58ztZmXwJP3iQSiCrNZO3oRYEKoki4WFhLz7T6/WULFmSkiVLSqhMISnodDoCAgIcmqZ9HmasqkynPF9HNbWa4fHxnD9/nqJFi0otJ9nExcVx+vRpihcv7nA4s1q1apErl3MjQKXpVwtRFKlQoQKbNm1yuI6pU6cmalE2NSCKIhGHDhHoJsbvOYEqFYcPH05WzFcF9yUiIoLsMtjhmRTKPTPWqSVQ9rlz56hQoQLnzp1zuI6tW7c6nLj8TbjPHeEC5OqbIleuXbvGo9hYAt3oTRogUK3mcXw8Fy9elFqKggREHDqEu6WfzSAI5NfpUo0BlGtfKy81KYxcL4pcOXz4MPB0ROVOPDfYz/UrpC0Oh4e73awFQKDdzuFDh6SW4RTk2tfKS00KI9cArXLl/v37qIBsbtaZZBIE9CoV9+/fl1qKQgojiiL3o6LI4Wb3LEAOQeB+ZKTUMpyCXPtaealJYeT6ViJXjEYjBrVallv/34WHSpXqwkspvJvn7i4e7njPCkKquWfl2tfKS00Ko9frCQ8PT1Z+qrp16zJq1CgnqpIvdrvdbW8YlSBgs7l/RnmFpPG843U/8/e0c04t92zJkiUJDw8nX758DpU3m83kz5+fdevWOVVXmnaDUKlUBAYmb3n8+vXrREXJJ6q9KzEYDJjcdCel0W7Hw8O1cUEV5If+WbQik7zifSQKoyjiIYNYu87A29s7WX2t3W7nypUrxMXFOVFVGh8BOoO05Ajv7e2NRRR54madiVEUSbDZ8Pb2llqKQgqjUqnwNBhwx1fUKMDbDaJhpQSumkJNGz23C0lLBvC54/gxN5uWOW63I4Li+J5GKRkQwFE3u2cBjgoCJZOYQDm1ohhAmZKWDGBAQAB6rZZwN5sGjbDZ0KjVlCpVSmopChIQWLEiEW7mu2oSRU5YLMleokktKAbQBSQkJDB79myuXbvmcB2dOnWievXqTlQlX7RaLaVLliTCzd6mI+x2ShQrhiGVrKcoJI2goCDOmUw8dqOp+xN2OxZRTDXZIG7evMns2bN58uSJQ+W1Wi0DBgxweli4NL0JJjY2ls6dO7Np0yby5MnjUB2DBw92sqqUQRRFHj58yO3bt4mJicFqtSIIAhqNBl9fX/z9/cmYMeMrLg+BFSuy6+RJiVQ7xiFBoHzFilLLUJCIwMBAROCwzUYNNwnifshmQ61SvTJrYbVauXfvHnfu3CE+Ph6r1YpKpUKj0ZA5c2b8/f3x8fGRSPWbOXXqFJ07d6Z+/foOrcV7eHgwduxYp+tyj7vBRSR2WB0ZGcmFCxdISEjAZDKh0+kwGAzkzZuXXLlyyd4v7tGjRxw8eJCwsDDCQkM5deokd+5GYrFY3lpOp9Phlz0bJUqUJLhSJSpWrEi1atWYPn06ERqNW4REO2Gzcdxs5vtkuLoouDcBAQHkzJ6dpQ8fuo0BXCqKBFeowJYtW16khLp48QL37j94Z0xbLy9Pcvj7Uy4w6EXqrrJly77YESsFcvUDdI+7wUW87qLY7Xb++ecf9uzZQ0R4OOGhody6d++NdWROn56gChUILF+eSpUqUbduXbQS58kTRZE9e/awYMEC9u3dw/kLT2NgZvD1Iah4PprVLI1/1gxky5QevyzpSefliVbz1JhZrDaiH8dx90E0dx9Ec+d+FMfP32D82N3EPH66Bdmg0TDNYmGeGxjAEIsFvyxZaNy4sdRSFCRCrVbTpUcPRv/wA2NFkfQyf2E9ZrOxz2yG0FD2tWhBLr8sBBbPS5WPKuGX5WkS5+yZ0+PloUetUiGKImarjYfRj188tzfuPuTo6cOsW7sGk9mCVqulTOlS1Hq/Np06daJAgQIp+psUAyhD/n1RoqKiWLBgAdOnTuXClStk0GgIFAS+EAQCDQaKq1R4CQJ6wAzEiyIXRJGI+Hgidu1i7q5djLJY8M+alc7du9OpUyf8/f1T9PfExsayZMkSQqZN49Tp0xTI7UetCsX56vNaBAXkJ3+ubA6PVu12Oxev3yX81GXmrvmLpaevMEGvJ4OMO5NYUWSx3c7X3btL/lKiIC0dO3Zk+A8/sNBioY8MkwD/mxCLhXQGHVOGdiS4VGGyZ07vcF1mi5VTF29w6OQlwk9eYtb0aYwdO5b69erRvUcPPvjgA9Qp8CIrVwOYpjPCX7t2jbx58/JB/fr8vWsXVouF5mo13TQaqiYy5FeMKGIA9ILAcZuN6RYLi+12jKJIkyZNGP3zzxQsWNClv+PkyZOEhISwePEiEhISaFCtHB2b1aJ6UDGXTM/eexhDQKN+jNZo+UrGnclUs5l+FgvXrl8nR44cUstRkJjPPv2UI+vWcVqvRy3TF7doUSRHfDy92zdiUMfGTq8/wWhm7Y4w5qzdxZHTl8mTOzddu3WjQ4cODudETQwbNmygcePG3Lt3z6F27HY7jx8/xsvLC00iprETa0fkZY5TEFEUOXDgAJnSp+fQjh18L4rc8PBgmcFANY0m0YajQFwcvzyLN1hKrWa6wcBtDw8ma7WEb9xIqRIlmDJlikty0UVERFCjenVKlizJ2lUr6P5pbY6vG8/in3tSo3xxl61NZs3ky6cfVGak1cJtmbpERNrtDLfZaPnZZ4rxUwCgX//+XLBamfKOtW8p+dpkQqXV0Lbxey6p38Ogo9VH1dg1byg75/2vvfuOb6r6Hz/+StKsDsqm7CVYdqGFMlWGOJAhWwGZglAQxAmKfBUB/bhZZYMgW0UElb2VAm0pUqCATKVlCRQ6crPO7w+gP5ECHUlv2pzn45E/mpzc8+5tT973npzxPs3qVOCD/xtPuXLl6N+/v9sWjO/QoQN2u53ixYvn6P2JiYkULlyYzZs3uzQur0yAly5doluXLrzwwgu0TE3lsNHIWKORUjm4PXcKcc9JLKTREGEwcMhoZKDTyciRI3miRQuX7UeXlpbGW2+9RaNGjbh64SwLJw4j/sdPGfvy85QtWdQldTzMhBE9MQb4MdhqxcM6ERBCMNRqRRsQwBdffaV2OJKHCA8PZ8SIEYy12TjugRduG+125tlsfDiyZ666PbOqQc0qzBg3iCM/fcG4Ic/z04/fUyM4mG+//dblbVqj0aDLxUL67tpNwusS4B9//EG92rXZsXYtK00mVplMlMzFSXVya6HlzPhrNEw1mdhmNvP3/v2E1K2b6yuYrVu3UrdOHaZ8/RXvDenMtgXv06l1I/R5PLqtSKA/X44dwM82G4vt9jyt+2FW2O2sttmYMXu2W7t1pPxn0qRJlC1fngE2Gw4PunBLFoJBNiuPNwim//Mt87TuooH+jOj1DHuXTqRlWHX69OnDs888k6v50a4mJ8K7wN69e3m8eXOCrl/nkNFINxcMjHDy8JP4hI8PfxiNtLDbaffMM6xZsybb9aSnp/Pyyy/TunVrSgUa+O3bCYzu+1yeJ75/e/ax+nRv25iRNisnPeSK+ozTyXC7nW5du9K1a1e1w5E8jJ+fH/MXLeI3q5VPbn91oTYhBMMVhWs6HVPHDVJtWlWJooWY++ErLP9sFIfioqlVqyazZs3yiB4emQBz6Y8//uDpJ5+kpsXCNqORIBedyKwkQLh1N7jGaKSDRkO3rl3ZuHFjluv4+++/adG8OUu+XcwXb73Euulv8UiFoBzH7EqfvN6b4kHFaKNYOK9yErzgdNLGaiWwTBmmz5ihaiyS53rsscd47733eNdqZb7K3wcKIXhLUfjWZuOLMf2oUDpn35G50tPNQ9iz9CO6t23EK6+8wuDBgzP2VVSLTIC5kJiYSNtWrahisfCL0ejSeUDzTSaezuIwYoNGw1KjkbYaDc937EhcXNxD3xMVFUXDsDAuJp5j/ayxDOjcyqOGEhcJ9Gf19HewFw6glVXhnEpJ8LzTSWubjfQiRdi8fbvs+pQe6MMPP+SVIUN4WVFUS4JCCMZarXxms/Hxa73o/nRTVeLITCE/M1++3Y9p7w1k0Tff0LpVKy49YD70w+zbt49hw4blOJGWLFmSpUuXUqNGjRzHkJkCPw1CCEH7du2I2byZPwwGSnhA8kgXgiaKgqhenf2xsRjuM5Xgl19+oXPnztQPrsiiSRGULBaYx5Fm3em/L9Fx2GTE1RusMxipm4eT5I84HLSz2XAUL87m7dupXr16ntUt5V9Op5OIYcOYOWsWHxsMvGkw3Pf7fFezCMEIRWGuzcbEkS8Q8YLnrlS0948T9BkzHf9CRdi6bVuOlo1cvHgxL730EhaLJU9WpJHTIG5btGgRP//6K7N9fDwi+QGYNRoW6vUcOXKEjz76KNMya9eupVOnTrQOr8WaqW96dPIDqFyuJOvnvk+hcqUIS09noqJgd/O1lV0IPlEU6lssmCtVYndUlEx+UpZptVpmREYyduxY3rFaaW21cioPejD2OxyEKha+cTqY+u4Aj05+AOF1q7Fpzrs4bWk81qIFZ86cyfYx3DWKM7c8KxoXO3/+PCOHD6ePXk97D1sDMESn4z29nkkTJxIbG3vXaxs2bKBr16480zyEbyYNw2jIH6uYlClZhC3f/B/D+zzL+zYbjRUL8W7aOeKow0Ezq5WxNhuvjh5NzMGDVKhQwS11SQWXRqNh4sSJbN68mdMlSlDXYmG61YrTDRdvihCMURQap6VhqFSGHYs+pE/7/LGTTMUyJfh5xtvohJVWLVty/vz5bL3fU1eC8axoXOyN11/HrCh8peIisA8y1mCgjo8PrwwalHGFFB8fT9euXWjZqCbzJgxRdZRnTpiMBsYP68bmeeNILV2cBunp9LZY+N3hyPVoMiEEex0O+loshFgsJJcvz+7ffuPTTz/FbDa76DeQvFHr1q05dPQofQYNYrii0MBqZY7VSqoLEuEVp5P/KQrBFgufO+yMHdKZTQvep2bVci6IPO+ULVmUn6a9hc2SQvvnniMtLS3L75UJMI8lJiayatUqxmq1FHVjv/52u52kHHab6DUaJul07D9wgL179/LPP//QsUMHKpUuxrwPX8l3ye/fGtSswvbFHzJ+RA9+L1aIZmlp1FcszLZaOe90ZisZJjqdzLNaCbNaaZyWxs5SpZgweTIHDh2iSZMmbvwtJG8SEBBA5MyZ7NixgwpPPskQq5WyFgsjLRZiHA5s2fifTReCHXY7L1kslEtP432ng8ZPNmLn4gm80b9Dvm3b5YOKsezTVzl27CgDBvTPcjsuXLgwderUyfEUj5SUFLZv387Nmzdz9P77KbCDYD744AM+nTCB82YzgW5KgEIItCkpzDOZGJDDOYVOIXhEUWjavTtJiYkcjIth67xxVCxTcEYxOp1Otu6NZ973W9nw20GcQlDKR0eYRkOoRksNrRZfjQYD/3+h8QSnkxghiAGSbDY0Gg3PPv00w4YP56mnnsqTBXwl73bmzBlmz57N3JkzuXztGkatlno+PoQKQX2tlmIaDWaNBie3BrVcEIIYh4NojYYjdhsOARVLFWVgtzb0eq4FxQp73j59ObVm6376jp3OxIkTGTt2rNvrO3DgAA0aNCA6OprQ0NCHls9qHimQCdBms1GpXDnaX7vGTDfuAu4QAp+UFBaYTPTLxaT6T29/l+UQgrXT36Z5A9fueuxJLly5zv74kxxMOEPc0dPEHTnNldvbLP1b8cKFCW3Y8NYjNJTw8HC5pqekCkVR2L9/PzExMbceUVEknDx5z/q+ep2WmpXLEFKrKiHBlQgJrkS9Ryt6XLefq0yc/QOfL1zH3r173b5zfUxMDGFhYcTGxlK/fv2Hls9qHsmf9+EPsW7dOhIvXWKor69b67nz75/b+8v+Pj68qyg0qf9ogU5+AEHFC9P+iVDaP3HrKk4IQUqahTc/W8xPOw7w22+/U7VqVfz9/T1+o2HJOxiNRpo3b07z5s0znlMUhSVLljBw4ECmjOlPh1Zh+JmN+bZrMyfeGtCB9bsPMnDAAKJjYty65ZicCJ8N27dvp6rBQD03d5PdSYC5PYnFtVqe0OnwzSejPV1Jo9EQ4Gfmk9d7E+hnZtx778nkJ3m81NRUxrzzDu2fCOOljo9TOMDPq5IfgN7Hh6lj+3P4yBE+++wzt9YlE2A2xOzbR1gezOdxVQIEaKjTcSjhjAuOlD8F+vvy2Zu9Wffzz6xYsULtcCTpgV5//XUUSxqfvtFb7VBUFRJcieEvPMUHH3zAsWPH3FaP3A0iixwOBwfi4gjLg0ESRuCory/PueDKL0yr5WJyCkmXr+U+sHyq3WMN6NiqIa+OGMHVq1fVDkeSMrVlyxYWLlzIhOHd82TbIk/3zqBOlC1ZhJf/NZ3rv5YvX06rVq1yXEe9evU4evSoyzcXL3AJ8NixY6RZLITmwRfPWo2GYJ3OJaNMQ28n7DgvvgsE+N/rvbh58wazZ89WOxRJytRHEyYQVrsqfTrkj0ns7mY2Gfjf6BfZtXs3u3btyrRMYmIi0dHROa/DbCY4ONjly6gVuASYkJAAQO18NvKqvEZDIZ2W42eS1A5FVaWKFaZzm0bMjIzE4aZVZCQpp44cOcL2HTsY2uNJ+T31v7RuXIdqFcswY8b0TF93Op0eORrW8yLKpdTUW0PqA/LZP6dGo8FfqyXNoqgdiuoGdWnF2XPn+PXXX9UORZLuEhkZSYmigbR/wr3D/vMbjUbDwM4t+f77H7hw4cI9r8sEmEcU5VYC8czFzx7MhAbFqu7+ZJ6gQc0q1K9ZhRnTM7+alCQ1pKSk8M03C+nb4TEMeu8a8ZkVPZ9tikGvY+7cufe8JhNgHvG5PSDFngd1pQnBl1bX7YZuQ+DjI1c4ARjYuSXrN2zg5MmTaociSQAsWbKE1NQ0+nZ6Qu1QPFLhAD+6tW3MrJkzsdvv/gSuVasWPXr0yPGxz549y5dffsmNGzdyG+ZdClwCvLMocnoe1JUiBKMVhSMuSoDpQmA2Zr43oLfp3KYRhQP8mDVrltqhSBJCCGZMn87TzUMoH1RM7XA81sAurfj7/HnWrVt31/Pt27dnei56dI4dO8bo0aO5fv16LiO8W4FLgOXK3VphPS/29XLlPMBkIbhid1CmZFEXHC3/8zUZebFdM+bNnUt6el5czkjS/f3+++/8cegQg7rkfCi/N6hbvSKN6lRj+rRpLj2unAifRSEhIWi1WmLyYAThnRpccRIP3I43JLiSC45WMLzYrjlXr13j999/VzsUycutWbOG0iWK8kTDmmqH4vFebNeMrdu2uXTnhjsjwmUCfAg/Pz+Cq1YlJp/dAcY4nfga9FSvWNoFRysYalQpS2CAH1FRUWqHInm5qD17aFSnqkcO5PA04XWr4XQ6czXv77/kHWA2hDZuTHQeTIMQgAlwxbCVaIeDOtXKo9MVyD9Jjmi1WhrUqEzUnj1qhyJ5MZvNRnRMNGG1qqgdSr7waKXSFPL3demFq0ajwWQyuXwbtAL5aRseHk6c3c4VN98FVtBqSQ8IoE0ul0KzC8EOBA3quHaZn4IgrHYV9u7dm+vd5CUpp+Lj40lPt9CwtmyfWaHVaqlfozJ79/7/BDh+/Hhq1KiR42M+99xzpKenU6KEa/dJdXkCnDx5Mg0bNiQgIICSJUvSqVMnty6SmpkePXqg1elYYM+LyRC595PdTpLdwQvPNlM7FI8TVqsKl69c4fTp02qHInmpqKgofHx01Hu0otqh5BthNSsTtScq48JVURRsNs+b4+zyBLhjxw4iIiKIiopi06ZN2Gw22rZtm7FCS14oXrw43Xv0INLpxJkP7hxmOByE16pC3eqygf1XWK2qAOzbt0/lSCRvtW/fPmo9UgGzSU5Ryqqw2lW5eOkSf/31F+BFE+HXr19Pv379qFWrFvXq1WPhwoWcO3eOmJgYV1f1QEOHDeO0zcYGD19P8pjTyRabjQFd26gdikcqVjiAIoX8OXPmjNqhSF7qzOnTPFK+pNph5CtVy5cCyGi3XpMA/ys5ORmAokUzn9+mKAo3bty46+EKjRs3JqROHT622z36LvBjq5ViAb50bCXXFryfoBJFSExMVDsMyUslJp6X2x5lU1CJIgAZ7dYrE6DT6WTUqFE0a9aM2rVrZ1pm8uTJBAYGZjzKly/vkro1Gg2ffvEFO202Zrqp7/m800nf9HT+zOFgmw12OwttNsZFdMckV4C5r1LFCpGU5N27ZEjqSbpwQSbAbArwNeFrNma0244dO/L222/n+Hh79uyhb9++WK1WV4UIuDkBRkREEB8fz/Lly+9bZsyYMSQnJ2c87vQZu0KbNm0YMngwb9ntnHbDiNBrQrDIbuefHNxhJgvBIJuVlmE16NvxcZfHVpCUKhbIBZkAJRWkpaVx82YKJYsGqh1KvqLRaChVvEjGzhCPP/44ffv2zfHxTp48yaJFi1w+GtxtCXD48OGsW7eObdu2ZSxPlhmj0UihQoXuerjSp599RvGgIAbabC7vCr2TUnMy43C0opDso2PKe4PkvmIPYTYaUBSL2mFIXshiufV/JwfAZJ/ZaMg4f7l1ZyK8qz8rXZ4AhRAMHz6c1atXs3XrVipXruzqKrIlICCAed98wzarlbcUxaVXEDldCSbSamW+zcZHo16UC+tmgU6rvWd1eUnKC3f+7+QCFdmn02pc1m7zzUowERERfPvttyxdupSAgAAuXLjAhQsXVF3QuHXr1kyZMoXPbTYmuLAPOScJ8FubjQhFYWjPtrwkuz6zxCmcLl8BQpKy4s7/ndPpuQPpPJXTKVzWbt2VAF2+q2NkZCQATzzxxF3PL1iwgH79+rm6uiwbMWIEN2/e5N133yUNmGww5Pp22hdorNXin8XjRFqtRCgKvdu3YOKrPWXXZxZZFBsGg+yCkvKe0Xhra225UXX2Way2jPOXlJSEoihUqlQpR8cqUaIEjRs3dvlnpssToCcvWTV27Fh8fX157bXXOCEEkQYDJXNxRRGs07HHz++h5VKE4G1FYYbNRpVyJfl6TH+PHBLsqS5fu0nJUnIdRinv+fn5YTabuHQ1We1Q8p3LV5MpWfLW/Mn333+fQ4cO5Xh90Pbt29O+fXtXhgcU0LVAH2TUqFF899137PT3p6bFwko3L8+z3W6nrqKwUKejYcOGlCxWWCa/bLr4TzKlS8tdMqS8p9FoKB0UxMUrMgFmR2q6wo2UtIx263Q6PbLHyys/ibt06cLhhATK1apFD4uFrhYLx1w8TeJvp5MIi4WW6emUa9iQP+LjadmyJRevumaivzdJunKdMmXKqB2G5KXKlClL0pVraoeRr1y8ch0go9165UR4T1ayZEle6tsXo9HIrkKFCE5NpY2i8IPNhj2H3bhCCLbY7XSxWKiUlsYivZ6vv/6a7bt2UbVqVcqXL8/5C/9gtckRjVl1MzWdK1eTXbZAgiRlV/kKFTibeEXtMPKVs4mXATLarUyAHkgIgdFo5Nz583z77bekhYTcSl6KwmCLhVlWKzEOB8p9EqJdCOIdDr6x2RhhsRBstdImPZ1jVaowZdo0zicl8eqrr2b84cPCwrDabBz+03WT/Qu62KO3doFo2LChypFI3qphw4YcPHYWm5yKk2XRh09RuHAgVaveWszeUxOgywfB5Cfdu3cnPDwco9FIr1696NWrF3FxccyZM4ddW7cy//hxHIqCXqPhEYMBP8AIWIF04ITVmpEcq1euTOMWLZgzcCAtWrTItL+7fv36GAwG9sefpH4NdedH5hfR8ScpVCiA4OBgtUORvFR4eDgWxcrhP/8mJLiS2uHkC9FHTtGoYaOMpDd27FgURcnx8b799lumTZvm0k12wcsTYPny5e/pWgsJCWH69OkApKenc/DgQWJiYjhx4gTp6ekoioLBYMBkMtHCbicyMpK4uDjq1av30PqMRiMh9eoRffgkg7vJ3R+yIvrw3Q1JkvJa/fr10ev1RMeflAkwC4QQRB8+RcSIDhnP5WYzXIDLly9z5MiR3IZ2D69OgA9jNptp3LgxjRs3zvT1LVu2EBkZSWBg1tcJbNykCetWr3JViAXanYY0eOizaocieTGz2Uy9unXYf/gkg7q2Vjscj3c28TL/XLtBeHi4y47pri5UmQBzISerE4SHhzNlyhSuXLtB8SKuXfc0L6SkWfjj+FniEs5w8NhZrl67iUWx4nQ6MRkN+PubqVm1PCHBFQkJrkSpYoVzXNfZxMtcvpp83wsQScorjZs05ec13yGEcNtw/pQ0C4eOnyPu2K229c/VG3e1LT8/MzWrliMkuBIhwZU8doeKfYf+BJAJsKDLyQKtrVu3xsfHh1Uboxjao627QnOppMvX+GbNDlZv2MPxvy8iBBg1Gur5+FBGCIpza0FwBbiqgZk7DnDt9kbEpYsE0KZ5CAO7tM5299HKDXvw9TXTokULV/9KkpQtTz/9NNOmTSP2yGlCa7luUYaL/1xn0Zqd/LD+d479dRGnEBg1Gur6+FD2P23rGjB75wGu3m5bQYUDaNWsHoO6tKJBTc9ZKGLVhigahoVRrJjr1jl21zxCr06Ahw4d4tSpU3Ts2DFH7y9XrhxDhw7F398/y+8pVaoUXbp0Zt4P23il+5MeOTkUbnU/7opNYN53m1m3IxYTGrprtbxlNBGq1VJTq0V/n9iFEJwRghiHg30pFpb/+juL1+4iLLgSA7s/yfOtGz50/0O73cHCH3fw4ou9stXFLEnu8PTTT1OpYkXmfr8l1wlQCMHvcceZ990WftoWjR5Bd50PrxsMhOp01HpI2zp7u23tT1NYviGKpT/vpsGjFRnYrQ2d24SrunPF6b8vsTnqEPPnz7/r+Z07dyKE4PHHc7b+cUhICAMHDnRFiHfRCA9bu+zGjRsEBgaSnJzs8q2R/mv8+PHMnz/fpXsQZsXOnTt5/PHHWTP1LR5vWDNP686KM+cvMXzCXHbHHaeG3odhWh199HoCc5is7ULws93ODIedjTY75YoXZsq4QbQKz3yTZIC122Po885UDhw4QEhISA5/E0lynU8++YTx74/j6NovKRqY9YvefzuXdIURE+ayIzaB6rfbVl+9nsI5bFsOIfjF4WCG3c56m43SRQvx9XsDadv04YPy3OH9qStY/Msezp9PxGw2ZzzfqVMnHA4Ha9euzZM4sppHvHponVpzU1q0aEGtmjWZ+/3WPK/7QZxOJ7NXbabpC+/yV/xJfjGbOWw0MdxgyHHyA/DRaOio17PBZCbBz4/g5FQ6j/yMVyfN50Zq5ruEzPthG00aN5bJT/IYffr0wWZ3sGTdrmy/VwjBgtXbaNpzLKf+OMFPZjMJRhMjDYYcJz8AnUZDex8ffjWZOO7nR92b6XQf/SXDPpzD9ZupOT5uTqRbrCxet5sBAwbelfzAc+cBel5EeUitP4pGo2FYRAS/7Irl/KWreV5/ZhIvXaP90I956/NveUnAIZOZZ3x8XN5F+6hWy0ajkZlGIz+s203THmPYHZtwV5kTZ5PYvi+eocOGubRuScqNXbt24XQ6mbdyc8b3/1lx8Z/rdIr4hNc++YaeTkG8yUx7N7StalotvxqNzDUaWbdhD026j2FHtOunDtzPj1v3cy35JkOGDLnnNZkAPZCaf5TevXtjNpv5Zs0OVer/t9N/X+LpQRM4c/gkW8xmIk0mAtz43aRGo2GIwUC82UzV5BQ6j/iUn3fGZrw+f/U2ihUtSrdu3dwWgyRl14ypUwnx8eHMxX/Yujc+S+85m3iZZwZ9xPE/TrDBbGaOyUQhN7etgQYD8SYzNVPS6DryM9Zs3e+2+v5t3g/baPvkk1SrVu2e1zw1AXr1IBin04nFYiEmJuaeq7HSpUvfswNBUlISSUlJd71fCEHZsmXvWaz5v2X/e9xChQrRp89LfLNqOaNfavfQQSHucuqvizw7+CMKpaSzyWiiQh7+k1bUatloNNFbUXjpnanM/2gYrcJrs2Tdbp7v0i1j4mtW/hZ3yLKyrDvK/vnnn+z87TeWG41M1sDsFZto06TuPcf4t7OJl3n25Y8wJqfym9FE5TxsW+W1Wn41muinKPR/dwazPxhC17bum04Ue+QU0fF/8uNHn2V6fq9fv86NGznfCMBdG+IiPExycrIARHJystvrOn78uKhZs6YA7nmMHz/+nvLjx4/PtOw777yT5bL/Pm5CQoLQ6/XijX7txfWohXn+OPLTl6JCiSKiut5HXPDzEyIgQJWH3d9fvKDXC71OK55pUV9otZoc/y1kWVnWXWWLg1D8/cUSk0kAYsXno+7bto79/JWoHFRcVPXxEedVbFsOf3/RV68XPlqtWP7Z/ePNzeOf3+aLsNqPiBrBwcJms933/DZt2lTs27fvnnOcFWPHjhWVKlXKcvms5hGvHgUKsGfPHj7++GM6dOhASEhIxp1gVq4cN27cyJgxYzhx4gSPPPLIA8ve8d/jfvDBB3z00QS2L/g/alfLux0PHA4nzw7+iMRjZ9mdx3d+mbEJQSfFwgabnf6DBjF06NCM1zz97kCWLdhlExMTebJlS4YoCpONRoQQPK1YOFzIj6iVHxPgd++Ajw7DPuZU/El+M5qopHLbsgtBN0VhkxZ+XzaJimVKuPT4M5Zv4N2vl7N7926aNm2a6fkVQnD48GGSkpJo0aIFTZs2zVYdY8aMYeXKlZw8eTJL5bOaR7w+AQIcO3aMr7/+mm7duvHEE09k+cvpZcuW8eKLL5Kamoqvr2+O6rZarTSoXx+DsLBpzrv4+OhydJzsmr5sA+99vYwdZjMtfDyjJ/y6EASnplKvdWvWb9zosXMkJe9y9uxZKlWqxFqzmedut5UzTie109N5sXNLPn2jz13l53y3mTc/+5YtZjOtPKRt3RCCOpZ0KtWtxo/T3nJZV+KZxMs07fUeAwa+zNSpUx9YVgjB6tWr+fvvvwkJCeGxxx7Lcj1vv/02P/zwAydOnMhSeTkNIhseffRRhg0bxi+//MKmTZvI6jWBK/qlDQYDc+fNIy7hNJErNub4ONnx57kLTJixilf1eo9JfgCFNRrmm0xs3Lz5nom0kqSW6OhoAEL/1c4rabVM1OuZ+90Wog7+/w/lM+cv8X9TV/CKXu8xyQ+gkEbDXL2BnbEJLFi93SXHFELw2iffUKxYCSZNmvTQ8hqNhs6dO1OqVClOnDjB1q1ZnwbmrkE0MgHeVrt2bV588UV27drFL7/8kqUk6KovZhs3bsyIESOYNOdHjp1OzNWxHsbpdDL8wzmUFTDRaHRrXTnxrI8P/fR6Ro8cmecLFEhSZmJiYiit11P6P+18uF5PI70PIybM4WZqOkIIhk+YS3GHk/95YNt60seHl/V63p+yLGPD2txYtGYH2/bGM3PWLAICArL8vh49emA0GklKSmLTpk1Zeo9MgHmgfv36tG/fnvj4eH766aeHlm/cuDGzZ89Gp8t9t+XEiROpXLkKL749les33DeBdXPUIaLiTzJbr8fPQ7sYvzQaMSgKn332mdqhSBKx0dGEZvK8TqNhod7AhaQrDBk/i2374tkdd5yZeoNbpxHlxudGIwEOJ18v/iVXx9l36E/e/HwJgwYN4tlns79bS+/evbHb7SQnJ7N+/fqHlu/WrRsffPBBTkJ9IPkdYCZ2797NgQMHKFOmDF26dMmzek+ePEnDhmHUr16elZ+Pcsv3gT1e+5zL0UeJNpo8+ju2MYrCDL2exIsX8fPzUzscyYuF1atH6NGjzDKZMn19rd1Ox/R0qlUIwpB0hT88vG39n6LwmUZw9JcpFPrPAJ6sOH/pKi37f0j14Fps2boVgyHnU7jmzp1LqVKl0Ol0OUqk9yO/A8yF5s2bU7t2ba5fv86KFSvyrN6qVauyatV37Ig+wpivlmb5u8isOpN4mY1Rhxim1Xl0AwUYotdzMzWVpUuXqh2K5OUsFgsPShPtfXx4S6/nxLkL+aJtvazXY7HaWPHrb9l+b3JKGi+8OQWj2Z/vf/ghV8kPYNCgQSQlJaHVavn5559zdayckAnwPlq2bEn58uWx2+0sWbIkz+pt3bo1kZGRzPluC+OnrXRpElzwwzYCtVpe0Otddkx3qaTV8pzBwIwpU1x+ISBJ2eF0OnlYStMDvkDvfNC2ymq1dPLRM2/l5my1rZQ0C91Hf8m5i9dYu24dJUuWdEk8gwcP5syZMxiNxjxbLPsOmQAfoG3bthQpUgSTycTixYvzrN6XX36Zr776iilLfuW9KctdlgC+//U3emt1+Hr4Feodg3U64uLjSUhIeHhhSXITk8mE8pAyy+12eun1Hvvd338N9vEh4dwF4v/M2kCz5JQ0Oo/8nCOnk9iwYSP16rl2t4lXXnmF48eP4+/vn6dJUCbAh3j22WfR6/UULlyYhQsX3vXapUuXiI2NzfyNuTRy5EimTZvG9GUbeOXDOVgUa66Od+mfZP7+J5nHXDBgJ6+0uB3rnWHokqSGgMBArj7gIvSaEPwpRL5qW810OrTAgaOnH1r2bOJlnn3lY47/dYktW7bSqFEjt8Q0bNgwDh8+nGkSPHv2LEePHnV5nTIBZkGHDh2w2+0EBQXdlQRXrVpFkyZN3FZvREQEy5YtY822GNoN+4Sky9dyfKy4Y2cACM1HjTRQo+ERg4GYmBi1Q5G8WN369Yl7QLuJub1De6gHLvZ8P34aDcEGPQcTzj6w3O7YBFoNnECaXcuuXbtp2LChW+MaPnw48fHxBAQEsG7duoznJ06cSL9+/VxeX/75i6ns+eefJzU1lXLlymVM0s6LFc579uzJrl27uXAtnZYDPmRnTM6ugg4cPUMRnY7K+aSL5o5Qp5PovXvVDkPyYqGhoRxXFG7e5y4wxuHAH6iejxIgQKhTcCA+86XFHA4nM5ZvoNOrn1IvJJT90dHUrn3/DaxdacSIERw6dAg/P7+MgTFyHqAH6NKlC9euXaNy5crMnz8/z7b4CAsLY390NNWDa9Mh4hNenTQ/25tdHkw4QwOtxuNHqP1XqE7Hgbi4bO2/JkmuFBoaigAO3L7T+68Yp5P6Oh3afNi2Dp/6G5vdftfzR0+d5+lXJjH2q2VERAxn/YYNFCtWLE9jGzFiBPHx8ZhMJn799ddbA5HccH5lAsymbt26cfnyZSpXrkxUVFSe7XFVunRptm3fTmRkJKu3xRL+wnv8tC3r341duXKdCvlwMGUFjYY0i4W0tDS1Q5G8VI0aNTAbjey7z0XYJSGokM+SH9xqW4rdQUqqBQDFamPynNU81nc8NxQtu3bt4quvvkKv0sjWESNGcPToUTQaDX/99ZdbPms9Z7G6fKR79+6sWLGCIkWKYLfbMx0I464V6zt27Mhzzz3HsKFDeWnMNJ57IpQBnVtStJD/PWWDihcmqHhhANItVkz5sJHemXq8d+9eihQpkvG8J+8eIMsWvLJPPvkkizdu5HUhuCAESf/qDr3idFJYpyPJ6bxnuTRPdufzICVdIeqPE3wQ+T1/nrtARERExnJldz7bMjtnkPl5c2XZpk2bsnLlSq5fv05KSkqOf9f7ytamTHkgL/cDzK2ZM2eKXr16qbJnmdPpFCtWrBBly5TJtBwg3h7YMWPPrloVS4vher1q+5Ll9LHWbPaoPeFkWe8su379egGI33x9xXiDIfOyBoPq7SU7j42321aZEkVv7dfXpIk4ePBgpucis3N2v/PmrrJDhgzJtGxm5H6AeUTtq1ebzcaiRYuYNWsW+/fvx9/XRIeWYXR4IpSQGpUz7gBbvfQ+DU4nMec+yzl5qu9sNrpZLGzfvv2uBXc96e5Ali34ZZ1OJ9WrVKFxYiKfGgx33QEOsFgop9Ewx2TKV3eAP91ewq1Hjx68/vrrGSM83X1X5+qymZH7AXqhhIQEBg4cyN6oKJzCyZPhdWjVpC4Na1dl/NTlBMWfYpU5+2v/qWme1cogRUFRlFwvuyRJufH5558z9q23OGs2E/SvRPd0WhpGjYY1+axtLbbZeMliISUlpcCttysToJe6fPkyj1SuTC2LBY1WS7TdjlUItBoNFTUaTuWzf/QRFgsby5Xj2KlTaociebmrV69SrUoV2qalsexfPSlvWCx8Z7dzxv/e7+E92esWCz+ULMnpv/9WOxSXk4the6kSJUrw1dSp7HE4eMfHhxt+fkT5+vKCTsdpp5Nkz7reeagYjYYwNy42IElZVbRoUabOmMFym40fbLaM50N1Os4KwT/5sW01bqx2GKqSCbAA6tevH88+/TRD7HbSgHCdjndudx/eby6TJ7ILQZzdTmhoZruxSVLee+GFF+jYvj1DHQ6u3J4WcWd1pZh81LacQhDrcBAaFqZ2KKqSCbAA0mg0zJ47lzSjkSGKglMIgrVazEB0PppQftTpJN3plAlQ8hgajYaZs2dj9/XlZasVhxA8otFQCIjORwnwhBDcdDi8vm3JBFhAlS1blrkLFvCd3c5IRUEHtNbpWGqz5ZvthZba7QT6+7t9/UFJyo6goCAWLFrET3Y7wxQFDfCkjw/L7Pb807ZsNvzNZhrLLlCpoOratSszZ85kms3Ga4rCEL2eA07nfVe08CSKEMx1Ouk3cCC+vr5qhyNJd+nQoQNz581jts3GcEVhiI8P8U4nu/PBXaBNCOYIQZ9+/e6aWuSN5EowBdzgwYNxOBxERETwjxBU0miYYbUS7uFDtr+z27litzN06FC1Q5GkTPXv3x+Hw8HgwYO5KgTVNRpm2Gy08PHsj9U1djtJNptsW8hpEF5jyZIlDBowAD+7nRSnk7/8/CjhoZN2hRA0s1rxbdqUzdu2qR2OJD3QqlWr6NunD742GzecTs76+Xn0hPiWioIjLIydv/2mdihuI6dBSHfp1asXcX/8QeV69bADbygP2+NaPcvtdvZYrbz2xhtqhyJJD9WtWzcOHjpEtdBQnMBoD25b39tsbJdtK4NMgF7k0UcfJWr/frp0784iu52f/7MNiie44HQy3G6ne7dutGvXTu1wJClLqlWrxm9RUbzQuzfL7XZW/2ueoKe47HQy1G6nU4cOdOrUSe1wPILsAvVCQggeb9GCE3v2cMTXlyIeskuEEILOisJvAQEcTkigRIkSaockSdkihODJ1q35Y8cOjvr6UsxD2hZAD4uFzb6+HE5IICgoSO1w3Ep2gUr3pdFoWLp8OWlmMxGK4jFDtxfa7fxosxE5e7ZMflK+pNFoWLxkCXZ//4w5uJ5gic3GSpuNaZGRBT75ZYdMgF6qXLlyzJo7l2U2G296QBL82W5nsKIwoH9/unTpomoskpQbpUuXZu6CBfxgtzPKalW9bW202+lvtdL7xRfp2bOnqrF4GpkAvVjPnj2ZMmUKn9tsjFWxof5it9NVUXiufXtmzZ6tSgyS5EqdO3dm5syZTLVaeUPFC8yNdjvPW620feop5i9ciMaDumQ9gWdPWJHcbsSIEdjtdkaPHs01IZhiNGLIw0ay2GZjgKLQ7rnnWL5yJT4ePodKkrJq8ODBWK1WRowYwXVghtGIMQ/b1nKbjZduJ79V33+PXq/Ps7rzC3kHKPHaa68xd+5c5jmdhFmtxObBahaXnU56WCy8ZLHQq08fvvvhB4xGo9vrlaS8NHz4cBYtWsRiIWhgtbI/D9rWP0LQy2LhBYuF7j16sHrNGswevvCFWmQClAAYOHAg+6Oj0VavTqP0dMYpClY3ddt8Z7NRS1HY4ufHsmXLWLBwobzzkwqsPn36EHvgAOYaNWicns4YRUFxU9v60WajlsXCLybTrcS7ZIm883sAmQClDCEhIeyPjWXc+PF87HBQS1H42mrlugsaq10IVttstFYUulksNG/XjsMJCfTs2VN+LyEVeLVr12bP/v18OGECnzud1FQUvrRaueaCtuUQgjU2G20tFp63WGj01FMcOXaMPn36yLb1EDIBSnfR6/WMHz+emNhYQp9/njfsdsqmpzPYYmGfw4E9Gw1WCMFpp5MJikIlRaGzxUJ6vXp8//33fL96NaVKlXLjbyJJnkWv1/Puu+8Se+AA4V268LbDQdn0dAZaLETloG2ddTqZpChUURQ6WSzcqFuXFStWsGbtWkqXLu3G36TgkBPhpQdKSkpi7ty5zJo+nfMXL2LWaqnn40OoEITqdJTRaDBz60rKAlwTggNOJzFCECME/9jt+JpM9OrTh6FDh1K/fn2VfyNJ8gwXL15k3rx5zJw2jb+SkjD9p22VzaRtxd1uW7HAZZsNs9HIi716MXTYMK/f2+/fsppHZAKUssRut/P7778THR1NTEwMMVFRHD99OtPh3aVLlCC0YcNbj9BQWrRoQeHChfM+aEnKB+x2O3v27CEmJuZW+4qK4tipU5m2raDixQlt1IjQsLCMtlWkSBEVovZsMgFKbpeSksLVq1exWCw4HA7MZjP+/v4UL15c7dAkKV+7X9sqVqyY/F4vC7KaR+TQOynH/P398ff3VzsMSSpwZNvKG3IQjCRJkuSVZAKUJEmSvJLbEuD06dOpVKkSJpOJ8PBw9u3b566qJEmSJCnb3JIAV6xYwejRoxk/fjyxsbHUq1ePp556ikuXLrmjOkmSJEnKNrckwC+++IKXX36Z/v37U7NmTWbOnImvry/z5893R3WSJEmSlG0uHwVqtVqJiYlhzJgxGc9ptVratGnDnj177imvKAqKomT8nJycDNwaxipJkiRJ2XUnfzxslp/LE+CVK1dwOBz3LHNVqlQpEhIS7ik/efJkPvjgg3ueL1++vKtDkyRJkrzIzZs3CQwMvO/rqs8DHDNmDKNHj874+fr161SsWJFz5849MHBvdOPGDcqXL89ff/0lFwn4F3le7k+em8zJ83J/BeHcCCG4efMmZcqUeWA5lyfA4sWLo9PpuHjx4l3PX7x4kaCgoHvKG43GTPeBCwwMzLcn390KFSokz00m5Hm5P3luMifPy/3l93OTlRsolw+CMRgMhIaGsmXLloznnE4nW7ZsoUmTJq6uTpIkSZJyxC1doKNHj6Zv376EhYXRqFEjvvrqK1JTU+nfv787qpMkSZKkbHNLAuzRoweXL1/m/fff58KFC4SEhLB+/fos7f9mNBoZP358pt2i3k6em8zJ83J/8txkTp6X+/Omc+Nxu0FIkiRJUl6Qa4FKkiRJXkkmQEmSJMkryQQoSZIkeSWZACVJkiSv5HEJUG6jdLfJkyfTsGFDAgICKFmyJJ06deLYsWNqh+WRPv74YzQaDaNGjVI7FNWdP3+e3r17U6xYMcxmM3Xq1CE6OlrtsFTncDgYN24clStXxmw2U7VqVSZMmPDQNSMLop07d9K+fXvKlCmDRqPhxx9/vOt1IQTvv/8+pUuXxmw206ZNG06cOKFOsG7iUQlQbqN0rx07dhAREUFUVBSbNm3CZrPRtm1bUlNT1Q7No+zfv59Zs2ZRt25dtUNR3bVr12jWrBl6vZ5ff/2VI0eO8Pnnn1OkSBG1Q1PdJ598QmRkJNOmTePo0aN88skn/O9//2Pq1Klqh5bnUlNTqVevHtOnT8/09f/9739MmTKFmTNnsnfvXvz8/HjqqaewWCx5HKkbCQ/SqFEjERERkfGzw+EQZcqUEZMnT1YxKs9y6dIlAYgdO3aoHYrHuHnzpqhWrZrYtGmTePzxx8XIkSPVDklVb7/9tmjevLnaYXikdu3aiQEDBtz1XOfOnUWvXr1UisgzAGL16tUZPzudThEUFCQ+/fTTjOeuX78ujEajWLZsmQoRuofH3AHe2UapTZs2Gc89aBslb3Vnu6iiRYuqHInniIiIoF27dnf973izn376ibCwMLp160bJkiWpX78+c+bMUTssj9C0aVO2bNnC8ePHATh48CC7d+/mmWeeUTkyz3L69GkuXLhwV5sKDAwkPDy8QH0eq74bxB3Z3UbJGzmdTkaNGkWzZs2oXbu22uF4hOXLlxMbG8v+/fvVDsVjnDp1isjISEaPHs3YsWPZv38/r776KgaDgb59+6odnqreeecdbty4QXBwMDqdDofDwcSJE+nVq5faoXmUCxcuAGT6eXzntYLAYxKg9HARERHEx8eze/dutUPxCH/99RcjR45k06ZNmEwmtcPxGE6nk7CwMCZNmgRA/fr1iY+PZ+bMmV6fAFeuXMmSJUtYunQptWrVIi4ujlGjRlGmTBmvPzfeyGO6QLO7jZK3GT58OOvWrWPbtm2UK1dO7XA8QkxMDJcuXaJBgwb4+Pjg4+PDjh07mDJlCj4+PjgcDrVDVEXp0qWpWbPmXc/VqFGDc+fOqRSR53jzzTd555136NmzJ3Xq1KFPnz689tprTJ48We3QPMqdz9yC/nnsMQlQbqOUOSEEw4cPZ/Xq1WzdupXKlSurHZLHaN26NYcOHSIuLi7jERYWRq9evYiLi0On06kdoiqaNWt2z1SZ48ePU7FiRZUi8hxpaWlotXd/7Ol0OpxOp0oReabKlSsTFBR01+fxjRs32Lt3b4H6PPaoLlC5jdK9IiIiWLp0KWvWrCEgICCj/z0wMBCz2axydOoKCAi457tQPz8/ihUr5tXfkb722ms0bdqUSZMm0b17d/bt28fs2bOZPXu22qGprn379kycOJEKFSpQq1YtDhw4wBdffMGAAQPUDi3PpaSk8Oeff2b8fPr0aeLi4ihatCgVKlRg1KhRfPTRR1SrVo3KlSszbtw4ypQpQ6dOndQL2tXUHob6X1OnThUVKlQQBoNBNGrUSERFRakdkqqATB8LFixQOzSPJKdB3LJ27VpRu3ZtYTQaRXBwsJg9e7baIXmEGzduiJEjR4oKFSoIk8kkqlSpIt59912hKIraoeW5bdu2ZfrZ0rdvXyHErakQ48aNE6VKlRJGo1G0bt1aHDt2TN2gXUxuhyRJkiR5JY/5DlCSJEmS8pJMgJIkSZJXkglQkiRJ8koyAUqSJEleSSZASZIkySvJBChJkiR5JZkAJUmSJK8kE6AkSZLklWQClCRJkrySTICSJEmSV5IJUJIkSfJKMgFKkiRJXun/Ab/XNo1txJUiAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -540,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 33,
    "id": "3cf52bd7-5892-48f7-bc6b-9a5f24ca6ae2",
    "metadata": {},
    "outputs": [
@@ -553,14 +534,12 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -590,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 34,
    "id": "a9d0f944-cfcf-40bd-b874-0d4b081788af",
    "metadata": {},
    "outputs": [
@@ -669,10 +648,10 @@
        "</svg>\n"
       ],
       "text/plain": [
-       "<rdkit.Chem.rdchem.Mol at 0x77b0c5b64ee0>"
+       "<rdkit.Chem.rdchem.Mol at 0x163069700>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -704,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 35,
    "id": "2154888a-8305-423b-8d99-341e5a52f44a",
    "metadata": {},
    "outputs": [
@@ -718,14 +697,12 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 144x144 with 1 Axes>"
+       "<Figure size 200x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -739,6 +716,175 @@
     "plot_molecule(mol);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "dde8fe19-556c-4c9e-b6cf-2d0dd5a34ef3",
+   "metadata": {},
+   "source": [
+    "#### Convert problematic PLAMS Molecule to RDKit Mol"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "7da2ed9d-f9b5-4fec-86d6-c37af291b3ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot: >"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 200x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mol = Molecule(badxyz_file)\n",
+    "mol.guess_bonds()\n",
+    "plot_molecule(mol)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c84e4100-2347-4490-b216-cd23a48b78e2",
+   "metadata": {},
+   "source": [
+    "This molecule will fail to convert to an RDKit Mol object, because RDKit does not like the AMS assignment of double bonds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "8fe9140c-8de7-4c01-a64b-49ca37ca97a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[13.12|17:47:23] RDKit Sanitization Error.\n",
+      "[13.12|17:47:23] Most likely this is a problem with the assigned bond orders: Use chemical insight to adjust them.\n",
+      "[13.12|17:47:23] Note that the atom indices below start at zero, while the AMS-GUI indices start at 1.\n",
+      "Failed to convert\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "RDKit ERROR: [17:47:23] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14\n",
+      "[17:47:23] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14\n",
+      "\n",
+      "RDKit ERROR: \n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    rdkit_mol = to_rdmol(mol)\n",
+    "except ValueError as exc:\n",
+    "    print(\"Failed to convert\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dce54ed2-5913-4230-9990-7f8f16c66fb9",
+   "metadata": {},
+   "source": [
+    "The problem can be fixed by passing the argument `presanitize` to the `to_rdmol` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "be1209b5-08a8-490f-a28c-961f423d9b9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "RDKit ERROR: [17:47:06] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14\n",
+      "[17:47:06] Can't kekulize mol.  Unkekulized atoms: 10 11 12 13 14\n",
+      "\n",
+      "RDKit ERROR: \n"
+     ]
+    },
+    {
+     "data": {
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+      "image/svg+xml": [
+       "<?xml version='1.0' encoding='iso-8859-1'?>\n",
+       "<svg version='1.1' baseProfile='full'\n",
+       "              xmlns:svg='http://www.w3.org/2000/svg'\n",
+       "                      xmlns:rdkit='http://www.rdkit.org/xml'\n",
+       "                      xmlns:xlink='http://www.w3.org/1999/xlink'\n",
+       "                  xml:space='preserve'\n",
+       "width='250px' height='250px' >\n",
+       "<rect style='opacity:1.0;fill:#FFFFFF;stroke:none' width='250' height='250' x='0' y='0'> </rect>\n",
+       "<path d='M 185.034,136.645 192.405,121.229' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 192.405,121.229 199.775,105.812' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 185.034,136.645 150.732,131.396' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 180.366,138.771 187.005,153.346' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 187.005,153.346 193.643,167.92' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 189.702,134.519 196.341,149.093' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 196.341,149.093 202.979,163.668' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 211.847,99.8459 217.832,101.782' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 217.832,101.782 223.817,103.718' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 67.199,60.7117 78.7917,85.1979' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 33.5933,124.182 58.4518,124.99' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 130.146,54.1026 123.718,68.3247' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 123.718,68.3247 117.29,82.5469' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 139.494,58.3277 133.066,72.5499' style='fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 133.066,72.5499 126.639,86.772' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 132.621,193.785 120.807,169.3' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 67.8752,190.072 79.5165,167.358' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 121.964,84.6594 137.538,120.597' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 121.964,84.6594 78.7917,85.1979' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 78.7917,85.1979 58.4518,124.99' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 84.8754,95.8359 70.6375,123.69' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 58.4518,124.99 79.5165,167.358' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 79.5165,167.358 120.807,169.3' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 86.1922,157.401 115.096,158.761' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<path d='M 120.807,169.3 136.634,139.405' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' />\n",
+       "<text x='195.878' y='105.812' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
+       "<text x='194.221' y='182.892' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
+       "<text x='55.7416' y='60.7117' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>H</tspan></text>\n",
+       "<text x='18.7735' y='132.491' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>H</tspan></text>\n",
+       "<text x='130.699' y='56.2152' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000' ><tspan>O</tspan></text>\n",
+       "<text x='129.335' y='210.883' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>H</tspan></text>\n",
+       "<text x='56.0838' y='207.17' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>H</tspan></text>\n",
+       "<text x='223.817' y='114.664' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>H</tspan></text>\n",
+       "<text x='132.494' y='139.405' style='font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000' ><tspan>C</tspan><tspan style='baseline-shift:super;font-size:12.75px;'>-</tspan><tspan></tspan></text>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<rdkit.Chem.rdchem.Mol at 0x162373d00>"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rdkit_mol = to_rdmol(mol, presanitize=True)\n",
+    "rdkit_mol"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3d18c8c5-a1f6-4ef7-bb63-44d14faa3333",
diff --git a/examples/MoleculeFormats/MoleculeFormats.py b/examples/MoleculeFormats/MoleculeFormats.py
index 8c6f20f9e..426573b05 100644
--- a/examples/MoleculeFormats/MoleculeFormats.py
+++ b/examples/MoleculeFormats/MoleculeFormats.py
@@ -13,6 +13,7 @@
 AMSHOME = os.environ["AMSHOME"]
 cif_file = f"{AMSHOME}/atomicdata/Molecules/IZA-Zeolites/ABW.cif"
 xyz_file = f"{AMSHOME}/scripting/scm/params/examples/benchmark/ISOL6/e_13.xyz"
+badxyz_file = f"{AMSHOME}/scripting/scm/plams/unit_tests/xyz/reactant2.xyz"
 
 assert Path(cif_file).exists(), f"{cif_file} does not exist."
 assert Path(xyz_file).exists(), f"{xyz_file} does not exist."
@@ -190,6 +191,27 @@ def head(filename, n: int = 4):
 plot_molecule(mol)
 
 
+# #### Convert problematic PLAMS Molecule to RDKit Mol
+
+mol = Molecule(badxyz_file)
+mol.guess_bonds()
+plot_molecule(mol)
+
+
+# This molecule will fail to convert to an RDKit Mol object, because RDKit does not like the AMS assignment of double bonds.
+
+try:
+    rdkit_mol = to_rdmol(mol)
+except ValueError as exc:
+    print("Failed to convert")
+
+
+# The problem can be fixed by passing the argument `presanitize` to the `to_rdmol` function.
+
+rdkit_mol = to_rdmol(mol, presanitize=True)
+rdkit_mol
+
+
 # ### SCM libbase UnifiedChemicalSystem Python class
 #
 # #### Convert PLAMS Molecule to UnifiedChemicalSystem
diff --git a/examples/PlotReaction2D/PlotReaction2D.ipynb b/examples/PlotReaction2D/PlotReaction2D.ipynb
new file mode 100644
index 000000000..d9995ea53
--- /dev/null
+++ b/examples/PlotReaction2D/PlotReaction2D.ipynb
@@ -0,0 +1,322 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6223ccee-4f30-47da-9796-3f08b3387fae",
+   "metadata": {},
+   "source": [
+    "## Creating 2D images of molecules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "d8479d8e-8f2b-4882-94a9-0914e838ffc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"200px\" height=\"100px\">\n",
+       "<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"200\" height=\"100\" x=\"0\" y=\"0\"> </rect>\n",
+       "<path d=\"M 42.6347,72.1497 61.3626,55.2487\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 63.8295,54.7214 61.6424,44.4889\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 61.6424,44.4889 59.4553,34.2565\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 58.8956,55.7759 56.7085,45.5435\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 56.7085,45.5435 54.5215,35.311\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 61.3626,55.2487 71.3995,58.4973\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 71.3995,58.4973 81.4364,61.7459\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 89.2901,59.4732 96.6906,52.7945\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 96.6906,52.7945 104.091,46.1159\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 104.091,46.1159 98.8183,21.4465\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 108.234,41.3609 104.543,24.0924\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 104.091,46.1159 128.092,53.8841\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 98.8183,21.4465 117.546,4.54545\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 117.546,4.54545 141.547,12.3137\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 119.593,10.5108 136.393,15.9486\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 141.547,12.3137 146.82,36.9831\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 146.82,36.9831 128.092,53.8841\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 140.63,35.7727 127.521,47.6034\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 128.092,53.8841 133.365,78.5535\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 132.588,80.9535 142.625,84.2022\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 142.625,84.2022 152.662,87.4508\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 134.141,76.1534 144.178,79.402\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 144.178,79.402 154.215,82.6507\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 133.365,78.5535 126.33,84.9018\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 126.33,84.9018 119.296,91.2501\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<text x=\"52.1629\" y=\"34.7837\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"81.4364\" y=\"67.2214\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"153.438\" y=\"90.5262\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"107.066\" y=\"99.659\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>HO</tspan></text>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import SVG\n",
+    "from scm.plams import ReactionEquation\n",
+    "from scm.plams import from_smiles, to_image, get_reaction_image\n",
+    "\n",
+    "aspirin = from_smiles(\"CC(=O)OC1=CC=CC=C1C(=O)O\")\n",
+    "text = to_image(aspirin)\n",
+    "SVG(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "530d601b-eac3-4849-b5a3-1f7418c3edd0",
+   "metadata": {},
+   "source": [
+    "It is also possible to write the image to file in a range of different formats (SVG, PNG, EPS, PDF, JPEG)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "7b452b79-2c62-4bdd-b129-268ff0c0117c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = to_image(aspirin, filename=\"aspirin.svg\")\n",
+    "text = to_image(aspirin, filename=\"aspiring.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79095131-3e8b-4abb-8dfd-5fa7ea11ca49",
+   "metadata": {},
+   "source": [
+    "## Creating 2D imageas of reactions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2c93688-af57-416d-ba52-389b69314435",
+   "metadata": {},
+   "source": [
+    "We can have aspirin react with water to form acetic acid and salicylic acid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "5366f0ff-6435-4b6c-802f-f521ced86d4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"200px\" height=\"100px\">\n",
+       "<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"200\" height=\"100\" x=\"0\" y=\"0\"> </rect>\n",
+       "<path d=\"M 47.5136,95.4545 100,65.1515\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 96.9697,70.4002 119.71,83.5296\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 119.71,83.5296 142.451,96.659\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 103.03,59.9029 125.771,73.0323\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 125.771,73.0323 148.512,86.1617\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 100,65.1515 100,38.5985\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 100,38.5985 100,12.0455\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<text x=\"145.482\" y=\"102.955\" style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"86.4946\" y=\"12.0455\" style=\"font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>OH</tspan></text>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "acetic_acid = from_smiles(\"CC(O)=O\")\n",
+    "text = to_image(acetic_acid)\n",
+    "SVG(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "5316bb6a-c850-41ac-b3dd-9a33eec653a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"200px\" height=\"100px\">\n",
+       "<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"200\" height=\"100\" x=\"0\" y=\"0\"> </rect>\n",
+       "<path d=\"M 157.423,88.2266 151.113,75.9126\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 151.113,75.9126 144.802,63.5986\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 151.363,91.3324 145.052,79.0184\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 145.052,79.0184 138.742,66.7044\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 141.772,65.1515 149.178,53.689\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 149.178,53.689 156.584,42.2266\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 141.772,65.1515 107.764,63.4485\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 107.764,63.4485 89.2858,92.0484\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 99.2727,64.0427 86.3376,84.0627\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 107.764,63.4485 92.2355,33.1454\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 89.2858,92.0484 55.2781,90.3454\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 55.2781,90.3454 39.7491,60.0424\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 59.0093,82.6941 48.1391,61.482\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 39.7491,60.0424 58.2279,31.4424\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 58.2279,31.4424 92.2355,33.1454\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 62.9884,38.4994 86.7938,39.6915\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 92.2355,33.1454 99.6415,21.683\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 99.6415,21.683 107.048,10.2205\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<text x=\"152.001\" y=\"101.13\" style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"150.032\" y=\"42.2266\" style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>OH</tspan></text>\n",
+       "<text x=\"100.495\" y=\"10.2205\" style=\"font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>OH</tspan></text>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "salicylic_acid = from_smiles(\"O=C(O)c1ccccc1O\")\n",
+    "text = to_image(salicylic_acid)\n",
+    "SVG(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed975357-5d66-4090-ac85-a13cad4cc95c",
+   "metadata": {},
+   "source": [
+    "We can create a 2D image of the reaction as well, and optionally store it to file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "2169688f-97ff-4777-b01b-747c643f06d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"1000px\" height=\"100px\">\n",
+       "<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"1000\" height=\"100\" x=\"0\" y=\"0\"> </rect>\n",
+       "<path d=\"M 42.6347,72.1497 61.3626,55.2487\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 63.8295,54.7214 61.6424,44.4889\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 61.6424,44.4889 59.4553,34.2565\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 58.8956,55.7759 56.7085,45.5435\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 56.7085,45.5435 54.5215,35.311\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 61.3626,55.2487 71.3995,58.4973\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 71.3995,58.4973 81.4364,61.7459\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 89.2901,59.4732 96.6906,52.7945\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 96.6906,52.7945 104.091,46.1159\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 104.091,46.1159 98.8183,21.4465\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 108.234,41.3609 104.543,24.0924\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 104.091,46.1159 128.092,53.8841\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 98.8183,21.4465 117.546,4.54545\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 117.546,4.54545 141.547,12.3137\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 119.593,10.5108 136.393,15.9486\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 141.547,12.3137 146.82,36.9831\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 146.82,36.9831 128.092,53.8841\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 140.63,35.7727 127.521,47.6034\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 128.092,53.8841 133.365,78.5535\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 132.588,80.9535 142.625,84.2022\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 142.625,84.2022 152.662,87.4508\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 134.141,76.1534 144.178,79.402\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 144.178,79.402 154.215,82.6507\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 133.365,78.5535 126.33,84.9018\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 126.33,84.9018 119.296,91.2501\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<text x=\"52.1629\" y=\"34.7837\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"81.4364\" y=\"67.2214\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"153.438\" y=\"90.5262\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"107.066\" y=\"99.659\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>HO</tspan></text>\n",
+       "<path d=\"M 310.675,56.6504 317.813,52.5293\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 317.813,52.5293 324.951,48.4082\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 302.822,56.6504 295.684,52.5293\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 295.684,52.5293 288.546,48.4082\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<text x=\"302.822\" y=\"63.122\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"324.951\" y=\"50.5087\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"><tspan>H</tspan></text>\n",
+       "<text x=\"281.258\" y=\"50.5087\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#000000\"><tspan>H</tspan></text>\n",
+       "<path d=\"M 684.902,63.122 706.749,50.5087\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 705.487,52.6934 714.447,57.8665\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 714.447,57.8665 723.407,63.0395\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 708.01,48.324 716.97,53.4971\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 716.97,53.4971 725.93,58.6701\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 706.749,50.5087 706.749,39.9976\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 706.749,39.9976 706.749,29.4866\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<text x=\"724.669\" y=\"67.3264\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"699.178\" y=\"29.4866\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>OH</tspan></text>\n",
+       "<path d=\"M 948.359,73.4821 943.684,64.3592\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 943.684,64.3592 939.009,55.2362\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 943.869,75.7831 939.194,66.6601\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 939.194,66.6601 934.519,57.5372\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 936.764,56.3867 942.251,47.8946\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 942.251,47.8946 947.738,39.4025\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 936.764,56.3867 911.569,55.125\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 911.569,55.125 897.879,76.3136\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 905.278,55.5652 895.695,70.3973\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 911.569,55.125 900.064,32.6746\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 897.879,76.3136 872.684,75.0519\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 872.684,75.0519 861.179,52.6015\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 875.448,69.3833 867.395,53.6681\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 861.179,52.6015 874.869,31.4129\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 874.869,31.4129 900.064,32.6746\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 878.396,36.6411 896.033,37.5243\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 900.064,32.6746 905.551,24.1825\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<path d=\"M 905.551,24.1825 911.038,15.6904\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
+       "<text x=\"944.342\" y=\"83.0415\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>O</tspan></text>\n",
+       "<text x=\"942.883\" y=\"39.4025\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>OH</tspan></text>\n",
+       "<text x=\"906.183\" y=\"15.6904\" style=\"font-size:8px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000\"><tspan>OH</tspan></text>\n",
+       "<text x=\"180\" y=\"55\" font-size=\"16\">+</text>\n",
+       "<text x=\"780\" y=\"55\" font-size=\"16\">+</text>\n",
+       "<defs> <marker id=\"arrow\" viewBox=\"0 0 10 10\" refX=\"5\" refY=\"5\" markerWidth=\"6\" markerHeight=\"6\" orient=\"auto-start-reverse\"> <path d=\"M 0 0 L 10 5 L 0 10 z\"/></marker></defs>\n",
+       "<line x1=\"460\" y1=\"50\" x2=\"540\" y2=\"50\" stroke=\"black\" marker-end=\"url(#arrow)\"/>\n",
+       "</svg>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.SVG object>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reactants = [aspirin, from_smiles(\"O\")]\n",
+    "products = [acetic_acid, salicylic_acid]\n",
+    "text = get_reaction_image(reactants, products, filename=\"reaction.svg\")\n",
+    "SVG(text)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/PlotReaction2D/PlotReaction2D.py b/examples/PlotReaction2D/PlotReaction2D.py
new file mode 100644
index 000000000..2151dcf56
--- /dev/null
+++ b/examples/PlotReaction2D/PlotReaction2D.py
@@ -0,0 +1,40 @@
+#!/usr/bin/env amspython
+# coding: utf-8
+
+# ## Creating 2D images of molecules
+
+from IPython.display import SVG
+from scm.plams import ReactionEquation
+from scm.plams import from_smiles, to_image, get_reaction_image
+
+aspirin = from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")
+text = to_image(aspirin)
+SVG(text)
+
+
+# It is also possible to write the image to file in a range of different formats (SVG, PNG, EPS, PDF, JPEG)
+
+text = to_image(aspirin, filename="aspirin.svg")
+text = to_image(aspirin, filename="aspiring.png")
+
+
+# ## Creating 2D imageas of reactions
+
+# We can have aspirin react with water to form acetic acid and salicylic acid
+
+acetic_acid = from_smiles("CC(O)=O")
+text = to_image(acetic_acid)
+SVG(text)
+
+
+salicylic_acid = from_smiles("O=C(O)c1ccccc1O")
+text = to_image(salicylic_acid)
+SVG(text)
+
+
+# We can create a 2D image of the reaction as well, and optionally store it to file
+
+reactants = [aspirin, from_smiles("O")]
+products = [acetic_acid, salicylic_acid]
+text = get_reaction_image(reactants, products, filename="reaction.svg")
+SVG(text)
diff --git a/interfaces/molecule/rdkit.py b/interfaces/molecule/rdkit.py
index d5c6ef7c2..8a5e601e8 100644
--- a/interfaces/molecule/rdkit.py
+++ b/interfaces/molecule/rdkit.py
@@ -1,6 +1,7 @@
-from typing import List, Literal, Optional, overload, TYPE_CHECKING
+from typing import List, Literal, Optional, overload, TYPE_CHECKING, Sequence, Dict, Any
 import random
 import sys
+import copy
 from warnings import warn
 
 from scm.plams.core.functions import add_to_class, log, requires_optional_package
@@ -32,6 +33,8 @@
     "get_conformations",
     "yield_coords",
     "canonicalize_mol",
+    "to_image",
+    "get_reaction_image",
 ]
 
 
@@ -108,7 +111,11 @@ def from_rdmol(rdkit_mol: "RDKitMol", confid: int = -1, properties: bool = True)
 
 @requires_optional_package("rdkit")
 def to_rdmol(
-    plams_mol: Molecule, sanitize: bool = True, properties: bool = True, assignChirality: bool = False
+    plams_mol: Molecule,
+    sanitize: bool = True,
+    properties: bool = True,
+    assignChirality: bool = False,
+    presanitize: bool = False,
 ) -> "RDKitMol":
     """
     Translate a PLAMS molecule into an RDKit molecule type.
@@ -119,6 +126,8 @@ def to_rdmol(
     :parameter bool sanitize: Kekulize, check valencies, set aromaticity, conjugation and hybridization
     :parameter bool properties: If all |Molecule|, |Atom| and |Bond| properties should be converted from PLAMS to RDKit format.
     :parameter bool assignChirality: Assign R/S and cis/trans information, insofar as this was not yet present in the PLAMS molecule.
+    :parameter bool presanitize: Iteratively adjust bonding and atomic charges, to avoid failure of sanitization.
+                                 Only relevant is sanitize is set to True.
     :type plams_mol: |Molecule|
     :return: an RDKit molecule
     :rtype: rdkit.Chem.Mol
@@ -213,10 +222,11 @@ def plams_to_rd_bonds(bo):
 
     if sanitize:
         try:
-            Chem.SanitizeMol(rdmol)
+            if presanitize:
+                rdmol = _presanitize(plams_mol, rdmol)
+            else:
+                Chem.SanitizeMol(rdmol)
         except ValueError as exc:
-            # rdkit_flag = Chem.SanitizeMol(rdmol,catchErrors=True)
-            # log ('RDKit Sanitization Error. Failed Operation Flag = %s'%(rdkit_flag))
             log("RDKit Sanitization Error.")
             text = "Most likely this is a problem with the assigned bond orders: "
             text += "Use chemical insight to adjust them."
@@ -1290,3 +1300,737 @@ def canonicalize_mol(mol, inplace=False, **kwargs):
         ret = mol.copy()
         ret.atoms = [at for _, at in sorted(zip(idx_rank, ret.atoms), reverse=True)]
         return ret
+
+
+@requires_optional_package("rdkit")
+@requires_optional_package("PIL")
+def to_image(
+    mol: Molecule,
+    remove_hydrogens: bool = True,
+    filename: Optional[str] = None,
+    fmt: str = "svg",
+    size: Sequence[int] = (200, 100),
+    as_string: bool = True,
+):
+    """
+    Convert single molecule to single image object
+
+    * ``mol`` -- PLAMS Molecule object
+    * ``remove_hydrogens`` -- Wether or not to remove the H-atoms from the image
+    * ``filename`` -- Optional: Name of image file to be created.
+    * ``fmt`` -- One of "svg", "png", "eps", "pdf", "jpeg"
+    * ``size`` -- Tuple/list containing width and height of image in pixels.
+    * ``as_string`` -- Returns the image as a string or bytestring. If set to False, the original format
+                       will be returned, which can be either a PIL image or SVG text
+                       We do this because after converting a PIL image to a bytestring it is not possible
+                       to further edit it (with our version of PIL).
+    * Returns -- Image text file / binary of image text file / PIL Image object.
+    """
+    from io import BytesIO
+    from PIL import Image
+    from rdkit.Chem import Draw
+    from rdkit.Chem.Draw import rdMolDraw2D
+    from rdkit.Chem.Draw import MolsToGridImage
+
+    extensions = ["svg", "png", "eps", "pdf", "jpeg"]
+
+    classes: Dict[str, Any] = {}
+    classes["svg"] = _MolsToGridSVG
+    for ext in extensions[1:]:
+        classes[ext] = None
+    # PNG can only be created in this way with later version of RDKit
+    if hasattr(rdMolDraw2D, "MolDraw2DCairo"):
+        for ext in extensions[1:]:
+            classes[ext] = MolsToGridImage
+
+    # Determine the type of image file
+    if filename is not None:
+        if "." in filename:
+            extension = filename.split(".")[-1]
+            if extension in classes.keys():
+                fmt = extension
+            else:
+                msg = [f"Image type {extension} not available."]
+                msg += [f"Available extensions are: {' '.join(extensions)}"]
+                raise Exception("\n".join(msg))
+        else:
+            filename = ".".join([filename, fmt])
+
+    if fmt not in classes.keys():
+        raise Exception(f"Image type {fmt} not available.")
+
+    rdmol = _rdmol_for_image(mol, remove_hydrogens)
+
+    # Draw the image
+    if classes[fmt.lower()] is None:
+        # With AMS version of RDKit MolsToGridImage fails for eps (because of paste)
+        img = Draw.MolToImage(rdmol, size=size)
+        buf = BytesIO()
+        img.save(buf, format=fmt)
+        img_text = buf.getvalue()
+    else:
+        # This fails for a C=C=O molecule, with AMS rdkit version
+        img = classes[fmt.lower()]([rdmol], molsPerRow=1, subImgSize=size)
+        img_text = img
+        if isinstance(img, Image.Image):
+            buf = BytesIO()
+            img.save(buf, format=fmt)
+            img_text = buf.getvalue()
+    # If I do not make this correction to the SVG text, it is not readable in JupyterLab
+    if fmt.lower() == "svg":
+        img_text = _correct_svg(img_text)
+
+    # Write to file, if required
+    if filename is not None:
+        mode = "w"
+        if isinstance(img_text, bytes):
+            mode = "wb"
+        with open(filename, mode) as outfile:
+            outfile.write(img_text)
+
+    if as_string:
+        img = img_text
+    return img
+
+
+@requires_optional_package("rdkit")
+@requires_optional_package("PIL")
+def get_reaction_image(
+    reactants: Sequence[Molecule],
+    products: Sequence[Molecule],
+    filename: Optional[str] = None,
+    fmt: str = "svg",
+    size: Sequence[int] = (200, 100),
+    as_string: bool = True,
+):
+    """
+    Create a 2D reaction image from reactants and products (PLAMS molecules)
+
+    * ``reactants`` -- Iterable of PLAMS Molecule objects representing the reactants.
+    * ``products`` -- Iterable of PLAMS Molecule objects representing the products.
+    * ``filename`` -- Optional: Name of image file to be created.
+    * ``fmt``  -- The format of the image (svg, png, eps, pdf, jpeg).
+                     The extension in the filename, if provided, takes precedence.
+    * ``size`` -- Tuple/list containing width and height of image in pixels.
+    * ``as_string`` -- Returns the image as a string or bytestring. If set to False, the original format
+                       will be returned, which can be either a PIL image or SVG text
+    *      Returns -- SVG image text file.
+    """
+    extensions = ["svg", "png", "eps", "pdf", "jpeg"]
+
+    # Determine the type of image file
+    if filename is not None:
+        if "." in filename:
+            extension = filename.split(".")[-1]
+            if extension in extensions:
+                fmt = extension
+            else:
+                msg = [f"Image type {extension} not available."]
+                msg += [f"Available extensions are: {' '.join(extensions)}"]
+                raise Exception("\n".join(msg))
+        else:
+            filename = ".".join([filename, fmt])
+
+    if fmt.lower() not in extensions:
+        raise Exception(f"Image type {fmt} not available.")
+
+    # Get the actual image
+    width = size[0]
+    height = size[1]
+    if fmt.lower() == "svg":
+        img_text = _get_reaction_image_svg(reactants, products, width, height)
+    else:
+        img_text = _get_reaction_image_pil(reactants, products, fmt, width, height, as_string=as_string)
+
+    # Write to file, if required
+    if filename is not None:
+        mode = "w"
+        if isinstance(img_text, bytes):
+            mode = "wb"
+        with open(filename, mode) as outfile:
+            outfile.write(img_text)
+    return img_text
+
+
+def _get_reaction_image_svg(
+    reactants: Sequence[Molecule], products: Sequence[Molecule], width: int = 200, height: int = 100
+):
+    """
+    Create a 2D reaction image from reactants and products (PLAMS molecules)
+
+    * ``reactants`` -- Iterable of PLAMS Molecule objects representing the reactants.
+    * ``products`` -- Iterable of PLAMS Molecule objects representing the products.
+    *      Returns -- SVG image text file.
+    """
+    from rdkit import Chem
+
+    def svg_arrow(x1, y1, x2, y2, prefix=""):
+        """
+        The reaction arrow in html format
+        """
+        # The arrow head
+        l = ['<%sdefs> <%smarker id="arrow" viewBox="0 0 10 10" refX="5" refY="5" ' % (prefix, prefix)]
+        l += ['markerWidth="6" markerHeight="6" ']
+        l += ['orient="auto-start-reverse"> ']
+        l += ['<%spath d="M 0 0 L 10 5 L 0 10 z" /></%smarker></%sdefs>' % (prefix, prefix, prefix)]
+        arrow = "".join(l)
+        # The line
+        l = ['<%sline x1="%i" y1="%i" x2="%i" y2="%i" ' % (prefix, x1, y1, x2, y2)]
+        l += ['stroke="black" marker-end="url(#arrow)" />']
+        line = "".join(l)
+        return [arrow, line]
+
+    def add_plus_signs_svg(img_text, width, height, nmols, nreactants, prefix=""):
+        """
+        Add the lines with + signs to the SVG image
+        """
+        y = int(0.55 * height)
+        t = []
+        for i in range(nmols - 1):
+            x = int(((i + 1) * width) - (0.1 * width))
+            if i + 1 in (nreactants, nreactants + 1):
+                continue
+            t += ['<%stext x="%i" y="%i" font-size="16">+</%stext>' % (prefix, x, y, prefix)]
+        lines = img_text.split("\n")
+        lines = lines[:-2] + t + lines[-2:]
+        return "\n".join(lines)
+
+    def add_arrow_svg(img_text, width, height, nreactants, prefix=""):
+        """
+        Add the arrow to the SVG image
+        """
+        y = int(0.5 * height)
+        x1 = int((nreactants * width) + (0.3 * width))
+        x2 = int((nreactants * width) + (0.7 * width))
+        t = svg_arrow(x1, y, x2, y, prefix)
+        lines = img_text.split("\n")
+        lines = lines[:-2] + t + lines[-2:]
+        return "\n".join(lines)
+
+    # Get the rdkit molecules
+    rdmols = [_rdmol_for_image(mol) for mol in reactants]
+    rdmols += [Chem.Mol()]  # This is where the arrow will go
+    rdmols += [_rdmol_for_image(mol) for mol in products]
+    nmols = len(rdmols)
+
+    # Place the molecules in a row of images
+    subimg_size = [width, height]
+    kwargs = {"legendFontSize": 16}  # ,"legendFraction":0.1}
+    img_text = _MolsToGridSVG(rdmols, molsPerRow=nmols, subImgSize=subimg_size, **kwargs)
+    img_text = _correct_svg(img_text)
+
+    # Add + and =>
+    nreactants = len(reactants)
+    img_text = add_plus_signs_svg(img_text, width, height, nmols, nreactants)
+    img_text = add_arrow_svg(img_text, width, height, nreactants)
+
+    return img_text
+
+
+def _get_reaction_image_pil(
+    reactants: Sequence[Molecule],
+    products: Sequence[Molecule],
+    fmt: str,
+    width: int = 200,
+    height: int = 100,
+    as_string: bool = True,
+):
+    """
+    Create a 2D reaction image from reactants and products (PLAMS molecules)
+
+    * ``reactants`` -- Iterable of PLAMS Molecule objects representing the reactants.
+    * ``products`` -- Iterable of PLAMS Molecule objects representing the products.
+    *      Returns -- SVG image text file.
+    """
+    from io import BytesIO
+    from PIL import Image
+    from PIL import ImageDraw
+    from rdkit import Chem
+    from rdkit.Chem.Draw import rdMolDraw2D, MolsToGridImage
+
+    def add_arrow_pil(img, width, height, nreactants):
+        """
+        Add the arrow to the PIL image
+        """
+        y1 = int(0.5 * height)
+        y2 = y1
+        x1 = int((nreactants * width) + (0.3 * width))
+        x2 = int((nreactants * width) + (0.7 * width))
+
+        # Draw a line
+        black = (0, 0, 0)
+        draw = ImageDraw.Draw(img)
+        draw.line(((x1, y1), (x2, y2)), fill=128)
+
+        # Draw the arrow head
+        headscale = 20
+        xshift = width // headscale
+        yshift = img.size[1] // headscale
+        p1 = (x2, y2 + yshift)
+        p2 = (x2, y2 - yshift)
+        p3 = (x2 + xshift, y2)
+        draw.polygon((p1, p2, p3), fill=black)
+
+        return img
+
+    def add_plus_signs_pil(img, width, height, nmols, nreactants):
+        """
+        Add the lines with + signs to the SVG image
+        """
+        black = (0, 0, 0)
+
+        I1 = ImageDraw.Draw(img)
+        y = int(0.5 * height)
+        # myfont = ImageFont.truetype('FreeMono.ttf', 25)
+        for i in range(nmols):
+            x = int(((i + 1) * width) - (0.05 * width))
+            if i + 1 in (nreactants, nreactants + 1):
+                continue
+            # I1.text((x, y), "+", font=myfont, fill=black)
+            I1.text((x, y), "+", fill=black)
+        return img
+
+    def join_pil_images(pil_images):
+        """
+        Create a new image which connects the ones above with text
+        """
+        white = (255, 255, 255)
+
+        widths = [img.width for img in pil_images]
+        width = sum(widths)
+        height = max([img.height for img in pil_images])
+        final_img = Image.new("RGB", (width, height), white)
+
+        # Concatenate the PIL images
+        for i, img in enumerate(pil_images):
+            pos = sum(widths[:i])
+            h = int((height - img.height) / 2)
+            final_img.paste(img, (pos, h))
+
+        return final_img
+
+    nreactants = len(reactants)
+    nmols = nreactants + len(products)
+
+    if not hasattr(rdMolDraw2D, "MolDraw2DCairo"):
+        # We are working with the old AMS version of RDKit
+        white = (255, 255, 255)
+        rimages = [to_image(mol, fmt=fmt, as_string=False) for i, mol in enumerate(reactants)]
+        pimages = [to_image(mol, fmt=fmt, as_string=False) for i, mol in enumerate(products)]
+        blanc = Image.new("RGB", (width, height), white)
+
+        # Get the image (with arrow)
+        nreactants = len(reactants)
+        all_images = rimages + [blanc] + pimages
+        img = join_pil_images(all_images)
+
+    else:
+        # We have a later version of RDKit that can regulate the font sizes
+        rdmols = [_rdmol_for_image(mol) for mol in reactants]
+        rdmols += [Chem.Mol()]  # This is where the arrow will go
+        rdmols += [_rdmol_for_image(mol) for mol in products]
+
+        # Place the molecules in a row of images
+        subimg_size = [width, height]
+        kwargs = {"legendFontSize": 16}  # ,"legendFraction":0.1}
+        img = MolsToGridImage(rdmols, molsPerRow=nmols + 1, subImgSize=subimg_size, **kwargs)
+
+    # Add + and =>
+    img = add_plus_signs_pil(img, width, height, nmols, nreactants)
+    img = add_arrow_pil(img, width, height, nreactants)
+
+    # Get the bytestring
+    img_text = img
+    if as_string:
+        buf = BytesIO()
+        img.save(buf, format=fmt)
+        img_text = buf.getvalue()
+
+    return img_text
+
+
+def _correct_svg(image):
+    """
+    Correct for a bug in the AMS rdkit created SVG file
+    """
+    if not "svg:" in image:
+        return image
+    image = image.replace("svg:", "")
+    lines = image.split("\n")
+    for iline, line in enumerate(lines):
+        if "xmlns:svg=" in line:
+            lines[iline] = line.replace("xmlns:svg", "xmlns")
+            break
+    image = "\n".join(lines)
+    return image
+
+
+def _presanitize(mol, rdmol):
+    """
+    Change bonding and atom charges to avoid failed sanitization
+
+    Note: Used by to_rdmol
+    """
+    from rdkit import Chem
+
+    mol = mol.copy()
+    for i in range(10):
+        try:
+            rdmol_test = copy.deepcopy(rdmol)
+            Chem.SanitizeMol(rdmol_test)
+            stored_exc = None
+            break
+        except ValueError as exc:
+            stored_exc = exc
+            text = repr(exc)
+        # Fix the problem
+        rdmol, bonds, charges = _kekulize(mol, rdmol, text)
+        # print ("REB bonds charges: ",bonds, charges)
+        # print (rdmol.Debug())
+        # rdmol = to_rdmol(mol, sanitize=False)
+        # print (rdmol.Debug())
+
+    if stored_exc is not None:
+        raise stored_exc
+    else:
+        rdmol = rdmol_test
+    return rdmol
+
+
+def _kekulize(mol, rdmol, text, use_dfs=True):
+    """
+    Kekulize the atoms indicated as problematic by RDKit
+
+    * ``mol`` - PLAMS molecule
+    * ``text`` - Sanitation error produced by RDKit
+
+    Note: Returns the changes in bond orders and atomic charges, that will make sanitation succeed.
+    """
+    from rdkit import Chem
+
+    # Find the indices of the problematic atoms
+    indices = _find_aromatic_sequence(rdmol, text)
+    if indices is None:
+        return rdmol, {}, {}
+
+    # Set the bond orders along the chain to 2, 1, 2, 1,...
+    altered_bonds = {}
+    if len(indices) > 1:
+        emol = Chem.RWMol(rdmol)
+        if use_dfs:
+            # It may be better to create the tree first, and then start at a leaf
+            altered_bonds = _alter_aromatic_bonds(emol, indices[0])
+            indices = list(set([ind for tup in altered_bonds.keys() for ind in tup]))
+        else:
+            indices = _order_atom_indices(emol, indices)
+            altered_bonds = _alter_bonds_along_chain(emol, indices)
+        _adjust_atom_aromaticity(emol, altered_bonds)
+        rdmol = emol.GetMol()
+        # Adjust the PLAMS molecule as well
+        for (iat, jat), order in altered_bonds.items():
+            for bond in mol.atoms[iat].bonds:
+                if mol.index(bond.other_end(mol.atoms[iat])) - 1 == jat:
+                    bond.order = order
+                    break
+
+    # If the atom at the chain end has the wrong bond order, give it a charge.
+    altered_charge = {}
+    atom_indices, dangling_bonds = _get_charged_atoms(rdmol, indices)
+    if len(atom_indices) > 0:
+        iat = atom_indices[0]
+        ndangling = dangling_bonds[iat]
+        altered_charge = _guess_atomic_charge(iat, ndangling, rdmol, mol)
+        rdmol.GetAtomWithIdx(iat).SetFormalCharge(altered_charge[iat])
+        for k, v in altered_charge.items():
+            mol.atoms[k].properties.rdkit.charge = v
+
+    return rdmol, altered_bonds, altered_charge
+
+
+def _find_aromatic_sequence(rdmol, text):
+    """
+    Find the sequence of atoms with 1.5 bond orders
+    """
+    indices = None
+    lines = text.split("\n")
+    line = lines[-1]
+    if "Unkekulized atoms:" in line:
+        text = line.split("Unkekulized atoms:")[-1].split("\\n")[0]
+        if '"' in text:
+            text = text.split('"')[0]
+        indices = [int(w) for w in text.split()]
+        if len(indices) > 1:
+            return indices
+        line = "atom %i marked aromatic" % (indices[0])
+    iat: Any
+    if "marked aromatic" in line:
+        iat = int(line.split("atom")[-1].split()[0])
+        indices = [iat]
+        while iat is not None:
+            icurrent = iat
+            iat = None
+            for bond in rdmol.GetAtomWithIdx(icurrent).GetBonds():
+                if str(bond.GetBondType()) == "AROMATIC":
+                    iat = bond.GetOtherAtomIdx(icurrent)
+                    if iat in indices:
+                        iat = None
+                        continue
+                    indices.append(iat)
+                    break
+    elif "Explicit valence for atom" in line:
+        iat = int(line.split("#")[-1].split()[0])
+        indices = [iat]
+    return indices
+
+
+def _alter_aromatic_bonds(emol, iat, depth=0, double_first=False):
+    """
+    Switch all thearomitic bonds to single/double, starting at iat
+
+    * ``emol`` -- RDKit EditableMol type, for which bond orders will be changed
+    * ``iat``  -- Starting point for depth first search
+    """
+    from collections import OrderedDict
+    from rdkit import Chem
+    from scm.plams import PeriodicTable as PT
+
+    # Use OrderedDict, so that the leaves of the tree
+    # will be at the end
+    bonds_changed = OrderedDict()
+
+    at = emol.GetAtomWithIdx(iat)
+    valence = PT.get_connectors(at.GetAtomicNum())
+    bonds = at.GetBonds()
+    are_aromatic = [str(b.GetBondType()) == "AROMATIC" for b in bonds]
+    int_orders = sum([b.GetBondTypeAsDouble() for i, b in enumerate(bonds) if not are_aromatic[i]])
+    numbonds = len([b for i, b in enumerate(bonds) if are_aromatic[i]])
+    valence -= int(int_orders)
+    # Here I place the double bond first.
+    # I could also start with a single bond instead.
+    orders = [1 for i in range(numbonds)]
+    if valence > numbonds:
+        for i in range(min(numbonds, valence - numbonds)):
+            if double_first:
+                orders[i] = 2
+            else:
+                orders[numbonds - i - 1] = 2
+
+    for i, bond in enumerate(bonds):
+        jat = bond.GetOtherAtomIdx(iat)
+        if are_aromatic[i]:
+            pair = tuple(sorted([iat, jat]))
+            order = orders.pop(0)
+            bond.SetBondType(Chem.BondType(order))
+            bond.SetIsAromatic(False)  # This is necessary with the newer RDKit
+            bonds_changed[pair] = order
+            d = _alter_aromatic_bonds(emol, jat, depth + 1)
+            bonds_changed.update(d)
+    return bonds_changed
+
+
+def _alter_bonds_along_chain(emol, indices):
+    """
+    Along the chain of atoms (indices), alternate double and single bonds
+    """
+    from scm.plams import PeriodicTable as PT
+    from rdkit import Chem
+
+    if len(indices) > 1:
+        # The first bond order is set to 2, if aromic
+        # Else it is flipped
+        first_bond = emol.GetBondBetweenAtoms(indices[0], indices[1])
+        if str(first_bond.GetBondType()) == "AROMATIC":
+            new_order = 2
+        else:
+            new_order = first_bond.GetBondTypeAsDouble()
+            new_order = (new_order % 2) + 1
+        # Unless this breaks valence rules.
+        iat = indices[0]
+        at = emol.GetAtomWithIdx(iat)
+        valence = PT.get_connectors(at.GetAtomicNum())
+        orders = [b.GetBondTypeAsDouble() for b in at.GetBonds()]
+        jat = indices[1]
+        at_next = emol.GetAtomWithIdx(jat)
+        valence_next = PT.get_connectors(at_next.GetAtomicNum())
+        orders_next = [b.GetBondTypeAsDouble() for b in at_next.GetBonds()]
+        if sum(orders) > valence or sum(orders_next) > valence_next:
+            new_order = 1
+    # Set the bond orders along the chain to 2, 1, 2, 1,...
+    altered_bonds = {}
+    for i, iat in enumerate(indices[:-1]):
+        bond = emol.GetBondBetweenAtoms(iat, indices[i + 1])
+        bond.SetBondType(Chem.BondType(new_order))
+        bond.SetIsAromatic(False)  # This is necessary with the newer RDKit versions
+        altered_bonds[iat, indices[i + 1]] = new_order
+        new_order = ((new_order) % 2) + 1
+    return altered_bonds
+
+
+def _order_atom_indices(rdmol, indices):
+    """
+    Order the atomic indices so that they are consecutive along a bonded chain
+    """
+    # Order the indices, so that they are consecutive in the molecule
+    start = 0
+    for i, iat in enumerate(indices):
+        at = rdmol.GetAtomWithIdx(iat)
+        neighbors = [b.GetOtherAtomIdx(iat) for b in at.GetBonds()]
+        relevant_neighbors = [jat for jat in neighbors if jat in indices]
+        if len(relevant_neighbors) == 1:
+            start = i
+            break
+    iat = indices[start]
+    atoms = [iat]
+    while 1:
+        at = rdmol.GetAtomWithIdx(iat)
+        neighbors = [b.GetOtherAtomIdx(iat) for b in at.GetBonds()]
+        relevant_neighbors = [jat for jat in neighbors if jat in indices and not jat in atoms]
+        if len(relevant_neighbors) == 0:
+            break
+        iat = relevant_neighbors[0]
+        atoms.append(iat)
+    if len(atoms) < len(indices):
+        raise Exception("The unkekulized atoms are not in a consecutive chain")
+    indices = atoms
+    return indices
+
+
+def _adjust_atom_aromaticity(emol, altered_bonds):
+    """
+    Assign aromaticity to the atoms based on the new bond orders
+    """
+    indices = list(set([ind for tup in altered_bonds.keys() for ind in tup]))
+    for ind in indices:
+        at = emol.GetAtomWithIdx(ind)
+        if not at.GetIsAromatic():
+            continue
+        # Only change this if we are sure the atom is not aromatic anymore
+        aromatic = False
+        for bond in at.GetBonds():
+            if str(bond.GetBondType()) == "AROMATIC":
+                aromatic = True
+                break
+        if not aromatic:
+            at.SetIsAromatic(False)
+
+
+def _get_charged_atoms(rdmol, indices):
+    """
+    Locate the atoms that need a charge
+    """
+    from scm.plams import PeriodicTable as PT
+
+    atom_indices = []
+    dangling_bonds = {}
+    for iat in indices[::-1]:
+        at = rdmol.GetAtomWithIdx(iat)
+        valence = PT.get_connectors(at.GetAtomicNum())
+        bonds = at.GetBonds()
+        orders = [b.GetBondTypeAsDouble() for b in bonds]
+        ndangling = int(valence - sum(orders))
+        if ndangling != 0:
+            dangling_bonds[iat] = ndangling
+            atom_indices.append(iat)
+    return atom_indices, dangling_bonds
+
+
+def _guess_atomic_charge(iat, ndangling, rdmol, mol):
+    """
+    Guess the best atomic charge for atom iat
+    """
+    # Get the total charge from atomic charges already set
+    charges = [at.GetFormalCharge() for at in rdmol.GetAtoms()]
+    totcharge = sum(charges) - charges[iat]
+
+    # Here I hope that the estimated charge will be more reliable than the
+    # actual (user defined) system charge, but am not sure
+    est_charges = mol.guess_atomic_charges(adjust_to_systemcharge=False, depth=0)
+    molcharge = int(sum(est_charges))
+    molcharge = molcharge - totcharge
+
+    # Adjust the sign based on the estimated charge
+    sign = ndangling / (abs(ndangling))
+    if molcharge != 0:
+        sign = molcharge / (abs(molcharge))
+    elif est_charges[iat] != 0:
+        sign = est_charges[iat] / abs(est_charges[iat])
+
+    # Then use the over/under valence with the new sign as the atomic charge
+    charge = int(sign * abs(ndangling))
+    # Perhaps we tried this already
+    if charge == charges[iat]:
+        charge = -charge
+    altered_charge = {iat: charge}
+    return altered_charge
+
+
+def _rdmol_for_image(mol, remove_hydrogens=True):
+    """
+    Convert PLAMS molecule to an RDKit molecule specifically for a 2D image
+    """
+    from rdkit.Chem import AllChem
+    from rdkit.Chem import RemoveHs
+
+    rdmol = to_rdmol(mol, presanitize=True)
+
+    # Flatten the molecule
+    AllChem.Compute2DCoords(rdmol)
+    # Remove the Hs only if there are carbon atoms in this system
+    # Otherwise this will turn an OH radical into a water molecule.
+    carbons = [i for i, at in enumerate(mol.atoms) if at.symbol in ["C", "Si"]]
+    if remove_hydrogens and len(carbons) > 0:
+        rdmol = RemoveHs(rdmol)
+    else:
+        for atom in rdmol.GetAtoms():
+            atom.SetNoImplicit(True)
+
+    ids = [c.GetId() for c in rdmol.GetConformers()]
+    for cid in ids:
+        rdmol.RemoveConformer(cid)
+    return rdmol
+
+
+def _MolsToGridSVG(
+    mols,
+    molsPerRow=3,
+    subImgSize=(200, 200),
+    legends=None,
+    highlightAtomLists=None,
+    highlightBondLists=None,
+    drawOptions=None,
+    **kwargs,
+):
+    """
+    Replaces the old version of this function in our RDKit for a more recent one, with more options
+    """
+    from rdkit.Chem.Draw import rdMolDraw2D
+
+    if legends is None:
+        legends = [""] * len(mols)
+
+    nRows = len(mols) // molsPerRow
+    if len(mols) % molsPerRow:
+        nRows += 1
+
+    fullSize = (molsPerRow * subImgSize[0], nRows * subImgSize[1])
+
+    d2d = rdMolDraw2D.MolDraw2DSVG(fullSize[0], fullSize[1], subImgSize[0], subImgSize[1])
+    if drawOptions is not None:
+        d2d.SetDrawOptions(drawOptions)
+    else:
+        dops = d2d.drawOptions()
+        for k, v in list(kwargs.items()):
+            if hasattr(dops, k):
+                setattr(dops, k, v)
+                del kwargs[k]
+
+    d2d.DrawMolecules(
+        list(mols),
+        legends=legends or None,
+        highlightAtoms=highlightAtomLists or [],
+        highlightBonds=highlightBondLists or [],
+        **kwargs,
+    )
+    d2d.FinishDrawing()
+    res = d2d.GetDrawingText()
+    return res
diff --git a/unit_tests/test_identify.py b/unit_tests/test_identify.py
index de61464da..91f8cf650 100644
--- a/unit_tests/test_identify.py
+++ b/unit_tests/test_identify.py
@@ -1,10 +1,7 @@
 import pytest
 
-from scm.plams.tools.periodic_table import PT
 from scm.plams.mol.molecule import Molecule
 
-PT.set_connectors("Mg", 4)
-
 
 class TestIdentify:
     """
diff --git a/unit_tests/test_molecule_interfaces.py b/unit_tests/test_molecule_interfaces.py
index 0f705672c..93ed460dc 100644
--- a/unit_tests/test_molecule_interfaces.py
+++ b/unit_tests/test_molecule_interfaces.py
@@ -6,7 +6,15 @@
 from scm.plams.interfaces.molecule.ase import toASE, fromASE
 from scm.plams.unit_tests.test_helpers import get_mock_find_spec, get_mock_open_function
 from scm.plams.core.errors import MissingOptionalPackageError
-from scm.plams.interfaces.molecule.rdkit import from_rdmol, to_rdmol, from_smiles, to_smiles, from_smarts
+from scm.plams.interfaces.molecule.rdkit import (
+    from_rdmol,
+    to_rdmol,
+    from_smiles,
+    to_smiles,
+    from_smarts,
+    to_image,
+    get_reaction_image,
+)
 from scm.plams.interfaces.molecule.packmol import packmol
 
 
@@ -22,7 +30,16 @@ def plams_mols(xyz_folder, pdb_folder, rkf_folder):
     water_molecule_in_box.lattice = [[100, 0, 0], [0, 100, 0], [0, 0, 100]]
     benzene = Molecule(xyz_folder / "benzene.xyz")
     chlorophyl = Molecule(xyz_folder / "chlorophyl1.xyz")
+    chlorophyl.guess_bonds()
     chymotrypsin = Molecule(pdb_folder / "chymotrypsin.pdb")
+    chymotrypsin.guess_bonds()
+    o_hydroxybenzoate = Molecule(xyz_folder / "reactant2.xyz")
+    o_hydroxybenzoate.guess_bonds()
+    hydronium = from_smiles("[OH3+]")
+    # Remove the charge, so that it becomes a difficult molecule for RDKit
+    for at in hydronium.atoms:
+        if at.symbol == "O":
+            del at.properties.rdkit.charge
 
     return {
         "water": water_molecule,
@@ -31,6 +48,8 @@ def plams_mols(xyz_folder, pdb_folder, rkf_folder):
         "benzene": benzene,
         "chlorophyl": chlorophyl,
         "chymotrypsin": chymotrypsin,
+        "o_hydroxybenzoate": o_hydroxybenzoate,
+        "hydronium": hydronium,
     }
 
 
@@ -177,7 +196,19 @@ def roundtrip_and_assert(self, molecules, from_mol, to_mol, level=4):
     def test_to_rdmol_from_rdmol_roundtrip(self, plams_mols):
         from_mol = lambda mol: to_rdmol(mol)
         to_mol = lambda mol: from_rdmol(mol)
-        self.roundtrip_and_assert(plams_mols, from_mol, to_mol)
+        from_bad_mol = lambda mol: to_rdmol(mol, presanitize=True)
+
+        # These molecules throw errors when sanitized by RDKit
+        badmolnames = ["chlorophyl", "chymotrypsin", "o_hydroxybenzoate", "hydronium"]
+
+        # Most molecules do not change at all during round trip
+        molecules = {k: v for k, v in plams_mols.items() if k not in badmolnames}
+
+        # This molecule needs presanitization, meaning bond orders and charge change
+        badmols = {k: v for k, v in plams_mols.items() if k in badmolnames}
+
+        self.roundtrip_and_assert(molecules, from_mol, to_mol)
+        self.roundtrip_and_assert(badmols, from_bad_mol, to_mol, level=1)
 
     @pytest.mark.parametrize("short_smiles,ff", [(True, None), (False, None), (False, "uff"), (False, "mmff")])
     def test_to_smiles_from_smiles_roundtrip(self, plams_mols, short_smiles, ff):
@@ -189,7 +220,10 @@ def test_to_smiles_from_smiles_roundtrip(self, plams_mols, short_smiles, ff):
 
         # For chlorophyl we get a kekulize error...
         # For chymotrypsin we get a aromatic error...
-        err_mols = {k: v for k, v in plams_mols.items() if k in ["chlorophyl", "chymotrypsin"]}
+        # For o_hydroxybenzoate we get a kekulize error...
+        # For hydronium we get a valence error...
+        errmolnames = ["chlorophyl", "chymotrypsin", "o_hydroxybenzoate", "hydronium"]
+        err_mols = {k: v for k, v in plams_mols.items() if k in errmolnames}
 
         # Everything else should match up to bond order level
         lvl2_mols = {k: v for k, v in plams_mols.items() if k not in lvl1_mols.keys() and k not in err_mols.keys()}
@@ -230,6 +264,38 @@ def test_to_smiles_and_from_smiles_requires_rdkit_package(self, plams_mols, smil
             with pytest.raises(MissingOptionalPackageError):
                 from_smiles(smiles[0])
 
+    def test_to_image_and_get_reaction_image_can_generate_img_files(self, xyz_folder):
+        from pathlib import Path
+        import shutil
+
+        # Given molecules
+        reactants = [Molecule(f"{xyz_folder}/reactant{i}.xyz") for i in range(1, 3)]
+        products = [Molecule(f"{xyz_folder}/product{i}.xyz") for i in range(1, 3)]
+
+        # When create images for molecules and reactions
+        result_dir = Path("result_images/rdkit")
+        try:
+            shutil.rmtree(result_dir)
+        except FileNotFoundError:
+            pass
+        result_dir.mkdir(parents=True, exist_ok=True)
+
+        for i, m in enumerate(reactants):
+            m.guess_bonds()
+            to_image(m, filename=f"{result_dir}/reactant{i+1}.png")
+
+        for i, m in enumerate(products):
+            m.guess_bonds()
+            to_image(m, filename=f"{result_dir}/product{i+1}.png")
+
+        get_reaction_image(reactants, products, filename=f"{result_dir}/reaction.png")
+
+        # Then image files are successfully created
+        # N.B. for this test just check the files are generated, not that the contents is correct
+        for f in ["reactant1.png", "reactant2.png", "product1.png", "product2.png", "reaction.png"]:
+            file = result_dir / f
+            assert file.exists()
+
 
 class TestPackmol:
     """
diff --git a/unit_tests/xyz/product1.xyz b/unit_tests/xyz/product1.xyz
new file mode 100644
index 000000000..9c479f92e
--- /dev/null
+++ b/unit_tests/xyz/product1.xyz
@@ -0,0 +1,5 @@
+3
+
+O      -0.0898298722      -0.1823078766      -0.4030695566
+H       0.0892757418       0.7223466689      -0.0923666662
+H       0.0251541304      -0.7229767923       0.3978492228
\ No newline at end of file
diff --git a/unit_tests/xyz/product2.xyz b/unit_tests/xyz/product2.xyz
new file mode 100644
index 000000000..6d6089de6
--- /dev/null
+++ b/unit_tests/xyz/product2.xyz
@@ -0,0 +1,18 @@
+16
+
+C      -0.5058742951       0.7197846249       1.4873502327
+O      -1.8467123470       1.0371356691       1.5578617129
+O       0.2426104621       0.8031328420       2.4444063939
+H      -1.2336711895      -0.1871011373      -3.0847664179
+H       1.1181735289      -0.9674586781      -3.3575709511
+O      -2.2996282296       0.6761194013      -0.9441961430
+H       1.8534242875      -0.1774201547       0.8154069061
+H      -2.5401517031       0.9226546484      -0.0164069755
+H       2.6629785819      -0.9669531279      -1.4040236989
+H      -2.0724918515       1.3639645877       2.4489574275
+C      -1.0116321368       0.2602883042      -0.9839670361
+C      -0.5476048834      -0.1835433773      -2.2354228233
+C       0.7686298918      -0.6221529110      -2.3859139467
+C       1.6373720529      -0.6205255079      -1.2863630033
+C       1.1825135366      -0.1770749455      -0.0425597678
+C      -0.1335817057       0.2638847621       0.1265310905
\ No newline at end of file
diff --git a/unit_tests/xyz/reactant1.xyz b/unit_tests/xyz/reactant1.xyz
new file mode 100644
index 000000000..81b5ba591
--- /dev/null
+++ b/unit_tests/xyz/reactant1.xyz
@@ -0,0 +1,6 @@
+4
+
+H      -0.2376962379      -0.7861223708      -0.7251893655
+H       0.2018867953       0.7880914552      -0.7285471665
+O      -0.2208830412       0.0589617158      -0.1564035523
+H       0.2343924838      -0.0668018003       0.7570340842
\ No newline at end of file
diff --git a/unit_tests/xyz/reactant2.xyz b/unit_tests/xyz/reactant2.xyz
new file mode 100644
index 000000000..3e4b30bae
--- /dev/null
+++ b/unit_tests/xyz/reactant2.xyz
@@ -0,0 +1,17 @@
+15
+
+         C       1.57069295      -0.27708399       0.90970331
+         O       2.12128736       0.87456945       1.36157624
+         O       2.07282352      -1.37947564       1.11228461
+         H      -1.99350927       2.19343258      -1.05192987
+         H      -3.07456482       0.09440138      -1.81135919
+         O       0.21528019       2.32492362       0.26785454
+         H       0.15858899      -2.19801533      -0.12644583
+         H      -1.98350403      -2.08943892      -1.37694501
+         H       2.92149221       0.61570028       1.85487304
+         C      -0.27365246       1.24312943      -0.09274131
+         C      -1.53614397       1.22738446      -0.83792248
+         C      -2.13094250       0.06375743      -1.26848661
+         C      -1.51494870      -1.17520266      -1.01801439
+         C      -0.30749228      -1.23200960      -0.31149813
+         C       0.30093635      -0.08278875       0.15391948