From d0ce99ba3cdb430bad05490e56135bd4544ec79f Mon Sep 17 00:00:00 2001
From: hannahbaumann <hbaumann@uci.edu>
Date: Fri, 26 Apr 2024 10:56:03 +0200
Subject: [PATCH] Addressing review comments

---
 showcase/openfe_showcase.ipynb | 433 +++++++++++++++++++--------------
 1 file changed, 254 insertions(+), 179 deletions(-)

diff --git a/showcase/openfe_showcase.ipynb b/showcase/openfe_showcase.ipynb
index ed15f91..a36817f 100644
--- a/showcase/openfe_showcase.ipynb
+++ b/showcase/openfe_showcase.ipynb
@@ -62,7 +62,6 @@
    },
    "source": [
     "# 0. Setup for Google Colab\n",
-    "TODO:  Clean this up and see if we can make it clean + hide it\n",
     "\n",
     "If you are running this example in Google Colab, run the following cells to setup the environment. If you are running this notebook locally, skip down to `1. Overview`"
    ]
@@ -183,7 +182,7 @@
    "source": [
     "### The dataset: Alchemical transformations of TYK2 ligands\n",
     "\n",
-    "Here we explore how OpenFE can be used to build a network for alchemical substitutions of the TYK2 ligands.\n",
+    "Here we explore how OpenFE can be used to build a network of alchemical transformations between the TYK2 ligands.\n",
     "\n",
     "First, we will use rdkit to visualize the TYK2 ligands."
    ]
@@ -232,8 +231,7 @@
     "\n",
     "Here is what we will achieve in this notebook and what software toolchains are\n",
     "used along the way.\n",
-    "\n",
-    "TODO Update this to include cinnabar IF this gets included \n",
+    " \n",
     "\n",
     "| **Actions**                    | **Software**                                                |\n",
     "|:------------------------------:|:-----------------------------------------------------------:|\n",
@@ -244,10 +242,7 @@
     "| Create hybrid OpenMM topology  | OpenFE interface - OpenMMTools (eventually - ex Perses)  |\n",
     "| Create Lambda Protocol         | OpenFE interface - OpenMMTools (eventually - ex Perses)  |\n",
     "| Setup and run RBFE calculation | OpenFE interface - OpenMM + OpenMMTools                  |\n",
-    "| Analysis RBFE calculation      | OpenFE interface - PyMBAR + OpenMMTools                  |\n",
-    "\n",
-    "\n",
-    "A complete overview of the setup and simulation process starting from initial SDF and PDB files can be found [in this diagram](./../openmm_rbfe/assets/RBFE_workflow.drawio.pdf)."
+    "| Analysis RBFE calculation      | OpenFE interface - PyMBAR + OpenMMTools                  |"
    ]
   },
   {
@@ -297,7 +292,7 @@
    "id": "fbc94d04-5d68-4123-82b2-97a1b8872375",
    "metadata": {},
    "source": [
-    "Load ligands using RDKit:"
+    "<u>Load ligands using RDKit:</u>"
    ]
   },
   {
@@ -323,7 +318,7 @@
    "id": "044045af-020c-4926-b295-0c9db5bd6a0c",
    "metadata": {},
    "source": [
-    "Load ligands using the OpenFF toolkit:"
+    "<u>Load ligands using the OpenFF toolkit:</u>"
    ]
   },
   {
@@ -352,7 +347,7 @@
    "source": [
     "OpenFE SmallMoleculeComponents have some useful built in attributes and methods.\n",
     "\n",
-    "For example the molecule's name (as defined by the SDF file) can be accessed"
+    "For example the molecule's name (as defined by the SDF file) can be accessed:"
    ]
   },
   {
@@ -386,7 +381,7 @@
     "id": "b24a3ffa"
    },
    "source": [
-    "As previously stated `SmallMoleculeComponent`s also use the OpenFF backend to allow conversion between different object types. For example, it's possible to obtain an openff Molecule:"
+    "As previously stated `SmallMoleculeComponent`s also use the OpenFF backend to allow conversion between different object types. For example, it's possible to obtain an OpenFF Molecule:"
    ]
   },
   {
@@ -440,6 +435,14 @@
     "Lomap and Kartograf can be passed to the atom mapper."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1eec9783-4620-43ff-8ef7-a647fd6f54ac",
+   "metadata": {},
+   "source": [
+    "**1. `LomapAtomMapper`**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -452,6 +455,16 @@
     "lomap_mapping = next(mapper.suggest_mappings(ligand_mols[0], ligand_mols[4]))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "fe2a416a-618a-44fa-ab79-c2a6e8ec7333",
+   "metadata": {},
+   "source": [
+    "We can also visualize the atom mappings by invoking the individual OpenFE AtomMapping objects directly.\n",
+    "\n",
+    "Unique atoms between each mapping are shown in red, and atoms which are mapped but undergo element changes are shown in blue. Bonds which either involve atoms that are unique or undergo element changes are highlighted in red."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -470,15 +483,20 @@
     }
    ],
    "source": [
+    "# We can display the atom mapping in 2D by calling it\n",
     "lomap_mapping"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "512938c0-4ec1-48f0-9351-169923319b30",
+   "id": "d3f10c24-e50c-46da-b8d5-8de1f3efae90",
    "metadata": {},
    "source": [
-    "It is also possible to visualize the mapping in 3D. Atoms that have the same sphere color in both ligands are mapped while atoms that do not have a coloured sphere are not mapped but are transformed to a dummy atoms."
+    "It is also possible to visualize the mapping in 3D using py3dmol:\n",
+    "\n",
+    "Here, the ``visualization_3D`` method displays the two endstate molecules (left and right), in addition to the hybrid molecule (middle).\n",
+    "\n",
+    "Atoms that have the same sphere color in both end states are mapped (i.e. will be interpolated between each other), whilst those that do not have a coloured sphere are unmapped (i.e. will be transformed into dummy atoms in the opposite end state)."
    ]
   },
   {
@@ -489,10 +507,10 @@
    "outputs": [
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       "text/html": [
-       "<div id=\"3dmolviewer_17131879268019998\"  style=\"position: relative; width: 600px; height: 600px;\">\n",
-       "        <p id=\"3dmolwarning_17131879268019998\" style=\"background-color:#ffcccc;color:black\">You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  You need to install the 3dmol extension: <br>\n",
+       "<div id=\"3dmolviewer_1714120145905783\"  style=\"position: relative; width: 600px; height: 600px;\">\n",
+       "        <p id=\"3dmolwarning_1714120145905783\" style=\"background-color:#ffcccc;color:black\">You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  You need to install the 3dmol extension: <br>\n",
        "        <tt>jupyter labextension install jupyterlab_3dmol</tt></p>\n",
        "        </div>\n",
        "<script>\n",
@@ -524,77 +542,77 @@
        "  $3Dmolpromise = loadScriptAsync('https://cdnjs.cloudflare.com/ajax/libs/3Dmol/2.0.4/3Dmol-min.js');\n",
        "}\n",
        "\n",
-       "var viewer_17131879268019998 = null;\n",
-       "var warn = document.getElementById(\"3dmolwarning_17131879268019998\");\n",
+       "var viewer_1714120145905783 = null;\n",
+       "var warn = document.getElementById(\"3dmolwarning_1714120145905783\");\n",
        "if(warn) {\n",
        "    warn.parentNode.removeChild(warn);\n",
        "}\n",
        "$3Dmolpromise.then(function() {\n",
-       "viewer_17131879268019998 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_17131879268019998\"),{backgroundColor:\"white\"});\n",
-       "viewer_17131879268019998.zoomTo();\n",
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-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": 6.179000091552734, \"y\": -2.795799970626831, \"z\": -16.497800827026367}, \"radius\": 0.6, \"color\": \"#ff0000\", \"alpha\": 0.8});\n",
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-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -20.524399948120116, \"y\": -11.933500289916992, \"z\": -18.456199645996094}, \"radius\": 0.6, \"color\": \"#6000ff\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": 1.3717996597290032, \"y\": -11.893699645996094, \"z\": -18.496299743652344}, \"radius\": 0.6, \"color\": \"#6000ff\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -21.34929962158203, \"y\": -12.50469970703125, \"z\": -18.552400588989258}, \"radius\": 0.6, \"color\": \"#9700ff\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": 0.546199989318847, \"y\": -12.466400146484375, \"z\": -18.60540008544922}, \"radius\": 0.6, \"color\": \"#9700ff\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -19.6197998046875, \"y\": -12.03849983215332, \"z\": -19.44879913330078}, \"radius\": 0.6, \"color\": \"#cf00ff\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": 2.314700317382812, \"y\": -12.01930046081543, \"z\": -19.457300186157227}, \"radius\": 0.6, \"color\": \"#cf00ff\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -18.529500198364257, \"y\": -11.470600128173828, \"z\": -19.47760009765625}, \"radius\": 0.6, \"color\": \"#fc00f4\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": 3.373600196838378, \"y\": -11.392600059509277, \"z\": -19.477399826049805}, \"radius\": 0.6, \"color\": \"#fc00f4\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -22.015299987792968, \"y\": -11.0423002243042, \"z\": -14.355199813842773}, \"radius\": 0.6, \"color\": \"#ff00bf\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -0.0908000946044929, \"y\": -11.061100006103516, \"z\": -14.366299629211426}, \"radius\": 0.6, \"color\": \"#ff00bf\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -20.612400245666503, \"y\": -9.040599822998047, \"z\": -14.031100273132324}, \"radius\": 0.6, \"color\": \"#ff0087\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": 1.3103000640869134, \"y\": -9.046699523925781, \"z\": -14.02810001373291}, \"radius\": 0.6, \"color\": \"#ff0087\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -15.701699924468993, \"y\": -7.623799800872803, \"z\": -15.822999954223633}, \"radius\": 0.6, \"color\": \"#ff0050\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": 6.2006999969482415, \"y\": -7.593500137329102, \"z\": -15.822199821472168}, \"radius\": 0.6, \"color\": \"#ff0050\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": -14.683700037002563, \"y\": -5.077700138092041, \"z\": -16.453100204467773}, \"radius\": 0.6, \"color\": \"#ff0018\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addSphere({\"center\": {\"x\": 7.220200014114379, \"y\": -5.04640007019043, \"z\": -16.444400787353516}, \"radius\": 0.6, \"color\": \"#ff0018\", \"alpha\": 0.8});\n",
-       "\tviewer_17131879268019998.addModel(\"lig_ejm_31\\n     RDKit          3D\\n\\n 32 33  0  0  0  0  0  0  0  0999 V2000\\n   -4.7726   -2.8276  -16.4990 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.3616   -3.6890  -16.2224 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7777   -4.9643  -16.1989 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.5357   -6.0885  -15.8273 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.9007   -5.9451  -15.4736 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.4915   -4.6571  -15.5270 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7187   -3.5398  -15.8968 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.1766   -2.5639  -15.9391 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.1733   -4.4152  -15.1872 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.7131   -7.1309  -15.0378 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.1428   -7.1983  -13.8857 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.8973   -8.0563  -15.9972 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.4216   -7.8671  -16.8645 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7209   -9.2140  -16.0056 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5775   -9.5988  -14.9513 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3850  -10.7324  -15.1337 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3675  -11.4892  -16.2527 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5395  -11.1490  -17.2696 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7142  -10.0001  -17.1743 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.0845   -9.6948  -17.9928 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5744  -11.9335  -18.4562 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3993  -12.5047  -18.5524 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.6698  -12.0385  -19.4488 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.5795  -11.4706  -19.4776 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.0521  -12.9529  -20.6079 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.1557  -13.9753  -20.2570 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.2854  -12.9378  -21.3838 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.9953  -12.6471  -21.0606 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -11.0653  -11.0423  -14.3552 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.6624   -9.0406  -14.0311 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7517   -7.6238  -15.8230 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.7337   -5.0777  -16.4531 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  7  2  0\\n  2  3  1  0\\n  3  4  2  0\\n  3 32  1  0\\n  4  5  1  0\\n  4 31  1  0\\n  5  6  2  0\\n  5 10  1  0\\n  6  7  1  0\\n  6  9  1  0\\n  7  8  1  0\\n 10 11  2  0\\n 10 12  1  0\\n 12 13  1  0\\n 12 14  1  0\\n 14 19  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 15 30  1  0\\n 16 17  2  0\\n 16 29  1  0\\n 17 18  1  0\\n 18 19  2  0\\n 18 21  1  0\\n 19 20  1  0\\n 21 22  1  0\\n 21 23  1  0\\n 23 24  2  0\\n 23 25  1  0\\n 25 26  1  0\\n 25 27  1  0\\n 25 28  1  0\\nM  END\\n\",\"molA_overlay\");\n",
-       "\tviewer_17131879268019998.addModel(\"lig_ejm_47\\n     RDKit          3D\\n\\n 39 41  0  0  0  0  0  0  0  0999 V2000\\n   -4.7710   -2.7958  -16.4978 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.3622   -3.6601  -16.2251 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7228   -3.5154  -15.9047 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.4990   -4.6316  -15.5395 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.9098   -5.9210  -15.4840 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.5411   -6.0619  -15.8372 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7773   -4.9364  -16.1997 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.7298   -5.0464  -16.4444 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7493   -7.5935  -15.8222 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.7221   -7.1086  -15.0399 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.1200   -7.1967  -13.8766 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.9177   -8.0239  -16.0075 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.4448   -7.8335  -16.8781 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7460   -9.1777  -16.0235 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5772   -9.5870  -14.9614 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3788  -10.7255  -15.1522 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3750  -11.4658  -16.2840 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5645  -11.1035  -17.3073 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7559   -9.9418  -17.2069 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.1406   -9.6119  -18.0290 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5782  -11.8937  -18.4963 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.4038  -12.4664  -18.6054 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.6353  -12.0193  -19.4573 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.5764  -11.3926  -19.4774 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.9229  -13.0284  -20.5859 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.6756  -12.5134  -22.0198 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.0186  -13.8555  -22.3469 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.7694  -14.0202  -20.8465 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.9493  -15.0312  -20.4860 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7826  -13.6664  -20.5416 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.1203  -13.7980  -22.9633 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7315  -14.5837  -22.7394 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.9803  -11.6755  -22.0897 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5770  -12.3187  -22.5983 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.8902  -13.5295  -20.4978 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -11.0408  -11.0611  -14.3663 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.6397   -9.0467  -14.0281 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.1773   -4.3783  -15.2060 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.1757   -2.5362  -15.9443 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  7  2  0\\n  2  3  1  0\\n  3  4  2  0\\n  3 39  1  0\\n  4  5  1  0\\n  4 38  1  0\\n  5  6  2  0\\n  5 10  1  0\\n  6  7  1  0\\n  6  9  1  0\\n  7  8  1  0\\n 10 11  2  0\\n 10 12  1  0\\n 12 13  1  0\\n 12 14  1  0\\n 14 19  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 15 37  1  0\\n 16 17  2  0\\n 16 36  1  0\\n 17 18  1  0\\n 18 19  2  0\\n 18 21  1  0\\n 19 20  1  0\\n 21 22  1  0\\n 21 23  1  0\\n 23 24  2  0\\n 23 25  1  0\\n 25 28  1  0\\n 25 26  1  0\\n 25 35  1  0\\n 26 27  1  0\\n 26 33  1  0\\n 26 34  1  0\\n 27 28  1  0\\n 27 31  1  0\\n 27 32  1  0\\n 28 29  1  0\\n 28 30  1  0\\nM  END\\n\",\"molB_overlay\");\n",
-       "\tviewer_17131879268019998.setStyle({\"stick\": {}});\n",
-       "\tviewer_17131879268019998.zoomTo();\n",
-       "viewer_17131879268019998.render();\n",
+       "viewer_1714120145905783 = $3Dmol.createViewer(document.getElementById(\"3dmolviewer_1714120145905783\"),{backgroundColor:\"white\"});\n",
+       "viewer_1714120145905783.zoomTo();\n",
+       "\tviewer_1714120145905783.addModel(\"lig_ejm_31\\n     RDKit          3D\\n\\n 32 33  0  0  0  0  0  0  0  0999 V2000\\n  -15.7226   -2.8276  -16.4990 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -16.3116   -3.6890  -16.2224 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -15.7277   -4.9643  -16.1989 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -16.4857   -6.0885  -15.8273 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -17.8507   -5.9451  -15.4736 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -18.4415   -4.6571  -15.5270 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -17.6687   -3.5398  -15.8968 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -18.1266   -2.5639  -15.9391 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -20.1233   -4.4152  -15.1872 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n  -18.6631   -7.1309  -15.0378 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -19.0928   -7.1983  -13.8857 O   0  0  0  0  0  0  0  0  0  0  0  0\\n  -18.8473   -8.0563  -15.9972 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  -18.3716   -7.8671  -16.8645 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -19.6709   -9.2140  -16.0056 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -20.5275   -9.5988  -14.9513 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -21.3350  -10.7324  -15.1337 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -21.3175  -11.4892  -16.2527 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  -20.4895  -11.1490  -17.2696 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -19.6642  -10.0001  -17.1743 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -19.0345   -9.6948  -17.9928 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -20.5244  -11.9335  -18.4562 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  -21.3493  -12.5047  -18.5524 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -19.6198  -12.0385  -19.4488 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -18.5295  -11.4706  -19.4776 O   0  0  0  0  0  0  0  0  0  0  0  0\\n  -20.0021  -12.9529  -20.6079 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -20.1057  -13.9753  -20.2570 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -19.2354  -12.9378  -21.3838 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -20.9453  -12.6471  -21.0606 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -22.0153  -11.0423  -14.3552 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -20.6124   -9.0406  -14.0311 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -15.7017   -7.6238  -15.8230 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n  -14.6837   -5.0777  -16.4531 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  7  2  0\\n  2  3  1  0\\n  3  4  2  0\\n  3 32  1  0\\n  4  5  1  0\\n  4 31  1  0\\n  5  6  2  0\\n  5 10  1  0\\n  6  7  1  0\\n  6  9  1  0\\n  7  8  1  0\\n 10 11  2  0\\n 10 12  1  0\\n 12 13  1  0\\n 12 14  1  0\\n 14 19  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 15 30  1  0\\n 16 17  2  0\\n 16 29  1  0\\n 17 18  1  0\\n 18 19  2  0\\n 18 21  1  0\\n 19 20  1  0\\n 21 22  1  0\\n 21 23  1  0\\n 23 24  2  0\\n 23 25  1  0\\n 25 26  1  0\\n 25 27  1  0\\n 25 28  1  0\\nM  END\\n\",\"molA\");\n",
+       "\tviewer_1714120145905783.addModel(\"lig_ejm_47\\n     RDKit          3D\\n\\n 39 41  0  0  0  0  0  0  0  0999 V2000\\n    6.1790   -2.7958  -16.4978 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.5878   -3.6601  -16.2251 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.2272   -3.5154  -15.9047 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.4510   -4.6316  -15.5395 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.0402   -5.9210  -15.4840 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.4089   -6.0619  -15.8372 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.1727   -4.9364  -16.1997 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    7.2202   -5.0464  -16.4444 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    6.2007   -7.5935  -15.8222 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n    3.2279   -7.1086  -15.0399 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8300   -7.1967  -13.8766 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.0323   -8.0239  -16.0075 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.5052   -7.8335  -16.8781 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.2040   -9.1777  -16.0235 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3728   -9.5870  -14.9614 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.5712  -10.7255  -15.1522 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.5750  -11.4658  -16.2840 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3855  -11.1035  -17.3073 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.1941   -9.9418  -17.2069 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.8094   -9.6119  -18.0290 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3718  -11.8937  -18.4963 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.5462  -12.4664  -18.6054 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.3147  -12.0193  -19.4573 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.3736  -11.3926  -19.4774 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.0271  -13.0284  -20.5859 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.2744  -12.5134  -22.0198 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.9314  -13.8555  -22.3469 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.1806  -14.0202  -20.8465 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.0007  -15.0312  -20.4860 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.1674  -13.6664  -20.5416 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.8297  -13.7980  -22.9633 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.2185  -14.5837  -22.7394 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.9697  -11.6755  -22.0897 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3730  -12.3187  -22.5983 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.0598  -13.5295  -20.4978 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.0908  -11.0611  -14.3663 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.3103   -9.0467  -14.0281 H   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.7727   -4.3783  -15.2060 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n    3.7743   -2.5362  -15.9443 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  7  2  0\\n  2  3  1  0\\n  3  4  2  0\\n  3 39  1  0\\n  4  5  1  0\\n  4 38  1  0\\n  5  6  2  0\\n  5 10  1  0\\n  6  7  1  0\\n  6  9  1  0\\n  7  8  1  0\\n 10 11  2  0\\n 10 12  1  0\\n 12 13  1  0\\n 12 14  1  0\\n 14 19  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 15 37  1  0\\n 16 17  2  0\\n 16 36  1  0\\n 17 18  1  0\\n 18 19  2  0\\n 18 21  1  0\\n 19 20  1  0\\n 21 22  1  0\\n 21 23  1  0\\n 23 24  2  0\\n 23 25  1  0\\n 25 28  1  0\\n 25 26  1  0\\n 25 35  1  0\\n 26 27  1  0\\n 26 33  1  0\\n 26 34  1  0\\n 27 28  1  0\\n 27 31  1  0\\n 27 32  1  0\\n 28 29  1  0\\n 28 30  1  0\\nM  END\\n\",\"molB\");\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -15.722600173950195, \"y\": -2.8276000022888184, \"z\": -16.499000549316406}, \"radius\": 0.6, \"color\": \"#ff0000\", \"alpha\": 0.8});\n",
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+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -16.311599922180175, \"y\": -3.688999891281128, \"z\": -16.222400665283203}, \"radius\": 0.6, \"color\": \"#ff3800\", \"alpha\": 0.8});\n",
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+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": 5.408899974822997, \"y\": -6.0619001388549805, \"z\": -15.837200164794922}, \"radius\": 0.6, \"color\": \"#ffa700\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -17.850700092315673, \"y\": -5.945099830627441, \"z\": -15.473600387573242}, \"radius\": 0.6, \"color\": \"#ffdf00\", \"alpha\": 0.8});\n",
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+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": 2.314700317382812, \"y\": -12.01930046081543, \"z\": -19.457300186157227}, \"radius\": 0.6, \"color\": \"#cf00ff\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -18.529500198364257, \"y\": -11.470600128173828, \"z\": -19.47760009765625}, \"radius\": 0.6, \"color\": \"#fc00f4\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": 3.373600196838378, \"y\": -11.392600059509277, \"z\": -19.477399826049805}, \"radius\": 0.6, \"color\": \"#fc00f4\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -22.015299987792968, \"y\": -11.0423002243042, \"z\": -14.355199813842773}, \"radius\": 0.6, \"color\": \"#ff00bf\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -0.0908000946044929, \"y\": -11.061100006103516, \"z\": -14.366299629211426}, \"radius\": 0.6, \"color\": \"#ff00bf\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -20.612400245666503, \"y\": -9.040599822998047, \"z\": -14.031100273132324}, \"radius\": 0.6, \"color\": \"#ff0087\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": 1.3103000640869134, \"y\": -9.046699523925781, \"z\": -14.02810001373291}, \"radius\": 0.6, \"color\": \"#ff0087\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -15.701699924468993, \"y\": -7.623799800872803, \"z\": -15.822999954223633}, \"radius\": 0.6, \"color\": \"#ff0050\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": 6.2006999969482415, \"y\": -7.593500137329102, \"z\": -15.822199821472168}, \"radius\": 0.6, \"color\": \"#ff0050\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": -14.683700037002563, \"y\": -5.077700138092041, \"z\": -16.453100204467773}, \"radius\": 0.6, \"color\": \"#ff0018\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addSphere({\"center\": {\"x\": 7.220200014114379, \"y\": -5.04640007019043, \"z\": -16.444400787353516}, \"radius\": 0.6, \"color\": \"#ff0018\", \"alpha\": 0.8});\n",
+       "\tviewer_1714120145905783.addModel(\"lig_ejm_31\\n     RDKit          3D\\n\\n 32 33  0  0  0  0  0  0  0  0999 V2000\\n   -4.7726   -2.8276  -16.4990 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.3616   -3.6890  -16.2224 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7777   -4.9643  -16.1989 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.5357   -6.0885  -15.8273 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.9007   -5.9451  -15.4736 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.4915   -4.6571  -15.5270 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7187   -3.5398  -15.8968 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.1766   -2.5639  -15.9391 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.1733   -4.4152  -15.1872 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.7131   -7.1309  -15.0378 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.1428   -7.1983  -13.8857 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.8973   -8.0563  -15.9972 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.4216   -7.8671  -16.8645 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7209   -9.2140  -16.0056 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5775   -9.5988  -14.9513 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3850  -10.7324  -15.1337 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3675  -11.4892  -16.2527 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5395  -11.1490  -17.2696 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7142  -10.0001  -17.1743 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.0845   -9.6948  -17.9928 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5744  -11.9335  -18.4562 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3993  -12.5047  -18.5524 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.6698  -12.0385  -19.4488 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.5795  -11.4706  -19.4776 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.0521  -12.9529  -20.6079 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.1557  -13.9753  -20.2570 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.2854  -12.9378  -21.3838 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.9953  -12.6471  -21.0606 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -11.0653  -11.0423  -14.3552 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.6624   -9.0406  -14.0311 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7517   -7.6238  -15.8230 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.7337   -5.0777  -16.4531 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  7  2  0\\n  2  3  1  0\\n  3  4  2  0\\n  3 32  1  0\\n  4  5  1  0\\n  4 31  1  0\\n  5  6  2  0\\n  5 10  1  0\\n  6  7  1  0\\n  6  9  1  0\\n  7  8  1  0\\n 10 11  2  0\\n 10 12  1  0\\n 12 13  1  0\\n 12 14  1  0\\n 14 19  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 15 30  1  0\\n 16 17  2  0\\n 16 29  1  0\\n 17 18  1  0\\n 18 19  2  0\\n 18 21  1  0\\n 19 20  1  0\\n 21 22  1  0\\n 21 23  1  0\\n 23 24  2  0\\n 23 25  1  0\\n 25 26  1  0\\n 25 27  1  0\\n 25 28  1  0\\nM  END\\n\",\"molA_overlay\");\n",
+       "\tviewer_1714120145905783.addModel(\"lig_ejm_47\\n     RDKit          3D\\n\\n 39 41  0  0  0  0  0  0  0  0999 V2000\\n   -4.7710   -2.7958  -16.4978 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.3622   -3.6601  -16.2251 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7228   -3.5154  -15.9047 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.4990   -4.6316  -15.5395 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.9098   -5.9210  -15.4840 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -5.5411   -6.0619  -15.8372 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7773   -4.9364  -16.1997 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -3.7298   -5.0464  -16.4444 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -4.7493   -7.5935  -15.8222 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.7221   -7.1086  -15.0399 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.1200   -7.1967  -13.8766 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.9177   -8.0239  -16.0075 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.4448   -7.8335  -16.8781 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7460   -9.1777  -16.0235 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5772   -9.5870  -14.9614 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3788  -10.7255  -15.1522 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.3750  -11.4658  -16.2840 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5645  -11.1035  -17.3073 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7559   -9.9418  -17.2069 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.1406   -9.6119  -18.0290 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5782  -11.8937  -18.4963 N   0  0  0  0  0  0  0  0  0  0  0  0\\n  -10.4038  -12.4664  -18.6054 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.6353  -12.0193  -19.4573 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.5764  -11.3926  -19.4774 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.9229  -13.0284  -20.5859 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.6756  -12.5134  -22.0198 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.0186  -13.8555  -22.3469 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.7694  -14.0202  -20.8465 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.9493  -15.0312  -20.4860 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -6.7826  -13.6664  -20.5416 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.1203  -13.7980  -22.9633 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -8.7315  -14.5837  -22.7394 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.9803  -11.6755  -22.0897 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.5770  -12.3187  -22.5983 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.8902  -13.5295  -20.4978 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  -11.0408  -11.0611  -14.3663 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.6397   -9.0467  -14.0281 H   0  0  0  0  0  0  0  0  0  0  0  0\\n   -9.1773   -4.3783  -15.2060 Cl  0  0  0  0  0  0  0  0  0  0  0  0\\n   -7.1757   -2.5362  -15.9443 H   0  0  0  0  0  0  0  0  0  0  0  0\\n  1  2  1  0\\n  2  7  2  0\\n  2  3  1  0\\n  3  4  2  0\\n  3 39  1  0\\n  4  5  1  0\\n  4 38  1  0\\n  5  6  2  0\\n  5 10  1  0\\n  6  7  1  0\\n  6  9  1  0\\n  7  8  1  0\\n 10 11  2  0\\n 10 12  1  0\\n 12 13  1  0\\n 12 14  1  0\\n 14 19  1  0\\n 14 15  2  0\\n 15 16  1  0\\n 15 37  1  0\\n 16 17  2  0\\n 16 36  1  0\\n 17 18  1  0\\n 18 19  2  0\\n 18 21  1  0\\n 19 20  1  0\\n 21 22  1  0\\n 21 23  1  0\\n 23 24  2  0\\n 23 25  1  0\\n 25 28  1  0\\n 25 26  1  0\\n 25 35  1  0\\n 26 27  1  0\\n 26 33  1  0\\n 26 34  1  0\\n 27 28  1  0\\n 27 31  1  0\\n 27 32  1  0\\n 28 29  1  0\\n 28 30  1  0\\nM  END\\n\",\"molB_overlay\");\n",
+       "\tviewer_1714120145905783.setStyle({\"stick\": {}});\n",
+       "\tviewer_1714120145905783.zoomTo();\n",
+       "viewer_1714120145905783.render();\n",
        "});\n",
        "</script>"
       ]
@@ -605,7 +623,7 @@
     {
      "data": {
       "text/plain": [
-       "<py3Dmol.view at 0x15014d090>"
+       "<py3Dmol.view at 0x10c8f5b70>"
       ]
      },
      "execution_count": 8,
@@ -614,12 +632,22 @@
     }
    ],
    "source": [
-    "# Visualize mapping in 3D\n",
+    "# Visualize the mapping in 3D\n",
     "from openfe.utils import visualization_3D\n",
     "\n",
     "visualization_3D.view_mapping_3d(lomap_mapping)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f21bf9e5-6cb8-41c1-b0fd-e70ee2e71852",
+   "metadata": {},
+   "source": [
+    "**2. `KartografAtomMapper`**\n",
+    "\n",
+    "We can also use the `KartografAtomMapper` which is based on the 3D geometries of the ligands."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -674,6 +702,7 @@
     }
    ],
    "source": [
+    "# We can display the atom mapping in 2D by calling it\n",
     "kartograf_mapping"
    ]
   },
@@ -695,12 +724,14 @@
     "* Loading in networks from external software (FEP+ or Orion)\n",
     "* Loading in a user defined network\n",
     "\n",
-    "In this section we will create and visualize the MST, LOMAP and radial networks for the TYK2 dataset."
+    "In this section we will create and visualize the MST, LOMAP and radial networks for the TYK2 dataset.\n",
+    "\n",
+    "Here, we will be using the `LomapAtomMapper` as the atom mapper for all networks."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "5b89da93",
    "metadata": {
     "id": "5b89da93",
@@ -710,12 +741,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "de06b8b695284aefafde095415b698b2",
+       "model_id": "e1179d421fcf4082b353700d4cf43633",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       " 67%|######6   | 30/45 [00:01<00:00, 19.92it/s]"
+       " 56%|#####5    | 25/45 [00:01<00:01, 16.16it/s]"
       ]
      },
      "metadata": {},
@@ -783,9 +814,17 @@
     "    mappers=[LomapAtomMapper(),])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "41cd9718-0c2e-4b59-86ee-8dc429915de1",
+   "metadata": {},
+   "source": [
+    "We can plot out the different networks to visualize their structure and to see how ligands are being tranformed."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "f4e3ac10-236b-4ff4-a69e-600706288164",
    "metadata": {
     "scrolled": true
@@ -798,7 +837,7 @@
        "<Figure size 800x800 with 1 Axes>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -814,6 +853,7 @@
     }
    ],
    "source": [
+    "# Visualize the MST network\n",
     "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n",
     "\n",
     "plot_atommapping_network(mst_network)"
@@ -821,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "3335c478-ce7c-41a4-b36f-2e4c79581050",
    "metadata": {},
    "outputs": [
@@ -832,12 +872,13 @@
        "<Figure size 800x800 with 1 Axes>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Visualize the LOMAP network\n",
     "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n",
     "\n",
     "plot_atommapping_network(lomap_network)"
@@ -845,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "e1c09036-efc7-4bc4-9851-ac16992bf041",
    "metadata": {},
    "outputs": [
@@ -856,12 +897,13 @@
        "<Figure size 800x800 with 1 Axes>"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# Visualize the radial network\n",
     "from openfe.utils.atommapping_network_plotting import plot_atommapping_network\n",
     "\n",
     "plot_atommapping_network(radial_network)"
@@ -879,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "ad80c243-82d7-4599-a4a6-5d651c662f24",
    "metadata": {},
    "outputs": [
@@ -887,9 +929,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "molecule A smiles:  CC(=O)Nc1cc(NC(=O)c2c(Cl)cccc2Cl)ccn1\n",
+      "molecule A smiles:  CCC(=O)Nc1cc(NC(=O)c2c(Cl)cccc2Cl)ccn1\n",
       "molecule B smiles:  O=C(CO)Nc1cc(NC(=O)c2c(Cl)cccc2Cl)ccn1\n",
-      "map between molecule A and B:  {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 26, 26: 27, 27: 25, 28: 29, 29: 30, 30: 31, 31: 32}\n"
+      "map between molecule A and B:  {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 31: 29, 32: 30, 33: 31, 34: 32}\n"
      ]
     }
    ],
@@ -905,23 +947,9 @@
     "print(\"map between molecule A and B: \", edge.componentA_to_componentB)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ee0b19b7",
-   "metadata": {
-    "id": "ee0b19b7"
-   },
-   "source": [
-    "We can also visualise the atom mappings by invoking the individual OpenFE\n",
-    "AtomMapping objects directly.\n",
-    "\n",
-    "Unique atoms between each mapping are shown in red, and atoms which are mapped\n",
-    "but undergo element changes are shown in blue. Bonds which either involve atoms that are unique or undergo element changes are highlighted in red."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "9f92262f",
    "metadata": {
     "colab": {
@@ -934,7 +962,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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CwsLTp0/X1tZ+6GPx8fGiUZrq6uqysjIybzdjxozO35wIDofDZDK1tLTIFsiakYSEhClTppC2qqqqunWGH+Y///lPbGws+VJfX18spa0dEx4evnjxYtF3sBDKCEwmk8vl/vjjj0ePHi0uLiYI4t27d+TlHRERUVRUtGLFCjIXnpSUxOPxyNFn3759mUxmJxMKovB4PCaTiRYdBIAFCxZER0cTBPH8+XO0LhqdTre1tW1sbBTv+ZLcvHnT0dGRfLl169anT59KyBaJQCDQ0dERfQcLIaYjnJ2dQ0NDz5w54+DgQBAEi8VycHBAJWfNzc0uLi6//fYbitIYGhoGBASgHGG/fv3IdIJAIOia6cbGxqNHj5LrZSxbtmzz5s2oEFxPTw9FhCTH8uXLY2JiyJf6+vqd8Wi7iVAonDlz5vnz5zMyMp4/f15QUICFUBZgs9m2trahoaGHDh06cuQIEkIzMzO0LjxBEKampgcPHkRFMS4uLgKBIDQ0dOzYseSIsLKysjsdaG5u/uOPPzQ0NACARqORbqKZmZnoXSAJXF1djx49Sr7csWOHhFzPNsyfP/+ff/4hX2IhxHyQ1NTUTZs2JSUlHT58+OrVqwRB1NbWqqurb9++nSCIyspKCwuLrVu3koXUOTk5CxYsIHVLLOmExsZGBoOBEvW9evVC41OxFG13zIkTJ06fPo2O09LSpk2bJmmLCBaLdenSpV9++YXBYFRWVp4/f54au5gOqKio+OmnnxYvXmxpaSn69JQEZMGImZmZjY2Nt7c3QRCmpqb19fU7d+4sLS2tq6sj8+6GhoZi7E9NTY29vb2SkpKenp6SkpLYq8/eS2RkpGgUZPLkyZIIbrUnPT192rRp33777ddff33u3DkshJ8xZWVle/bssba2PnToUJcnBn0StbW1FhYWixcvjoqKQkLY0NAQExPz3hyhGKmsrESj1AsXLoi35Q/R2Ng4b968X375xdHRcfLkyR2U+Yidixcvrlq1SjQwi5Eus2bNevHiBUEQVVVVEyZM+NRQf9cwMzMrKCgYP358c3OzqakpKl1+8OCBtrY2ACgqKh48eFAS9czHjh0DgHXr1om95Q/x888///jjjzdv3rS2tt63bx9ldtGzq6ioiCAILpdLgepTjEwIIYvFMjY2/ueffxobG//66y9zc/MuByE7DxLClJQUc3PzsrIyCwsLgiAaGxsnTJiAgirr1q2rqakRr0U0MwRNTOy4lFS8CASCpKSkt2/f1tbW1tbWdiEB0zW++eYbAKAmQIT5KEVFRaKlUi4uLkeOHKHArpmZGYfDcXZ23r9/PymEP/30EwDMnDkzOTm5+yaam5vRQ6O2tvbRo0cMBqOgoOD69esAsGHDhu6333liYmI8PDySkpKoNHrhwgUA2LFjB5VGqUQm1hp9/vz5nDlzFi5c2KtXr++++65///5RUVHUmB4zZsysWbNu3ryJXvbq1WvUqFH6+vp+fn63b99Gkwi7TF5eHoPBAAAej7d9+/b+/fvr6uoKhUJUJtrU1NT9/ncGf39/Q0NDBoMxbdo0AwMDTU3N+vp6akxTv1kVpgOqqqrIFWsBQEtLi5qFZxFbtmx5/fp1RUUFennmzJlbt269efOGTBB2mebm5n379lVUVHC53J07d6I6gPT0dLRiPmW727e0tEyaNGnz5s0rV648deqUpqZmd5bw/iQoPlPqkQkhLCoqEt3u0sDAoLCwkDLrBw8evH//Pvny6tWrycnJZBl3d9DX10ch1tu3b2tpaamqqhIE0dLS0qtXL6BWHvLz89EDiGJlwkLYoxg2bFhOTg75MiMjw9DQkDLrcnJyTk5OpBD269fvp59+Esv+XOrq6mjJ35s3b3733XdKSkoAwGKxKN7UV0FBISYmBq3O39raWldXR5npnrB9sUSRCSEcNGhQSUkJ+bKoqIiCjWfpdDqa5Kempnb8+HFywl/fvn3R8EpctLS0FBcX5+XloYu1ublZimqEhVCW0dTUnDZt2tGjR0tKSgICAu7du7d27VoK7D59+lQoFObm5vbu3Ts/Px8JlRgRCoVJSUmpqal6enoxMTFoozE2m03xpr6KiopycnI8Hq+1tZViF4367YspRiaE0NLS8sWLF6mpqQAQGhqam5tLLgAhOTQ0NB48eJCUlHTt2rU+ffo8ePBA7CYqKipUVVULCwuPHTu2detWVNLd1NSE3MSWlhaxW3wvUhRCis8U81GuXbumpaV16NChwMDAFy9eoNlBkmbw4MGJiYmGhoZr164dMmSI2HdpptPpO3funDp1qqWlpYODw8iRIwGAzWajoSeVAUPSM6PYRaP+TClGXtodoILevXs/fvz46NGjtbW1urq6Pj4+aD4fBQQHB9vZ2W3btm3OnDlib1xLS2vDhg3o2MzMjIyIUqxGyC5KSUrFI6QsG4r5KFeuXCktLT1x4kR8fPyvv/66fPnyH374gQK7kn5S9+3bFx3Iy8uT3hj1fpKKigra44Vi09gj/BJobW1NSkqytLT08/OzsrK6cuVKUlISNaapFAbSFsXyIEWPkPpsKKZj7ty5c+rUqdLS0vT0dC8vL8qq0qj0kNq4ZVT6SaQgUWz6i/cIZUIIAWDlypXr1q0DgMePH584cSIuLo4au1IRQorlAecIMSTkE/MLdllIj5D6EhLy55VKaBR7hJ838vLyysrKAoGAzWZ/wbG7Nh4hZeeorKwsLy/P5XL5fL4UnVFMxzQ0NKSnpwNAQkJCdna2hKxIy1ui0hx5jtRPKiD1XirjDOwRfvaQeawvOHYnLSGEnuGMYjomNjY2ICAgLS3txYsX6HeTBG1id1+kRyitihVo5xFSPM7AHqEEuX///qVLl+rq6i5duoSmyEgCaYkE9aFR6sUeZOPn/RA8Hu/69eto6Q0vLy+02kgPZO7cuTweT1tbe/DgwUwmU0JW2oRGKXtSKykp0el0DocjFAolbUtabhm0c0a/yHGGVJC+EK5atUooFBYUFNTX14u97plEFp7U0qoaBRENluLMDWmhqKg4ZcqU2tra0tJSgiC4XK4UO9MBe/bsqa2trays5HA4aGcSSSAtb4lGoykrKwMAh8ORtC1S7BUUFOTl5fl8PppZSAEyXizj7e198uTJ/Px8FxeXS5cuibFl6U+fiIuLGzZs2JgxY8aPH3/8+HETExNJWJEFISRFiHp5kJYGU3+mMTExCgoK6CrNysqqqKgwMzNTVVWl0WhoTcuMjAzKOvNJnD17Fh2MGjVKclakO7WAxWKRpiVqCP49NRUVlaamJhaLhXZ9kTSkIKFJw5T9vMrKynQ6ncvlCgQCyuaetWfWrFl+fn4DBgzYvHkzWl1SXEjZIyQIwt/fv6qqqrCw8NatW1ZWVhIyJAtCKK3pE+1Nf5G1SIigoKCwsDB0HB8f//z5cxUVldLSUhsbm0WLFq1atWrv3r2UdabLiMZvb9y4IcaWpTi1gDIfVFTjKdZ7KdbpUOZwIxITEwMDA9FxYWHhkydPAKC0tNTCwiIzM/PMmTNoawFxIWUhpNFo+/bts7W1HT58+IYNG0xNTSVkSFr5MzU1NTqdzmKxKEhdiKoRjUZraWmhwGh70yD5n7epqenmzZteXl49ZB7h7NmzR4wYgY6HDBki3c50BtHR9KlTp8R4nUirWAYo1CRRjadY76mfuZGdnV1VVQWUF44mJSUFBwej48LCQrS8OJ1O19HRGTRokL6+flZWlhjNST9HSNLY2Ci68pmRkZEYG5dW7I5Go5FrYUvaFin2dDodGaXsqqXYGe3Vq5eJiUlBQYHoxBiJWhTl8uXLS5YsWbJkyYkTJygz+rkgrWoOoFCT2oRGQXoeoaTttra2FhcXo0KwnlA4Onr06Dlz5mhpaVlbW1taWoqxZennCEkIguDz+eRLHo8nxsalGzZsbm5uampCYiw5RN0jdXX1lpYW8nwlDfnzol0+JD3O4HK548aN8/X1BQA1NTUOh9PY2IhuVArYtm3bli1bAMDLyysmJoYao+Klurp6yZIl6Li0tFSMLUurvh8o9AilGBqluFhGXl5+wIABbUxL1KIo9+/fj4yMBID6+nqJJrahRwkhADQ1Nfn7+6Nj8RZiSWuiGwD06tWrvLycAottVnipqKigPldHjcMtEAgePnw4ffp0BweH5uZmTU3N2bNnHzt27Ntvv5VQ4TGHw2EwGDU1NcOGDZNE+xTTv39/Pz8/dEwGdcWC7HiEUgmNUvzz8ni8t2/fysvLV1dXo01Gnzx5snfvXjpdsqFE5AWtWbPm6NGjABAaGirpWUk9KDQKAGw2O/FfBAKBGFuWYkUlZRalvubn69evr169SoFdVVVVHR2dzZs3nzp1SiAQ0Gi0jIyMlStXTp48GbmJ4iU4ONjExOTIkSOurq7kYBwAlJSU0HWFISEf0GhiH6ozTEhIuHHjRnBw8KNHj86cOSOhNAFlsTtRQ1IplsnMzHz48CFIXoAVFRU3bNhgaGhoZGRUUVGhoqKyb98+ExMTLy8vCVmsr6/fsWPHnDlzCIKQkIn3I5mN77tCfX39hAkTyJcGBgZibPz8+fMAsHPnztraWgDo06cPQRBxcXHXrl1LSEh4+PDhpUuX+Hy+GC2SWFhYAMDr168l0bgolZWVBw4cuHbtGkEQM2bMAICQkBBJG0UcPnxY9KJSUVGpqKiQkK26ujpbW1vk+ZmYmERGRvJ4PCaTSe4xOW3aNG9vb7HYqq+vt7OzQ+NfY2PjiIgIsTQrdaZOnUoem5ubC4VCcbXs6ekJAN9++y1BEGiU0NzcTBAEm80+f/48QRD379/Pz88XlzlRrK2tAcDT05MgCIFA4O7uzuPxJGGIz+dnZmais/jqq68A4MmTJ5Iw1B5nZ2cAIMMecnJybDZbcuays7MXLlyIbM2ePfvChQv6+vropbm5eXBwsHjN/fXXX9ra2gCgqKh47tw5Jycn9H50dPRvv/0mXlttkBUhvHbtGgBs3LgROd3y8vIEQfD5fBaLdeHCBT6ff/PmTQk9u5cuXQoAPj4+BEFUVFSsXLkyISFBEoZIhEIhqr89ffq0RA0h7t6926dPH3T5oulNAKChoXHkyJHGxkbx2vL29kaCp6Ki4ujoyOVyCYLIy8urqKjgcrlMJhPdSAAwY8aMoKCgbtrS1dUFAAUFBXt7e2Try6O4uPj7778vKCgQS2vPnz8HgGXLlhEE0b9/fwCorKxsbm6+dOlSQ0NDXFzcgwcPxGKoPWgT4Dt37hAE4e7uDgDDhg1jMpmtra0SslhSUjJs2DBlZWVLS0vJjfxI/vnnH3Tx02g0ckkEPT09Nzc3gUAgXls8Ho/BYKApE5qamkwmE42W0F2mpaWFrC9YsCA2Nrb75nJzc9FzEgBmzpyJpuRSSQ8SQh6PJzqw8vDwSEhIENdY9f79+wDw3XffEQSBNrBms9lsNvvixYv19fWpqal3794Vi6H2oIGqh4cHQRB79uwBADqdvmrVqrS0NEmYy83NRYO4gQMHIj2QnDNaUlKCRsRowBgSEuLu7h4SEkLOB9XU1HR0dBSLHJaWln7zzTeoWQsLC9Ffb/ny5WpqanZ2digtymAwyN3jFixYEBUV9am2ysrKbGxsSEFNTU3tfv97LEg/li9fLpbWAgICAGDu3LkEQQwdOhQACgoKwsLCrly5EhQUdP36dSaTWVJSIhZbbVixYgUA/P777wRBvHz5kiw7Hz9+PBqGihGBQODs7Iwm0aPoqLq6+sGDBxsaGsRrCFFTU0NGQYyNjV+9euXn5/fy5UtyvpmRkZGnp6e4npYhISFjx45FimtjY1NVVYXed3d3d3BwqK2tbWpqYjAYaNRLp9Otra2zsrK6ZovP5zs5OaFUTp8+fZycnMQu6p1BbELo5+fn5eXF4/Hu3LkTFhbW/QYPHTpEp9Nv3brV/aYIgvD29gYAKysrgiDQrtlVVVUvX750c3OLjY29ePGip6dnbW2tWGyJwuPxJk+ejDSYzWZXVVXZ29ujQH83L6D28Pn8M0sFqBEAACAASURBVGfOoNty4MCB1tbW6Eql0WjLly+Pj48XlyGCIIRCIZPJRKnBPn36oAHjyZMny8vLXV1dCYIICwubN28euksHDBjAYDC6HMNBttBDR0NDo82twuVyly9fLuqGNjQ0iN6oHYxbfXx8AgIC2thyc3NDV0jv3r2ldVtSSVlZGXLoHz161P3WwsPDUXSarPST0IBPlObmZnt7ezqdjrant7a2zszMFAgEnp6eBgYGZCivmxECkoSEhGnTpqFmraysAgICrK2tkUppamoyGAwWiyUWQ8S/FyTyrckoyKlTpxITE69fv47O0dDQEHVm6tSpba7nT6Wuro7MBQwfPtzf35/8E5/PR5Viffv2PXnyZEtLS3V1tb29PfIaFRQUbG1t0SqDnScmJmbSpEmo89bW1hR41R9CbEJYWFh48ODBJ0+eJCQkHD9+vPsN3rt3D11Y5eXl3W8NLVKAcrDovzMvL6/7zXZMVFQUGrIhB2XgwIEMBoPD4RQVFdnZ2SHHVEFBwcbGJjc3t5u2EhISpkyZQl5SlZWVBEHU1NQ4OjoiCaHRaFZWVmKJyiYlJZEPAtHL9927d66urmfPniU/GRISMmvWLPRJXV1dJycnDofzSbaysrLmzp1LPnSKiore+7F37961cUMbGhqqq6sdHR2RWqNhR3p6OvmV1tbWwsLCY8eOke/k5OQsWLAANbJs2bLCwsJP6urny5UrVwBAR0enrq6um03Fx8ejqxoA5OXlAUAsobMOePz4MQoYKigomJubk8/lzZs3l5SUtAmYdzOUx2KxHB0dUVhSR0cH5SMRERER5MhPV1eXyWR2v+YgKytr/vz5qM25c+dmZGSg94OCgm7dunXixAn0EuXIdXR0yHOMjo7ugjlPT08URkK5gPa3amRkJHmXodEth8MpLCy0tbVF/9eqqqr29vZtrqLW1tZr166h8TEJyr6j1doMDAz8/Py60GExIjYhrKurO3LkyJ9//pmZmUn+D3UTNNJfuXJl95tC81FUVVX79OmDbpWkpKTuN/shWCyWvb09+d986tQpcuAzdOhQdJMUFBSQF5CiomIXxlMINptN3px6enrtL6n2bmhmZmbXzgtlDpCE6+joPH78WPSvsbGxly9fbh/48vf3nzhxIjp9lLbpzDOCz+eTWQptbW0vL6+PfkXUDe3fvz8am1dWVpLj1jZeeH5+Pgo5oPgMKu7Q0tJyc3Pr7C/yRSAQCKZPnw4A27Zt6047PB5v7969bWawTJ8+vQvR6c5QUlJCrrM1ceJE9PQvKioSfS7b2dmhJCUZIUAu43sjMQKBAAVOUlJSRMdMiMDAwJEjR6KryNbW9r1RUH9/fzMzM9SlUaNGdTl7J3qjtb8gExMTL1y40MbVRueInHt0jqRwfpScnJzFixejbltYWHScCwgNDUUFgCCShU1LS+vALRYIBCdPniRfent7owWY5OXl7ezsUDmVdBGbEN69e/fy5csNDQ1Xr159cvVy9YXjZXs2VZ//g53Y9fFXQUEBGs53vygLFcuQ0Gi0b775RkLVa69fv0ZBIfTf3NTURBCEUCj09vYmY/r6+vroAsrNzbW1tUWSie7b9h5wbm6uo6MjQRDnz58nK6kQISEho0ePJm/ODrJxFRUV9vb2om5oTk7OJ51XeHj4mDFj0K/3oQfBh0CnT66oPnr0aDc3tw6qGOLi4pB2oixFdXV152291w0tLCwU9cJtbW0zMzN37drl6ekZFxeHwtcAYG1tTWZEZIqkpCQFBQU6nR4eHt61FmJiYkgNsLa2joyMbJOsFWONmEAgaBMtb3Mtpaenk8/lXr162dvbNzY2tg/ltRm05ebmHjhwQCgU2tvbHz58mHy/vLyczBmPHz++4+JhoVDo6emJJBMAxo0bJ+o4doY3b96gBGcXLv6ampo2o96OQ1+iQ8C+ffuSRTEfxd/fn7ydx44di84xMjKSdGFJlxF9ODAwkCCIkpISlMoFADMzs5iYmM6fmkQRc7GMkM+v+H1Hzgyj7EkG2RP1ciYZ5M4YU753q7Crdcxor43uBG1qa2vJPPP48eMZDAY5YHzvzdAd2lf2t/kAiumTN8mYMWNQijs5OZm8b9XV1duHF5ydnQmC2Lx5M1lGLFrZP27cuLdv33amh11zQ1taWkgHd/jw4eia7gLo9MkZ3Oj+aXPjiTrThoaGr1696pqt97qh+fn5oqe/fv363bt3ozievr7+y5cvu2bry8DBwQEAjI2NRWcdVFZWIo1pbGxsaWl57xc7CHM1NjYyGAzR6HT3swDx8fHkWoxWVlYdRLATExNRqZroc7mNy2hvby9aHODs7Mxms+3t7X/99Vc+ny+aM1ZVVXV0dGwzJaO+vp4giJqamjZKzOfz3dzc0EJL0OmaNdGH1YgRI7qc8CsuLrazs0NRInSbvzf9FhYWNm7cOHLs8qkpujZZ2GnTpqEsrL+/PxkAGzZsmLOz8+XLlz08PFxdXdGV8N6xi3QRsxBW/rEvZ7rRHA2V7Il66N9cDZWc6aMrDv3atQYFAsHMmTMBYMuWLV34uqenJ1oiSEVFhcFgkD99Xl4e6YehC6WsrKxrPSQhK/uVlZXJyn5RhEJhTU1NfX19VlbW9evXyRS3sbExGk8lJCSQ9y2Z6ELfdXZ2rqurc3Z2Pnv2bFNT07Nnz1Bs4b3R/OTkZAaD0dLScuPGjevXr7fvamfcUBJfX19U/vehzMGnwuPx3NzcyPtnypQp5LS/4OBgJJNiiZkgN3T8+PFt3NC0tDQbGxt0+vLy8nJycrt37/7QU152YLFY6JokZ93k5uY+efKEwWAIhcI9e/b8/fff7b9FhrkUFBQ+9F+GotPIHUe3W9cS/y0tLY6OjmjgMmjQoIcPH773Y69evbp582ZsbCyTyTx79uxHQ3l9+/ZFtc1lZWW7du2KjY09dOjQ4cOHk5OTyfy0paVle9cqISHBwcGhsbHxyZMnBw8ebN8TlKFEiTfkFndQs0Y+rNADpPs3GnrKobEyGl6TzxPRsYuhoWF3hoDts7AxMTFCodDLywsFqwDAyMiIPF65cmXXckASRZxCyC8pyjEfnT1Rb4iSPCmEQ5Xksybq5Uwfzcvv4kgwPT0d7Yb15s0b9E5xcbGLiwuakHT79u33JlpLSkr++9//op9+1qxZ7w2Xp6am2tjYoAtFTU2tzdiw85SVlZH+/syZMz9UJvfmzRsGg1FRUREREXH69GmU4h40aBA5nkLeT0hIyOzZs9GbWlpajx8/Li4uZjKZcXFxQUFBXl5eZJRm+vTpKSkp77WFPMjo6GjRYpA2pKSktHFD8/LyNm/ejAa51dXVFy5c+OGHH5CtqVOnijeryuFwnJycyPtn+vTp5Cq6ZmZmcXFx4jL0ITc0Pj5ejNWSXwYvX75EYyMybF5cXHzjxo179+6FhYW1EcLi4mJyQsv06dM/enkgdxw9fNH1hq60TvL8+XNU5vbRLADx7/UfFBRECo+/vz85JEKRGOL/hvLQVxAo746UW1tbu4Oc8aVLlwiCCA8P76Awor6+/vfff0fhRzk5uR9//PHt27erV69GspSbm3v27FmySmv+/PldTuG/l6SkJHJ4jRLnDx8+JCfIiitF1z4LSxbu6uvr0+l0NTW1QYMG9dh7TZxC2PDofs50o+yJeoMV5UPG6aJ/gxXlsybq5ZiPqv/rTpdbRguXjBw5kizBFwqFDAYjLS3t3r179+/fF/3weyv7O2gcXSii6YTO358ocqKpqQnvq+xvD7ptPD09yerH9tPA0XoNZKJLdPkGT09PMkrDYDA6sOXs7NzS0lJbW3v06NGOTyEqKopcf/n8+fN9+/b95ZdfCILIz89fvHjx9u3bkS0JxTFaWlqcnJzQ/NzevXujsbAkFgRBww7kuygqKqLff8KECQDQtRK7L5XVq1cDwJIlSwiCSEtL27BhQ3R0dFBQ0NWrV8n8NEosiYa5Ol8SgrIA6Hrr169fZ+bViE7rNDU1fffu3UetIFW7ePGi6JsdhPK+/vprshvBwcHIfUEpupqamg9ZKSoq+uWXXzIyMh48eHDo0KGOu4Rq1lCG8saNG9ra2tu3bycIIjY29rvvvvv5559F562LneDgYHNzc3TiaOg/c+bMDw2ju0xVVdWuXbvILOyWLVsaGhrYbDbydCmYSNNlxCmEtbeuZE8yzJ6opyFHX9m/F/qnLkfPmqiXPVGvxuV8l1vmcrlogueBAwcIguDxeFevXi0tLb1+/frNmzdFx2KZmZlz5sxB/99WVlbFxcWdNBEREUFWBqP786OxsuzsbLJGsYPKfpK4uLj169fX19dfuXKlzZ8+NA2cnJGZk5NDrnVkaWnZ8TogGRkZTCYzNTX10aNHnSwWDwkJWbt2bV5eHtpgKDIyEglhY2OjROeZ1NTUbNq0qaGhYfjw4QBw7949ydkiCILD4Vy8eJF8ZrUfamCqqqrQrLUHDx5wOJza2lrkewkEAjRAEa0t6sxl/17evn1L3qdDhgz5UCGx6ECz8wOyqKgoT0/PxsbG7Ozs9n99bygP/anNvPWP1g2h34fL5TY1NXVypJibm3vmzJnExMTVq1cvWrQoKioKCWFdXd0nFcV8Kmw228XF5enTp3379lVUVDx06JCEFJcQKdzV09NDAV5UPNi+ELfnIE4hbPLzzrUY+97QaPaMMY3P3h/Q7yQRERFycnLy8vKxsbHZ2dnu7u4ok8xms9Hl3qay/0P5g44JDQ0lswIdTAPvWrU9ctH4fP6Hov+osoAML1hZWcXFxYmuvDBw4ECJVvYXFRUtWbIkNTV12rRpqKJacrYQZ8+evXDhAkEQy5YtA4Bnz55lZWUxGAx3d3dJmyYIAgVjnz9/ToGtzwhUYq2trd0mUyBaM6Wvr+/r69tNQ8+ePSMrD8eMGfPixQsDAwM0BGSxWFZWVuRSCcuXLxfvtM72obzTp0+3mbcuRnNtQEKYkpJibm4eHR2NVrySKJcvX0bTu9Fs44iICA6Hk5OTgyYcS4K0tDRyiImq5SU9o7Q7iFMIBc1NuTOM3iuEqZNHtdZ3d67u9u3bAWDy5MntB1/h4eGiawJ1EM3oDL6+vqJVTx4eHsOHDydroKdMmfL9998jWxs2bOj+HOQ2VFZW7t69myyAJufJrl27VqJjRuJfISQIwsHBYd++fZIWwrq6unPnzq1fv76srGzVqlUAcP/+/RcvXgAABRpMEMTKlSuR60OBrc8IoVCIhoObN28m33z27BmqmRKdFCQWW56enige4OHhoaurO3PmzNbW1paWlgkTJvz55586OjqSG/yhCRXoXkNjzUWLFn3qtKIugISQIAh7e/utW7dKWggLCgrWrVv33XffVVRUIEc8KCjo9u3bAPDjjz9K1DQCRWXFsuKYhBBz1Wjjs4c5M4z2DOpLCuGeQX3DJgz/btm+26+7EkIRpbm5Ga19jpaxR4iOUg0NDbu5wpAo/v7+KId09erVESNGTJ48GSW3R48eHRcXN3LkSNH1h8QOKrRTUFAYNmyYtrY2NSsvkELY0tIyatQopEY8Hu/atWuVlZWBgYGSiCKiqc0bN24EgGvXroWEhKBcqdgNtWf9+vUoYUOBrc+L9PR0tI9SaGhoaWkpmaKbMGGCJGbH83i8+/fvcziciRMnOjo6Xrx4EQkhQRDiUtwOKCwsROWyKGlHAaQQNjc3GxgYICFsbGw8e/Ysh8N5+PBh+5lX3efFixeEyB4AHh4eIKblSj4KqkiS6AOzm4h5P8JeVisGHjy1dfgQunovnrxyE11loLLq3JSSB3X8nXdTS+s43WlcTU0NrQV14MCBnJwcAPDz8xszZsypU6doNJq9vX1ycjKZtOs+aKUiLy+vDRs2KCkpbd++/ffff0d/MjU1TUtLIwu9JAEKzK5bt66goGD79u3kug8SRVFREY3NVVVVz549iyot5eTkdHV1a2trp0+fnpiYKHajyCK5gSKVOylSvznl5wJLSUdz8ndCodDa2nr06NF3797t3bu3s7NzdHQ0GSwRIwoKCqtXr0bJOXt7+1u3bpWVlaE/of8jiTJkyBA0R4tcEEDSaGhooKmQ6JmGjlVVVfv168fj8SwsLLKyssRuFNXEUbzHPYKyrSK7jPg35lVf/B+9V9Ha55i9HE7YD9+yf8AKHo8NMcyG2upd7mndbHzJkqXTFv6XxWKtX79+06ZNS5cuLSgoMDU1jYiIIJfjEiN0Ov3bb79F81J/+OGH5OTkmJgY8k/itfVe0OQKtHUUBQwcONDZ2TksLOz48eNKSkpo8zM6nY5mH6P8q4RoI4RNTU2Ss9XeKAW2PhdauAKHBxmTD4aVqZmCnGJtXX1jY6OVlVVycvK2bdsouOxVVFT++OOP/fv3S9pQG6NA4UbzQ4cO3bFjh4eHx6ZNm9TV1Xfu3AkAKKwF/67RKiHIM6VyS2GKty/uAhL5xWkKCiqTzHUnwbd9iv65kQSFoVAWC7E3PJX3rDYf9PUkra41m1fF2nQzOaL3SpD3CwkJefPmjZqa2rFjx8iZoRKFRqM5Ozvb2dkRFG6djOpxJLSj94cIDQ09cODA3r17yTrVgoICLpdbV1fH5/O5XK4kFJE8U1SUjz1CqfAstnLbnZTCijpIugfpT4EQcAWgqKh44cIFNPOEGqysrG7evImO09PTw8LChgwZIhQKc3Jyli9fLomeSOVJHRIScu3aNWNjY+SP8ni8hoaG+Ph4NptdV1cnIaOkc0a9R0jZOKMLSHDoAQAb5wzxeFsa0LIdfLZCfhAMs9h6W2nOGM0+qgqf1I6QIG4EFe2+l9ZcVwVRV6GVDXIKCnRaUFAQWclNAePGjZs4cWJqaioAVFVVoRVQhw4dWlRUNGLECLLcVIxQ6R61MSoqDyiBBwDkphOSM4pDo1KhqIaz3S3laUwFlEZBlAu0VACNBsOXAJ/DKwj++eefAwMD26ymLXbk5OTWrVuHjp2cnJ4+fQoAo0ePHjFixPnz5/l8/q5du1xdXXft2iV201J5UrdRX0VFxR07dqDjRYsWSdooleHKnu8RSjbQQaOBy/pxKn11wOR7AIBol7Kq+v0eGZ/USGJhk7nj2003k5qTfeC5LRS/BUU1opcun887f/68RPrdDhS7AIDDhw8fOHAAAAYMGLBx48a6urq4uDhbW9uIiAhJ2KXSPSKRovo2NTWpqqrS6XQ2my0QCKgxKuNCKCSIa4GFY/e+eRqWDm/PQfBhaKmAvvqw6Dxt6vaV2w737z8gODgYbYsmUeTk5LZv3/748eOFCxf6+fnZ2dkBAJfLdXFx+emnn4RCYVNTE7ojxI5UklhSUV+phEZ7vkco8Yj/CG21Q/8dDqO+hv6joaUKEv9kBhSFZNR25rscvnC/R8akA6GRsQnwzx6IdAY+GwZPgWWuvZccU1ZVf/DgARo2SpoNGza8fft2ypQpv//++7Zt2wCAzWZfvXp148aNcnJy5eXlEsqfSeVJLRX1JY3SaDQ1NTWCICgICGMhTChsRKPMprSX4LMF8gJBXglMf4Ill4xNzcIczT32zD5z5jQA7Nq1q7q6moIulZaWvnr1KikpCb1MSkrS0NDIzMy0trZ++fIlueafeJGiEErLKC6WEUWyoVHEnmUGDyPLY6ZsB78dkPlMONRi43XVhJMWygodyXBoRt3PN5LSi+sh/W9IdAchH1Q0YdIWGDLdasJAl/XjHpvX7NixY+vWrbNnz0aLRkoUFosVFRVFDkhramq0tLTi4uJ++eWXwMDArVu3SsKoVJ7UUjeqrq7e1NTU3NyM9tmhxqisweIJjj7OPueb21qTB5HOUJ0OADBoMkzeqtJXZ6+Vwf6vDBXl6QDw448/ovUrHBwcbty4IemOtfFUJk2aRNapogVKKDBKDVKRB/JMqfcIe7IQUlH6KC9Hu2lrrNBPH4xWAEHAu4uZJfV//J39oc83sFp3/Jk6+1hEemI0vLCD+NsgbIXhS8CKqW0819NuwrM9k3Q1lbdt2zZjxozS0lIUq5Q0bR6aurq61tbWCxcuVFdXX758udgLVt9rlBqkbpSyDkglCNwT8ImrHPPbm1N/p7XGu4PfDqhOBxVNMP8V5hyeO9ko/sTMwytGIBUEABqN5uLioqysfOvWrcDAQEn3TboBQyqNIimS1plSecpSOdNPggohBIDxQ3vvXKoPxqtBYyg0FkOK16lnOXH5je0/+Tyu0tjhzSWfDGHcLfD/DRoKoNcgmH+CNnW7zdwRyacsrKf+b6UVOp1+48YNJSUlFxeX0NBQSZ+C1OVBdoxSpk9SCQJLjvr6eg8PD7QrkJeXV2NjIwBw+MLAlBqvd2Vvs+oEQqK8nrvWJcHqbHRBSiS82A5J90EoQKNMLVNLt83jA3+fOlJHrU3LI0aM2LdvH0EQW7Zs4XC6NRv4o8hglFIqRlVUVGg0GlrukTKjkjbUZagIjSKOrBjxJKo8e+oO8N8DKR6tQ2dsutn77ZHpcvT/laJVNHB/u59+N7QESqMh6jK0VAFdHoy+AZMfDLQ1mBvGLRjXv02bo0ePdnBwOHLkyMaNG+Pj4yXkliGkLg+yY5QyffrCQqOVlZWLFi26fv367t27XV1dlZWVb78p2vlnGo0GAiHQaUADEAI01VVD3E3ICwIgoI8+TNlGGzD6hxmDL9gY9VNX/FDjDg4OHh4eqampp06dcnR0lNxZSCVKKYNG6XS6oqIil8vlcrkSfXICrhoVRUVR7vrPxrQBo2H4UhC2QsTFqJy64b8Gf3Mhxj205MHb0rF7Q+4GpkOkMwQ7QksV9DWEReflzdbbLR2RyLBor4KI/fv3jx07NiMj48SJExLtP3o6S30mAwVI8UyRUYpDo1+MEI4cOfLVq1dLlixhsVjy8vLXgsu230ltbOE0pAc3x3k25r5rYLc2lWTA882QFwjyimD6Eyy9OGKc2at9U//cMr4DFQQARUVFV1dXGo128uTJtLTurozRAbITGpW6Ucr0CVeN/h/mGPVbP3sImP4Eqv2hJgOyfPKr2E+iK9YxE1dfjq9JDYRnmyDb79+6NafxphPeHp5+ce0YNaUPzpdXVFS8efOmnJzcyZMn4+PjJdd5KfpJsqO+UskRfjFC6Ovrm5aWxufzQ0JCLBYss/8rvYXDh6CD0FQCffWhJAreXYI+w0ClL2iZwFJn5fErHVeMTmJYzBvbrzPtW1hYrF+/nsvlosW4JXQWMuicUWlUNEpJmT5hj7At57436t27F0zZBgAQfweaKwBA0FQOQQch9CRwG2CgMSy9rGK6ynHFqKg/Zkwy0Phom1OnTt2yZUtra+umTZskN/lMUVFRUVERLa0iIRPtUVZWVlBQ4PF4lK2yBgAqKiry8vIcDqe1tZVKo3Jycmw2u7W1lTJ9UlNTo9PpLBZLKBRK2hYFWFpaHjp0yNTUdPHixQFZPACA0ihQ14axq0B7AkzaDPV50FIJCxgw/+T8aSaJDIvDK0YodVi83YbTp09raWm9efPmzp07EjoLqftJX7ZR0boV7BGSUC2E8nI0bisBgybDUAto5UDUVcj2A99foCwWFNVhynZYcNJi0ri4EzMPrxihINfZlSwYDIa+vn5kZCRaHlNCSD15RhlowTMqjaLpgwDQ0tIi6VNubW2tr69HRlVVVamZs0gxScVNLF4rNBRBH/3//25fQ2gspilp/DBjkP++qSO02xbFfBRNTc1z584BwJ49eyorK8XYYRKp+0mUIfUzxR4hCdVC+C67XlleDgBg0mZQUIOy6P9Nk9ebA/+5pjh6KXOD8euD00a1q1vrGHJjit9//z03N1cSPQdZEkLpGpVoQDghIcHc3JzcV+gLi46SaKjI02k0UFQHvshjjt8CimpqynJLxg/s8nJp33///bJly2pra/fs2SOWrrahJ6x2Rg1SP1NJy7+vr29tbS1gj7A9YVm1TRw+CFsh9xW0ckBOCWh0mPYrTP8NlDSmDhISWS+eP38WHBx869atyMjIzre8dOnS1atXs1isn3/+WUIJDBnUJKkYRQdi99JYLNZvv/02adKk6OjotLQ0tMsxWq49I+PTlv3r+cwd009NWR60TaEoHFo5AACsGqjLhb7DBUJi5qi+3Wn88uXLampqd+/effXqlXi6K4KsrXYmxTOV3PS+8vLytWvXLlu2zMHBAT6H6RPUCSGaJn/4YbawMgV8f4H42wBCUOoNhBAq4gFAnk6zmGC4adOmlJSUOXPmCAQCtOl857l06dKAAQMCAwPd3d0lcQqyULrS2tqK1rhCK4kXFhZSYxdEznTr1q0lJSWHDh0SY+Nv3ryZOHHi2bNnAcDOzi4+Pr6xsXHRokUlJSV9+vRZtGjRpk2bSktLxWhRuiw07j9EU1mutzaMXQlBhyDYEd6egWm7lJVVLMcPHNZfpTuN6+npHThwQF5eXhLlaVKcZk7lk7q8vBwA6uvrS0tL6+rq8vPzqbEreqZbtmw5deoU2hBUXAiFwqtXr6I9LNXV1dEzHG2vWF5eTm5j1+OgZv9f75gK3W0BsPIRjPkWUFCm1yCYz4CvboG8CgDAbEfaGp+I7NobN27k5eXxeDxXV9cuGPrzzz8BYPz48UKhUOxnIZV9lufNm6empvb69WtqzBUUFMyZM4d8aWBgQI1dHo+H9gFmMBjibbmurs7W1hbtnGBiYvLu3Ts+n3/q1Cn0RBgwYMCcOXPQNnsqKip79uypqqoSbwekRX4Va9DWV7Q1vvDNPVjsBP+5TlvjY7ovpIHF737jPB4vOjra19eXfAdtxiIWFBQUAIDH44mrwY8iEAgYDMa5c+cos3jp0qULFy6g47///nvnzp3U2EXLLKipqUniCZmYmGhubo6UxcrKKj8/v7GxEW2Th0IvNBrN2to6IyND7Ka7icSFsLSO882FGFjjA3MOg9oAAAC6PIz5Fr77G9b4wBofmLABAEBtIKx8uPrEM7SMU2FhYXl5edcsrQt1TwAAIABJREFUOjs7p6ent7a2opcVFRVsNlss5/LVV18BgBhv+I4RCoWi+hcUFESBUakIYVxcHNocHK0ZO3XqVG9vb7G07O3tPXjwYABQVlZ2dHTkcrkJCQnk1l3W1tZxcXFPnz5NTU21sbFBcqimpmZvb19bWyuWDkiXBhZfe8srmLwVAGCEJe17Xy5fbI+/6urqyZMnky/FeKkYGBgYGBg0NjaKq8GO8fPzc3FxQcfx8fGHDh2iwKhUhDA3N3fx4sWqqqrKysrGxsaenp7iapnFYjk6OqI9zHV0dNzc3AiC8Pb2Hjp0KADIy8vb2tru2rULjT7pdLq1tXVubq64rHcfCQqhUEi4vSnuZ+sP39wD/Xn/80D7j4ZlV+V+8O1n66+6zk/5xxew+hlojgAAGP0V/XvfN+k13Tc9e/bswsJCdLx27dqQkJDut0kQxM6dO83MzERHwRKFy+WOHj2afKmvr0+B0YKCghEjRlz8F0kLYXNz865du9Bocfjw4Vu3bh04cCC6UubMmRMaGtrllktLS1esWIGamjlzZlpamui9qq+v//LlS4IgampqnJ2d0VeSkpKsra2R79irVy97e/v6+nrxnKf0mHE4HKb9CgBgMB/W+HB4AnG1LAkhLC0tjYiIQMcNDQ0BAQHdb/Oj3Lt378CBA+g4NDTUxsaGAqOXLl1auHDh/v379+/fb21tLWkh5PF4DAYDhZ179+49YMAAdGvMnTuX/MG7TFBQEFoPnU6n29raNjQ0lJaWWltbIxMTJ06Miop6+PDhixcviouL7ezs0F49ioqKtra2FRUVYjnBbiIpIcypaFlw4h2s8YGZ+0CpNwD8b5r86mfjHUKicuoJgsitbPFLqBy9+zVYXga6PNBosOjsyN3B7G7fq5IQwlu3bpHH7u7uXC63+212jLSE0NjY+Pm/SFQIg4ODR44ciQaMdnZ2gYGBV65cSUlJcXJyIuVwwYIFkZGRn9SsUCh0c3PT1NQEAA0NDScnJ4FA8ObNm9GjR5P3alNTE/l5UggRERERVlZWyHq/fv0YDEZLS4t4TlgaLDjxDmY6AAAMnQlrfGqbxRZvrK6u1tTUXPUvgwcP7n6bL1++RLP1CYJIS0uztLTsfpsf5d69e7t27SovLy8vL3/27BllQrh79+7o6Ojo6Ohz585JVAhjYmJQxAVFQV68ePHXX38xmUzRuyw+Pr4LLZeXl5MF2CYmJm/fvhUIBEwmE+0b07t3bycnJxSca2lpuXHjBvpWXl6era0tCsCoq6vb29s3NDSI84Q/HfELIb9V6PQiT+0nP/jqFuhM/J8jqGMGX91WWefH8M5uFfyf4ExcfoO8jS+MXQkA0GcYfPf0oFd3I8izZ88+cuQI8mmmTJkiFiEUlYTx48dT8D/H5XL79u279V/69+8vaYsEVaFR0bzd+PHjo6KiCIK4du2au7s7KuZsampiMBjk7loLFiyIjY3tTMvZ2dnz5s0jsxRFRUX19fV2dnborjM2Nn737p3o57Oysk6cOFFUVNSmndDQ0Dlz5qB2BgwYwGAwxBVgp5j/nI2C2Y4AAIOnwBqfklqxnUV1dfXEiRPr/kUsl4q0hNDExGTt2rVr165dunQpZUJIQWgUXfwo4mJgYODn50cQxPnz5wsKCoh/7zIkWihcmZ2d3cmW0XCzX79+KLmO8g7x8fFTp04l7z7SGyH+rxAiUADmf4HC/v2le4uJWQjjCxomHwiF1c9hyvb/VcGgafJrfGYdfZte2vzeb+12T4Xv/obeQwAATH6Qt/GNzeuWzMyePdvNzc3Hx8fHx2f+/PmfrxAaGhpm/cvQoUMlbZHoqhBGRUXdv3+/k4FEb2/vQYMGkfcPWRPB4/FYLNbZs2fJT9bU1Dg6OqIbFaXZ09PTP9Qsn89nMBho+WBtbW0yS6GrqyuaI+xMD0n8/f3JhOKQIUOYTCafL4ZKEypZdSkW5h0HANAaD2t8ssvF5t1KIjT68uVLQ0PDlStXrly5cunSpZQJoVRCo10QwhcvXnS+VsDb23vIkCFkxKW5+X+P34iIiKNHj5aUlKCXVVVV9vb26MZRUFCwtbUtKyvruOXMzExyuGlpaZmXl9fS0uLo6IiqnAYNGvTw4cM2X3n27NmlS5faV+iEh4fPnj0bNTV06FAmk0mWd1CJ2ISQxW11fJipYOMLlleg37/7Zw6dCSvu99n4DzOgoIMapRZuq+GuIFhwCoAGdAVY5jL5QGgbx/GTkERodODAgYv/pVevXtQIIfWhUQ6Hk5SURL6Mjo7uzLdOnjx5586djzptpaWl33zzDbo0LCws2qhaTEzMjRs3SktL23wL3aiiafasrKw2n0lPTzcxMUF6uXHjxrq6urKysm+//RbZmjFjRmpqamdO5L34+/tPmDABNaWnp8dkMhMTE1+8eIH+WlJS8tdff3W5cUmzzjUBFp0FAOg/Gtb4JBaKrQJFQkK4fv36+vr6+vr6qKioL1gI0Tmi4+bm5s7UKre2tv72228nTpz46CdLSkrIBPn06dNF72iCIDw8PE6cOEHqIqKgoMDW1hb5jqherK6urn3LbDbb0dERJfnI4aaPj4+enh6Zd+hClZO/v7+pqSnqsJGRkaenpySKWjugu0L466+/lpeXv0mvGbU7GEx/gslbgC4PAKA2EOYcgTU+VmeiijsRjQlMqaZ97wPDl/7vjl39/MKLrtcUSUIIpeIRUi+EXePs2bOPHj0itaE9QqGQzBz06dOHyWR+6oVeVFREptkVFBRsbGxEq86qq6sHDhxoYGDg7+8vGrRBWQqBoLtZZ4FAcP/+fZTRpNFo586d27NnD/pTdHS0tbV1N9uXHFtvJ8NSZwCAvvqwxudd9nuebj0HqYRGo6OjfXx80HFubu7du3cpMNoFamtrz5w5Y29v38HEEj6f7+TkhJawJxPknTeRmppK1otpamoyGAwWiyX6gS1btiDB27x5MxpukjlCU1PTNnmHT0IgEHh6epKTGqdOnRoQEHDt2jVy+oCXl1cHAaFu0l0hnDt37vXn0bTvfWCNDxjMh7l/gLoODF8C1g8H/xLwd/QnTIH4iZkA1l6g2h8AYPJW1XV+ORVdDOMkJSWRQbDMzEyx1GFTL4QEQYgW8dfUiKGetpOg6ASPx+tkmOLZs2fXr1/Py8t771+zsrLmzp0rmrfrcsfy8/NtbW3l5eXh36oz0oOMiYlhsVjZ2dlouicALFu2TDRL0X34fP6tW7fs7OwePXr0uQjhbvdUsGICAPQaDGt8glKrJWQoJCQEjYR8fX3Dw8O71ohUhFBatLa2okdTJ+8IFovl7Ozs5OT0oUFkXFwcGcnvzo0WERFBRj51dXVFMwKFhYXm5uZhYWGiJWmqqqoMBkMsIU0ej8dkMnV0dJAWLlu2jIzlbNmyBeU4JYEYhDApJXXMrn9g5WPQmwsLTsPKR7TvfWyuxtc0fVp9Wn0Lf9AvAf9L7CuowNd35h2P6L5/zOFwIiMjW1tbm5qa3r1712WP28zMjDyePXu2aNnhF0ZGRsb27ds5HM7169cdHBy60AIZdUFF22QgxcvLSyw9TEtLs7GxIcM4dnZ2FRUVaCyMVu7W0tJCQRsJ8ejRIz09PRQnNzc378lCeMAzA76+AwCg2h/W+PjGV0rIUE5OzpkzZ/z9/W/fvi31IsDPAm9vbxcXl8jISF9f32vXrnWnqZaWFnt7e3RHDB48+PHjx93vnr+/P1lrOmrUKDc3N9K5bD9xvvvmRGlubv7jjz9CQkI+JyFcsmTJnEVWtCHTQW0ALDhtuCsoILmLo06Pt6WwxgeGzgQAGDQZ1vj8GVLczR5GRUWFh4ffuHHjwIEDaWlp3WyNeoqLiwmCKCkpoXLCzaVLlwiCaG1tPX78eGc+n52d/dtvv61fv97Dw6OhocHc3JwgiNjY2IkTJ6JYoo2Njdg92oSEhOXLl6MwTu/evdHUXRqNtn79eklPh/+MPMLjf2fDir8AAJR6wRqfR5EfqYPoMrW1tU5OTu7u7g8fPnR0dJSQFQnh6+vr7e2dnJx88eLF7s+r6yR8Ph9N5H/79i2a1fpRwsLC/vjjD2dn55qamgsXLqCR5bNnz8h563Z2dmIcowuFQk9PT5QRAABjY2N3d/f2E+clx7Jly2bOnIlGnEOGDJGcEIphrVFXV9egl8+2/3Gdpj3+2ynaCSc7u89ne1ZO0/l6khZM2gyK6lAaBQVvdt1Nq2zs1lZ8kyZNQnXe+fn59+7dQ0v8fS7k5+efPXu2ubn59u3b586dk9xui6LU1dUVFRXl5+fv3r17+vTpH/18aWnp0qVLFy1adPDgwdTUVLTe/O7duydPnhwbGzty5Mjg4OA///wTRVHEiImJydOnT+Pj462trRsbG/l8vq6urp+f34oVKyoqKsRrq5MIhUIPD4/w8HCpWH8vqkp0kFMCABDwAIDFlcglJBQK9+zZo6SkNG3atJycHCMjoy43VVNTgxZSyM7Opmzxz6VLlxYUFAQGBi5ZsuTp06fUGA0LC4uNjX358uXNmzfJKX0dcOfOnYMHD5qbm2tqal6+fJnNZldWVn7zzTf/+c9/CgsLp0yZEhUVdfHiRbRmr1hApdrJyclMJnPw4MFJSUlr1649cuRIa2vrtm3b0tPT165dKy5bH+LatWt+fn5+fn7k7F5J8GlCyOfz0ZLTaIqx6J+Orxz1nwkD7ZbodbCbfGe4sm5sH82BaN01hVjmvgyXvK/nFa+xqrl6VlBX24UGHz9+/ObNGwUFhREjRtDpdDSW6QJCofDZs2cFBQW1tbWJiYlda+RT0dPTMzQ0VFZWlpeXr6iooGYlYkVFxc2bN6urq9vZ2Q0bNuyjn7979+769esXLFigp6d3+PBhJHgEQdBoNDs7u9jY2FmzZkmutyYmJp6ennPmzCkrKzt79mxdXZ2ysnJnut0devXqRT65lJWV0WwQAAgPD9fS0goKCpKo9U9CRUEO5BQBaNDKAyDYfIlsQUyn02/evLl582ZDQ8O9e/euWrWqy03l5ubW1tb6+/t7eHhQvAw6mjmAousUYGZmdurUqZkzZ54+fZr0ujrg5MmT7u7u8+bNW7NmDVqSXiAQvHr1Sk1NjcFghIeHk4WX4gXNqcjKytq/f7+CgoK6unp4ePj8+fMfP35M5S7lohQUFLi5uYlx/4NPE8JHjx4dOXIkLi7O1dX15MmTALBkyRI0AFFXllu+ZJ62tnY3OzSor/KJVaPAcOGkYcae+hpfNcf1byjhZqQ03L1R+NUcTkL0pzb4zTffnDlzZty4cYcOHTpy5EiX/RJULO7m5lZWVhYQENC1Rj6VkpKSsLCwpKT/1969R0VV7n0A/83duQgCxjVUAnm5mJeUo1SEFYimKxCsRF2WssCsrKNHfY9oy14qT+YNF6aktlYqRGUWB7WT5hn1rJZaBIjhyEUBYcRLMggzDDMwl/ePfZo1S0HA5ubs7+evPXtmnueHMnxnP/t59v4tJCQkPDycmQxmb1Kp9LHHHhs+fPhjjz0WEhLS7+ubm5tDQ0Pv2vn++++XlZVt377dMX9WmAuK9vT0NDY26nS6HTt22LW7xMTEVatWMdvR0dG5ubnMtkajGTp0KLN430WIhTzicIgnIDKTscdOR4Q2FBMTo1Qqn3jiCWZc3THOnj0rFoubm5vr6+uZiZEOIJPJvLy8pFKpl5cXc/Gz+zCZTB0dHcwsEgsfH5+ioqKamhrLCUL7EYvF77zzjl6vF4vFkydPPnToEHPK0K6dhoWFMeumiGjEiBHMtHMi2r9//0svvbRnzx5bdcQf1Kvnzp175coVb2/v+vp6Zp3y6tWrLc9mZGTYpKYlzwc3f1mUPlyfXX87y88zSiIkoo0NN56QiaYtWzSi+CTPe7hNOhoUX1/fioqKcePGRUdHy+Vyx3QaFBRUVFRERJZ1bC7I19f3+vXrd+2USqXMwj7HsNzCafr06SdOnLDtnWUGLj4+fu/evc7qvVcSZoSGJyJjNxn1Xd2uHoQffvihVqtta2uTy+U3b9689zuWPcTGxjKzP1zq/84al8vlcDgGg4GZMm0xc+ZMh9VgfQun0aNHjxo1qrKy0nIpGXuwfMUkIubWhgwejycWi7u7/9RZM2uDC8KVK1eOGTOmuro6JibGfjfQ4nI4GQ3fkLnbYCbL9w0jmU1mMvd0txd+5r3sf+3U9X0oFIrc3NyUlJSzZ89WVVW1trYyK9XgpZdemjNnzpw5cwIDA5uamizXRXMk5lhZrVaPGzdu3Lhxji+AIRaLly1b5qzeeyUWcImI4taS2Uj8IXYaGrWhtWvXMhubNm1ybiWuJjExMT8//6233iIn3e3d+u72ycnJdXV1llUWDhYbG/vpp5+mpKTYqsHBBSHzO+rp6dne3m6/IS/DjRaOrpOJwPOd+jaDkYiu6Y0TpWTu7u786aRTgjAqKmrfvn3MtmXqMBBRZGRkbm5uZmam0Wj08/PbunWrI48FGcxvo81vau8GLv/2C/2cR5PfISLqvLk/Z9H/pf3k7KLgQWzbtu3tt9/+7rvveDzezJkzhw4d2u+Aqm3xeDyhUNjd3d3d3e3cb5zPPvusZWmyTQwuCL28vO7asAezrov+GO++qu/Rm81E1PbHhEmzTme/ruHBJCYmJiYmWh7m5+c7uADL0KiD+3V9Qj6HLCdyzGQwuPrQKPTF29u7oKDAuTUwA5JarfaBZx26psEFoWPwAx+lnv8O/iZ7y6IlQiK62WMgIiKOMOx/+n4rsBSCsC8iPoe6Wul6GRFR1x2Tyb6zG8C9icXi9vb2rq4up5wBsR9XDEKOUCRLeEHz49F7n+IOEXvOW+T4ksDFMUFow+nUbiPIWxzxCGf6k3wuh9OtFVaWOXQwDdyM9WlCd+KKQUhEw1et7yr7eUFAV5DgvzPRXxgmfWSoVDYzRTxxinNrAxfETJbBEeG9hgi4sRPHbvtgDRE1NDRkZpY6uyJ4iDETR50yVceuXGjBkzWux7BHi44+n5ziJR7Clcm4EukT/r6hy1Y9suYDk8nkft9H4E/C0CiAA+CI0NF4nl7+/8gzabUG5VWOSFR1u62mrk7z2WctLS1GozEzM5NZyAhACMK+RUREWBb4PvLII5brAAA8AHc9InTdIGRwJRJheCQRTRhJIrH40qVLbW1ter2+uroaQQgWCMK++Pn5+fn5MdsymSwpKcm59cBDzXpNvTtx0aHRe/388887d+6MiopKTEw0m81OXMICLgiTZQAcAEOjThYWFrZixQoPDw8Oh7N8+XIbXmEd3AAmy9zH119/feHChaysrC+//FIikTCXJgF4ABgadTIfHx9c0gz6gqHR+3j55ZeZe2PV1NQwN64DeDAYGgVwXUKhUCgU9vT0OOu+MK7sn//8Z0BAQHNzc2pqqsnk6tcaBVfmrkOjCEJwEzgo7MuwYcOkUml0dLRIJJo/f76zy4GHGIZGAVyaTCZTqVQajQZD6HeJj49nNhISEpxbCTzsMDQK4KJ0Ol1dXd2FCxd0Ol1wcHBVVZWzKwJwT1OmTMnKyho/fryzC7ExBCE89BoaGpYvX+7p6SkSiXQ6XXp6urMrAnBDa9asiY+PZ24EuGXLlqamJmdXZDMIQgAA6N+xY8csN4X/6aef7ty549x6bAjnCMEd1NbWrl+/nogMBoOzawGAhwyCENyBr6/vCy+8QER6vb6kpMTZ5QC4p7S0ND6fT0QVFRXOrsWWEITgDoYNGzZ58mRyx4ndAK7j0KFDnp6eRDR79mxn12JLOEcIAACshiCEh15oaGh+fj6zLRaLDx8+7Nx6ANxSXFycQCBgtidNmuTh4eHcemyIYzabnV0DAACA0+AcIbgJjUZz69YtsVgcEBDg7FoA4GGCoVFwE3q9XqVS7d2719mFALgtrVa7e/fuDz74wM2u3o4gBDfh4+OjUqnS0tKcXQiA25JIJFlZWZ6enlyuW2WHW/0wwHKXLl2KiopydhUA7qysrGzixInOrsLGMFkG3IderxeJRM6uAsCdyeXy5557ztlV2BiCEAAAWA1DowAAwGoIQgAAYDUEIQAAsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCEAADAaghCAABgNQQhAACwGoIQAABYDUEIAACshiAEAABWQxACAACrIQgBAIDVEIQAAMBqCEIAAGA1BCEAALAaghAAAFgNQQgAAKyGIAQAAFZDEAIAAKshCAEAgNUQhAAAwGoIQgAAYDUEIQAAsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCEAADAaghCAABgNQQhAACwGoIQAABYDUEIAACshiAEAABWQxACAACrIQgBAIDVEIQAAMBqCEIAAGA1BCEAALAaghAAAFgNQQgAAKyGIAQAAFZDEAIAAKshCAEAgNUQhAAAwGoIQgAAYDUEIQAAsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCEAADAaghCAABgNQQhAACwGoIQAABYDUEIAACshiC0mStXrty4ccPZVQCwV0tLS0NDg7OrgIcPx2w2O7sGNxESEpKYmLh79+7GxsZz584lJyeLxWJ7dHT58uWKiorZs2fz+XybN67X60tKSoho/Pjxo0ePtuwvKysrLS3lcDjjx4+fPHmyzfsF+PPmz59fWVlZVVWl0WiOHDny9NNPP/roo/boqLW1VS6Xx8fH+/r62qrNX3/99a4Uf/755729vZntjo6O77//XqlUBgcHz5gxw8PDw1b9AhGRGWxk1KhRmZmZZrN53759RKRUKu3UUV5eHhGp1Wp7NL5+/fohQ4YQ0bZt25g9Go3mxRdfJKKQkBDmz0p6enpPT489egf4M+bNmxcdHW02m2tra4no4MGDduro7NmzRCSXy23YZmpqqkAg8LLS2NjIPFVeXu7v7y8SiSIjI0Uikb+/f3l5uQ27BgyN2t68efPUanVgYKCd2s/MzFSpVFKp1OYtV1dXf/TRR2vXrrXeuXnz5lOnTp06daq+vr65uTknJ6eoqKiwsNDmvQPYSlhYmFqtnj17tp3anzRpkkqliouLs2GbLS0tqampKisjR44kIoPBkJ6ezuVyq6qqFApFbW3tkCFD0tPTjUajDXtnOQSh7alUqoqKip6eHsuetra2I0eOFBQUHD9+vLS0VKFQDLCdkpKSAwcO/Prrr9b7b926VVZWZjKZiOj06dO///57Z2fnkSNHCgsL6+vrmdeUlpYWFRWdOXPGPOChb5PJlJGRMXbs2Ndff916f3Z29rlz5+Lj45mHq1ev5vP5586dG2CzAI6n1+srKira2tose3Q63b///e+CgoLDhw9XVFQM8BdYp9MdP378wIEDcrncYDBY9nd2dpaVlanVaiIqLy+/cuWKwWCQy+WFhYVlZWXMa+rq6r744osff/xRr9cPpK+WlpZevz2fPHmypqYmOzs7LCyMiEaMGLFq1aqamppTp04NpFkYEGcfkrqPvoZG8/PzpVKpVCqNjIyUyWRElJGR0W9rBw8elMlkXl5e4eHhHA5n/vz5lqesh0a9vLwyMjL8/f1Hjx7t5+fH5/P379+fkpLi7e0dGRnJ4XBSU1MHWH9ubi6PxysvL1epVGQ1NHoXo9EoEokWLlw4wGYBHKavodHTp08HBgYKBIKIiAgfHx8iGjNmTL+tnT9/Pjg4WCKRREZGCgSCiRMndnR0ME9ZD40mJibOmjVr3LhxQUFBISEhRLRmzZrs7OyhQ4dGRUWJRKLo6Oh+T2SYTCaRSLRx40a9Xn/79m2TyWR5atOmTUTU0NBg2dPY2EhEmzZtGty/DvQNR4T2derUqaVLl77yyiutra0KhUKtVgcFBfX7LqVSuXDhwhkzZly/fr2mpiY/P7+wsLCvL4AlJSWHDh2qra1tamqaMGHCq6+++vjjj9+4cUOhUGzfvv3bb78tLS3tt8erV6+uW7fur3/964QJE+7/yjNnzuj1+qeeeqrfNgFcwc2bN1988cWRI0cqlcpLly7dvn07NTW133cZjcaXX3552LBhjY2NCoXizJkzlZWVu3bt6vXFP/zww8qVK5ubm+vr6xcvXvzRRx9VV1crlcqLFy+ePHny4sWLBQUF9+9OpVLp9fqdO3fKZLLhw4f7+/tv3bqVeUqpVBKR9cFiYGAgh8O5du3aQP8JoD8IQvvatWvX8OHD8/LyRCIRs4fD4fT7rn/9619dXV0rV67UarVtbW0pKSkikejo0aO9vnju3LlPPvkkEQmFwri4OKFQmJOTIxAIiCghIYGILl++3G+Pb731lpeX13vvvXf/lxkMhr///e9hYWGvvvpqv20CuILCwsL29vZPP/3UMsNzIJ/Bqqqq2traN954g8/nt7W1hYaGTpgwoa/PYExMzIIFC5hmExISzGbze++9x0zsjI2NlUqldXV19+/Ow8Nj7969eXl5CoWirKwsMTHxb3/72+7du4lIrVYLBAKhUGh5sUAgEAgEzMAs2ITt59+DtYsXLz7++OMSiWRQ77py5QoR3bVKobm5udcXW3+qRSKR2eqkIJO+3d3d9+/uwIEDR44cKS4uZkZu+2IymZYuXVpeXn7s2DFLrgO4OIVCIZVKx4wZM6h3MZ/BpUuXLl261LKTGfm8l/VnkEmsuz6G1jMGeiUQCDIyMiwP9+3b98svv+Tn52dlZQUEBPT09HR0dFiWTHR0dHR3d9tvOh4LIQjtSyKR8Hi8B3gXl8u9du2add4wB3n28O677xLRokWLmIfMZ3jt2rUlJSVyuZzZqdVqMzMzv/nmm+LiYttOlgOwK+bTNJCjQGvMIuAffvjhL3/5i2Unl+ugITQejzd69OiKigoiYtYsVVdXWyqpqakhouDgYMcUwwYIQvsKCwuTy+V6vZ6JNJVK1dra2u+7IiMjTSaTQqF47rnn7F8j5eTk6HQ6y0OtVrt8+fJZs2bNnTuX2dODnBAvAAAEXklEQVTU1JSWlnb16tVjx45NnTrVASUB2AqzlKK6ujoiIoKIzGZzvwOVRBQZGUlE58+fT0pKsnuJREajsb6+3nIJC61WW1lZyRSckJDA4XAKCgosQfjFF19wuVzmxAfYBILQvl577bWioqLXX389OztbrVavWrWqq6ur33fNmjUrNDR0yZIlO3fujIqKUiqVxcXFq1ev9vLyIiJm4YQNv5wuXLjQ+mFbW9vy5ctjY2OZZVhHjx5dsGABEeXm5hoMhhMnThCRWCzGfBl4KKSlpa1bt27x4sU7duzw8PDYvHnzhQsX+h0pHTVqVHJy8oYNG4KCgqZOnapSqY4fPz516tRJkyaRHT6DW7ZsycnJWbFixbRp0zQazccff3zz5s39+/cTUVhY2MKFC3fs2OHn55eUlHTy5MkdO3YsWrSor3FaeAAIQpvh8/nMKCiXy+XxeMxQzLRp0/bs2ZOTk/P555/LZLLFixdfunSp36bEYvHx48eXLFkyY8YMo9EoFotnzpx5584dJggbGxu9vb0He97xgW3cuPHOnTtE9Nprr1l2hoSEWNYsArgIHo/HXHeQw+HweDwmqIKCgg4fPvz222/HxMTweLykpKRnn332999/77e1AwcOLFu2LDMzU6fT8fn8p556asqUKcxTV69epT8GLW1i2bJlHR0de/fuff/994lo4sSJ33//vWVAaNeuXRKJZMOGDevWrZNIJEuWLNm8ebOtugbCtUYdpqOjQyqV8ni8gICAlJSUXbt23bp1q9cLBHt4eDDDMkSk1Wo1Go319Qy1Wm1ERERSUtKePXsGVUB1dXV7e/u9+0eNGuXn5zeopgAeRlqtlsfjiUSilJSUpqam8vJyrVb722+/3ftKLpcbExPDbOv1+vb2dm9vb+tL+06fPv3GjRvnz58fVAHNzc0tLS337vf19bUc3t2+fVsikfT6Nbenp4f5NmyPiwyznTMXMbKPSqXicrnMStj8/Pxe/0fi4uLu08Lq1at9fHxaWloG23Vfpxvz8vIe/OcBeNiYTKaIiIg5c+aYzWZmNsq9hELhfVo4ePAgj8c7ffr0YLteuXJlr90tWrTowX8esAUcEdrXf/7znw0bNsyfPz80NLSzs3PLli2nT5+urKwMDw/XarW9HqKJRCLLJefvYjab09LSVqxY8fTTTw+2EmbF7r37PT09HTbKCuB4ra2tycnJr7zyytixY7lc7ldfffXJJ58cPHhwzpw5BoOh1zFSDofj7+/fV4PvvvtuUFDQXVciHAi1Wq3RaO7dL5FIPD09B9sa2BCC0L7q6uo++eSTEydONDY2MouZ1q9f/8wzzzi7LgC2aG1tzc/PLy4urq+vNxgM4eHhb775pvUJbwAEIQAAsBousQYAAKyGIAQAAFZDEAIAAKshCAEAgNUQhAAAwGoIQgAAYDUEIQAAsBqCEAAAWA1BCAAArIYgBAAAVkMQAgAAqyEIAQCA1RCEAADAaghCAABgtf8HFN/mXZ5BSg0AAAJNelRYdHJka2l0UEtMIHJka2l0IDIwMjMuMDkuNAAAeJx7v2/tPQYg4GdAAGUgVgHiBkZGBQ0gzcjIlgCimVmIpWH6BBUUQDSbAkSYA0IzsUNoZkLGs0NoZlzGY5iDZg8b2Hq4coQyrMK4aKgnYFxuBkagjzKYGJkSmJgzmJiUgBYmMCsysLBmMLFwKrCyJbByMLCxM3ByaTBxcitw8zBw8yrwCiXw8mUw8fEn8Ckw8AtkMPHLMwgIJggKZTAJiigICTOIiDKIiCmIiWswiUkwSEgySEgxSEgzSMswSMsySMsxiDAB7WZjZGJmYWVj4+MXEBTiFV/HCHQOPM4+bGV2PHzs334Q58ihOw7/jywDs3lrbjmskPWwA7E/XVrv8IkryR7E7itod+h7MdkWxHbd3umg3bUNrD4hYYvDd1f2AyC2+PJtDoFa6WD24ZVODr0a/GD2sz5Lh5kNTmBzyhlNHRpaBBxAbJspF+3TrwTsBbEl19+yD/c/BjbTfv9sO8dvSmD1M5bn2b1ayAFWX3m+Z/+ZRSZgdouO+IGQ/Qxgtth8oQMvs5nB6uuNS/dXxxntA7GP/U3a3yxyDWzmveTkA++cZ4DF86/FHVh/8ylYnCt6zgGlw8fB/j24cN6Bmp0fwebk7T1yYF7EdbB6rfX3DlxICweLbzq96sD2+OtgvZfk3x6QmDsXzJY+yHWQZRMT2L9iepcPBE+WB7OPdzAf/Cn72wbEDns1df9k5y6wm5vi9tsntdqA2dY9ax3E0iXAbIt8RselpxrAdokBAMQzqdkRk5n8AAADN3pUWHRNT0wgcmRraXQgMjAyMy4wOS40AAB4nH1Wy27UQBC85yvmB9bq1zz6SDYRQSgbCQLXnBACwY3/F9XjZLxILbwrxW7X9LO6sr9+fH/59vP3i8lNievT3ccff8q65O4GdvrP193LVyWim8cSN+X2/v2HSzk/v7t9s5yfvlyePxetRRvO4PMv9t3z0+ObhctDGZsoO49y4s2dzLjQRvM6jko5l7YNH73XACrJGD0B6g6U3kzxmr2atARnwNXN3GpYtzHEuye4CpwhQzEPHHURyRJsE6ekTSQSRFRySYB9Bm7aekMlsjFrs5oAB3pTt059eC8n3RqjmJEAvZx/Fd3IGJBwKdpGpQTJmE4RtNHVonm9evQzAXJ5CqAMYcON1EFpu1nKpTDK6YppnwCo5MMypKIeRj3DmkeHqrl4NhqO2RB6aewxQ/Sq9rScOoGiSo2RJauNoRkwpoOQDOuQWZiYprE76kELMcYYn2zE3HrWdRD2PJvt5C2yq4wWpM30QNLmtQ/4xx1345GxTQhNOgUdG0YYKbculdKN4MhUt1rBXIpD+KuelQ9GPgTUBg+de9aRC2X1S+zPySIBEDOm4ARaZUgDRYCEJwF/GUeG9TR8jOnUNkZ8nEKmJlQ9aypW9SGgIDo2MvbS2Dh12gNZNwXz2t6pgZVLnc5J9c2YwJCAQnC0ZnQWD68jus4215NMmLOpKu2pVixTbwE1BwFSTeLdqwiNfUAgbM2WROekQNTOsXgQFKFuGVN0XyfzCkkCTyFkLVVOVAx5gNRhTdoAUjGFVJmg2KHGyLFVhW/CVqWrfH+5+0fFd12/fbrcHboeHznUGyJS+qHR8aiHFFuYDsVlPGo9lDUA67HjNfALPuJlaYcYcni4ljyDixWbI7FW/ErAGD7HlUxJHGK+kiOellUOR4pxcJUAgdktKy0sus2jR6TxalmlQBhsOlvZy2wd/nMuzxLVhvtVgUTGsOjR3mgoAvKqQqJlCHhEl5kz5r/qkpmzI+bVTnEElMPzeLUc+eBABJRV6Rx3WFZdOodfEeGK17vl4IC+WlbOGn1GQFk5a90telC0vVpWFcHFa+bF89uvEdzf/AVrCal+lnO1ewAAAjV6VFh0U01JTEVTIHJka2l0IDIwMjMuMDkuNAAAeJxNUrtuG0EM/JWUMnA+8LW7JIVUblw56Q0XgZAusYLApT8+w1Ngs9BhOSSHw6GeH18u/Ho5PZ2eH1/uHk5fv+FTz//xx7N+l9Pt24ovcjk9/PrItK8UzHh+eT/d886LhTfbVWiN7VwIEfkmu4uYAZFdp1AAIebpByJBMTfaB6vOatN9DIm13RNyS0MPzJzdNnCuGIsB2e4+mdGpQTxvEHIyNkbKbQGZO6NRisuERviBxUAejW5sXPRjV126in66HFxrNyYkAYmyTkC+zyUMEZBvAtbiGhHqBVmA46gSIQ/Qk9IcGsCohDkfO/Ey9huIBaaPY2rJPmMVMsbCtE94ATZkIMyqjwaFYxSwRbUd2oaVO/AXIga61ohV1pfjjlvA3eHw+2xYQuwQtUTkBinhGkVjMgtRrFVG1lG0hJ1H6VizEObjOgPDV2w4CLzH3c4Tw3150ShJLeZlWBzEEVR3R42saZjOMTCsamDRrEsRTfbitbBawR1HjwOQMhR/GRwwyhzEKAbCan4YYTGoStAK+rvtx9v19/e/1z9Jez2frm8/d+Xkz4hHSotmaotW2mcklKNFnLNFkqtFmt4iy2jRSG5iZCY3NbKSmxzx5KZHIrkJUkpuihjFTRKjuGliTW6iGCY0VSwpTVWkdIsopYmylKYJ9jVJntIUzZQmaKV0PSlNDiZ0j1L7vSy1qdHUJkYR9ouhtjsk7/8AOFQ+rO9mel4AAAI0elRYdHJka2l0UEtMMSByZGtpdCAyMDIzLjA5LjQAAHice79v7T0GIOBnQABFIFYC4gZGRgUNIM3IyJYAoplZiKVh+gQVFEA0mwJEmANCM7FDaGZCxrNDaGZcxmOYg2YPG9h6uHIYzQEWZkIXRnM0jMvNwAj0QQYTI1MCE3MGE5MC0IIEZnkGFtYMJhZOBVa2BFYOBjZ2Bk4uDSZObgVuHgZuXgVeoQRevgwmPv4EPjkGfoEMJn5ZBgHBBEGhDCZBEQUhYQYRUQYRMQUxcQ0mMQkGCUkGCSkGCWkGaRkGESagrWyMTMwsrGxsfPwCgkK84tMYgQ6Bx872LUyOSXHMB0Ccwr+3HLSer9kPYk+2uunwcq+4HYg97dw6hw2zIu1B7HLVNoeCxghbEDv0RbvDHamdYPVBIZscrCs4weboi29xWJ+eCWbn1Nk5iEziB7O/FJk7+Bo7gs2xfGvs4G4j4ABi5zqesT/yRXcviP3E57J9r99RsJlPXbvsXp7WAKsXMIixy1PiAqv/qNm335rXHMw2T5c4MJWDBcyufihyoC1FAKyeWbhh/z4p3n0gtjh/0X77potgM3capx3w3pEHFv+3NunAx613weI3W+cecP31Huzf7R7zD1Tc/Q0254fesQNvtFaA1ftsvH+gybgQLL50zZoD5uvPgPWK33h3gF1kAphtJfr2QPlPdbB/M0yn7g+v6wa7rU5rn72Iui2YvYN7rYNCmBiYffMjg6MwUxHYTDEAHYiXWRl+l7gAAAMKelRYdE1PTDEgcmRraXQgMjAyMy4wOS40AAB4nH2Wy27VSBCG93mKfoFj1b2rlyRBkxHiRILANiuEQLCb99f81UlsI7Wwz8Iuf133Kp1fP74/f/v5+9npptX16f7Dj//afsn9DeT0l98Yo31VIrr52Oqh3b7/599ru3t6d/smuXv8cn363FSbGs7g/pN99/T48U3C7aHlxjFYqF1kI5fQaLTRvI6j0u5abGlqmu3Cmzol+QLUCfZMK+nGlpwrzsD5ZiGdE5/TibgvOAdnGw+hrvXZJT0XXExOrDNzOWgWrLIA+zTsXfrQCpmtj9AFmMiNb3Aw8XLRLSyGr8DR7n412UYHEaVSTGXwgmRUB2RG6Pze3SLHCuT2WCCzuuFBXKMvQWnXxpsP79bbBYAhB7QiFfHwFk49rTLkxhZLsmpDm3SiqNKFD41lOP4CspN0eMme6kuwqgOTzNDoMwPEucoleuBaKdRkdDlICiWxFZmlE6QEbgAeRrokx4t1CD2pksTa+1h1G6bgoYBh7qZ1COPQc4lyeapIfQyaqRdVllWvi5RWrYwnagOtXYV5idb8XGwbIj47Aw3SaTmRhhYpsnckAJUd0WMsySrTJTBEKkgQPFVJ89VwQNFDoWOQjgKGCY8l2Yv0TUdQ99lPOLROapanHaFo9rk9kKbgVaPIKK1ACQuEq7whaNIVqrNU0BUe8NCwkgb3VU8pz8639OhSg+ruvGp87AtMMrYS9UgGqTaIViGpzsVJPsh8psnVVr33/nr/x8J9WcG3j9f7YwXXLceixby3fqzTetVja1qJjuXIeNX9c0zAj1XHxe+6sz62OPYWl4bzdjKo2G1zORZtnHYNQ2eeNorUIebT5uAp2U1yuVgHdx+xC14kexSYeZtHD0v5KtlDwQzbVLZ7LzN1juqeJtKm+j0CKY8h0fN8WRnkPQqplMHgYV2mzwl9p7HgckHO7c9lUI7C5atkjxTNLGVQ9khnuUuyx6Wz+FiJe1wqr5KjB/RVsvuslWcYlN3n6rNzV9X7258CPN/8D3+8kx15F5w8AAACGHpUWHRTTUlMRVMxIHJka2l0IDIwMjMuMDkuNAAAeJxdkb1uHDEMhF8l5R2wJ/CfEo1UblzZ6Q0XwSFdYgeBSz98SB0CE2l2l7Pk6OPo+eHl6f70/PBy3o/709en8+P+vOL19j49fv660vV0//P870970n/yK375OF18CMwpx4WGkdhxV4rxnMcFB5IapmQDFxMfFxhMU5S2thbwOmAsIVwl6eBl4DWpa9WgjEWk1SQ2HewmuRsfOJa5p8DZbNmd5sScR940wbmdnAnLivJoI0srNQGWlHAAgM6aRHZf5VY4qsI1amy+jrv0dQDTHDVdXBsVoLrWJEiuvWZppuB77zy8oqAxzfaKrsm/toLIetAgZcshyWQoV04fpam+JRJP5PQRMeSaWm7ilTEJU/roUKdkSwXFl5Vi5DOxeVjyZAg2prBI+bBCGs+BmRPVUB5mUi1ed5fry8Sp1QK6QPalaA2nr6QxpTIVAH0r4MmegcoC3PmgAnkqqDO3yyhkqpWwVBV31IgVYUUCOG+Kqa1Dkm6Vb93QzHWTzhjoOB/f399+ffvz9jtg1Ofj2/uPQTOwVR7UKglulYa0ykJbRWGt4vBWQcxWYazPCj2wwWDCNBpcgQ0HObDxIAY2IKTARpSjjQghsCFJYEPSwIY0gxqRBTWgTKfzBDUcCOrxBDUYzrLRcFCD4fRpNChBDYchuCekwf2+VnAnso+/ENcrL6gxHUoAAAAASUVORK5CYII=",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
@@ -944,12 +972,13 @@
     }
    ],
    "source": [
+    "# We can display the atom mapping of an edge by calling it\n",
     "edge"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "id": "66dd7d32",
    "metadata": {
     "colab": {
@@ -962,12 +991,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 22,
    "id": "23b778d6",
    "metadata": {
     "id": "23b778d6"
@@ -1246,7 +1275,7 @@
     "\n",
     "10. `solvation_settings`: Settings for solvating the system, including the solvent model and the solvent padding.\n",
     "\n",
-    "11. `partial_charge_settings`: Settings for assigning partial charges to small molecules, including the partial charge method (e.g. `am1bcc`) and the openff toolkit backend (e.g. `ambertools` or `openeye`)."
+    "11. `partial_charge_settings`: Settings for assigning partial charges to small molecules, including the partial charge method (e.g. `am1bcc`) and the OpenFF toolkit backend (e.g. `ambertools` or `openeye`)."
    ]
   },
   {
@@ -1265,7 +1294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "id": "309c059b-85c7-4911-a417-69889a474fe1",
    "metadata": {},
    "outputs": [
@@ -1307,7 +1336,7 @@
     "id": "ab0eaea9"
    },
    "source": [
-    "### Creating the RelativeLigandTransform Protocol\n",
+    "### Creating the RFE Protocol\n",
     "\n",
     "With the Settings inspected and adjusted, we can provide these to the Protocol.\n",
     "This Protocol defines the procedure to estimate a free energy difference between two chemical systems,\n",
@@ -1316,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "id": "d1829ab6",
    "metadata": {
     "id": "d1829ab6"
@@ -1324,7 +1353,7 @@
    "outputs": [],
    "source": [
     "# Create RBFE Protocol class\n",
-    "rbfe_transform = RelativeHybridTopologyProtocol(\n",
+    "rbfe_protocol = RelativeHybridTopologyProtocol(\n",
     "    settings=rbfe_settings\n",
     ")"
    ]
@@ -1337,6 +1366,45 @@
     "# 3. Running a Relative Ligand Binding Free Energy Calculation"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "06d9d8a4-d570-42ea-9936-222b5e1728b2",
+   "metadata": {},
+   "source": [
+    "### Creating the Transformations\n",
+    "\n",
+    "Once we have the ChemicalSystems, and the Protocol, we can create the Transformation.\n",
+    "\n",
+    "The `Transformation` requires as input:\n",
+    "\n",
+    "the two ChemicalSystem objects defining either end of the alchemical transformation, a mapping between the two systems, the protocol, and optionally a name.\n",
+    "\n",
+    "As previously detailed, we create two sets of transformation, for the complex and the solvent legs of the thermodynamic cycle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "853d6eef-e716-4284-b02f-8b06f2beb916",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformation_complex = openfe.Transformation(\n",
+    "            stateA=ejm_31_complex,\n",
+    "            stateB=ejm_47_complex,\n",
+    "            mapping=ejm_31_to_ejm_47.componentA_to_componentB,\n",
+    "            protocol=rbfe_protocol,  # use protocol created above\n",
+    "            name=f\"{ejm_31_complex.name}_{ejm_47_complex.name}_complex\"\n",
+    "        )\n",
+    "transformation_solvent = openfe.Transformation(\n",
+    "            stateA=ejm_31_solvent,\n",
+    "            stateB=ejm_47_solvent,\n",
+    "            mapping=ejm_31_to_ejm_47.componentA_to_componentB,\n",
+    "            protocol=rbfe_protocol,  # use protocol created above\n",
+    "            name=f\"{ejm_31_solvent.name}_{ejm_47_solvent.name}_solvent\"\n",
+    "        )"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "026ad9fc-635f-44d2-937c-bbc58edf64da",
@@ -1358,33 +1426,55 @@
    "id": "0e2e7d25-5bb6-4344-963d-8cf5298982b4",
    "metadata": {},
    "source": [
-    "With the method we will be applying (the Protocol) defined, we can move onto applying to the alchemical transformation of interest.\n",
-    "\n",
-    "The `Protocol.create()` method requires as input:\n",
-    "\n",
-    "the two `ChemicalSystem` objects defining either end of the alchemical transformation\n",
-    "a mapping between the two systems, as a dict\n",
-    "This creates a directed-acyclic-graph (DAG) of computational tasks necessary for creating an estimate of the free energy difference between the two chemical systems.\n",
+    "With the `Transformation` defined, we can move onto creating the `ProtocolDAG`.\n",
     "\n",
-    "As previously detailed, we create two sets of simulations, defining both the complex and solvent transformations."
+    "The `Transformation.create()` method creates a directed-acyclic-graph (DAG) of computational tasks necessary for creating an estimate of the free energy difference between the two chemical systems."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 47,
+   "id": "a140488f-045c-479e-9d9d-739c6b9be42e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{0: 0, 1: 1, 2: 6, 3: 5, 4: 4, 5: 3, 6: 2, 7: 38, 8: 37, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 28: 35, 29: 36, 30: 8, 31: 7}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(transformation_complex.mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
    "id": "77e72e9a-bac0-4059-b0bf-96c15c9b2696",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "A single LigandAtomMapping is expected for this Protocol",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[46], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m complex_dag \u001b[38;5;241m=\u001b[39m \u001b[43mtransformation_complex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m solvent_dag \u001b[38;5;241m=\u001b[39m transformation_solvent\u001b[38;5;241m.\u001b[39mcreate()\n",
+      "File \u001b[0;32m~/mambaforge/envs/openfe_dev/lib/python3.10/site-packages/gufe/transformations/transformation.py:124\u001b[0m, in \u001b[0;36mTransformation.create\u001b[0;34m(self, extends, name)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    116\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    117\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[1;32m    118\u001b[0m     extends: Optional[ProtocolDAGResult] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    119\u001b[0m     name: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    120\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ProtocolDAG:\n\u001b[1;32m    121\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    122\u001b[0m \u001b[38;5;124;03m    Returns a ``ProtocolDAG`` executing this ``Transformation.protocol``.\u001b[39;00m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprotocol\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    125\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstateA\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    126\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstateB\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateB\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    127\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmapping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    128\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextends\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextends\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    129\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    130\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtransformation_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    131\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/mambaforge/envs/openfe_dev/lib/python3.10/site-packages/gufe/protocols/protocol.py:224\u001b[0m, in \u001b[0;36mProtocol.create\u001b[0;34m(self, stateA, stateB, mapping, extends, name, transformation_key)\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    175\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    176\u001b[0m     \u001b[38;5;241m*\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    182\u001b[0m     transformation_key: Optional[GufeKey] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    183\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ProtocolDAG:\n\u001b[1;32m    184\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Prepare a `ProtocolDAG` with all information required for execution.\u001b[39;00m\n\u001b[1;32m    185\u001b[0m \n\u001b[1;32m    186\u001b[0m \u001b[38;5;124;03m    A :class:`.ProtocolDAG` is composed of :class:`.ProtocolUnit` \\s, with\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    220\u001b[0m \u001b[38;5;124;03m        A directed, acyclic graph that can be executed by a `Scheduler`.\u001b[39;00m\n\u001b[1;32m    221\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    222\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ProtocolDAG(\n\u001b[1;32m    223\u001b[0m         name\u001b[38;5;241m=\u001b[39mname,\n\u001b[0;32m--> 224\u001b[0m         protocol_units\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    225\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstateA\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstateA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    226\u001b[0m \u001b[43m            \u001b[49m\u001b[43mstateB\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstateB\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    227\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmapping\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    228\u001b[0m \u001b[43m            \u001b[49m\u001b[43mextends\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextends\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    229\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m    230\u001b[0m         transformation_key\u001b[38;5;241m=\u001b[39mtransformation_key,\n\u001b[1;32m    231\u001b[0m         extends_key\u001b[38;5;241m=\u001b[39mextends\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;28;01mif\u001b[39;00m extends \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    232\u001b[0m     )\n",
+      "File \u001b[0;32m~/openfe/openfe/protocols/openmm_rfe/equil_rfe_methods.py:524\u001b[0m, in \u001b[0;36mRelativeHybridTopologyProtocol._create\u001b[0;34m(self, stateA, stateB, mapping, extends)\u001b[0m\n\u001b[1;32m    520\u001b[0m \u001b[38;5;66;03m# Get alchemical components & validate them + mapping\u001b[39;00m\n\u001b[1;32m    521\u001b[0m alchem_comps \u001b[38;5;241m=\u001b[39m system_validation\u001b[38;5;241m.\u001b[39mget_alchemical_components(\n\u001b[1;32m    522\u001b[0m     stateA, stateB\n\u001b[1;32m    523\u001b[0m )\n\u001b[0;32m--> 524\u001b[0m \u001b[43m_validate_alchemical_components\u001b[49m\u001b[43m(\u001b[49m\u001b[43malchem_comps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapping\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    525\u001b[0m ligandmapping \u001b[38;5;241m=\u001b[39m mapping[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapping, \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m mapping  \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m    527\u001b[0m \u001b[38;5;66;03m# Validate solvent component\u001b[39;00m\n",
+      "File \u001b[0;32m~/openfe/openfe/protocols/openmm_rfe/equil_rfe_methods.py:214\u001b[0m, in \u001b[0;36m_validate_alchemical_components\u001b[0;34m(alchemical_components, mapping)\u001b[0m\n\u001b[1;32m    212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mapping \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapping) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    213\u001b[0m     errmsg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mA single LigandAtomMapping is expected for this Protocol\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m--> 214\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(errmsg)\n\u001b[1;32m    216\u001b[0m \u001b[38;5;66;03m# Check that all alchemical components are mapped & small molecules\u001b[39;00m\n\u001b[1;32m    217\u001b[0m mapped \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstateA\u001b[39m\u001b[38;5;124m'\u001b[39m: [m\u001b[38;5;241m.\u001b[39mcomponentA \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m mapping],\n\u001b[1;32m    218\u001b[0m           \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstateB\u001b[39m\u001b[38;5;124m'\u001b[39m: [m\u001b[38;5;241m.\u001b[39mcomponentB \u001b[38;5;28;01mfor\u001b[39;00m m \u001b[38;5;129;01min\u001b[39;00m mapping]}\n",
+      "\u001b[0;31mValueError\u001b[0m: A single LigandAtomMapping is expected for this Protocol"
+     ]
+    }
+   ],
    "source": [
-    "complex_dag = rbfe_transform.create(\n",
-    "    stateA=ejm_31_complex, stateB=ejm_47_complex,\n",
-    "    mapping=ejm_31_to_ejm_47,\n",
-    ")\n",
+    "complex_dag = transformation_complex.create()\n",
     "\n",
-    "solvent_dag = rbfe_transform.create(\n",
-    "    stateA=ejm_31_solvent, stateB=ejm_47_solvent,\n",
-    "    mapping=ejm_31_to_ejm_47,\n",
-    ")"
+    "solvent_dag = transformation_solvent.create()"
    ]
   },
   {
@@ -1416,7 +1506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "981cde0c",
    "metadata": {
     "colab": {
@@ -1426,7 +1516,19 @@
     "outputId": "812389bc-3730-416b-8154-79e0e1fb4346",
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'function' object has no attribute 'protocol_units'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[40], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# complex dry-run\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m complex_unit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mcomplex_dag\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprotocol_units\u001b[49m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m      4\u001b[0m complex_unit\u001b[38;5;241m.\u001b[39mrun(dry\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'function' object has no attribute 'protocol_units'"
+     ]
+    }
+   ],
    "source": [
     "# complex dry-run\n",
     "complex_unit = list(complex_dag.protocol_units)[0]\n",
@@ -1461,32 +1563,7 @@
    "source": [
     "## 3.2. Using the CLI\n",
     "\n",
-    "Even when using the Python API to set up the RBFE calculations, you can dump all inputs to a JSON file and run the calculations using the `openfe quickrun` command. Here, we will show you how to save the inputs to the JSON file.\n",
-    "\n",
-    "Once we have the ChemicalSystems, and the Protocol, we can create the Transformation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f1be2fdc-3983-41a5-b969-f2f62dcef7c6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transformation_complex = openfe.Transformation(\n",
-    "            stateA=ejm_31_complex,\n",
-    "            stateB=ejm_47_complex,\n",
-    "            mapping=ejm_31_to_ejm_47.componentA_to_componentB,\n",
-    "            protocol=rbfe_transform,  # use protocol created above\n",
-    "            name=f\"{ejm_31_complex.name}_{ejm_47_complex.name}_complex\"\n",
-    "        )\n",
-    "transformation_solvent = openfe.Transformation(\n",
-    "            stateA=ejm_31_solvent,\n",
-    "            stateB=ejm_47_solvent,\n",
-    "            mapping=ejm_31_to_ejm_47.componentA_to_componentB,\n",
-    "            protocol=rbfe_transform,  # use protocol created above\n",
-    "            name=f\"{ejm_31_solvent.name}_{ejm_47_solvent.name}_solvent\"\n",
-    "        )"
+    "Even when using the Python API to set up the RBFE calculations, you can dump all `Transformation`s to a JSON file and run the calculations using the `openfe quickrun` command. Here, we will show you how to save the `Transformation`s to the JSON file."
    ]
   },
   {
@@ -1499,7 +1576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "12d35b4c-b737-4c3c-9fda-dc387aaa6915",
    "metadata": {},
    "outputs": [],
@@ -1543,9 +1620,7 @@
     "energies for both phases.\n",
     "\n",
     "This can be achieved by passing the results of executing the DAGs calling the `gather()` methods of `RelativeLigandTransform`.\n",
-    "This takes a **list** of completed DAG results, catering for when simulations have been extended.\n",
-    "\n",
-    "TODO: add cinnabar"
+    "This takes a **list** of completed DAG results, catering for when simulations have been extended."
    ]
   },
   {
@@ -1574,8 +1649,8 @@
    ],
    "source": [
     "# Get the complex and solvent results\n",
-    "complex_results = rbfe_transform.gather([complex_dag_results])\n",
-    "solvent_results = rbfe_transform.gather([solvent_dag_results])\n",
+    "complex_results = rbfe_protocol.gather([complex_dag_results])\n",
+    "solvent_results = rbfe_protocol.gather([solvent_dag_results])\n",
     "\n",
     "print(f\"Complex dG: {complex_results.get_estimate()}, err {complex_results.get_uncertainty()}\")\n",
     "print(f\"Solvent dG: {solvent_results.get_estimate()}, err {solvent_results.get_uncertainty()}\")"
@@ -1612,7 +1687,7 @@
    "id": "68cc5f26-71e2-4135-b72b-6c5a4a1d4072",
    "metadata": {},
    "source": [
-    "**1. Setup**\n",
+    "### 5.1. Setup\n",
     "\n",
     "The setup, as described above, can also be carried out using the CLI command `openfe plan-rbfe-network`.\n",
     "\n",
@@ -1637,7 +1712,7 @@
    "id": "26fef157-6989-406d-b719-0ed597ef820e",
    "metadata": {},
    "source": [
-    "**2. Execution**\n",
+    "### 5.2. Execution\n",
     "\n",
     "You can run each leg individually by using the `openfe quickrun` command. It takes a transformation JSON as input, and the flags `-o` to give the final output JSON file and `-d` for the directory where simulation results should be stored. For example,\n",
     "\n",
@@ -1645,7 +1720,7 @@
     "\n",
     "`openfe quickrun tyk2_json/lig_ejm_31_lig_ejm_47_solvent.json -o results_solvent.json -d working-directory`\n",
     "\n",
-    "**3. Anaylsis**\n",
+    "### 5.3. Anaylsis\n",
     "\n",
     "To gather the \n",
     " estimates into a single file, use the `openfe gather` command from within the working directory used above:\n",