diff --git a/models/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb b/models/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb
index 01c1172..ca648f5 100644
--- a/models/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb
+++ b/models/v3/.ipynb_checkpoints/Aragon_Conviction_Voting_Model-checkpoint.ipynb
@@ -70,22 +70,18 @@
     "* https://www.desmos.com/calculator/x9uc6w72lm\n",
     "* https://www.desmos.com/calculator/0lmtia9jql\n",
     "\n",
-    "See ALPHA NOTEBOOK here\n",
-    "Deriving_Alpha.ipynb\n",
+    "See the [Deriving_Alpha](Deriving_Alpha.ipynb) notebook for more details around alpha and how it is derived.\n",
     "\n",
     "\n",
     "## Converting Signals to Discrete Decisions\n",
     "\n",
-    "Conviction can be considered like a fluctuating kinetic energy, with the Trigger function acting as a required activation energy for proposals to pass. This is the mechanism by which a continuous community preference turns into a discrete action event: passing a proposal. See [Trigger Function Explanation ](Trigger_Function_Explanation.ipynb)"
+    "Conviction can be considered like a fluctuating kinetic energy, with the Trigger function acting as a required activation energy for proposals to pass. This is the mechanism by which a continuous community preference turns into a discrete action event: passing a proposal. See [Trigger Function Explanation](Trigger_Function_Explanation.ipynb) for more details around the trigger function and how it works."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "These plots demonstrate the increasing conviction required to pass a proposal when either % of funds requested, effective supply, or alpha parameter is high. Blue areas represent lower required conviction, and red areas represent impossibly high conviction. This ensures that the reserve pool of funds are not depleted by a small number of large proposals.\n",
-    "\n",
-    "\n",
     "### Resolving Passed Proposals\n",
     "\n",
     "![](images/stockflow_cv_trigger.png)\n",
@@ -362,7 +358,9 @@
     }
    ],
    "source": [
-    "from model import config"
+    "from model import config\n",
+    "from model.parts.sys_params import initial_values\n",
+    "from model.parts.utils import *"
    ]
   },
   {
@@ -379,44 +377,29 @@
     }
    ],
    "source": [
-    "# pull out configurations to illustrate\n",
-    "sim_config,state_variables,seeds,partial_state_update_blocks = config.get_configs()"
+    "from copy import deepcopy\n",
+    "from cadCAD import configs\n",
+    "\n",
+    "# Initialize network x\n",
+    "for c in configs:\n",
+    "    c.initial_state = deepcopy(c.initial_state)\n",
+    "\n",
+    "    print(\"Params (config.py) : \", c.sim_config['M'])\n",
+    "\n",
+    "    c.initial_state['network'] = initialize_network(initial_values['n'],initial_values['m'],\n",
+    "                                            initial_values['initial_funds'],\n",
+    "                                            initial_values['supply'],c.sim_config['M'])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'N': 1,\n",
-       "  'T': range(0, 100),\n",
-       "  'M': {'beta': 0.2,\n",
-       "   'rho': 0.0025,\n",
-       "   'alpha': 0.7937005259840998,\n",
-       "   'gamma': 0.001,\n",
-       "   'sensitivity': 0.75,\n",
-       "   'tmin': 1,\n",
-       "   'min_supp': 1,\n",
-       "   'base_completion_rate': 45,\n",
-       "   'base_failure_rate': 180,\n",
-       "   'base_engagement_rate': 0.3,\n",
-       "   'lowest_affinity_to_support': 0.3},\n",
-       "  'subset_id': 0,\n",
-       "  'subset_window': deque([0, None]),\n",
-       "  'simulation_id': 0,\n",
-       "  'run_id': 0}]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sim_config"
+    "# pull out configurations to illustrate\n",
+    "sim_config,state_variables,seeds,partial_state_update_blocks = config.get_configs()\n",
+    "state_variables['network'] = c.initial_state['network']"
    ]
   },
   {
@@ -483,10 +466,6 @@
     }
    ],
    "source": [
-    "from model.parts.sys_params import initial_values\n",
-    "from model.parts.utils import *\n",
-    "\n",
-    "\n",
     "initial_values"
    ]
   },
@@ -537,7 +516,7 @@
     {
      "data": {
       "text/plain": [
-       "{'network': 0,\n",
+       "{'network': <networkx.classes.digraph.DiGraph at 0x7fea9537c410>,\n",
        " 'funds': 4867.21,\n",
        " 'sentiment': 0.6,\n",
        " 'effective_supply': 14020.008000000002,\n",
@@ -561,15 +540,15 @@
     "## Hyperparameter explanations:\n",
     "* $\\beta$ = .2  Upper bound on share of funds dispersed in the example Trigger Function\n",
     "* $\\rho$ = 0.002  Scale Parameter for the example Trigger Function\n",
-    "* $\\alpha$ : 0.875 The decay rate for previously accumulated conviction\n",
+    "* $\\alpha$ : 0.79370 timescale set in days with 3 day halflife\n",
     "* $\\gamma$: 0.001 The expansion of supply per per day\n",
     "* sensitivity of participant decisions to changes in affinity \n",
     "* tmin = 1 unit days; minimum periods passed before a proposal can pass\n",
     "* min_supp = 50 number of tokens that must be stake for a proposal to be a candidate\n",
-    "* base_completion_rate': 45, \n",
-    "* base_failure_rate': [180],\n",
-    "* base_engagement_rate' :[0.3],\n",
-    "* lowest_affinity_to_support': [0.3],"
+    "* base_completion_rate': 45,  # expected number of days to complete a proposals.\n",
+    "* base_failure_rate': [180], # expected number of days until a proposal will fail\n",
+    "* base_engagement_rate' :[0.3], # Probability of being active on a certain day if 100% sentiment\n",
+    "* lowest_affinity_to_support': [0.3],# lowest affinity to required to support a proposal\n"
    ]
   },
   {
@@ -579,28 +558,6 @@
     "# TODO Pull out. Only read, not set variables"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # # initialize \n",
-    "# network = initialize_network(initial_values['n'],initial_values['m'],\n",
-    "#                                             initial_values['initial_funds'],\n",
-    "#                                             initial_values['supply'],sim_config[0]['M'])\n",
-    "# initial_funds = initial_values['initial_funds']\n",
-    "\n",
-    "# genesis_states = { \n",
-    "#                 'network': network,\n",
-    "#                 'funds':initial_values['initial_funds'],\n",
-    "#                 'sentiment': initial_values['initial_sentiment'],\n",
-    "#                 'effective_supply': initial_values['supply']-initial_values['initial_funds'],\n",
-    "#                 'total_supply': initial_values['supply']\n",
-    "\n",
-    "# }\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -615,51 +572,74 @@
     "Let's explore!"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n",
+    "proposals = get_nodes_by_type(state_variables['network'], 'proposal')\n",
+    "participants = get_nodes_by_type(state_variables['network'], 'participant')\n",
+    "supporters = get_edges_by_type(state_variables['network'], 'support')\n",
+    "influencers = get_edges_by_type(state_variables['network'], 'influence')\n",
+    "competitors = get_edges_by_type(state_variables['network'], 'conflict')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'participant',\n",
+       " 'holdings': 486.5022169604887,\n",
+       " 'sentiment': 0.9223857537413497}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sample a participant\n",
+    "state_variables['network'].nodes[participants[0]]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'int' object has no attribute 'nodes'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-2ac646ef48d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mproposals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_nodes_by_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'network'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'proposal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mparticipants\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_nodes_by_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'participant'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msupporters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_edges_by_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'support'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0minfluencers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_edges_by_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'influence'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/repos/Aragon_Conviction_Voting/models/v3/model/parts/utils.py\u001b[0m in \u001b[0;36mget_nodes_by_type\u001b[0;34m(g, node_type_selection)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m     '''\n\u001b[0;32m--> 159\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m \u001b[0mnode_type_selection\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_sentimental\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentiment\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'nodes'"
-     ]
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Count of Participants')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
-   "source": [
-    "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n",
-    "proposals = get_nodes_by_type(network, 'proposal')\n",
-    "participants = get_nodes_by_type(network, 'participant')\n",
-    "supporters = get_edges_by_type(network, 'support')\n",
-    "influencers = get_edges_by_type(network, 'influence')\n",
-    "competitors = get_edges_by_type(network, 'conflict')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#sample a participant\n",
-    "network.nodes[participants[0]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# Let's look at the distribution of participant holdings at the start of the sim\n",
-    "plt.hist([ network.nodes[i]['holdings'] for i in participants])\n",
+    "plt.hist([ state_variables['network'].nodes[i]['holdings'] for i in participants])\n",
     "plt.title('Histogram of Participants Token Holdings')\n",
     "plt.xlabel('Amount of Honey')\n",
     "plt.ylabel('Count of Participants')\n"
@@ -667,22 +647,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:563: MatplotlibDeprecationWarning: \n",
+      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
+      "  if not cb.iterable(width):\n",
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:660: MatplotlibDeprecationWarning: \n",
+      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
+      "  if cb.iterable(node_size):  # many node sizes\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Participants Social Network')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "nx.draw_spring(network, nodelist = participants, edgelist=influencers)\n",
+    "nx.draw_spring(state_variables['network'], nodelist = participants, edgelist=influencers)\n",
     "plt.title('Participants Social Network')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'proposal',\n",
+       " 'conviction': 0,\n",
+       " 'status': 'candidate',\n",
+       " 'age': 0,\n",
+       " 'funds_requested': 492.2258454429512,\n",
+       " 'trigger': 20663.16831435627}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#lets look at proposals\n",
-    "network.nodes[proposals[0]]"
+    "state_variables['network'].nodes[proposals[0]]"
    ]
   },
   {
@@ -701,19 +730,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "funds_array = np.array([ network.nodes[i]['funds_requested'] for i in proposals])\n",
+    "funds_array = np.array([ state_variables['network'].nodes[i]['funds_requested'] for i in proposals])\n",
     "conviction_required = np.array([trigger_threshold(r, initial_values['initial_funds'], initial_values['supply'], sim_config[0]['M']['alpha'],sim_config[0]['M']) for r in funds_array])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.bar( proposals, funds_array/initial_values['initial_funds'])\n",
     "plt.title('Bar chart of Proposals Funds Requested')\n",
@@ -723,9 +775,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Amount of Conviction')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.bar( proposals, conviction_required)\n",
     "plt.title('Bar chart of Proposals Conviction Required')\n",
@@ -742,9 +817,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 55.73999999999998, 'Participant_id')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "m = len(proposals)\n",
     "n = len(participants)\n",
@@ -754,7 +852,7 @@
     "    for j_ind in range(m):\n",
     "        i = participants[i_ind]\n",
     "        j = proposals[j_ind]\n",
-    "        affinities[i_ind][j_ind] = network.edges[(i,j)]['affinity']\n",
+    "        affinities[i_ind][j_ind] = state_variables['network'].edges[(i,j)]['affinity']\n",
     "\n",
     "dims = (20, 5)\n",
     "fig, ax = plt.subplots(figsize=dims)\n",
@@ -783,41 +881,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from cadCAD.configuration import Experiment\n",
+    "# from cadCAD.configuration import Experiment\n",
     "\n",
-    "# Create configuration\n",
-    "exp = Experiment()\n",
+    "# # Create configuration\n",
+    "# exp = Experiment()\n",
     "\n",
-    "exp.append_configs(\n",
-    "    sim_configs=sim_config,\n",
-    "    initial_state=genesis_states,\n",
-    "    seeds=seeds,\n",
-    "    partial_state_update_blocks=partial_state_update_blocks\n",
-    ")"
+    "# exp.append_configs(\n",
+    "#     sim_configs=sim_config,\n",
+    "#     initial_state=state_variables,\n",
+    "#     seeds=seeds,\n",
+    "#     partial_state_update_blocks=partial_state_update_blocks\n",
+    "# )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                  ___________    ____\n",
+      "  ________ __ ___/ / ____/   |  / __ \\\n",
+      " / ___/ __` / __  / /   / /| | / / / /\n",
+      "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
+      "\\___/\\__,_/\\__,_/\\____/_/  |_/_____/\n",
+      "by cadCAD\n",
+      "\n",
+      "Execution Mode: local_proc\n",
+      "Configuration Count: 1\n",
+      "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (100, 11, 1, 5)\n",
+      "Execution Method: local_simulations\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "simulation() missing 1 required positional argument: 'configured_N'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-0f5e7f8620b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mrdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/repos/Aragon_Conviction_Voting/models/v3/model/run.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0msimulation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_mode_ctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0mraw_system_events\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_field\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msessions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimulation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m     \u001b[0;31m# Result System Events DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_system_events\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/cadCAD/engine/__init__.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    151\u001b[0m             simulations_results = self.exec_method(\n\u001b[1;32m    152\u001b[0m                 \u001b[0msim_executors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_lists\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs_structs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSimIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRunIDs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0mExpIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubsetIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubsetWindows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_N\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             )\n\u001b[1;32m    155\u001b[0m             \u001b[0mfinal_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_final_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulations_results\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartial_state_updates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremote_threshold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/cadCAD/engine/execution.py\u001b[0m in \u001b[0;36mlocal_simulations\u001b[0;34m(simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, SimIDs, Ns, ExpIDs, SubsetIDs, SubsetWindows, exec_method)\u001b[0m\n\u001b[1;32m    140\u001b[0m             return single_proc_exec(\n\u001b[1;32m    141\u001b[0m                 \u001b[0msimulation_execs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_lists\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs_structs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSimIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m                 \u001b[0mExpIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubsetIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubsetWindows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexec_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m             )\n\u001b[1;32m    144\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfigs_structs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconfig_amt\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mremote_threshold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/cadCAD/engine/execution.py\u001b[0m in \u001b[0;36msingle_proc_exec\u001b[0;34m(simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, SimIDs, Ns, ExpIDs, SubsetIDs, SubsetWindows, exec_method)\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     )\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimulation_exec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msim_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset_window\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: simulation() missing 1 required positional argument: 'configured_N'"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from model.parts.utils import *\n",
     "from model import run\n",
-    "from cadCAD import configs\n",
+    "#from cadCAD import configs\n",
     "pd.options.display.float_format = '{:.2f}'.format\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
-    "rdf = run.run(configs)\n"
+    "rdf = run.run()\n"
    ]
   },
   {
diff --git a/models/v3/.ipynb_checkpoints/Trigger_Function_Explanation-checkpoint.ipynb b/models/v3/.ipynb_checkpoints/Trigger_Function_Explanation-checkpoint.ipynb
index e33bcc9..3fc7acb 100644
--- a/models/v3/.ipynb_checkpoints/Trigger_Function_Explanation-checkpoint.ipynb
+++ b/models/v3/.ipynb_checkpoints/Trigger_Function_Explanation-checkpoint.ipynb
@@ -91,6 +91,13 @@
    "source": [
     "trigger_grid(supply_sweep, alpha_sweep)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These plots demonstrate the increasing conviction required to pass a proposal when either % of funds requested, effective supply, or alpha parameter is high. Blue areas represent lower required conviction, and red areas represent impossibly high conviction. This ensures that the reserve pool of funds are not depleted by a small number of large proposals.\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/models/v3/Aragon_Conviction_Voting_Model.ipynb b/models/v3/Aragon_Conviction_Voting_Model.ipynb
index 01c1172..ca648f5 100644
--- a/models/v3/Aragon_Conviction_Voting_Model.ipynb
+++ b/models/v3/Aragon_Conviction_Voting_Model.ipynb
@@ -70,22 +70,18 @@
     "* https://www.desmos.com/calculator/x9uc6w72lm\n",
     "* https://www.desmos.com/calculator/0lmtia9jql\n",
     "\n",
-    "See ALPHA NOTEBOOK here\n",
-    "Deriving_Alpha.ipynb\n",
+    "See the [Deriving_Alpha](Deriving_Alpha.ipynb) notebook for more details around alpha and how it is derived.\n",
     "\n",
     "\n",
     "## Converting Signals to Discrete Decisions\n",
     "\n",
-    "Conviction can be considered like a fluctuating kinetic energy, with the Trigger function acting as a required activation energy for proposals to pass. This is the mechanism by which a continuous community preference turns into a discrete action event: passing a proposal. See [Trigger Function Explanation ](Trigger_Function_Explanation.ipynb)"
+    "Conviction can be considered like a fluctuating kinetic energy, with the Trigger function acting as a required activation energy for proposals to pass. This is the mechanism by which a continuous community preference turns into a discrete action event: passing a proposal. See [Trigger Function Explanation](Trigger_Function_Explanation.ipynb) for more details around the trigger function and how it works."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "These plots demonstrate the increasing conviction required to pass a proposal when either % of funds requested, effective supply, or alpha parameter is high. Blue areas represent lower required conviction, and red areas represent impossibly high conviction. This ensures that the reserve pool of funds are not depleted by a small number of large proposals.\n",
-    "\n",
-    "\n",
     "### Resolving Passed Proposals\n",
     "\n",
     "![](images/stockflow_cv_trigger.png)\n",
@@ -362,7 +358,9 @@
     }
    ],
    "source": [
-    "from model import config"
+    "from model import config\n",
+    "from model.parts.sys_params import initial_values\n",
+    "from model.parts.utils import *"
    ]
   },
   {
@@ -379,44 +377,29 @@
     }
    ],
    "source": [
-    "# pull out configurations to illustrate\n",
-    "sim_config,state_variables,seeds,partial_state_update_blocks = config.get_configs()"
+    "from copy import deepcopy\n",
+    "from cadCAD import configs\n",
+    "\n",
+    "# Initialize network x\n",
+    "for c in configs:\n",
+    "    c.initial_state = deepcopy(c.initial_state)\n",
+    "\n",
+    "    print(\"Params (config.py) : \", c.sim_config['M'])\n",
+    "\n",
+    "    c.initial_state['network'] = initialize_network(initial_values['n'],initial_values['m'],\n",
+    "                                            initial_values['initial_funds'],\n",
+    "                                            initial_values['supply'],c.sim_config['M'])"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'N': 1,\n",
-       "  'T': range(0, 100),\n",
-       "  'M': {'beta': 0.2,\n",
-       "   'rho': 0.0025,\n",
-       "   'alpha': 0.7937005259840998,\n",
-       "   'gamma': 0.001,\n",
-       "   'sensitivity': 0.75,\n",
-       "   'tmin': 1,\n",
-       "   'min_supp': 1,\n",
-       "   'base_completion_rate': 45,\n",
-       "   'base_failure_rate': 180,\n",
-       "   'base_engagement_rate': 0.3,\n",
-       "   'lowest_affinity_to_support': 0.3},\n",
-       "  'subset_id': 0,\n",
-       "  'subset_window': deque([0, None]),\n",
-       "  'simulation_id': 0,\n",
-       "  'run_id': 0}]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "sim_config"
+    "# pull out configurations to illustrate\n",
+    "sim_config,state_variables,seeds,partial_state_update_blocks = config.get_configs()\n",
+    "state_variables['network'] = c.initial_state['network']"
    ]
   },
   {
@@ -483,10 +466,6 @@
     }
    ],
    "source": [
-    "from model.parts.sys_params import initial_values\n",
-    "from model.parts.utils import *\n",
-    "\n",
-    "\n",
     "initial_values"
    ]
   },
@@ -537,7 +516,7 @@
     {
      "data": {
       "text/plain": [
-       "{'network': 0,\n",
+       "{'network': <networkx.classes.digraph.DiGraph at 0x7fea9537c410>,\n",
        " 'funds': 4867.21,\n",
        " 'sentiment': 0.6,\n",
        " 'effective_supply': 14020.008000000002,\n",
@@ -561,15 +540,15 @@
     "## Hyperparameter explanations:\n",
     "* $\\beta$ = .2  Upper bound on share of funds dispersed in the example Trigger Function\n",
     "* $\\rho$ = 0.002  Scale Parameter for the example Trigger Function\n",
-    "* $\\alpha$ : 0.875 The decay rate for previously accumulated conviction\n",
+    "* $\\alpha$ : 0.79370 timescale set in days with 3 day halflife\n",
     "* $\\gamma$: 0.001 The expansion of supply per per day\n",
     "* sensitivity of participant decisions to changes in affinity \n",
     "* tmin = 1 unit days; minimum periods passed before a proposal can pass\n",
     "* min_supp = 50 number of tokens that must be stake for a proposal to be a candidate\n",
-    "* base_completion_rate': 45, \n",
-    "* base_failure_rate': [180],\n",
-    "* base_engagement_rate' :[0.3],\n",
-    "* lowest_affinity_to_support': [0.3],"
+    "* base_completion_rate': 45,  # expected number of days to complete a proposals.\n",
+    "* base_failure_rate': [180], # expected number of days until a proposal will fail\n",
+    "* base_engagement_rate' :[0.3], # Probability of being active on a certain day if 100% sentiment\n",
+    "* lowest_affinity_to_support': [0.3],# lowest affinity to required to support a proposal\n"
    ]
   },
   {
@@ -579,28 +558,6 @@
     "# TODO Pull out. Only read, not set variables"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# # # initialize \n",
-    "# network = initialize_network(initial_values['n'],initial_values['m'],\n",
-    "#                                             initial_values['initial_funds'],\n",
-    "#                                             initial_values['supply'],sim_config[0]['M'])\n",
-    "# initial_funds = initial_values['initial_funds']\n",
-    "\n",
-    "# genesis_states = { \n",
-    "#                 'network': network,\n",
-    "#                 'funds':initial_values['initial_funds'],\n",
-    "#                 'sentiment': initial_values['initial_sentiment'],\n",
-    "#                 'effective_supply': initial_values['supply']-initial_values['initial_funds'],\n",
-    "#                 'total_supply': initial_values['supply']\n",
-    "\n",
-    "# }\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -615,51 +572,74 @@
     "Let's explore!"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n",
+    "proposals = get_nodes_by_type(state_variables['network'], 'proposal')\n",
+    "participants = get_nodes_by_type(state_variables['network'], 'participant')\n",
+    "supporters = get_edges_by_type(state_variables['network'], 'support')\n",
+    "influencers = get_edges_by_type(state_variables['network'], 'influence')\n",
+    "competitors = get_edges_by_type(state_variables['network'], 'conflict')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'participant',\n",
+       " 'holdings': 486.5022169604887,\n",
+       " 'sentiment': 0.9223857537413497}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#sample a participant\n",
+    "state_variables['network'].nodes[participants[0]]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "ename": "AttributeError",
-     "evalue": "'int' object has no attribute 'nodes'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-2ac646ef48d4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mproposals\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_nodes_by_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'network'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'proposal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mparticipants\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_nodes_by_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'participant'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msupporters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_edges_by_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'support'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0minfluencers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_edges_by_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'influence'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/repos/Aragon_Conviction_Voting/models/v3/model/parts/utils.py\u001b[0m in \u001b[0;36mget_nodes_by_type\u001b[0;34m(g, node_type_selection)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m     '''\n\u001b[0;32m--> 159\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m \u001b[0mnode_type_selection\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_sentimental\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msentiment\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecay\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'nodes'"
-     ]
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Count of Participants')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
-   "source": [
-    "# To explore our model prior to the simulation, we extract key components from our networkX object into lists.\n",
-    "proposals = get_nodes_by_type(network, 'proposal')\n",
-    "participants = get_nodes_by_type(network, 'participant')\n",
-    "supporters = get_edges_by_type(network, 'support')\n",
-    "influencers = get_edges_by_type(network, 'influence')\n",
-    "competitors = get_edges_by_type(network, 'conflict')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#sample a participant\n",
-    "network.nodes[participants[0]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# Let's look at the distribution of participant holdings at the start of the sim\n",
-    "plt.hist([ network.nodes[i]['holdings'] for i in participants])\n",
+    "plt.hist([ state_variables['network'].nodes[i]['holdings'] for i in participants])\n",
     "plt.title('Histogram of Participants Token Holdings')\n",
     "plt.xlabel('Amount of Honey')\n",
     "plt.ylabel('Count of Participants')\n"
@@ -667,22 +647,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:563: MatplotlibDeprecationWarning: \n",
+      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
+      "  if not cb.iterable(width):\n",
+      "/home/aclarkdata/anaconda3/lib/python3.7/site-packages/networkx/drawing/nx_pylab.py:660: MatplotlibDeprecationWarning: \n",
+      "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
+      "  if cb.iterable(node_size):  # many node sizes\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Participants Social Network')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "nx.draw_spring(network, nodelist = participants, edgelist=influencers)\n",
+    "nx.draw_spring(state_variables['network'], nodelist = participants, edgelist=influencers)\n",
     "plt.title('Participants Social Network')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'type': 'proposal',\n",
+       " 'conviction': 0,\n",
+       " 'status': 'candidate',\n",
+       " 'age': 0,\n",
+       " 'funds_requested': 492.2258454429512,\n",
+       " 'trigger': 20663.16831435627}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#lets look at proposals\n",
-    "network.nodes[proposals[0]]"
+    "state_variables['network'].nodes[proposals[0]]"
    ]
   },
   {
@@ -701,19 +730,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "funds_array = np.array([ network.nodes[i]['funds_requested'] for i in proposals])\n",
+    "funds_array = np.array([ state_variables['network'].nodes[i]['funds_requested'] for i in proposals])\n",
     "conviction_required = np.array([trigger_threshold(r, initial_values['initial_funds'], initial_values['supply'], sim_config[0]['M']['alpha'],sim_config[0]['M']) for r in funds_array])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Amount of Honey requested(as a Fraction of Funds available)')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.bar( proposals, funds_array/initial_values['initial_funds'])\n",
     "plt.title('Bar chart of Proposals Funds Requested')\n",
@@ -723,9 +775,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Amount of Conviction')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.bar( proposals, conviction_required)\n",
     "plt.title('Bar chart of Proposals Conviction Required')\n",
@@ -742,9 +817,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 55.73999999999998, 'Participant_id')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x360 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "m = len(proposals)\n",
     "n = len(participants)\n",
@@ -754,7 +852,7 @@
     "    for j_ind in range(m):\n",
     "        i = participants[i_ind]\n",
     "        j = proposals[j_ind]\n",
-    "        affinities[i_ind][j_ind] = network.edges[(i,j)]['affinity']\n",
+    "        affinities[i_ind][j_ind] = state_variables['network'].edges[(i,j)]['affinity']\n",
     "\n",
     "dims = (20, 5)\n",
     "fig, ax = plt.subplots(figsize=dims)\n",
@@ -783,41 +881,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from cadCAD.configuration import Experiment\n",
+    "# from cadCAD.configuration import Experiment\n",
     "\n",
-    "# Create configuration\n",
-    "exp = Experiment()\n",
+    "# # Create configuration\n",
+    "# exp = Experiment()\n",
     "\n",
-    "exp.append_configs(\n",
-    "    sim_configs=sim_config,\n",
-    "    initial_state=genesis_states,\n",
-    "    seeds=seeds,\n",
-    "    partial_state_update_blocks=partial_state_update_blocks\n",
-    ")"
+    "# exp.append_configs(\n",
+    "#     sim_configs=sim_config,\n",
+    "#     initial_state=state_variables,\n",
+    "#     seeds=seeds,\n",
+    "#     partial_state_update_blocks=partial_state_update_blocks\n",
+    "# )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "                  ___________    ____\n",
+      "  ________ __ ___/ / ____/   |  / __ \\\n",
+      " / ___/ __` / __  / /   / /| | / / / /\n",
+      "/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
+      "\\___/\\__,_/\\__,_/\\____/_/  |_/_____/\n",
+      "by cadCAD\n",
+      "\n",
+      "Execution Mode: local_proc\n",
+      "Configuration Count: 1\n",
+      "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (100, 11, 1, 5)\n",
+      "Execution Method: local_simulations\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "simulation() missing 1 required positional argument: 'configured_N'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-0f5e7f8620b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mrdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/repos/Aragon_Conviction_Voting/models/v3/model/run.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m()\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0msimulation\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExecutor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexec_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlocal_mode_ctx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     \u001b[0mraw_system_events\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor_field\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msessions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimulation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     44\u001b[0m     \u001b[0;31m# Result System Events DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_system_events\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/cadCAD/engine/__init__.py\u001b[0m in \u001b[0;36mexecute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    151\u001b[0m             simulations_results = self.exec_method(\n\u001b[1;32m    152\u001b[0m                 \u001b[0msim_executors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_lists\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs_structs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSimIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRunIDs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 153\u001b[0;31m                 \u001b[0mExpIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubsetIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubsetWindows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moriginal_N\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    154\u001b[0m             )\n\u001b[1;32m    155\u001b[0m             \u001b[0mfinal_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_final_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msimulations_results\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpartial_state_updates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremote_threshold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/cadCAD/engine/execution.py\u001b[0m in \u001b[0;36mlocal_simulations\u001b[0;34m(simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, SimIDs, Ns, ExpIDs, SubsetIDs, SubsetWindows, exec_method)\u001b[0m\n\u001b[1;32m    140\u001b[0m             return single_proc_exec(\n\u001b[1;32m    141\u001b[0m                 \u001b[0msimulation_execs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_lists\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfigs_structs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSimIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m                 \u001b[0mExpIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubsetIDs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSubsetWindows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexec_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m             )\n\u001b[1;32m    144\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfigs_structs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mconfig_amt\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mremote_threshold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/cadCAD/engine/execution.py\u001b[0m in \u001b[0;36msingle_proc_exec\u001b[0;34m(simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, SimIDs, Ns, ExpIDs, SubsetIDs, SubsetWindows, exec_method)\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     )\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msimulation_exec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar_dict_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msim_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubset_window\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: simulation() missing 1 required positional argument: 'configured_N'"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
-    "from model.parts.utils import *\n",
     "from model import run\n",
-    "from cadCAD import configs\n",
+    "#from cadCAD import configs\n",
     "pd.options.display.float_format = '{:.2f}'.format\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
-    "rdf = run.run(configs)\n"
+    "rdf = run.run()\n"
    ]
   },
   {
diff --git a/models/v3/Trigger_Function_Explanation.ipynb b/models/v3/Trigger_Function_Explanation.ipynb
index e33bcc9..3fc7acb 100644
--- a/models/v3/Trigger_Function_Explanation.ipynb
+++ b/models/v3/Trigger_Function_Explanation.ipynb
@@ -91,6 +91,13 @@
    "source": [
     "trigger_grid(supply_sweep, alpha_sweep)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These plots demonstrate the increasing conviction required to pass a proposal when either % of funds requested, effective supply, or alpha parameter is high. Blue areas represent lower required conviction, and red areas represent impossibly high conviction. This ensures that the reserve pool of funds are not depleted by a small number of large proposals.\n"
+   ]
   }
  ],
  "metadata": {
diff --git a/models/v3/model/__pycache__/config.cpython-37.pyc b/models/v3/model/__pycache__/config.cpython-37.pyc
index 87db61a..cfdfc33 100644
Binary files a/models/v3/model/__pycache__/config.cpython-37.pyc and b/models/v3/model/__pycache__/config.cpython-37.pyc differ
diff --git a/models/v3/model/__pycache__/run.cpython-37.pyc b/models/v3/model/__pycache__/run.cpython-37.pyc
index f0749c9..552307e 100644
Binary files a/models/v3/model/__pycache__/run.cpython-37.pyc and b/models/v3/model/__pycache__/run.cpython-37.pyc differ
diff --git a/models/v3/model/config.py b/models/v3/model/config.py
index 2a77e39..735fd0a 100644
--- a/models/v3/model/config.py
+++ b/models/v3/model/config.py
@@ -11,7 +11,6 @@ from cadCAD import configs
 from .state_variables import state_variables
 from .partial_state_update_block import partial_state_update_blocks
 from .parts.sys_params import * 
-
 from .parts.utils import * 
 
 
@@ -49,6 +48,7 @@ def get_configs():
     '''
     Function to extract the configuration information for display in a notebook.
     '''
+
     
     sim_config,state_variables,seeds,partial_state_update_blocks
     
diff --git a/models/v3/model/parts/__pycache__/sys_params.cpython-37.pyc b/models/v3/model/parts/__pycache__/sys_params.cpython-37.pyc
index 6b0f118..83e7d0b 100644
Binary files a/models/v3/model/parts/__pycache__/sys_params.cpython-37.pyc and b/models/v3/model/parts/__pycache__/sys_params.cpython-37.pyc differ
diff --git a/models/v3/model/parts/sys_params.py b/models/v3/model/parts/sys_params.py
index 1e93c03..9f5f664 100644
--- a/models/v3/model/parts/sys_params.py
+++ b/models/v3/model/parts/sys_params.py
@@ -9,10 +9,6 @@ initial_values = {
     'supply': 22392.22, # Honey total supply balance as of 8-5-2020  
 }
 
-# andrew TODO
-# <There alpha to their half life then rescale half life to our alpha >
-# Explaing how alpha works, refine alpha notebook and v3.
-
 
 # Alpha from solidity code - uint256 _decay = 9999599; // 3 days halftime. halftime_alpha = (1/2)**(1/t)
 # Half life associated with solidity code alpha (in number of blocks on xDai). 
@@ -28,22 +24,7 @@ params = {
     'min_supp': [1], #number of tokens that must be stake for a proposal to be a candidate
     'base_completion_rate': [45], # expected number of days to complete a proposals.
     'base_failure_rate': [180], # expected number of days until a proposal will fail
-    'base_engagement_rate' :[0.3], # Probability of being active on a certain day if 100% sentiment (Andrew AUDIT)
+    'base_engagement_rate' :[0.3], # Probability of being active on a certain day if 100% sentiment 
     'lowest_affinity_to_support': [0.3], # lowest affinity to required to support a proposal
 }
 
-
-# Pull out trigger stuff from v3 and add to notebook. Link to and trigger notebook. 
-# ANDREW AUDIT explanations
-
-# add metrics - think about health matrics of the system 
-# fraction of supply of use for voting (effectiev supply over total supply)
-# fraction of supply in the funding pool
-# fraction of projects that are active vs completed vs killed
-# Fraction of projects in each of the states
-# fraction of money in the different states
-
-# cadCAD model is micro founded, metrics are macro or at the insitutional level. If you are interested in insitutional dynamics, link to cryptoeconomics paper
-
-# write what metrics are: views on a complex data structure. 
-# Update differential spec
\ No newline at end of file
diff --git a/models/v3/model/run.py b/models/v3/model/run.py
index 5596232..a7136f3 100644
--- a/models/v3/model/run.py
+++ b/models/v3/model/run.py
@@ -6,7 +6,28 @@ exec_mode = ExecutionMode()
 from cadCAD.engine import Executor
 from cadCAD import configs
 
-def run(input_config=configs):
+# def run(input_config=configs):
+#     '''
+#     Definition:
+#     Run simulation
+
+#     Parameters:
+#     input_config: Optional way to pass in system configuration
+#     '''
+#     # Single
+#     exec_mode = ExecutionMode()
+#     local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)
+
+#     simulation = Executor(exec_context=local_mode_ctx, configs=input_config)
+#     raw_system_events, tensor_field, sessions = simulation.execute()
+#     # Result System Events DataFrame
+#     df = pd.DataFrame(raw_system_events)
+
+#     return df
+
+
+
+def run():
     '''
     Definition:
     Run simulation
@@ -18,7 +39,7 @@ def run(input_config=configs):
     exec_mode = ExecutionMode()
     local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)
 
-    simulation = Executor(exec_context=local_mode_ctx, configs=input_config)
+    simulation = Executor(exec_context=local_mode_ctx, configs=configs)
     raw_system_events, tensor_field, sessions = simulation.execute()
     # Result System Events DataFrame
     df = pd.DataFrame(raw_system_events)