{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "from hatch import contributions_to_token_batches, TokenBatch, Commons\n",
    "\n",
    "from cadCAD.configuration import Configuration\n",
    "\n",
    "\n",
    "def initialize_network_of_hatchers(participants: List[TokenBatch]):\n",
    "    \"\"\"\n",
    "    The role of this function is simply to fill up the directed graph.\n",
    "    Helper functions setting up the tokens etc should be defined elsewhere\n",
    "    and only provide values to this dumb function to put in the graph.\n",
    "    \"\"\"\n",
    "    network = nx.DiGraph()\n",
    "    for i, p in enumerate(participants):\n",
    "        network.add_node(i)\n",
    "        network.nodes[i]['type'] = \"participant\"\n",
    "        network.nodes[i]['holdings_vesting'] = p\n",
    "        network.nodes[i]['holdings_nonvesting'] = TokenBatch(0, 5, 5)  # just random numbers that don't mean anything if this is not a hatch token\n",
    "        network.nodes[i]['sentiment'] = np.random.rand()\n",
    "    \n",
    "    return network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sellout_asap(params, step, sL, s):\n",
    "    network = s[\"network\"]\n",
    "    how_much_to_sell = []\n",
    "    today = datetime.datetime.today()\n",
    "    for i in network.nodes:\n",
    "        node = network.nodes[i]\n",
    "        token_batch = node[\"holdings_vesting\"]\n",
    "        token_batch.current_date = token_batch.creation_date + datetime.timedelta(days=s[\"timestep\"])\n",
    "        how_much_to_sell.append(node[\"holdings_vesting\"].spendable())\n",
    "    return {\"update_network_spending\": how_much_to_sell, \"commons\": None}\n",
    "\n",
    "def update_network(params, step, sL, s, _input):\n",
    "    network = s[\"network\"]\n",
    "    participants_expenditure = _input[\"update_network_spending\"]\n",
    "    for i in network.nodes:\n",
    "        node = network.nodes[i]\n",
    "        token_batch = node[\"holdings_vesting\"]\n",
    "        token_batch.current_date = token_batch.creation_date + datetime.timedelta(days=s[\"timestep\"])\n",
    "        token_batch.spend(participants_expenditure[i])\n",
    "    return \"network\", network\n",
    "\n",
    "def update_commons(params, step, sL, s, _input):\n",
    "    commons = s[\"commons\"]\n",
    "    participants_expenditure = _input[\"update_network_spending\"]\n",
    "    for expenditure in participants_expenditure:\n",
    "        if expenditure > 0:\n",
    "            commons.burn(expenditure)\n",
    "            s[\"funding_pool\"] = commons._funding_pool\n",
    "            s[\"collateral_pool\"] = commons._collateral_pool\n",
    "            s[\"token_supply\"] = commons._token_supply\n",
    "    return \"commons\", commons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "contributions = [5e5, 5e5, 2.5e5]\n",
    "token_batches, initial_token_supply = contributions_to_token_batches(contributions, 0.1, 60)\n",
    "\n",
    "n = initialize_network_of_hatchers(token_batches)\n",
    "commons = Commons(sum(contributions), initial_token_supply)\n",
    "initial_conditions = {\n",
    "    \"network\": n,\n",
    "    \"commons\": commons,\n",
    "    \"funding_pool\": commons._funding_pool,\n",
    "    \"collateral_pool\": commons._collateral_pool,\n",
    "    \"token_supply\": commons._token_supply\n",
    "}\n",
    "\n",
    "partial_state_update_blocks = [\n",
    "    {\n",
    "        \"policies\": {\n",
    "            \"sellout_asap\": sellout_asap\n",
    "        },\n",
    "        \"variables\": {\n",
    "            \"network\": update_network,\n",
    "            \"commons\": update_commons,\n",
    "        }\n",
    "    },\n",
    "\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "\n                            __________   ____ \n          ________ __ _____/ ____/   |  / __ \\\n         / ___/ __` / __  / /   / /| | / / / /\n        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n        by BlockScience\n        \nExecution Mode: single_proc: [<cadCAD.configuration.Configuration object at 0x7fe0ad4d3220>]\nConfigurations: [<cadCAD.configuration.Configuration object at 0x7fe0ad4d3220>]\n"
    }
   ],
   "source": [
    "simulation_parameters = {\n",
    "    'T': range(150),\n",
    "    'N': 1,\n",
    "    'M': {}\n",
    "}\n",
    "\n",
    "# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n",
    "# The configurations above are then packaged into a `Configuration` object\n",
    "config = Configuration(initial_state=initial_conditions, #dict containing variable names and initial values\n",
    "                       partial_state_update_blocks=partial_state_update_blocks, #dict containing state update functions\n",
    "                       sim_config=simulation_parameters #dict containing simulation parameters\n",
    "                      )\n",
    "\n",
    "\n",
    "from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
    "exec_mode = ExecutionMode()\n",
    "exec_context = ExecutionContext(exec_mode.single_proc)\n",
    "executor = Executor(exec_context, [config]) # Pass the configuration object inside an array\n",
    "raw_result, tensor = executor.execute() # The `execute()` method returns a tuple; its first elements contains the raw results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(raw_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0ad5c1cd0>"
     },
     "metadata": {},
     "execution_count": 27
    },
    {
     "output_type": "display_data",
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      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "df.plot(\"timestep\", \"collateral_pool\", grid=True)\n",
    "df.plot(\"timestep\", \"token_supply\", grid=True)\n",
    "df.plot(\"timestep\", \"funding_pool\", grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python38264bitcadcadvirtualenv759203ea8fcd4eb59bfff73a3b8619e8",
   "display_name": "Python 3.8.2 64-bit ('cadcad': virtualenv)"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}