storage-econ-model/test_main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'N': 3, 'T': range(0, 182), 'M': {'block_time': 30, 'i_storage': 500, 'i_in_bandwidth': 500, 'i_out_bandwidth': 140, 'j_storage': 500, 'j_in_bandwidth': 400, 'j_out_bandwidth': 100, 'p_storage': 4000, 'p_in_bandwidth': 400, 'p_out_bandwidth': 600, 'k_storage': 8000, 'k_in_bandwidth': 400, 'k_out_bandwidth': 80, 'j_avail_to_i': 0.9, 'k_avail_to_i': 0.8, 'j_avail_to_k': 0.7, 'p_avail_to_i': 0.6, 'depth': 15}}, {'N': 3, 'T': range(0, 182), 'M': {'block_time': 30, 'i_storage': 500, 'i_in_bandwidth': 1234, 'i_out_bandwidth': 140, 'j_storage': 500, 'j_in_bandwidth': 400, 'j_out_bandwidth': 100, 'p_storage': 4000, 'p_in_bandwidth': 400, 'p_out_bandwidth': 600, 'k_storage': 8000, 'k_in_bandwidth': 400, 'k_out_bandwidth': 80, 'j_avail_to_i': 0.9, 'k_avail_to_i': 0.8, 'j_avail_to_k': 0.7, 'p_avail_to_i': 0.6, 'depth': 15}}]\n",
      "[{'N': 3, 'T': range(0, 182), 'M': {'block_time': 30, 'i_storage': 500, 'i_in_bandwidth': 500, 'i_out_bandwidth': 140, 'j_storage': 500, 'j_in_bandwidth': 400, 'j_out_bandwidth': 100, 'p_storage': 4000, 'p_in_bandwidth': 400, 'p_out_bandwidth': 600, 'k_storage': 8000, 'k_in_bandwidth': 400, 'k_out_bandwidth': 80, 'j_avail_to_i': 0.9, 'k_avail_to_i': 0.8, 'j_avail_to_k': 0.7, 'p_avail_to_i': 0.6, 'depth': 15}}, {'N': 3, 'T': range(0, 182), 'M': {'block_time': 30, 'i_storage': 500, 'i_in_bandwidth': 1234, 'i_out_bandwidth': 140, 'j_storage': 500, 'j_in_bandwidth': 400, 'j_out_bandwidth': 100, 'p_storage': 4000, 'p_in_bandwidth': 400, 'p_out_bandwidth': 600, 'k_storage': 8000, 'k_in_bandwidth': 400, 'k_out_bandwidth': 80, 'j_avail_to_i': 0.9, 'k_avail_to_i': 0.8, 'j_avail_to_k': 0.7, 'p_avail_to_i': 0.6, 'depth': 15}}]\n"
     ]
    }
   ],
   "source": [
    "from src.sim import run\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                            __________   ____ \n",
      "          ________ __ _____/ ____/   |  / __ \\\n",
      "         / ___/ __` / __  / /   / /| | / / / /\n",
      "        / /__/ /_/ / /_/ / /___/ ___ |/ /_/ / \n",
      "        \\___/\\__,_/\\__,_/\\____/_/  |_/_____/  \n",
      "        by BlockScience\n",
      "        \n",
      "Execution Mode: multi_proc: [<cadCAD.configuration.Configuration object at 0x00000255875CDA58>, <cadCAD.configuration.Configuration object at 0x00000255875CDA90>]\n",
      "Configurations: [<cadCAD.configuration.Configuration object at 0x00000255875CDA58>, <cadCAD.configuration.Configuration object at 0x00000255875CDA90>]\n"
     ]
    }
   ],
   "source": [
    "experiments = run.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'src.model.utils.Event'>: {'sending_node': 'i', 'receiving_node': 'j', 'hash_file': 2, 'size': 3, 'block_init': 4, 'reward': 5, 'block_end': None, 'storing_node': None, 'proving_node': None, 'time': None}\n"
     ]
    }
   ],
   "source": [
    "print(experiments.dataset[0]['event'][4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'src.model.utils.History'>: {'active': [<src.model.utils.Event object at 0x000002558AF62E10>], 'routed': [], 'solved': []}\n"
     ]
    }
   ],
   "source": [
    "print(experiments.dataset[0]['history'][4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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