Experiment_results_block2_sigma500_short_singlerun.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Hydra Multi-Asset Monte Carlo Experiment with varying trade sizes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testcase Definition\n",
    "\n",
    "#### Simulation Settings\n",
    "- There are 100 timesteps for each simulation run\n",
    "- Each testcase has 10 MC runs with different randomized variables\n",
    "- Block 2 randomizes trade sizes \n",
    "\n",
    "\n",
    "\n",
    "#### Agent Activities - Action Sequence\n",
    "\n",
    "There are two agents acting during the simulation. The trading agent performs \"R-for-R\" trades of varying direction and size over most simulation timesteps. The liquidity agent provides liquidity to the pool at timestep 10 and removes the liquidity from the pool at timestep 90.\n",
    "\n",
    "##### Trading agent\n",
    "\n",
    "- One Agent performs random \"R-for-R\" trades during the simulation. \n",
    "- The direction of the trade is a random coin flip for each timestep.\n",
    "- The size of the trade is drawn from $\\mathcal{N}(\\mu, \\sigma)$ for each timestep.\n",
    "- The agent does not act at timestep 10 nor timestep 90.\n",
    "\n",
    "##### Liquidity agent\n",
    "\n",
    "- One Agent provides liquidity in Risk asset \"i\" to the pool \n",
    "- He provides the liquidity at timestep 10\n",
    "- He removes the liquidity at timestep 90\n",
    "- He does not act at other timesteps\n",
    "\n",
    "#### Block 2:\n",
    "| #   | Run | a |  $\\mu_i$  | $\\sigma_i$ |  $\\mu_j$  | $\\sigma_j$ | $\\mu$   | $\\sigma$ | Total runs |\n",
    "| --- |:---:|:-------:|:---------:|:----------:|:---------:|:----------:| ------- | -------- | ---------- |\n",
    "| 1   | 01 - 10    |   0.5   | $1.000.000$ |    $0$     | $1.000.000$ |    $0$     | $1.000$ | $50$     | $10$       |\n",
    "| 2   | 11 - 20    |   0.5   | $1.000.000$ |    $0$     | $1.000.000$ |    $0$     | $1.000$ | $500$    | $10$        |\n",
    "| 3   | 21 - 30    |    1    | $1.000.000$ |    $0$     | $1.000.000$ |    $0$     | $1.000$ | $50$     | $10$        |\n",
    "| 4  |  31 - 40   |    1    | $1.000.000$ |    $0$     | $1.000.000$ |    $0$     | $1.000$ | $500$    | $10$        |\n",
    "| 5  | 41 - 50    |   1.5   | $1.000.000$ |    $0$     | $1.000.000$ |    $0$     | $1.000$ | $50$     | $10$        |\n",
    "| 6  | 51 - 60    |   1.5   | $1.000.000$ |    $0$     | $1.000.000$ |    $0$     | $1.000$ | $500$    | $10$        |\n",
    "\n",
    "All tests in block 2 are performed as $10$ Monte-Carlo runs for the given parameters which results in $60$ runs in total.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model.config_wrapper import ConfigWrapper\n",
    "\n",
    "from model import run_wrapper\n",
    "from cadCAD import configs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running file: run.py\n",
      "running file: asset_utils.py\n",
      "end of file: asset_utils.py\n",
      "running file: sim_setup.py\n",
      "end of file: sim_setup.py\n",
      "end of file: run.py\n"
     ]
    }
   ],
   "source": [
    "# Dependences\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Experiments\n",
    "from model import run\n",
    "from model.parts.utils import *\n",
    "from model.plot_utils import *\n",
    "\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# df = run.run()\n",
    "# rdf = run.postprocessing(df)\n",
    "# rdf = df # debugging substeps\n",
    "# pd.set_option(\"mode.chained_assignment\", None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "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, 15, 3, 24)\n",
      "Execution Method: local_simulations\n",
      "SimIDs   : [0, 0, 0]\n",
      "SubsetIDs: [0, 1, 2]\n",
      "Ns       : [0, 1, 2]\n",
      "ExpIDs   : [0, 0, 0]\n",
      "Execution Mode: parallelized\n",
      "{'q_sold': 2498, 'ri_sold': 1249, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1249\n",
      " R removed from pool of  j  =  4943.14135172253\n",
      "{'q_sold': 1862, 'ri_sold': 3724, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3724\n",
      " R removed from pool of  i  =  942.3211207670392\n",
      "{'q_sold': 2648, 'ri_sold': 1324, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1324\n",
      " R removed from pool of  j  =  5222.99407985562\n",
      "{'q_sold': 3524, 'ri_sold': 7048, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  7048\n",
      " R removed from pool of  i  =  1782.7556610940956\n",
      "{'q_sold': 1766, 'ri_sold': 883, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  883\n",
      " R removed from pool of  j  =  3505.692192252376\n",
      "{'q_sold': 1766, 'ri_sold': 3532, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3532\n",
      " R removed from pool of  i  =  889.5985089257592\n",
      "{'q_sold': 3580, 'ri_sold': 7160, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  7160\n",
      " R removed from pool of  i  =  1780.6694380386034\n",
      "{'q_sold': 2768, 'ri_sold': 1384, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1384\n",
      " R removed from pool of  j  =  5575.480101840687\n",
      "{'q_sold': 1532, 'ri_sold': 3064, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3064\n",
      " R removed from pool of  i  =  762.8318314308999\n",
      "{'q_sold': 2544, 'ri_sold': 1272, 'ri_deposit': 50000, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'AddLiquidity', 'agent_id': 2, 'asset_id': 'i', 'direction': '', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'N/A'}\n",
      "{'q_sold': 1538, 'ri_sold': 769, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  769\n",
      " R removed from pool of  j  =  3088.7920998225454\n",
      "{'q_sold': 1536, 'ri_sold': 768, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  768\n",
      " R removed from pool of  j  =  3062.576521699433\n",
      "{'q_sold': 2242, 'ri_sold': 1121, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1121\n",
      " R removed from pool of  j  =  4430.909845791408\n",
      "{'q_sold': 88, 'ri_sold': 44, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  44\n",
      " R removed from pool of  j  =  172.97246643994004\n",
      "{'q_sold': 276, 'ri_sold': 138, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  138\n",
      " R removed from pool of  j  =  542.0449363953667\n",
      "{'q_sold': 1438, 'ri_sold': 719, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  719\n",
      " R removed from pool of  j  =  2812.90329130413\n",
      "{'q_sold': 988, 'ri_sold': 1976, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1976\n",
      " R removed from pool of  i  =  505.5828074084129\n",
      "{'q_sold': 2316, 'ri_sold': 4632, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4632\n",
      " R removed from pool of  i  =  1175.905799495522\n",
      "{'q_sold': 1092, 'ri_sold': 546, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  546\n",
      " R removed from pool of  j  =  2157.062155554653\n",
      "{'q_sold': 588, 'ri_sold': 1176, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1176\n",
      " R removed from pool of  i  =  298.0166718130931\n",
      "{'q_sold': 3466, 'ri_sold': 6932, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6932\n",
      " R removed from pool of  i  =  1739.9814127760474\n",
      "{'q_sold': 1776, 'ri_sold': 888, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  888\n",
      " R removed from pool of  j  =  3551.8787304523867\n",
      "{'q_sold': 2068, 'ri_sold': 4136, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4136\n",
      " R removed from pool of  i  =  1033.3279145192355\n",
      "{'q_sold': 576, 'ri_sold': 1152, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1152\n",
      " R removed from pool of  i  =  286.0327784740366\n",
      "{'q_sold': 1456, 'ri_sold': 2912, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2912\n",
      " R removed from pool of  i  =  719.6026871486101\n",
      "{'q_sold': 2112, 'ri_sold': 4224, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4224\n",
      " R removed from pool of  i  =  1035.1814347753534\n",
      "{'q_sold': 850, 'ri_sold': 1700, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1700\n",
      " R removed from pool of  i  =  413.7651828962844\n",
      "{'q_sold': 2376, 'ri_sold': 4752, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4752\n",
      " R removed from pool of  i  =  1148.0080525418743\n",
      "{'q_sold': 1400, 'ri_sold': 2800, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2800\n",
      " R removed from pool of  i  =  670.5767191760242\n",
      "{'q_sold': 1710, 'ri_sold': 3420, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3420\n",
      " R removed from pool of  i  =  813.2421059801709\n",
      "{'q_sold': 1400, 'ri_sold': 2800, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2800\n",
      " R removed from pool of  i  =  661.0972441389458\n",
      "{'q_sold': 3854, 'ri_sold': 7708, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  7708\n",
      " R removed from pool of  i  =  1798.3268329645507\n",
      "{'q_sold': 1988, 'ri_sold': 3976, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3976\n",
      " R removed from pool of  i  =  915.453346307273\n",
      "{'q_sold': 944, 'ri_sold': 1888, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1888\n",
      " R removed from pool of  i  =  431.84563818364404\n",
      "{'q_sold': 2824, 'ri_sold': 1412, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1412\n",
      " R removed from pool of  j  =  6143.666595511953\n",
      "{'q_sold': 780, 'ri_sold': 390, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  390\n",
      " R removed from pool of  j  =  1682.0227744234726\n",
      "{'q_sold': 2210, 'ri_sold': 4420, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4420\n",
      " R removed from pool of  i  =  1021.6806418604683\n",
      "{'q_sold': 42, 'ri_sold': 84, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  84\n",
      " R removed from pool of  i  =  19.31818049098365\n",
      "{'q_sold': 672, 'ri_sold': 1344, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1344\n",
      " R removed from pool of  i  =  308.59516411821824\n",
      "{'q_sold': 2198, 'ri_sold': 1099, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1099\n",
      " R removed from pool of  j  =  4767.952730307006\n",
      "{'q_sold': 2740, 'ri_sold': 1370, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1370\n",
      " R removed from pool of  j  =  5872.630011951784\n",
      "{'q_sold': 2172, 'ri_sold': 4344, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4344\n",
      " R removed from pool of  i  =  1015.1452629073756\n",
      "{'q_sold': 1886, 'ri_sold': 3772, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3772\n",
      " R removed from pool of  i  =  873.4157657832839\n",
      "{'q_sold': 1700, 'ri_sold': 3400, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3400\n",
      " R removed from pool of  i  =  780.9425576347858\n",
      "{'q_sold': 522, 'ri_sold': 261, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  261\n",
      " R removed from pool of  j  =  1139.2126076727873\n",
      "{'q_sold': 1282, 'ri_sold': 2564, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2564\n",
      " R removed from pool of  i  =  586.4892704453086\n",
      "{'q_sold': 1540, 'ri_sold': 3080, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3080\n",
      " R removed from pool of  i  =  700.0806110248668\n",
      "{'q_sold': 3058, 'ri_sold': 6116, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6116\n",
      " R removed from pool of  i  =  1375.989045121707\n",
      "{'q_sold': 2344, 'ri_sold': 4688, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4688\n",
      " R removed from pool of  i  =  1042.1508131484734\n",
      "{'q_sold': 238, 'ri_sold': 119, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  119\n",
      " R removed from pool of  j  =  537.770301243756\n",
      "{'q_sold': 2326, 'ri_sold': 4652, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4652\n",
      " R removed from pool of  i  =  1024.7659266074188\n",
      "{'q_sold': 1616, 'ri_sold': 3232, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3232\n",
      " R removed from pool of  i  =  705.82395303878\n",
      "{'q_sold': 1324, 'ri_sold': 2648, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2648\n",
      " R removed from pool of  i  =  574.5798244641628\n",
      "{'q_sold': 2612, 'ri_sold': 5224, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5224\n",
      " R removed from pool of  i  =  1123.8659865924856\n",
      "{'q_sold': 3032, 'ri_sold': 6064, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6064\n",
      " R removed from pool of  i  =  1288.7267446451588\n",
      "{'q_sold': 2932, 'ri_sold': 1466, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1466\n",
      " R removed from pool of  j  =  6891.956638929434\n",
      "{'q_sold': 1162, 'ri_sold': 2324, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2324\n",
      " R removed from pool of  i  =  496.78479073301423\n",
      "{'q_sold': 1692, 'ri_sold': 3384, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3384\n",
      " R removed from pool of  i  =  718.9230413991027\n",
      "{'q_sold': 2332, 'ri_sold': 1166, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1166\n",
      " R removed from pool of  j  =  5475.98896063026\n",
      "{'q_sold': 2976, 'ri_sold': 5952, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5952\n",
      " R removed from pool of  i  =  1266.7069705225294\n",
      "{'q_sold': 1522, 'ri_sold': 761, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  761\n",
      " R removed from pool of  j  =  3584.9619175179396\n",
      "{'q_sold': 1816, 'ri_sold': 3632, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3632\n",
      " R removed from pool of  i  =  770.9459753873525\n",
      "{'q_sold': 894, 'ri_sold': 1788, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1788\n",
      " R removed from pool of  i  =  377.3181842860067\n",
      "{'q_sold': 804, 'ri_sold': 1608, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1608\n",
      " R removed from pool of  i  =  338.09653853892814\n",
      "{'q_sold': 2814, 'ri_sold': 1407, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1407\n",
      " R removed from pool of  j  =  6655.436842401279\n",
      "{'q_sold': 3358, 'ri_sold': 1679, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1679\n",
      " R removed from pool of  j  =  7818.680560015608\n",
      "{'q_sold': 1928, 'ri_sold': 3856, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3856\n",
      " R removed from pool of  i  =  831.6084196603624\n",
      "{'q_sold': 3004, 'ri_sold': 6008, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6008\n",
      " R removed from pool of  i  =  1281.9344642226351\n",
      "{'q_sold': 2362, 'ri_sold': 1181, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1181\n",
      " R removed from pool of  j  =  5537.779518709751\n",
      "{'q_sold': 1356, 'ri_sold': 2712, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2712\n",
      " R removed from pool of  i  =  580.1358104590327\n",
      "{'q_sold': 2362, 'ri_sold': 4724, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R added to pool of  j  =  4724\n",
      " R removed from pool of  i  =  1002.4430488174548\n",
      "{'q_sold': 3540, 'ri_sold': 7080, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  7080\n",
      " R removed from pool of  i  =  1483.4496712385444\n",
      "{'q_sold': 1966, 'ri_sold': 983, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  983\n",
      " R removed from pool of  j  =  4703.522302528145\n",
      "{'q_sold': 3566, 'ri_sold': 7132, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  7132\n",
      " R removed from pool of  i  =  1486.677757042693\n",
      "{'q_sold': -618, 'ri_sold': -309, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  -309\n",
      " R removed from pool of  j  =  -1496.083849835908\n",
      "{'q_sold': 2822, 'ri_sold': 5644, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5644\n",
      " R removed from pool of  i  =  1156.9110203745076\n",
      "{'q_sold': 2088, 'ri_sold': 1044, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1044\n",
      " R removed from pool of  j  =  5096.129284389783\n",
      "{'q_sold': 1702, 'ri_sold': 3404, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3404\n",
      " R removed from pool of  i  =  698.5973085017176\n",
      "{'q_sold': 2092, 'ri_sold': 1046, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1046\n",
      " R removed from pool of  j  =  5087.643589272164\n",
      "{'q_sold': 14, 'ri_sold': 28, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  28\n",
      " R removed from pool of  i  =  5.78769717074465\n",
      "{'q_sold': 1782, 'ri_sold': 3564, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3564\n",
      " R removed from pool of  i  =  733.8849702854641\n",
      "{'q_sold': 2358, 'ri_sold': 1179, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1179\n",
      " R removed from pool of  j  =  5712.537598673487\n",
      "{'q_sold': 3478, 'ri_sold': 1739, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1739\n",
      " R removed from pool of  j  =  8300.640330755617\n",
      "{'q_sold': 1482, 'ri_sold': 2964, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2964\n",
      " R removed from pool of  i  =  624.5205741917016\n",
      "{'q_sold': 1192, 'ri_sold': 2384, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2384\n",
      " R removed from pool of  i  =  499.4372983366484\n",
      "{'q_sold': 1500, 'ri_sold': 3000, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3000\n",
      " R removed from pool of  i  =  624.8756817203248\n",
      "{'q_sold': 2916, 'ri_sold': 5832, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5832\n",
      " R removed from pool of  i  =  1203.3819176177494\n",
      "{'q_sold': 2330, 'ri_sold': 4660, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4660\n",
      " R removed from pool of  i  =  950.9067290468374\n",
      "{'q_sold': 1472, 'ri_sold': 2944, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2944\n",
      " R removed from pool of  i  =  595.9409657929791\n",
      "50065.995822158664\n",
      "{'q_sold': 2514, 'ri_sold': 1257, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'RemoveLiquidity', 'agent_id': 2, 'asset_id': 'i', 'direction': '', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'N/A', 'UNI_burn': 50065.995822158664}\n",
      "Ri_plus =  968276.0161558609\n",
      "{'q_sold': 2098, 'ri_sold': 4196, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4196\n",
      " R removed from pool of  i  =  842.9966490752995\n",
      "{'q_sold': 2970, 'ri_sold': 1485, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1485\n",
      " R removed from pool of  j  =  7366.699619094143\n",
      "{'q_sold': 1298, 'ri_sold': 2596, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2596\n",
      " R removed from pool of  i  =  525.9588022369426\n",
      "{'q_sold': 1674, 'ri_sold': 837, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  837\n",
      " R removed from pool of  j  =  4124.499420945067\n",
      "{'q_sold': 1608, 'ri_sold': 804, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  804\n",
      " R removed from pool of  j  =  3928.0171410392504\n",
      "{'q_sold': 538, 'ri_sold': 1076, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1076\n",
      " R removed from pool of  i  =  220.91119911440182\n",
      "{'q_sold': 2298, 'ri_sold': 1149, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1149\n",
      " R removed from pool of  j  =  5569.543400126509\n",
      "{'q_sold': 2262, 'ri_sold': 1131, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1131\n",
      " R removed from pool of  j  =  5417.850519106025\n",
      "{'q_sold': 2006, 'ri_sold': 1003, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1003\n",
      " R removed from pool of  j  =  4752.091729989508\n",
      "{'q_sold': 1766, 'ri_sold': 3532, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3532\n",
      " R removed from pool of  i  =  746.4675963150803\n",
      "{'q_sold': 586, 'ri_sold': 1172, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1172\n",
      " R removed from pool of  i  =  293.96202588942833\n",
      "{'q_sold': 1580, 'ri_sold': 3160, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3160\n",
      " R removed from pool of  i  =  788.3210405415157\n",
      "{'q_sold': 1658, 'ri_sold': 3316, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3316\n",
      " R removed from pool of  i  =  820.5988268777728\n",
      "{'q_sold': 1198, 'ri_sold': 2396, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2396\n",
      " R removed from pool of  i  =  588.7447195135755\n",
      "{'q_sold': 1840, 'ri_sold': 920, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  920\n",
      " R removed from pool of  j  =  3737.874986491981\n",
      "{'q_sold': 2406, 'ri_sold': 1203, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1203\n",
      " R removed from pool of  j  =  4835.896466928185\n",
      "{'q_sold': 3888, 'ri_sold': 1944, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1944\n",
      " R removed from pool of  j  =  7693.072013758239\n",
      "{'q_sold': 2176, 'ri_sold': 1088, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1088\n",
      " R removed from pool of  j  =  4241.41338549077\n",
      "{'q_sold': 2258, 'ri_sold': 4516, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4516\n",
      " R removed from pool of  i  =  1158.0355055065593\n",
      "{'q_sold': 1926, 'ri_sold': 963, 'ri_deposit': 50000, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'AddLiquidity', 'agent_id': 2, 'asset_id': 'i', 'direction': '', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'N/A'}\n",
      "{'q_sold': 82, 'ri_sold': 41, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  41\n",
      " R removed from pool of  j  =  160.77115187537856\n",
      "{'q_sold': 1974, 'ri_sold': 987, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  987\n",
      " R removed from pool of  j  =  3850.8874689600198\n",
      "{'q_sold': 2062, 'ri_sold': 4124, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4124\n",
      " R removed from pool of  i  =  1056.6396296827588\n",
      "{'q_sold': 4464, 'ri_sold': 8928, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  8928\n",
      " R removed from pool of  i  =  2250.6481163811404\n",
      "{'q_sold': 1808, 'ri_sold': 3616, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3616\n",
      " R removed from pool of  i  =  897.4992906239349\n",
      "{'q_sold': 2302, 'ri_sold': 1151, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1151\n",
      " R removed from pool of  j  =  4631.5335206282325\n",
      "{'q_sold': 1966, 'ri_sold': 983, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  983\n",
      " R removed from pool of  j  =  3913.970731515088\n",
      "{'q_sold': 832, 'ri_sold': 1664, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1664\n",
      " R removed from pool of  i  =  419.0817847494036\n",
      "{'q_sold': 3144, 'ri_sold': 1572, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1572\n",
      " R removed from pool of  j  =  6206.5426434119\n",
      "{'q_sold': 2752, 'ri_sold': 1376, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1376\n",
      " R removed from pool of  j  =  5354.844731649384\n",
      "{'q_sold': 2792, 'ri_sold': 5584, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5584\n",
      " R removed from pool of  i  =  1434.4739418886602\n",
      "{'q_sold': 1092, 'ri_sold': 2184, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2184\n",
      " R removed from pool of  i  =  555.6201755502261\n",
      "{'q_sold': 3404, 'ri_sold': 6808, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6808\n",
      " R removed from pool of  i  =  1712.7555786063895\n",
      "{'q_sold': 600, 'ri_sold': 300, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  300\n",
      " R removed from pool of  j  =  1200.7914978482295\n",
      "{'q_sold': 2588, 'ri_sold': 1294, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1294\n",
      " R removed from pool of  j  =  5138.864742968115\n",
      "{'q_sold': 4192, 'ri_sold': 2096, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  2096\n",
      " R removed from pool of  j  =  8186.66923636687\n",
      "{'q_sold': 1010, 'ri_sold': 505, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  505\n",
      " R removed from pool of  j  =  1947.576185752987\n",
      "{'q_sold': 1434, 'ri_sold': 717, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  717\n",
      " R removed from pool of  j  =  2748.842495635152\n",
      "{'q_sold': 2100, 'ri_sold': 4200, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4200\n",
      " R removed from pool of  i  =  1093.5153268948197\n",
      "{'q_sold': 1498, 'ri_sold': 2996, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2996\n",
      " R removed from pool of  i  =  773.0046458751895\n",
      "{'q_sold': 450, 'ri_sold': 900, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  900\n",
      " R removed from pool of  i  =  231.0782244980801\n",
      "{'q_sold': 2070, 'ri_sold': 4140, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4140\n",
      " R removed from pool of  i  =  1056.2864311989397\n",
      "{'q_sold': 938, 'ri_sold': 1876, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1876\n",
      " R removed from pool of  i  =  475.06717647193\n",
      "{'q_sold': 2474, 'ri_sold': 4948, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4948\n",
      " R removed from pool of  i  =  1242.4411049652845\n",
      "{'q_sold': 1082, 'ri_sold': 541, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  541\n",
      " R removed from pool of  j  =  2161.982746525202\n",
      "{'q_sold': 3550, 'ri_sold': 7100, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  7100\n",
      " R removed from pool of  i  =  1765.8855996666243\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'q_sold': 1218, 'ri_sold': 2436, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2436\n",
      " R removed from pool of  i  =  598.8009990241844\n",
      "{'q_sold': 1678, 'ri_sold': 3356, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3356\n",
      " R removed from pool of  i  =  819.1246213256381\n",
      "{'q_sold': 2814, 'ri_sold': 1407, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1407\n",
      " R removed from pool of  j  =  5747.683277689968\n",
      "{'q_sold': 770, 'ri_sold': 385, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  385\n",
      " R removed from pool of  j  =  1558.7413491117768\n",
      "{'q_sold': 2228, 'ri_sold': 1114, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1114\n",
      " R removed from pool of  j  =  4476.810646338039\n",
      "{'q_sold': 3308, 'ri_sold': 1654, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1654\n",
      " R removed from pool of  j  =  6556.694822927238\n",
      "{'q_sold': 394, 'ri_sold': 197, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  197\n",
      " R removed from pool of  j  =  773.8563114812132\n",
      "{'q_sold': 2186, 'ri_sold': 1093, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1093\n",
      " R removed from pool of  j  =  4266.543769885786\n",
      "{'q_sold': 2260, 'ri_sold': 4520, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4520\n",
      " R removed from pool of  i  =  1157.5640497358982\n",
      "{'q_sold': 2782, 'ri_sold': 1391, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1391\n",
      " R removed from pool of  j  =  5425.339242838789\n",
      "{'q_sold': 764, 'ri_sold': 382, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  382\n",
      " R removed from pool of  j  =  1477.0892809394281\n",
      "{'q_sold': 680, 'ri_sold': 1360, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1360\n",
      " R removed from pool of  i  =  351.7704938664101\n",
      "{'q_sold': 2522, 'ri_sold': 5044, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5044\n",
      " R removed from pool of  i  =  1294.2237265247386\n",
      "{'q_sold': 2298, 'ri_sold': 4596, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4596\n",
      " R removed from pool of  i  =  1165.1610035710037\n",
      "{'q_sold': 2252, 'ri_sold': 4504, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4504\n",
      " R removed from pool of  i  =  1129.0107481379528\n",
      "{'q_sold': 2348, 'ri_sold': 4696, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4696\n",
      " R removed from pool of  i  =  1163.8475199566456\n",
      "{'q_sold': 1320, 'ri_sold': 2640, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2640\n",
      " R removed from pool of  i  =  648.4204585945699\n",
      "{'q_sold': 2234, 'ri_sold': 1117, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1117\n",
      " R removed from pool of  j  =  4537.20040732401\n",
      "{'q_sold': 2294, 'ri_sold': 1147, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1147\n",
      " R removed from pool of  j  =  4606.939863584703\n",
      "{'q_sold': 1286, 'ri_sold': 643, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  643\n",
      " R removed from pool of  j  =  2559.847824986442\n",
      "{'q_sold': 3866, 'ri_sold': 7732, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  7732\n",
      " R removed from pool of  i  =  1929.8274014025228\n",
      "{'q_sold': 2474, 'ri_sold': 1237, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1237\n",
      " R removed from pool of  j  =  4973.1050227478845\n",
      "{'q_sold': 810, 'ri_sold': 1620, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1620\n",
      " R removed from pool of  i  =  404.62339368951507\n",
      "{'q_sold': 2658, 'ri_sold': 1329, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1329\n",
      " R removed from pool of  j  =  5296.749453145312\n",
      "{'q_sold': 1026, 'ri_sold': 2052, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2052\n",
      " R removed from pool of  i  =  516.9349849065766\n",
      "{'q_sold': 2788, 'ri_sold': 1394, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1394\n",
      " R removed from pool of  j  =  5509.779933669954\n",
      "{'q_sold': 3160, 'ri_sold': 1580, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1580\n",
      " R removed from pool of  j  =  6154.662337435759\n",
      "{'q_sold': 1180, 'ri_sold': 590, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  590\n",
      " R removed from pool of  j  =  2274.0861067910446\n",
      "{'q_sold': 2964, 'ri_sold': 5928, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5928\n",
      " R removed from pool of  i  =  1530.9544336078689\n",
      "{'q_sold': 2414, 'ri_sold': 1207, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1207\n",
      " R removed from pool of  j  =  4680.968204897596\n",
      "{'q_sold': 2824, 'ri_sold': 1412, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1412\n",
      " R removed from pool of  j  =  5406.844434913597\n",
      "{'q_sold': 3898, 'ri_sold': 1949, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1949\n",
      " R removed from pool of  j  =  7343.309892269317\n",
      "{'q_sold': 1756, 'ri_sold': 3512, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3512\n",
      " R removed from pool of  i  =  936.6755690902937\n",
      "{'q_sold': 1248, 'ri_sold': 2496, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2496\n",
      " R removed from pool of  i  =  660.6217647755984\n",
      "{'q_sold': 1112, 'ri_sold': 2224, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2224\n",
      " R removed from pool of  i  =  585.1124374244828\n",
      "{'q_sold': 1186, 'ri_sold': 2372, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2372\n",
      " R removed from pool of  i  =  620.4284436993767\n",
      "{'q_sold': 1924, 'ri_sold': 962, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  962\n",
      " R removed from pool of  j  =  3671.830053359503\n",
      "{'q_sold': 2342, 'ri_sold': 4684, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4684\n",
      " R removed from pool of  i  =  1225.6168494685553\n",
      "{'q_sold': 2278, 'ri_sold': 4556, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4556\n",
      " R removed from pool of  i  =  1178.3134925323538\n",
      "{'q_sold': 2828, 'ri_sold': 5656, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5656\n",
      " R removed from pool of  i  =  1444.2078203374986\n",
      "{'q_sold': 2014, 'ri_sold': 4028, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4028\n",
      " R removed from pool of  i  =  1016.1747837653384\n",
      "{'q_sold': 3454, 'ri_sold': 1727, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1727\n",
      " R removed from pool of  j  =  6821.818884574226\n",
      "{'q_sold': 1736, 'ri_sold': 868, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  868\n",
      " R removed from pool of  j  =  3385.3320684358478\n",
      "{'q_sold': 4722, 'ri_sold': 9444, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  9444\n",
      " R removed from pool of  i  =  2403.2533719299827\n",
      "{'q_sold': 2626, 'ri_sold': 1313, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1313\n",
      " R removed from pool of  j  =  5187.216583437636\n",
      "{'q_sold': 1144, 'ri_sold': 572, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  572\n",
      " R removed from pool of  j  =  2238.9696594525594\n",
      "{'q_sold': 930, 'ri_sold': 465, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  465\n",
      " R removed from pool of  j  =  1810.9422424943186\n",
      "{'q_sold': 2484, 'ri_sold': 4968, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4968\n",
      " R removed from pool of  i  =  1270.6252099361736\n",
      "{'q_sold': 1778, 'ri_sold': 889, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  889\n",
      " R removed from pool of  j  =  3482.365889279172\n",
      "{'q_sold': 2716, 'ri_sold': 5432, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5432\n",
      " R removed from pool of  i  =  1383.3527916215826\n",
      "{'q_sold': 2474, 'ri_sold': 4948, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4948\n",
      " R removed from pool of  i  =  1243.9285438738298\n",
      "{'q_sold': 1928, 'ri_sold': 964, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  964\n",
      " R removed from pool of  j  =  3839.80663950101\n",
      "{'q_sold': 1154, 'ri_sold': 2308, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2308\n",
      " R removed from pool of  i  =  580.5323828828987\n",
      "49924.846237721744\n",
      "{'q_sold': 486, 'ri_sold': 243, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'RemoveLiquidity', 'agent_id': 2, 'asset_id': 'i', 'direction': '', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'N/A', 'UNI_burn': 49924.846237721744}\n",
      "Ri_plus =  986478.7875024456\n",
      "{'q_sold': 1554, 'ri_sold': 777, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  777\n",
      " R removed from pool of  j  =  3085.990256692865\n",
      "{'q_sold': 2858, 'ri_sold': 5716, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5716\n",
      " R removed from pool of  i  =  1434.4753446625546\n",
      "{'q_sold': 2216, 'ri_sold': 1108, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1108\n",
      " R removed from pool of  j  =  4422.251838698401\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'q_sold': 756, 'ri_sold': 1512, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1512\n",
      " R removed from pool of  i  =  380.2120374905644\n",
      "{'q_sold': 2174, 'ri_sold': 4348, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4348\n",
      " R removed from pool of  i  =  1085.391639059526\n",
      "{'q_sold': 2386, 'ri_sold': 4772, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4772\n",
      " R removed from pool of  i  =  1177.8013297870057\n",
      "{'q_sold': 1118, 'ri_sold': 2236, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2236\n",
      " R removed from pool of  i  =  547.1093330418225\n",
      "{'q_sold': 2154, 'ri_sold': 4308, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4308\n",
      " R removed from pool of  i  =  1045.6298773932504\n",
      "{'q_sold': 2060, 'ri_sold': 1030, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1030\n",
      " R removed from pool of  j  =  4243.9400429481175\n",
      "{'q_sold': 858, 'ri_sold': 429, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  429\n",
      " R removed from pool of  j  =  1754.6159833987476\n",
      "{'q_sold': 2358, 'ri_sold': 4716, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4716\n",
      " R removed from pool of  i  =  1177.3110222422983\n",
      "{'q_sold': 2562, 'ri_sold': 1281, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1281\n",
      " R removed from pool of  j  =  5128.569517608965\n",
      "{'q_sold': 3084, 'ri_sold': 6168, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6168\n",
      " R removed from pool of  i  =  1538.5378155827057\n",
      "{'q_sold': 3054, 'ri_sold': 6108, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6108\n",
      " R removed from pool of  i  =  1499.3719621979399\n",
      "{'q_sold': 624, 'ri_sold': 1248, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1248\n",
      " R removed from pool of  i  =  303.4434173393529\n",
      "{'q_sold': 1064, 'ri_sold': 2128, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2128\n",
      " R removed from pool of  i  =  515.1590356209781\n",
      "{'q_sold': 2516, 'ri_sold': 1258, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1258\n",
      " R removed from pool of  j  =  5176.034211397171\n",
      "{'q_sold': 2514, 'ri_sold': 1257, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1257\n",
      " R removed from pool of  j  =  5103.420420501148\n",
      "{'q_sold': 2516, 'ri_sold': 1258, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1258\n",
      " R removed from pool of  j  =  5040.292451580521\n",
      "{'q_sold': 5854, 'ri_sold': 2927, 'ri_deposit': 50000, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'AddLiquidity', 'agent_id': 2, 'asset_id': 'i', 'direction': '', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'N/A'}\n",
      "{'q_sold': 2572, 'ri_sold': 1286, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1286\n",
      " R removed from pool of  j  =  5084.711228380329\n",
      "{'q_sold': 3136, 'ri_sold': 6272, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6272\n",
      " R removed from pool of  i  =  1583.836225108942\n",
      "{'q_sold': 2956, 'ri_sold': 1478, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1478\n",
      " R removed from pool of  j  =  5856.076604239526\n",
      "{'q_sold': 2652, 'ri_sold': 5304, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5304\n",
      " R removed from pool of  i  =  1339.6237213455606\n",
      "{'q_sold': 1686, 'ri_sold': 3372, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3372\n",
      " R removed from pool of  i  =  842.0956444051117\n",
      "{'q_sold': 2760, 'ri_sold': 5520, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5520\n",
      " R removed from pool of  i  =  1362.7607786960434\n",
      "{'q_sold': 1228, 'ri_sold': 614, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  614\n",
      " R removed from pool of  j  =  2496.8052036386216\n",
      "{'q_sold': 1764, 'ri_sold': 3528, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3528\n",
      " R removed from pool of  i  =  866.4366658022627\n",
      "{'q_sold': 1516, 'ri_sold': 758, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  758\n",
      " R removed from pool of  j  =  3088.21311303298\n",
      "{'q_sold': 2082, 'ri_sold': 4164, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4164\n",
      " R removed from pool of  i  =  1020.6412269501016\n",
      "{'q_sold': 4316, 'ri_sold': 2158, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  2158\n",
      " R removed from pool of  j  =  8752.108278717613\n",
      "{'q_sold': 134, 'ri_sold': 67, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  67\n",
      " R removed from pool of  j  =  268.58615573705174\n",
      "{'q_sold': 2688, 'ri_sold': 5376, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5376\n",
      " R removed from pool of  i  =  1332.2438629340613\n",
      "{'q_sold': 388, 'ri_sold': 194, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  194\n",
      " R removed from pool of  j  =  787.507336538285\n",
      "{'q_sold': 1530, 'ri_sold': 3060, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3060\n",
      " R removed from pool of  i  =  751.6209896892542\n",
      "{'q_sold': 3090, 'ri_sold': 6180, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6180\n",
      " R removed from pool of  i  =  1500.1019528898178\n",
      "{'q_sold': 2066, 'ri_sold': 1033, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1033\n",
      " R removed from pool of  j  =  4266.135157210869\n",
      "{'q_sold': 924, 'ri_sold': 462, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  462\n",
      " R removed from pool of  j  =  1893.0112373090815\n",
      "{'q_sold': 1286, 'ri_sold': 643, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  643\n",
      " R removed from pool of  j  =  2619.4101453257026\n",
      "{'q_sold': 2680, 'ri_sold': 1340, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1340\n",
      " R removed from pool of  j  =  5402.554824066465\n",
      "{'q_sold': 1270, 'ri_sold': 2540, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2540\n",
      " R removed from pool of  i  =  632.3309053343255\n",
      "{'q_sold': 2218, 'ri_sold': 1109, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1109\n",
      " R removed from pool of  j  =  4443.719963102951\n",
      "{'q_sold': 2046, 'ri_sold': 4092, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4092\n",
      " R removed from pool of  i  =  1021.6875477391295\n",
      "{'q_sold': 1350, 'ri_sold': 2700, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2700\n",
      " R removed from pool of  i  =  668.2296649524942\n",
      "{'q_sold': 4144, 'ri_sold': 2072, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  2072\n",
      " R removed from pool of  j  =  8311.184109441121\n",
      "{'q_sold': 2634, 'ri_sold': 5268, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5268\n",
      " R removed from pool of  i  =  1318.5002158507705\n",
      "{'q_sold': -24, 'ri_sold': -12, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  -12\n",
      " R removed from pool of  j  =  -48.27761190570891\n",
      "{'q_sold': 2188, 'ri_sold': 4376, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4376\n",
      " R removed from pool of  i  =  1081.514209295041\n",
      "{'q_sold': 1340, 'ri_sold': 2680, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2680\n",
      " R removed from pool of  i  =  656.3571528730681\n",
      "{'q_sold': 2854, 'ri_sold': 5708, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5708\n",
      " R removed from pool of  i  =  1383.0054476256482\n",
      "{'q_sold': 1208, 'ri_sold': 2416, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2416\n",
      " R removed from pool of  i  =  579.3383569553262\n",
      "{'q_sold': 1886, 'ri_sold': 943, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  943\n",
      " R removed from pool of  j  =  3925.0079701560317\n",
      "{'q_sold': 2506, 'ri_sold': 1253, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1253\n",
      " R removed from pool of  j  =  5155.161134142894\n",
      "{'q_sold': 2866, 'ri_sold': 5732, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5732\n",
      " R removed from pool of  i  =  1392.1799151783343\n",
      "{'q_sold': 800, 'ri_sold': 400, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  400\n",
      " R removed from pool of  j  =  1655.539449593285\n",
      "{'q_sold': 1666, 'ri_sold': 833, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  833\n",
      " R removed from pool of  j  =  3425.3186195790768\n",
      "{'q_sold': 1526, 'ri_sold': 3052, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3052\n",
      " R removed from pool of  i  =  742.5673115709797\n",
      "{'q_sold': 1348, 'ri_sold': 2696, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2696\n",
      " R removed from pool of  i  =  651.1457595109241\n",
      "{'q_sold': 3766, 'ri_sold': 1883, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1883\n",
      " R removed from pool of  j  =  7746.0157268807525\n",
      "{'q_sold': 2406, 'ri_sold': 4812, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4812\n",
      " R removed from pool of  i  =  1174.1522320234217\n",
      "{'q_sold': 740, 'ri_sold': 370, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  370\n",
      " R removed from pool of  j  =  1522.7811196600087\n",
      "{'q_sold': 2918, 'ri_sold': 5836, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5836\n",
      " R removed from pool of  i  =  1410.2298533022404\n",
      "{'q_sold': 4124, 'ri_sold': 2062, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  2062\n",
      " R removed from pool of  j  =  8504.06244849891\n",
      "{'q_sold': 3034, 'ri_sold': 6068, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6068\n",
      " R removed from pool of  i  =  1475.8961348986486\n",
      "{'q_sold': 482, 'ri_sold': 241, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  241\n",
      " R removed from pool of  j  =  997.3088576253504\n",
      "{'q_sold': 1516, 'ri_sold': 3032, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3032\n",
      " R removed from pool of  i  =  730.7861471702345\n",
      "{'q_sold': 3268, 'ri_sold': 1634, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1634\n",
      " R removed from pool of  j  =  6747.212560381857\n",
      "{'q_sold': 1294, 'ri_sold': 2588, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2588\n",
      " R removed from pool of  i  =  630.0820760176284\n",
      "{'q_sold': 2444, 'ri_sold': 1222, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1222\n",
      " R removed from pool of  j  =  5003.680078529636\n",
      "{'q_sold': 2776, 'ri_sold': 1388, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1388\n",
      " R removed from pool of  j  =  5606.252279849607\n",
      "{'q_sold': 1074, 'ri_sold': 537, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  537\n",
      " R removed from pool of  j  =  2147.339889170369\n",
      "{'q_sold': 1942, 'ri_sold': 971, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  971\n",
      " R removed from pool of  j  =  3852.602341015241\n",
      "{'q_sold': -1240, 'ri_sold': -2480, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  -2480\n",
      " R removed from pool of  i  =  -630.2411360552069\n",
      "{'q_sold': 976, 'ri_sold': 1952, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1952\n",
      " R removed from pool of  i  =  496.4031870160252\n",
      "{'q_sold': 1748, 'ri_sold': 3496, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3496\n",
      " R removed from pool of  i  =  882.7463246949483\n",
      "{'q_sold': 754, 'ri_sold': 377, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  377\n",
      " R removed from pool of  j  =  1496.9609843932558\n",
      "{'q_sold': 3634, 'ri_sold': 1817, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1817\n",
      " R removed from pool of  j  =  7133.7572683861945\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'q_sold': 570, 'ri_sold': 285, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  285\n",
      " R removed from pool of  j  =  1106.921494341339\n",
      "{'q_sold': 1560, 'ri_sold': 780, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  780\n",
      " R removed from pool of  j  =  3013.04562465474\n",
      "{'q_sold': 2132, 'ri_sold': 1066, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1066\n",
      " R removed from pool of  j  =  4079.346800038009\n",
      "{'q_sold': 3442, 'ri_sold': 6884, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  6884\n",
      " R removed from pool of  i  =  1792.251999597298\n",
      "{'q_sold': 566, 'ri_sold': 1132, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  1132\n",
      " R removed from pool of  i  =  291.6073913106229\n",
      "{'q_sold': 3164, 'ri_sold': 1582, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1582\n",
      " R removed from pool of  j  =  6100.993274726672\n",
      "{'q_sold': 2012, 'ri_sold': 4024, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4024\n",
      " R removed from pool of  i  =  1046.291529881768\n",
      "{'q_sold': 1020, 'ri_sold': 2040, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2040\n",
      " R removed from pool of  i  =  526.1926012029871\n",
      "{'q_sold': 2464, 'ri_sold': 4928, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4928\n",
      " R removed from pool of  i  =  1259.5321062104777\n",
      "{'q_sold': 2200, 'ri_sold': 4400, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4400\n",
      " R removed from pool of  i  =  1110.9389130333439\n",
      "{'q_sold': 1400, 'ri_sold': 2800, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2800\n",
      " R removed from pool of  i  =  700.3639773244504\n",
      "{'q_sold': 2070, 'ri_sold': 1035, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1035\n",
      " R removed from pool of  j  =  4130.685793826939\n",
      "{'q_sold': 1616, 'ri_sold': 808, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  808\n",
      " R removed from pool of  j  =  3194.115379321971\n",
      "{'q_sold': 2114, 'ri_sold': 4228, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4228\n",
      " R removed from pool of  i  =  1068.0979609244969\n",
      "{'q_sold': 2664, 'ri_sold': 1332, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1332\n",
      " R removed from pool of  j  =  5265.482961672242\n",
      "{'q_sold': 3588, 'ri_sold': 7176, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  7176\n",
      " R removed from pool of  i  =  1810.798398370389\n",
      "{'q_sold': 764, 'ri_sold': 382, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  382\n",
      " R removed from pool of  j  =  1525.0335447207326\n",
      "{'q_sold': 4134, 'ri_sold': 8268, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  8268\n",
      " R removed from pool of  i  =  2053.0480827232823\n",
      "{'q_sold': 48, 'ri_sold': 96, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  96\n",
      " R removed from pool of  i  =  23.581412013503723\n",
      "{'q_sold': 1850, 'ri_sold': 3700, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3700\n",
      " R removed from pool of  i  =  904.4472669996321\n",
      "{'q_sold': 2590, 'ri_sold': 5180, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5180\n",
      " R removed from pool of  i  =  1251.915427699103\n",
      "{'q_sold': 2282, 'ri_sold': 1141, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1141\n",
      " R removed from pool of  j  =  4723.829023029888\n",
      "49998.9911830038\n",
      "{'q_sold': 1378, 'ri_sold': 689, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'RemoveLiquidity', 'agent_id': 2, 'asset_id': 'i', 'direction': '', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'N/A', 'UNI_burn': 49998.9911830038}\n",
      "Ri_plus =  982238.1291414407\n",
      "{'q_sold': 1792, 'ri_sold': 3584, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3584\n",
      " R removed from pool of  i  =  866.9042812746484\n",
      "{'q_sold': 1508, 'ri_sold': 754, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  754\n",
      " R removed from pool of  j  =  3119.0987078243634\n",
      "{'q_sold': 1412, 'ri_sold': 706, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  706\n",
      " R removed from pool of  j  =  2897.924598289188\n",
      "{'q_sold': 2850, 'ri_sold': 1425, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1425\n",
      " R removed from pool of  j  =  5783.618723432068\n",
      "{'q_sold': 2358, 'ri_sold': 1179, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1179\n",
      " R removed from pool of  j  =  4720.028165059397\n",
      "{'q_sold': 1308, 'ri_sold': 2616, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  2616\n",
      " R removed from pool of  i  =  655.2423533330439\n",
      "{'q_sold': 2900, 'ri_sold': 1450, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1450\n",
      " R removed from pool of  j  =  5765.0498984101\n",
      "{'q_sold': 2308, 'ri_sold': 1154, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'i', 'direction': 'ij', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'j'}\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1154\n",
      " R removed from pool of  j  =  4526.359593419358\n",
      "{'q_sold': 2814, 'ri_sold': 5628, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5628\n",
      " R removed from pool of  i  =  1432.7820934368065\n",
      "{'q_sold': 2630, 'ri_sold': 5260, 'ri_deposit': 0, 'q_deposit': 0, 'Si_burn': 0, 'action_id': 'R_Swap', 'agent_id': 5, 'asset_id': 'j', 'direction': 'ji', 'direction_q': 'test_r_for_r', 'purchased_asset_id': 'i'}\n",
      " R to R swap called \n",
      " R added to pool of  j  =  5260\n",
      " R removed from pool of  i  =  1320.0206826209323\n",
      "Total execution time: 0.93s\n"
     ]
    }
   ],
   "source": [
    "(data, tensor_field, sessions) = run_wrapper.run(drop_midsteps=True)\n",
    "\n",
    "experiments = data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results dataframe analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excerpt from the results (head & tail)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>UNI_Qi</th>\n",
       "      <th>UNI_Ri</th>\n",
       "      <th>UNI_Si</th>\n",
       "      <th>UNI_Qj</th>\n",
       "      <th>UNI_Rj</th>\n",
       "      <th>UNI_Sj</th>\n",
       "      <th>UNI_ij</th>\n",
       "      <th>UNI_ji</th>\n",
       "      <th>UNI_Sij</th>\n",
       "      <th>...</th>\n",
       "      <th>C</th>\n",
       "      <th>asset_random_choice</th>\n",
       "      <th>trade_random_size</th>\n",
       "      <th>trade_random_direction</th>\n",
       "      <th>purchased_asset_id</th>\n",
       "      <th>simulation</th>\n",
       "      <th>subset</th>\n",
       "      <th>run</th>\n",
       "      <th>substep</th>\n",
       "      <th>timestep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>i</td>\n",
       "      <td>1000</td>\n",
       "      <td>test_q_for_r</td>\n",
       "      <td>N/A</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>i</td>\n",
       "      <td>1249</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>j</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>j</td>\n",
       "      <td>931</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>i</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>i</td>\n",
       "      <td>1324</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>j</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>j</td>\n",
       "      <td>1762</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>i</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   UNI_Qi   UNI_Ri  UNI_Si   UNI_Qj   UNI_Rj  UNI_Sj   UNI_ij  \\\n",
       "0      0  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "1      3  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "2      6  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "3      9  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "4     12  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "\n",
       "    UNI_ji        UNI_Sij  ...              C  asset_random_choice  \\\n",
       "0  1000000  1000000000000  ...  1000000000000                    i   \n",
       "1  1000000  1000000000000  ...  1000000000000                    i   \n",
       "2  1000000  1000000000000  ...  1000000000000                    j   \n",
       "3  1000000  1000000000000  ...  1000000000000                    i   \n",
       "4  1000000  1000000000000  ...  1000000000000                    j   \n",
       "\n",
       "   trade_random_size  trade_random_direction purchased_asset_id  simulation  \\\n",
       "0               1000            test_q_for_r                N/A           0   \n",
       "1               1249            test_r_for_r                  j           0   \n",
       "2                931            test_r_for_r                  i           0   \n",
       "3               1324            test_r_for_r                  j           0   \n",
       "4               1762            test_r_for_r                  i           0   \n",
       "\n",
       "   subset  run  substep timestep  \n",
       "0       0    1        0        0  \n",
       "1       0    1        3        1  \n",
       "2       0    1        3        2  \n",
       "3       0    1        3        3  \n",
       "4       0    1        3        4  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>UNI_Qi</th>\n",
       "      <th>UNI_Ri</th>\n",
       "      <th>UNI_Si</th>\n",
       "      <th>UNI_Qj</th>\n",
       "      <th>UNI_Rj</th>\n",
       "      <th>UNI_Sj</th>\n",
       "      <th>UNI_ij</th>\n",
       "      <th>UNI_ji</th>\n",
       "      <th>UNI_Sij</th>\n",
       "      <th>...</th>\n",
       "      <th>C</th>\n",
       "      <th>asset_random_choice</th>\n",
       "      <th>trade_random_size</th>\n",
       "      <th>trade_random_direction</th>\n",
       "      <th>purchased_asset_id</th>\n",
       "      <th>simulation</th>\n",
       "      <th>subset</th>\n",
       "      <th>run</th>\n",
       "      <th>substep</th>\n",
       "      <th>timestep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>890</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>j</td>\n",
       "      <td>654</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>i</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>893</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>i</td>\n",
       "      <td>1450</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>j</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>896</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>i</td>\n",
       "      <td>1154</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>j</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>899</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>j</td>\n",
       "      <td>1407</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>i</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>902</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>2000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000000000</td>\n",
       "      <td>j</td>\n",
       "      <td>1315</td>\n",
       "      <td>test_r_for_r</td>\n",
       "      <td>i</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index   UNI_Qi   UNI_Ri  UNI_Si   UNI_Qj   UNI_Rj  UNI_Sj   UNI_ij  \\\n",
       "298    890  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "299    893  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "300    896  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "301    899  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "302    902  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "\n",
       "      UNI_ji        UNI_Sij  ...              C  asset_random_choice  \\\n",
       "298  1000000  1000000000000  ...  1000000000000                    j   \n",
       "299  1000000  1000000000000  ...  1000000000000                    i   \n",
       "300  1000000  1000000000000  ...  1000000000000                    i   \n",
       "301  1000000  1000000000000  ...  1000000000000                    j   \n",
       "302  1000000  1000000000000  ...  1000000000000                    j   \n",
       "\n",
       "     trade_random_size  trade_random_direction purchased_asset_id  simulation  \\\n",
       "298                654            test_r_for_r                  i           0   \n",
       "299               1450            test_r_for_r                  j           0   \n",
       "300               1154            test_r_for_r                  j           0   \n",
       "301               1407            test_r_for_r                  i           0   \n",
       "302               1315            test_r_for_r                  i           0   \n",
       "\n",
       "     subset  run  substep timestep  \n",
       "298       2    2        3       96  \n",
       "299       2    2        3       97  \n",
       "300       2    2        3       98  \n",
       "301       2    2        3       99  \n",
       "302       2    2        3      100  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Individual MonteCarlo results\n",
    "\n",
    "- Choose the subset from $\\{0,1,2\\}$ ... for different values for `a`\n",
    "- Choose the simulation from $\\{0,\\dots,9\\}$ ... for the distinct simulation run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>UNI_Qi</th>\n",
       "      <th>UNI_Ri</th>\n",
       "      <th>UNI_Si</th>\n",
       "      <th>UNI_Qj</th>\n",
       "      <th>UNI_Rj</th>\n",
       "      <th>UNI_Sj</th>\n",
       "      <th>UNI_ij</th>\n",
       "      <th>UNI_ji</th>\n",
       "      <th>UNI_Sij</th>\n",
       "      <th>...</th>\n",
       "      <th>C</th>\n",
       "      <th>asset_random_choice</th>\n",
       "      <th>trade_random_size</th>\n",
       "      <th>trade_random_direction</th>\n",
       "      <th>purchased_asset_id</th>\n",
       "      <th>simulation</th>\n",
       "      <th>subset</th>\n",
       "      <th>run</th>\n",
       "      <th>substep</th>\n",
       "      <th>timestep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [index, UNI_Qi, UNI_Ri, UNI_Si, UNI_Qj, UNI_Rj, UNI_Sj, UNI_ij, UNI_ji, UNI_Sij, uni_agents, UNI_P_RQi, UNI_P_RQj, UNI_P_ij, pool, Q, H, Sq, Y, hydra_agents, C, asset_random_choice, trade_random_size, trade_random_direction, purchased_asset_id, simulation, subset, run, substep, timestep]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiment1 = experiments[experiments.subset == 2]\n",
    "experiment1 = experiment1[experiment1.run == 8]\n",
    "experiment2 = experiment1.sort_values(by=['timestep'], ascending=True)\n",
    "experiment2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Starting balances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000000], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.UNI_Ri.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000000], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starting_Ri = experiments[experiments.timestep == 0]\n",
    "starting_Ri = starting_Ri['UNI_Ri']\n",
    "starting_Ri.reset_index(drop=True)\n",
    "starting_Ri.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "a = starting_Ri.unique()\n",
    "_ = plt.hist(a, bins=10)  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram for starting R_i\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000000], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.UNI_Rj.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000000], dtype=int64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "starting_Rj = experiments[experiments.timestep == 0]\n",
    "starting_Rj = starting_Rj['UNI_Rj']\n",
    "starting_Rj.reset_index(drop=True)\n",
    "starting_Rj.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "a = starting_Rj.unique()\n",
    "_ = plt.hist(a, bins=10)  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram for starting R_j\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trade Sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1000, 1249,  931, 1324, 1762,  883, 1790, 1384,  766, 1272,  769,\n",
       "        768, 1121,   44,  138,  719,  494, 1158,  546,  294, 1733,  888,\n",
       "       1034,  288,  728, 1056,  425, 1188,  700,  855, 1927,  994,  472,\n",
       "       1412,  390, 1105,   21,  336, 1099, 1370, 1086,  943,  850,  261,\n",
       "        641,  770, 1529, 1172,  119, 1163,  808,  662, 1306, 1516, 1466,\n",
       "        581,  846, 1166, 1488,  761,  908,  447,  402, 1407, 1679,  964,\n",
       "       1502, 1181,  678, 1770,  983, 1783, -309, 1411, 1044,  851, 1046,\n",
       "          7,  891, 1179, 1739,  741,  596,  750, 1458, 1165,  736, 1257,\n",
       "       1049, 1485,  649,  837,  804,  269, 1149, 1131, 1003,  293,  790,\n",
       "        829,  599,  920, 1203, 1944, 1088, 1129,  963,   41,  987, 1031,\n",
       "       2232,  904, 1151,  416, 1572, 1376, 1396, 1702,  300, 1294, 2096,\n",
       "        505,  717, 1050,  749,  225, 1035,  469, 1237,  541, 1775,  609,\n",
       "        839,  385, 1114, 1654,  197, 1093, 1130, 1391,  382,  340, 1261,\n",
       "       1126, 1174,  660, 1117, 1147,  643, 1933,  405, 1329,  513, 1394,\n",
       "       1580,  590, 1482, 1207, 1949,  878,  624,  556,  593,  962, 1171,\n",
       "       1139, 1414, 1007, 1727,  868, 2361, 1313,  572,  465, 1242,  889,\n",
       "       1358,  577,  243,  777, 1429, 1108,  378, 1087, 1193,  559, 1077,\n",
       "       1030,  429, 1281, 1542, 1527,  312,  532, 1258, 2927, 1286, 1568,\n",
       "       1478, 1326,  843, 1380,  614,  882,  758, 1041, 2158,   67, 1344,\n",
       "        194,  765, 1545, 1033,  462, 1340,  635, 1109, 1023,  675, 2072,\n",
       "       1317,  -12, 1094,  670, 1427,  604, 1253, 1433,  400,  833,  763,\n",
       "        674, 1883,  370, 1459, 2062, 1517,  241, 1634,  647, 1222, 1388,\n",
       "        537,  971, -620,  488,  874,  377, 1817,  285,  780, 1066, 1721,\n",
       "        283, 1582, 1006,  510, 1232, 1100, 1057, 1332, 1794, 2067,   24,\n",
       "        925, 1295, 1141,  689,  896,  754,  706, 1425,  654, 1450, 1154,\n",
       "       1315], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "experiments.trade_random_size.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "a = experiments.trade_random_size.unique()\n",
    "_ = plt.hist(a, bins=30)  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram for trade sizes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_M(k, v):\n",
    "    if k == 'sim_config':\n",
    "        k, v = 'M', v['M']\n",
    "    return k, v\n",
    "\n",
    "config_ids = [\n",
    "    dict(\n",
    "        get_M(k, v) for k, v in config.__dict__.items() if k in ['simulation_id', 'run_id', 'sim_config', 'subset_id']\n",
    "    ) for config in configs\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Liquidity dynamics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hydra -- base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fan Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_fan_plot3(experiments, config_ids, 'a','Q')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_fan_plot3(experiments, config_ids, 'a','H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_fan_plot3(experiments, config_ids, 'a','Sq')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_fan_plot3(experiments, config_ids, 'a','Y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hydra -- pool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk asset i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_pool_simulation_plot(experiments, config_ids, 'a','i', 'R', 'S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDUAAAHwCAYAAAC2bVRBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAvc0lEQVR4nO3de7xlZXkn+N9TVCEqIgrEFAUKKklEVILVxksuRjMdNIhJ252I8RIzNtPTJsZMMmr6Mmh3Mpk4MVFz0SFqvAY70bRBRGNajXYStQFFRNAGr5QcBFEEFIGynvljb/QAp+qcgrNr77fq+/18zufUXu/a73rWPk9RZ/9419rV3QEAAAAYzYZ5FwAAAABwRwg1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUA4E6oqsdU1SVVdX1V/WxV3aeqPlRV11XVy+Zd3zxVVVfVA+ddx1pV1bur6lnzrgMAWDuhBgDsQlW9uKpunoYWt3xds2yX/5Tkj7v7wO5+R5JTk3w1yUHd/Rt34rivr6rfvlPFr/1Yj62qHdNzu66qPlNVz95Dx359VW2vqsNnfJxfqqp/2NU+3f2E7n7DLOsAANaXUAMAVvdfpqHFLV8HLxu7X5JP3ebxRd3de7TCO+/y7j4wyUFJXpjkz6rq2FkesKrunuQpSb6R5BdneSwAYO8k1ABgn1NVL6qqz05XJVxUVT93B+f5bJL7J3nndJXDGUmeleQF08c/VVUblh3v6qr6y6q697I5frSq/qmqrqmqy6YrCk7N5E3+LfO8cyfHf3RVnVNV35h+f/Sysb+vqv9cVf84Pc/3VtWhq51TT7wjydeTHFtVd6mql1fV5dOvl1fVXZYd519X1aVV9bWqOnM3V1w8Jck1max2udVlH1X1iKo6t6quraqvVNUfTLcfUFVvnr6W10zP+z7TsXtW1WuraqmqvlxVv11V+1XVg5K8OsmjVlhps/yYf19Vz9mN+gGAORNqALAv+mySH0tyzyQvSfLmqtq8u5N09wOSfCnJk6YrOE5J8pYkL50+/m9JnpfkZ5P8RJLDMwkL/iRJquq+Sd6d5I+SHJbk+CTnd/fpt5nnSbc99jQYeVeSVyY5JMkfJHlXVR2ybLenJXl2ku9Lsn+S31ztnKYhzM8lOTjJJ5P8+ySPnNb2sCSPSPIfpvs+LsnvJvn5JJuTfDHJW1c7xjLPSnLG9Dk/VFUnLBt7RZJXdPdBSR6Q5C+XPeeeSY6cnve/SXLDdOwNSbYneWCSH07yz5M8p7svnu734RVW2gAAAxNqALDP6e6/6u7Lu3tHd/+XJJdk8mZ9Z35+uirglq8P7Mbh/rck/767t3X3jUlenORfVtXGTFZj/LfuPqO7b+7uq7v7/DXO+zNJLunuN3X39u4+I8mnkywPQP68u/9nd9+QSShw/C7mO3y6guGrSU5L8ozu/sy0xv/U3Vd291WZhEDPmD7nF5O8rrs/Nj2338pkNcRRqxU/DXR+MslfdPdXkrwvt16tcXOSB1bVod19fXd/ZNn2Q5I8sLu/093ndfe109UaT0jy/O7+ZndfmeQPkzx1tVoAgHEJNQDY51TVM6vq/FtCiiTHJdnVpRl/2d0HL/v6yd043P2S/Ndlx7o4yXeS3CeT1QafvWNnkcMzWRmx3BeTbFn2+Iplf/5WkgN3Md/l03O7d3cf3923rLi47XG+ON12u7Huvj7J1bepYWeekeTiZSHOW5I8rao2TR//r0l+IMmnp5eYnDTd/qYkf5vkrdPLYV46fc79kmxKsrTstf7/MlmlAgDspTbOuwAA2JOq6n5J/izJ4zO5HOE7VXV+kprRIS9L8svd/Y8r1HJZdr5CZLUbjV6eyRv55e6b5D27XeHajnPLzVDvO912uxqmN/48JMmX1zDvM5Pct6puCV42Tp/7hCRndvclSU6pqg1J/kWSt1XVId39zUxWi7xkuiLk7CSfmX6/Mcmh3b19heONduNWAGANrNQAYF9z90ze4F6VJNOPLj1uhsd7dZLfmYYpqarDqurJ07G3JPmpqvr5qtpYVYdU1fHTsa9kchPSnTk7yQ9U1dOmz/2FJMcmOWud6z8jyX+Y1n1okv8ryZunY3+R5NlVdfz05qH/d5KPdvcXdjVhVT0qk/tkPCKTS2KOz+Rn8BeZXoJSVU+vqsO6e0cmNxNNku9U1U9W1UOqar8k12ZyOcp3unspyXuTvKyqDpreG+QBVfUT0+d+JckRVbX/nXw9AIAFItQAYJ/S3RcleVmSD2fyRvchSW63iuI2fmH6qRnLv9Z6WcMrkpyZ5L1VdV2SjyT5kWktX0ryxCS/keRrSc7P5GacSfLaTD595JqqescK53F1kpOmz706yQuSnNTdX11jXWv120nOTXJBJjcO/dh0W7r7fUn+Y5K3J1nKJKhYyz0snpXkb7r7k919xS1fmbxWJ01vgnpikk9V1fXT7U/t7m8n+f4kb8sk0Lg4yQfzvZDlmZncEPWiTG7I+rZMbmCaJO/PZLXJFVW13q8RADAn1W01JgAAADAeKzUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AIAkSVX9YlW9d951AACslVADAPaAqvrRqvqnqvpGVX2tqv6xqv7ZvOtarrvf0t3/fD3mqqpfqapzq+rGqnr9eswJAHBbG+ddAADs7arqoCRnJfnfk/xlkv2T/FiSG+dZ13JVtbG7t6/jlJcn+e0kP53krus4LwDAd1mpAQCz9wNJ0t1ndPd3uvuG7n5vd19wyw5V9a+r6uKquq6qLqqqE6bbD6+qt1fVVVX1+ap63rLnvLiq/rKq3jh93qeqauuy8RdV1WeXzflzy8Z+abpa5A+r6mtJXjzd9g/L9nl0VZ0zXV1yTlU9eq0n3N1/3d3vSHL1avtW1QOq6v1VdXVVfbWq3lJVB6/1WADAvkuoAQCz9z+TfKeq3lBVT6iqey0frKp/leTFSZ6Z5KAkJye5uqo2JHlnkk8k2ZLk8UmeX1U/vezpJyd5a5KDk5yZ5I+XjX02kxUh90zykiRvrqrNy8Z/JMnnknxfkt+5TU33TvKuJK9MckiSP0jyrqo6ZDr+oqo66468GCuoJL+b5PAkD0pyZCavBwDALgk1AGDGuvvaJD+apJP8WZKrqurMqrrPdJfnJHlpd5/TE5d29xeT/LMkh3X3f+rum7r7c9PnP3XZ9P/Q3Wd393eSvCnJw5Yd96+6+/Lu3tHd/yXJJUkesey5l3f3H3X39u6+4TZl/0ySS7r7TdPxM5J8OsmTpnP/P9190jq9Ppd29991943dfVUmAcpPrMfcAMDezT01AGAP6O6Lk/xSklTVDyV5c5KXJzklk5UJn13hafdLcnhVXbNs235J/vuyx1cs+/O3khxwy/0xquqZSf6PJEdNxw9Mcuiy/S/bRcmHJ/nibbZ9MZMVI+uqqr4vkxUhP5bkHpn8T5evr/dxAIC9j5UaALCHdfenk7w+yXHTTZclecAKu16W5PPdffCyr3t09xNXO0ZV3S+TVR2/kuSQ7j44yYWZXOrx3VJ2McXlmYQqy903yZdXO/Yd8LvTWh7a3QcleXpuXScAwIqEGgAwY1X1Q1X1G1V1xPTxkZms0PjIdJfXJPnNqnp4TTxwGkr8jyTXVtULq+quVbVfVR23xo+CvXsmQcFV02M+O98LUdbi7CQ/UFVPq6qNVfULSY7N5FNc1nLOG6vqgExWluxXVQdU1c5WiN4jyfVJrqmqLUn+z92oEwDYhwk1AGD2rsvkppwfrapvZhJmXJjkN5LJvS8yuVHnX0z3fUeSe0/vk/GkJMcn+XySr2YSgNxztQN290VJXpbkw0m+kuQhSf5xrQV399VJTprWeHWSFyQ5qbu/miRV9e+q6t27mOI/JLkhyYsyWXlxw3TbSl6S5IQk38jk5qR/vdY6AYB9W3XvauUpAAAAwGKyUgMAAAAYklADAAAAGJJQAwAAABiSUAMAAAAY0s4+Wm1Ihx56aB911FHzLmNVN998czZt2jTvMmBF+pNFp0dZdHqURaY/WXSj9Oh555331e4+bN517K7zzjvv+zZu3PiaTD7mfYRFDjuSXLh9+/bnPPzhD79ypR32qlDjqKOOyrnnnjvvMla1tLSUzZs3z7sMWJH+ZNHpURadHmWR6U8W3Sg9WlVfnHcNd8TGjRtf8/3f//0POuyww76+YcOGhf8o1B07dtRVV1117BVXXPGaJCevtM8IyQwAAABw5x132GGHXTtCoJEkGzZs6MMOO+wbmawsWXmfPVgPAAAAMD8bRgk0bjGtd6fZhVADAAAAGJJQAwAAANhjvvSlL2086aST7n/kkUce94AHPODBP/ETP/HAd7/73QeeeOKJ99/dufaqG4UCAAAAi2vHjh05+eSTH/i0pz3t6rPOOutzSfJP//RPd7322mv3e8973vO53Z3PSg0AAABgRV+59tsbP/K5q+/2lWu/vS6LIs4666x7bNy4sV/wghdcdcu2Rz/60TccffTRNx1zzDEP3t35rNQAAAAAbuctH/3ivV7yzouO2rihevuOrhc/6dgvPO1H7vf1OzPnBRdccNeHPexh31qvGq3UAAAAAG7lK9d+e+NL3nnRUTdt37HhWzd9Z7+btu/Y8OJ3XnTUeq3YWC9CDQAAAOBWPv/Vb+6/cUPd6uNfN26o/vxXv7n/nZn3IQ95yA2f+MQn7nbnqvseoQYAAABwK0cfevebtu/oWr5t+46uow+9+013Zt4nPelJ19100031spe97NBbtn3wgx+826WXXnqHwhKhBgAAAHAr9znogO0vftKxX9h/44Ydd9t/v+/sv3HDjhc/6dgv3OegA7bfmXk3bNiQM88887Pve9/7DjryyCOPe+ADH/jg00477fD73ve+N9+R+RbqWhgAAABgMTztR+739cc/6D7Xff6r39z/6EPvftOdDTRucdRRR9189tln3+7jWy+55JJP7e5cQg0AAABgRfc56IDt6xVmzILLTwAAAIAhCTUAAABg37Bjx44dtfpui2Na746djQs1GNrV19+YT1x2Ta6+/sY7NL43zbFex1j+fZ51jPJ6jTDHKHWudY7l3xe5Tj/X9ZtjlDpv2Wf590Wuc95zjFLnesyxSHUu/z7POuZ9jPWaAwZ04VVXXXXPUYKNHTt21FVXXXXPJBfubB/31GBYf3P+l/PCt1+QTRs25OYdO/LSpzw0Jx+/Zc3je9Mc63mMD/zb4/OY33v/wp7ror1eiz7HKHXuzhw769FFq9PPdd891zvaoyOe6770c91bznXR/51ftNdrV3PAiLZv3/6cK6644jVXXHHFcRljkcOOJBdu3779OTvbobp7D9YzW1u3bu1zzz133mWsamlpKZs3b553GUO7+vob85jfe3++ffP3ViEdsGlD/vGFj8shB95l1fG9aY71PsaHf+2EPOoVH1vIc13E12uR5xilzt2dY6UeXcQ6/Vz33XO9Iz066rnuSz/XveVcF/nf+UV8vXY2B7Mzynulqjqvu7fOuw7GSGbgdrZ9/YZs2nDr9t20YUO2ff2GNY3vTXOMUueizDFKnc7VuY5ep3N1rqPX6Vy9XruaA1gcQg2GdMS97pqbd9z6XjE379iRI+511zWN701zjFLnoswxSp3O1bmOXqdzda6j1+lcvV67mgNYHEINhnTIgXfJS5/y0BywaUPucZeNOWDThrz0KQ/97pLA1cb3pjnW+xhJFvZcF/H1WuQ5RqlzPXp0Eev0c913z/WO9Oio57ov/Vz3lnNdqT8X5VwW8fXa2RzA4nBPjTkY5TqxEVx9/Y3Z9vUbcsS97rriPzSrje9Nc6zXMW667mvZ/x73XuhzXY85RqnTud5+fFc9ukh1+rnuu+d6Z3p0tHPdl36ue8u5jvDv/CK9XqvNwfob5b2Se2osDqHGHIzyF5V9k/5k0elRFp0eZZHpTxbdKD0q1FgcM7v8pKpeV1VXVtWKnydbVT9UVR+uqhur6jdvM3ZiVX2mqi6tqhfNqkYAAABgXLO8p8brk5y4i/GvJXlekt9fvrGq9kvyJ0mekOTYJKdU1bEzqhEAAAAY1MxCje7+UCbBxc7Gr+zuc5LcfJuhRyS5tLs/1903JXlrkifPqk4AAABgTBvnXcAKtiS5bNnjbUl+ZGc7V9WpSU5Nki1btmRpaWm21a2TUepk36Q/WXR6lEWnR1lk+pNFp0fZHYsYatQK23Z6N9PuPj3J6cnkRqEj3FRmlJvfsG/Snyw6Pcqi06MsMv3JotOj7K5Z3lPjjtqW5Mhlj49IcvmcagEAAAAW1CKGGuckOaaqjq6q/ZM8NcmZc64JAAAAWDAzu/ykqs5I8tgkh1bVtiSnJdmUJN396qr6/iTnJjkoyY6qen6SY7v72qr6lSR/m2S/JK/r7k/Nqk4AAABgTDMLNbr7lFXGr8jk0pKVxs5OcvYs6gIAAAD2Dot4+QkAAADAqoQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQZhZqVNXrqurKqrpwJ+NVVa+sqkur6oKqOmHZ2K9X1aeq6sKqOqOqDphVnQAAAMCYZrlS4/VJTtzF+BOSHDP9OjXJq5KkqrYkeV6Srd19XJL9kjx1hnUCAAAAA5pZqNHdH0rytV3s8uQkb+yJjyQ5uKo2T8c2JrlrVW1Mcrckl8+qTgAAAGBMG+d47C1JLlv2eFuSLd19blX9fpIvJbkhyXu7+707m6SqTs1kpUe2bNmSpaWlGZa8fkapk32T/mTR6VEWnR5lkelPFp0eZXfMM9SoFbZ1Vd0rk1UcRye5JslfVdXTu/vNK03S3acnOT1Jtm7d2ps3b15pt4WytLSUEepk36Q/WXR6lEWnR1lk+pNFp0fZXfP89JNtSY5c9viITC4z+akkn+/uq7r75iR/neTRc6gPAAAAWGDzDDXOTPLM6aegPDLJN7p7KZPLTh5ZVXerqkry+CQXz7FOAAAAYAHN7PKTqjojyWOTHFpV25KclmRTknT3q5OcneSJSS5N8q0kz56OfbSq3pbkY0m2J/l4ppeXAAAAANxiZqFGd5+yyngnee5Oxk7LJAQBAAAAWNE8Lz8BAAAAuMOEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQZhZqVNXrqurKqrpwJ+NVVa+sqkur6oKqOmHZ2MFV9baq+nRVXVxVj5pVnQAAAMCYZrlS4/VJTtzF+BOSHDP9OjXJq5aNvSLJe7r7h5I8LMnFM6oRAAAAGNTGWU3c3R+qqqN2scuTk7yxuzvJR6arMzYn+WaSH0/yS9N5bkpy06zqBAAAAMY0z3tqbEly2bLH26bb7p/kqiR/XlUfr6rXVNXd51EgAAAAsLhmtlJjDWqFbZ1JTSck+dXu/mhVvSLJi5L8xxUnqTo1k8tXsmXLliwtLc2o3PU1Sp3sm/Qni06Psuj0KItMf7Lo9Ci7Y56hxrYkRy57fESSyzMJNrZ190en29+WSaixou4+PcnpSbJ169bevHnzbKpdR0tLSxmhTvZN+pNFp0dZdHqURaY/WXR6lN01z8tPzkzyzOmnoDwyyTe6e6m7r0hyWVX94HS/xye5aG5VAgAAAAtpZis1quqMJI9NcmhVbUtyWpJNSdLdr05ydpInJrk0ybeSPHvZ0381yVuqav8kn7vNGAAAAMBMP/3klFXGO8lzdzJ2fpKtMygLAAAA2EvM8/ITAAAAgDtMqAEAAAAMSagBAAAADGnN99SoqnslOTzJDUm+0N07ZlYVAAAAwCp2GWpU1T0zuZnnKUn2T3JVkgOS3KeqPpLkT7v7AzOvEgAAAOA2Vlup8bYkb0zyY919zfKBqnp4kmdU1f27+7Uzqg8AAABgRbsMNbr7f9nF2HlJzlv3igAAAADWYHfuqbElyf2WP6e7PzSLogAAAABWs6ZQo6p+L8kvJLkoyXemmzuJUAMAAACYi7Wu1PjZJD/Y3TfOsBYAAACANduwxv0+l2TTLAsBAAAA2B1rXanxrSTnV9X7knx3tUZ3P28mVQEAAACsYq2hxpnTLwAAAICFsKZQo7vfMOtCAAAAAHbHWj/95Jgkv5vk2CQH3LK9u+8/o7oAAAAAdmmtNwr98ySvSrI9yU8meWOSN82qKAAAAIDVrDXUuGt3vy9JdfcXu/vFSR43u7IAAAAAdm2tNwr9dlVtSHJJVf1Kki8n+b7ZlQUAAACwa2tdqfH8JHdL8rwkD0/y9CTPmlFNAAAAAKta66efnJMkVdXd/ezZlgQAAACwujWt1KiqR1XVRUkunj5+WFX96UwrAwAAANiFtV5+8vIkP53k6iTp7k8k+fEZ1QQAAACwqrWGGunuy26z6TvrXAsAAADAmq31008uq6pHJ+mq2j+TG4ZePLuyAAAAAHZtrSs1/k2S5ybZkmRbkuOnjwEAAADmYq2ffvLVJL8441oAAAAA1mxNoUZVHZ3kV5Mctfw53X3ybMoCAAAA2LW13lPjHUlem+SdSXbMrBoAAACANVprqPHt7n7lTCsBAAAA2A1rDTVeUVWnJXlvkhtv2djdH5tJVQAAAACrWGuo8ZAkz0jyuHzv8pOePgYAAADY49Yaavxckvt3902zLAYAAABgrTascb9PJDl4hnUAAAAA7Ja1rtS4T5JPV9U5ufU9NXykKwAAADAXaw01TptpFQAAAAC7aU2hRnd/cNaFAAAAAOyOtd5TAwAAAGChCDUAAACAIQk1AAAAgCHdoVCjqt5QVa+qquPWuyAAAACAtbijKzX+OMl/S/KMdawFAAAAYM3W+pGut9Ld5yQ5J8nb17ccAAAAgLXZZahRVS/v7udX1TuT9G3Hu/vkmVUGAAAAsAurrdR40/T778+6EAAAAIDdsVqo8aWqOra7P7h8Y1U9OMmVsysLAAAAYNdWu1HoHyU5bIXtRyR5xfqXAwAAALA2q4UaD7ntKo0k6e6/TfLQ2ZQEAAAAsLrVQo1Nd3AMAAAAYKZWCzUuqaon3nZjVT0hyedmUxIAAADA6la7UeivJzmrqn4+yXnTbVuTPCrJSbMsDAAAAGBXdrlSo7v/Z5KHJPlgkqOmXx9M8tDpGAAAAMBc7HKlRlVVd9+Y5M9X2afXvTIAAACAXVjtnhofqKpfrar7Lt9YVftX1eOq6g1JnjW78gAAAABWtto9NU5M8stJzqiqo5Nck+SumYQh703yh919/iwLBAAAAFjJLkON7v52kj9N8qdVtSnJoUlu6O5r9kBtAAAAADu12kqN7+rum5MszbAWAAAAgDVb7Z4aAAAAAAtJqAEAAAAMSagBAAAADEmoAQAAAAxJqAEAAAAMSagBAAAADEmoAQAAAAxJqAEAAAAMSagBAAAADEmoAQAAAAxpZqFGVb2uqq6sqgt3Ml5V9cqqurSqLqiqE24zvl9VfbyqzppVjQAAAMC4ZrlS4/VJTtzF+BOSHDP9OjXJq24z/mtJLp5JZQAAAMDwZhZqdPeHknxtF7s8Ockbe+IjSQ6uqs1JUlVHJPmZJK+ZVX0AAADA2DbO8dhbkly27PG26balJC9P8oIk91htkqo6NZOVHtmyZUuWlpbWvdBZGKVO9k36k0WnR1l0epRFpj9ZdHqU3THPUKNW2NZVdVKSK7v7vKp67GqTdPfpSU5Pkq1bt/bmzZvXtchZWFpaygh1sm/Snyw6Pcqi06MsMv3JotOj7K55fvrJtiRHLnt8RJLLkzwmyclV9YUkb03yuKp6854vDwAAAFhk8ww1zkzyzOmnoDwyyTe6e6m7f6u7j+juo5I8Ncn7u/vpc6wTAAAAWEAzu/ykqs5I8tgkh1bVtiSnJdmUJN396iRnJ3likkuTfCvJs2dVCwAAALD3mVmo0d2nrDLeSZ67yj5/n+Tv168qAAAAYG8xz8tPAAAAAO4woQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMKSZhRpV9bqqurKqLtzJeFXVK6vq0qq6oKpOmG4/sqo+UFUXV9WnqurXZlUjAAAAMK5ZrtR4fZITdzH+hCTHTL9OTfKq6fbtSX6jux+U5JFJnltVx86wTgAAAGBAMws1uvtDSb62i12enOSNPfGRJAdX1ebuXuruj03nuC7JxUm2zKpOAAAAYEwb53jsLUkuW/Z423Tb0i0bquqoJD+c5KM7m6SqTs1kpUe2bNmSpaWlne26UEapk32T/mTR6VEWnR5lkelPFp0eZXfMM9SoFbb1dwerDkzy9iTP7+5rdzZJd5+e5PQk2bp1a2/evHm961x3S0tLGaFO9k36k0WnR1l0epRFpj9ZdHqU3TXPTz/ZluTIZY+PSHJ5klTVpkwCjbd091/PoTYAAABgwc0z1DgzyTOnn4LyyCTf6O6lqqokr01ycXf/wRzrAwAAABbYzC4/qaozkjw2yaFVtS3JaUk2JUl3vzrJ2UmemOTSJN9K8uzpUx+T5BlJPllV50+3/bvuPntWtQIAAADjmVmo0d2nrDLeSZ67wvZ/yMr32wAAAAD4rnlefgIAAABwhwk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCEJNQAAAIAhCTUAAACAIQk1AAAAgCHNLNSoqtdV1ZVVdeFOxquqXllVl1bVBVV1wrKxE6vqM9OxF82qRgAAAGBcs1yp8fokJ+5i/AlJjpl+nZrkVUlSVfsl+ZPp+LFJTqmqY2dY5x519fU33ur7SuOfuOyanY6vZZ87Oz7SHAAAMC9+L1//Yyz/DmuxcVYTd/eHquqoXezy5CRv7O5O8pGqOriqNic5Ksml3f25JKmqt073vWhWte4pf3P+l/PCt1+QD/zb4/OY33t/XvqUh+bk47fcbnzThg25eceO242vZZ87Oz7SHAAAMC9+L5/Nue7svRLszDzvqbElyWXLHm+bbtvZ9qFdff2NeeHbL8i3b96RJPn2zTvygrdfcKs08pbx627cfrvxtexzZ8dHmgMAAObF7+WzO9fk9u+VYFdqslBiRpNPVmqc1d3HrTD2riS/293/MH38viQvSHL/JD/d3c+Zbn9Gkkd096/u5BinZnL5SrZs2fLwc845ZxanAgAAAEmSww8//Lzu3jrvOpjh5SdrsC3JkcseH5Hk8iT772T7irr79CSnJ8nWrVt78+bN61/pOrj6+hvzmN97f7598458+NdOyKNe8bEcsGlD/vGFj8shB97lVuO3WD5+2zlW2ufOjq/HMfbUHMzO0tJSFvXvESR6lMWnR1lk+nN9+L18due60nsl2JV5Xn5yZpJnTj8F5ZFJvtHdS0nOSXJMVR1dVfsneep036EdcuBd8tKnPDQHbJq85Ads2pCXPuWh3/1Lunz8HnfZeLvxtexzZ8dHmgMAAObF7+WzO9fk9u+VYFdmdvlJVZ2R5LFJDk3ylSSnJdmUJN396qqqJH+cySekfCvJs7v73Olzn5jk5Un2S/K67v6dtRxz69atfe65567viayzq6+/MTdd97Xsf497r/iX9Orrb8y2r9+QI+51153+JV5tnzs7PtIcrD//B4dFp0dZdHqURaY/15ffy9f/GLt6r7RIqsrlJwtipvfU2NNGCDUS/5iw2PQni06Psuj0KItMf7LoRulRocbimOflJwAAAAB3mFADAAAAGJJQAwAAABiSUAMAAAAYklADAAAAGJJQAwAAABiSUAMAAAAYklADAAAAGJJQAwAAABiSUAMAAAAYklADAAAAGJJQAwAAABiSUAMAAAAYklADAAAAGFJ197xrWDdVdVWSL867jjU4NMlX510E7IT+ZNHpURadHmWR6U8W3Sg9er/uPmzeRbCXhRqjqKpzu3vrvOuAlehPFp0eZdHpURaZ/mTR6VF2l8tPAAAAgCEJNQAAAIAhCTXm4/R5FwC7oD9ZdHqURadHWWT6k0WnR9kt7qkBAAAADMlKDQAAAGBIQg0AAABgSEKNPaiqTqyqz1TVpVX1onnXA1V1ZFV9oKourqpPVdWvTbffu6r+rqoumX6/17xrZd9VVftV1cer6qzpY/3Jwqiqg6vqbVX16el/Sx+lR1kUVfXr03/fL6yqM6rqAP3JPFXV66rqyqq6cNm2nfZkVf3W9L3TZ6rqp+dTNYtOqLGHVNV+Sf4kyROSHJvklKo6dr5VQbYn+Y3uflCSRyZ57rQvX5Tkfd19TJL3TR/DvPxakouXPdafLJJXJHlPd/9Qkodl0qt6lLmrqi1Jnpdka3cfl2S/JE+N/mS+Xp/kxNtsW7Enp7+TPjXJg6fP+dPpeyq4FaHGnvOIJJd29+e6+6Ykb03y5DnXxD6uu5e6+2PTP1+XyS/jWzLpzTdMd3tDkp+dS4Hs86rqiCQ/k+Q1yzbrTxZCVR2U5MeTvDZJuvum7r4mepTFsTHJXatqY5K7Jbk8+pM56u4PJfnabTbvrCefnOSt3X1jd38+yaWZvKeCWxFq7Dlbkly27PG26TZYCFV1VJIfTvLRJPfp7qVkEnwk+b45lsa+7eVJXpBkx7Jt+pNFcf8kVyX58+klUq+pqrtHj7IAuvvLSX4/yZeSLCX5Rne/N/qTxbOznvT+iTURauw5tcI2n6fLQqiqA5O8Pcnzu/vaedcDSVJVJyW5srvPm3ctsBMbk5yQ5FXd/cNJvhlL+VkQ0/sSPDnJ0UkOT3L3qnr6fKuC3eL9E2si1NhztiU5ctnjIzJZAghzVVWbMgk03tLdfz3d/JWq2jwd35zkynnVxz7tMUlOrqovZHLJ3uOq6s3RnyyObUm2dfdHp4/flknIoUdZBD+V5PPdfVV335zkr5M8OvqTxbOznvT+iTURauw55yQ5pqqOrqr9M7npzZlzrol9XFVVJteCX9zdf7Bs6Mwkz5r++VlJ/mZP1wbd/VvdfUR3H5XJfzPf391Pj/5kQXT3FUkuq6ofnG56fJKLokdZDF9K8siqutv03/vHZ3LvLP3JotlZT56Z5KlVdZeqOjrJMUn+xxzqY8FVtxU8e0pVPTGT68P3S/K67v6d+VbEvq6qfjTJf0/yyXzvngX/LpP7avxlkvtm8kvRv+ru297UCfaYqnpskt/s7pOq6pDoTxZEVR2fyY1s90/yuSTPzuR/GulR5q6qXpLkFzL5tLOPJ3lOkgOjP5mTqjojyWOTHJrkK0lOS/KO7KQnq+rfJ/nlTHr4+d397j1fNYtOqAEAAAAMyeUnAAAAwJCEGgAAAMCQhBoAAADAkIQaAAAAwJCEGgAAAMCQhBoAsKCq6pCqOn/6dUVVfXn65+ur6k9neNzHVtWjZzU/AMB62TjvAgCAlXX31UmOT5KqenGS67v79/fAoR+b5Pok/7QHjgUAcIdZqQEAg5mupDhr+ucXV9Ubquq9VfWFqvoXVfXSqvpkVb2nqjZN93t4VX2wqs6rqr+tqs3T7c+rqouq6oKqemtVHZXk3yT59emqkB+rqsOq6u1Vdc706zHLjv2mqnp/VV1SVf96un1zVX1o+vwLq+rH5vJCAQB7PSs1AGB8D0jyk0mOTfLhJE/p7hdU1X9N8jNV9a4kf5Tkyd19VVX9QpLfSfLLSV6U5OjuvrGqDu7ua6rq1Vm2KqSq/iLJH3b3P1TVfZP8bZIHTY/90CSPTHL3JB+fHuuUJH/b3b9TVfsludueeRkAgH2NUAMAxvfu7r65qj6ZZL8k75lu/2SSo5L8YJLjkvxdVWW6z9J0nwuSvKWq3pHkHTuZ/6eSHDt9bpIcVFX3mP75b7r7hiQ3VNUHkjwiyTlJXjddJfKO7j5/Hc4RAOB2hBoAML4bk6S7d1TVzd3d0+07Mvm3vpJ8qrsftcJzfybJjyc5Ocl/rKoHr7DPhiSPmoYX3zUNOfo2+3Z3f6iqfnw695uq6v/t7jfewXMDANgp99QAgL3fZ5IcVlWPSpKq2lRVD66qDUmO7O4PJHlBkoOTHJjkuiT3WPb89yb5lVseVNXxy8aeXFUHVNUhmdxg9Jyqul+SK7v7z5K8NskJszoxAGDfJtQAgL1cd9+U5F8m+b2q+kSS85M8OpPLUN48vWzl45ncN+OaJO9M8nO33Cg0yfOSbJ3eTPSiTG4keov/keRdST6S5D939+WZhBvnV9XHkzwlyStmf5YAwL6ovrdCFQBg7fbwx8wCANyOlRoAAADAkKzUAAAAAIZkpQYAAAAwJKEGAAAAMCShBgAAADAkoQYAAAAwJKEGAAAAMKT/H3y1JpztOpnhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_pool_simulation_plot(experiments, config_ids, 'a','i','C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Risk asset j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_pool_simulation_plot(experiments, config_ids, 'a','j', 'R', 'S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_pool_simulation_plot(experiments, config_ids, 'a','j','C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Asset i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_pool_simulation_plot(experiments, config_ids, 'a','i','P')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Asset j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABDAAAAHhCAYAAACYzk47AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABSDklEQVR4nO3deXxU9b3/8XcmGSaRBLOwmGQCgqEYAnFU4pbWtYKiRjRKiWKlgJQretEqcH/XtYsFYn24lD4elHKLC0LsbUoDWFIriBtKyrVJSuOCyJKEoDCsgWQyyeT3h82YmIQkZM7MmZnX8/Hw0c6ZMzOfCd+cnHnP5/s9ES0tLS0CAAAAAAAwMUugCwAAAAAAAOgOAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAAAMD0CDAAAAAAAIDpEWAAAAAAAADTI8AAAOA0vP/++xo5cqRiY2P15z//WV9++aUuv/xyxcXF6aGHHgp0eQEVERGhzz//PNBl9Nj111+vl156KdBlAACAbhBgAADQiSeffFJWq1WxsbHe/+Lj4733P/7447rvvvtUV1enSZMmadmyZRo4cKCOHTumZ5555rRfd9q0aXr00Ud98A66t3nzZlksFsXGxiouLk6jRo3SihUr/PLa06ZNU1RUlPbt22fo67z44ov67ne/e8p9NmzYoLvvvtvQOgAAQN8RYAAA0IUf/OAHqqur8/535MgR73179uxRZmZmu9ujR49WREREACo9fSkpKaqrq9OxY8e0ePFi3XPPPaqsrDT0NU+cOKGioiKdeeaZevXVVw19LQAAEDoIMAAAYWPRokU655xzFBcXp9GjR2vNmjWn9TznnHOOvvjiC910002KjY1Vfn6+XnrpJRUUFCg2NlZvvvmmPB6P9/WSkpI0efJkHTp0yPsc7733ni677DLFx8crLS1NL774opYtW6ZXX33V+zw33XRTp6+/ZcsWZWdn68wzz1R2dra2bNnive/KK6/UY489ppycHMXFxWn8+PE6ePBgt+8pIiJCkyZNUkJCgiorK+VyufTAAw8oJSVFKSkpeuCBB+Ryubz7/+53v1N6eroSExOVm5vbq06KoqIixcfH6/HHH+8wdaO0tFTjxo3TgAEDNGTIEP3kJz+RJDU0NGjq1KlKSkpSfHy8srOz9eWXX0qSjh49qhkzZig5OVmpqal69NFH1dzcrI8//lizZ8/WBx980KGDpq0rr7xSy5cv73H9AAAgMAgwAABh45xzztG7776ro0eP6oknntDUqVNVW1vb6+fZuXOnhg4dqnXr1qmurk6rV6/WnXfeqfnz56uurk7f//739cILL+jPf/6z3n77be3bt08JCQmaM2eOJGnv3r26/vrrdf/99+vAgQMqKyuTw+HQrFmz2j3PunXrOrz2oUOHdMMNN+g///M/5XQ69ZOf/EQ33HCDnE6nd59Vq1ZpxYoV+uqrr9TY2Khf/epX3b4nj8ejNWvW6MiRIxo7dqyeeuopffjhhyorK1N5eblKS0v1i1/8QpK0adMm/b//9//0hz/8QbW1tRo2bJimTJnS45/fSy+9pPz8fE2ZMkWffPKJPvroI+99c+fO1dy5c3Xs2DHt3LlTkydP9j7m6NGjqqqqktPp1NKlSxUTEyNJuvvuuxUVFaXPP/9c//jHP/TGG29o+fLlysjI0NKlS3XppZd26KABAADBhwADABA2br/9dqWkpMhisegHP/iBRo4cqdLS0i73/8Mf/qD4+Hjvf1dddVWPX+u3v/2tnnrqKdntdtlsNj355JP64x//qKamJr366qv6/ve/r/z8fFmtViUlJcnhcPToeV9//XWNHDlSd911l6KiopSfn69zzz23Xdjxox/9SN/5zncUExOjyZMnq6ysrMvn27dvn+Lj4zVw4ED99Kc/1SuvvKJRo0bp1Vdf1eOPP67Bgwdr0KBBeuKJJ/TKK69Ikl599VVNnz5dF1xwgWw2mxYuXKgPPvhAu3fv7rb+vXv36q233tIdd9yhIUOG6JprrmnXhWG1WvX555/r4MGDio2N1SWXXOLd7nQ69fnnnysyMlIXXnihBgwYoC+//FIbNmzQc889p/79+2vw4MF68MEHVVhY2KOfJwAACB4EGACAsPHyyy/L4XB4A4nt27efcnrF5MmTdeTIEe9/b731Vo9fa8+ePbrlllu8r5WRkaHIyEh9+eWXqqqq0jnnnHNa72Hfvn0aNmxYu23Dhg1TTU2N9/ZZZ53l/f9nnHGG6urquny+lJQUHTlyRIcOHVJZWZm3k+LbrzNs2DDvNJFv3xcbG6ukpKR2NXTllVdeUUZGhjewufPOO7Vq1Sq53W5J0v/8z//os88+07nnnqvs7GytX79eknTXXXdpwoQJmjJlilJSUjR//ny53W7t2bNHbrdbycnJ3p/1j3/8Y3311Vfd1gIAAIJLVKALAADAH/bs2aN77rlHGzdu1KWXXqrIyEg5HA61tLQY8nppaWn6/e9/r5ycnE7v66rzo7tFQFNSUrRnz5522/bu3avrrrvu9Is9xeu0LlS6d+9epaSkdFrDiRMn5HQ6lZqa2u3zvvzyy9q7d683ZGlqapLT6dSGDRuUm5urkSNHavXq1fJ4PPrTn/6k2267TU6nU/3799cTTzyhJ554Qrt379bEiRM1atQoTZw4UTabTQcPHlRUVMfTmmBbVBUAAHSNDgwAQFg4ceKEIiIiNGjQIEnSihUrtH37dsNeb/bs2XrkkUe8H/QPHDig4uJiSV93Hbz55pv6wx/+4P0A3zrNY8iQIfriiy+6fN6JEyfqs88+06pVq9TU1KTXXntNlZWVuvHGG31af35+vn7xi1/owIEDOnjwoH72s59p6tSpkqQ77rhDK1asUFlZmVwul/77v/9bF198sc4+++xTPucHH3ygnTt3qrS0VGVlZSorK9P27dt1xx13eKeRrFy5UgcOHJDFYvEuuhkZGam33npL//znP9Xc3KwBAwbIarUqMjJSycnJGj9+vB566CEdO3ZMHo9HO3fu1Ntvvy3p659ndXW1GhsbffrzAQAA/keAAQAIC6NHj9ZDDz2kSy+9VEOGDNE///nPTrsj2nrttdcUGxvb7r+eTk2YO3eucnNzNX78eMXFxemSSy7R1q1bJUlDhw7VX/7yFz3zzDNKTEyUw+FQeXm5JGnGjBmqrKxUfHy8Jk2a1OF5k5KStH79ej3zzDNKSkpSQUGB1q9fr4EDB/buB9KNRx99VOPGjVNWVpbGjh2rCy64QI8++qgk6ZprrtHPf/5z5eXlKTk5WTt37uzRmhMvvfSSbr75Zo0dO1ZnnXWW97+5c+dq/fr1OnTokEpKSpSZmanY2FjNnTtXhYWFio6O1v79+3XbbbdpwIABysjI0BVXXOENVF5++WU1NjZq9OjRSkhI0G233eZdnPXqq69WZmamzjrrLJ//jAAAgH9FtBjVOwsAABAELr/8cs2cOVM//OEPA10KAAA4BTowAABA2Dp58qS++OILDR8+PNClAACAbhBgAACAsPTVV1/prLPO0hVXXKHvfve7gS4HAAB0gykkAAAAAADA9OjAAAAAAAAApkeAAQBAmHj11Vc1fvz4QJcBAABwWggwAADwgffee0+XXXaZzjzzTCUmJionJ0d///vfA11WO3feeafeeOMNnzzXkiVLNG7cONlsNk2bNu2U+27fvl0TJkzQwIEDFRER4ZPXBwAA4YcAAwCAPjp27JhuvPFG3X///Tp06JBqamr0xBNPyGazBbo0r6amJp8+X0pKih599FFNnz69232tVqsmT56s//mf//FpDQAAILwQYAAA0EefffaZJCk/P1+RkZGKiYnR+PHjlZWV5d3nd7/7nTIyMhQXF6fRo0fro48+kiTt27dPeXl5GjRokIYPH64XXnjB+5gnn3xSkydP1g9/+EPFxcUpMzNT27Zt896/aNEinXPOOd7nXLNmjfe+F198UTk5OXrwwQeVmJioJ598Ui+++GK7q21s2bJF2dnZOvPMM5Wdna0tW7b0+D3feuutmjRpkpKSkrrdd9SoUZoxY4YyMzN79Nxz585VWlqaBgwYoAsvvFDvvvtuj+sCAAChiwADAIA++s53vqPIyEjdfffd2rBhgw4fPtzu/v/93//Vk08+qZdfflnHjh3T2rVrlZSUJI/Ho5tuuknnnXeeampqtHHjRj333HP661//6n3s2rVrNWXKFB05ckS5ubm67777vPedc845evfdd3X06FE98cQTmjp1qmpra733b926VSNGjNBXX32lRx55pF1Nhw4d0g033KD//M//lNPp1E9+8hPdcMMNcjqdkr4OR2688UYjflzdys7OVllZmQ4dOqQ77rhDt99+uxoaGgJSCwAAMA8CDAAA+mjAgAF67733FBERoXvuuUeDBg1Sbm6uvvzyS0nS8uXLNX/+fGVnZysiIkLp6ekaNmyY/v73v+vAgQN6/PHH1a9fP40YMUL33HOPCgsLvc/93e9+VxMnTlRkZKTuuusulZeXe++7/fbblZKSIovFoh/84AcaOXKkSktLvfenpKTo/vvvV1RUlGJiYtrV/Prrr2vkyJG66667FBUVpfz8fJ177rlat26dJOm//uu/tH79eiN/bF2aOnWqkpKSFBUVpYceekgul0uffvppQGoBAADmQYABAIAPZGRk6MUXX1R1dbW2b9+uffv26YEHHpAkVVVV6ZxzzunwmD179mjfvn2Kj4/3/vfLX/7SG3xI0llnneX9/2eccYYaGhq861m8/PLLcjgc3sdu375dBw8e9O6flpbWZb379u3TsGHD2m0bNmyYampqTuv9+9IzzzyjjIwMnXnmmYqPj9fRo0fbvS8AABCeogJdAAAAoebcc8/VtGnT9Nvf/lbS10HCzp07O+yXlpam4cOHa8eOHb1+jT179uiee+7Rxo0bdemllyoyMlIOh0MtLS3efU51xY+UlBTt2bOn3ba9e/fquuuu63UtvvTuu+9q8eLF2rhxozIzM2WxWJSQkNDufQEAgPBEBwYAAH30ySef6JlnnlF1dbWkrzsuVq9erUsuuUSSNHPmTP3qV7/S//3f/6mlpUWff/659uzZo4suukgDBgzQ4sWLVV9fr+bmZm3fvr1Hl189ceKEIiIiNGjQIEnSihUrtH379h7XPHHiRH322WdatWqVmpqa9Nprr6mysrLH6140NTWpoaFBzc3Nam5ubtcZ8m0tLS1qaGhQY2OjJKmhoUEul6vTfY8fP66oqCgNGjRITU1N+tnPfqZjx471+H0BAIDQRYABAEAfxcXFaevWrbr44ovVv39/XXLJJRozZoyeeeYZSV+vVfHII4/ojjvuUFxcnCZNmqRDhw4pMjJS69atU1lZmYYPH66BAwdq5syZOnr0aLevOXr0aD300EO69NJLNWTIEP3zn/9UTk5Oj2tOSkrS+vXr9cwzzygpKUkFBQVav369Bg4cKEn65S9/qeuvv77Lx//iF79QTEyMFi1apJUrVyomJka/+MUvJH3dyREbG6u9e/dK+rpbJCYmxnsVkpiYGI0aNarT550wYYKuv/56fec739GwYcMUHR19yqkwAAAgfES00JMJAAAAAABMjg4MAAAAAABgegQYAAAAAADA9AgwAAAAAACA6RFgAAAAAAAA0yPAAAAAAAAAphcV6AJ8aeDAgTr77LMDXUa33G63rFZroMsAOsX4hFkxNmFmjE+YGeMTZhYs43P37t06ePBgoMvoNbfbrerqajU0NAS6lB6Jjo6W3W7vckyEVIBx9tlna9u2bYEuo1u1tbVKTk4OdBlApxifMCvGJsyM8QkzY3zCzIJlfI4bNy7QJZyW6upqxcXF6eyzz1ZERESgyzmllpYWOZ1OVVdXa/jw4Z3uwxQSAAAAAABCUENDg5KSkkwfXkhSRESEkpKSTtktQoABAAAAAECICobwolV3tRJgAAAAAAAAQ0RGRsrhcGjMmDG6/fbbdfLkSUnSZZdd1uvnIsAAAAAAAACGiImJUVlZmbZv365+/fpp6dKlkqQtW7b0+rkIMAAAAAAAgCTJWedSedUROetcPn/u733ve/r8888lSbGxsb1+fEhdhQQAAAAAAJye4rIaLSiqkNVikdvjUUFelnIdqT557qamJm3YsEHXXXfdaT8HHRgAAAAAAIQ5Z51LC4oq1OD26LirSQ1uj+YXVfS5E6O+vl4Oh0Pjxo3T0KFDNWPGjNN+LjowAAAAAAAIc9WH62W1WNQgj3eb1WJR9eF6JcXaTvt5W9fA8AU6MAAAAAAACHP2hBi5PZ5229wej+wJMQGqqCMCDAAAAAAAwlxSrE0FeVmKtloUZ4tStNWigrysPnVfnEpERESvH8MUEgAAAAAAoFxHqnLSB6r6cL3sCTE+CS/q6uo6bHM6nUpMTOz1cxFgAAAAAAAASV93YhjVdSFJ+/bt05VXXqmHH364148lwAAAAAAAAH6RkpKizz777LQeyxoYftZ6CZq+XooGAAAAAIBwQoDhR8VlNcpZvEmSlLN4k9aW1QS4IgAAAABAKGtpaQl0CT3WXa0EGH7irHNpQVGFGtxfX5amwe3R/KIKOjEAAAAAAIaIjo6W0+kMihCjpaVFTqdT0dHRXe7DGhh+Un24XlaLRQ365rq6VotF1YfrDV0gBQAAAAAQnux2u6qrq3XgwIFAl9Ij0dHRstvtXd5PgOEn9oQYuT2edtvcHo/sCTEBqggAAAAAEMqsVquGDx8e6DJ8hikkfpIUa1NBXpairV//yKOtFhXkZdF9AQAAAABAD9CB4Ue5jlTlpA9U4/FDen/B1YQXAAAAAAD0EB0YftYaWhBeAAAAAADQcwQYAAAAAADA9AgwAAAAAACA6RFgAAAAAAAA0yPAAAAAAAAApkeAAQAAAAAATI8AAwAAAAAAmB4BBgAAAAAAMD0CDAAAAAAAYHqGBhglJSUaNWqU0tPTtWjRog73Hz16VDfddJPOO+88ZWZmasWKFT1+LAAAAAAACB+GBRjNzc2aM2eONmzYoMrKSq1evVqVlZXt9vnNb36j0aNHq7y8XJs3b9ZDDz2kxsbGHj0WAAAAAACED8MCjNLSUqWnp2vEiBHq16+fpkyZouLi4nb7RERE6Pjx42ppaVFdXZ0SExMVFRXVo8cCAAAAAIDwEWXUE9fU1CgtLc172263a+vWre32ue+++5Sbm6uUlBQdP35cr732miwWS48e22rZsmVatmyZJGn//v2qra014N34XrDUifDE+IRZMTZhZoxPmBnjE2bG+ERPGRZgtLS0dNgWERHR7vZf//pXORwObdq0STt37tS1116r733vez16bKtZs2Zp1qxZkqRx48YpOTnZB9Ubq7a2NijqRHhifMKsGJswM8YnzIzxCTNjfKI3DJtCYrfbVVVV5b1dXV2tlJSUdvusWLFCt956qyIiIpSenq7hw4frk08+6dFjAQAAAABA+DAswMjOztaOHTu0a9cuNTY2qrCwULm5ue32GTp0qDZu3ChJ+vLLL/Xpp59qxIgRPXosAAAAAAAIH4ZNIYmKitKSJUs0YcIENTc3a/r06crMzNTSpUslSbNnz9Zjjz2madOmaezYsWppadHixYs1cOBASer0sQAAAAAAIDwZFmBI0sSJEzVx4sR222bPnu39/ykpKXrjjTd6/FgAAAAAABCeDJtCAgAAAAAA4CsEGAAAAAAAwPQIMAAAAAAAgOkRYAAAAAAAANMjwAAAAAAAAKZHgAEAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAAAMD0CDAAAAAAAIDpEWAAAAAAAADTI8AAAAAAAACmR4ABAAAAAABMjwADAAAAAACYHgEGAAAAAAAwPQIMAAAAAABgegQYAAAAAADA9AgwAAAAAACA6RFgAAAAAAAA0yPAAAAAAAAApkeAAQAAAAAATI8AAwAAAAAAmB4BBgAAAAAAMD0CDAAAAAAAYHoEGAAAAAAAwPQIMAAAAAAAgOkRYAAAAAAAANMjwAAAAAAAAKZHgAEAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAwDWedS+VVR+SscwW6FACAyUQFugAAAABAkorLarSgqEJWi0Vuj0cFeVnKdaQGuiwAgEnQgQEAAICAc9a5tKCoQg1uj467mtTg9mh+UQWdGAAALwIMAAAABFz14XpZLe1PTa0Wi6oP1weoIgCA2RBgAAAAIODsCTFyezzttrk9HtkTYgJUEQDAbAgwAAAAEHBJsTYV5GUp2mpRnC1K0VaLCvKylBRrC3RpAACTYBFPAAAAmEKuI1U56QNVfbhe9oQYwgsAQDsEGAAAADCNpFgbwQUAoFNMIQEAAAAAAKZHgAEAAAAAAEyPAAMAAAAAAJieoQFGSUmJRo0apfT0dC1atKjD/U8//bQcDoccDofGjBmjyMhIHTp0SJL0/PPPa8yYMcrMzNRzzz1nZJkAAAAAAMDkDAswmpubNWfOHG3YsEGVlZVavXq1Kisr2+0zb948lZWVqaysTAsXLtQVV1yhxMREbd++Xb/73e9UWlqq8vJyrV+/Xjt27DCqVAAAAAAAYHKGBRilpaVKT0/XiBEj1K9fP02ZMkXFxcVd7r969Wrl5+dLkj7++GNdcsklOuOMMxQVFaUrrrhCa9asMapUAAAAAABgcoYFGDU1NUpLS/Pettvtqqmp6XTfkydPqqSkRHl5eZKkMWPG6J133pHT6dTJkyf1l7/8RVVVVUaVCgAAAAAATC7KqCduaWnpsC0iIqLTfdetW6ecnBwlJiZKkjIyMrRgwQJde+21io2N1XnnnaeoqM5LXbZsmZYtWyZJ2r9/v2pra330DowVLHUiPDE+YVaMTZgZ4xNmxviEmTE+0VOGBRh2u71d10R1dbVSUlI63bewsNA7faTVjBkzNGPGDEnSf//3f8tut3f62FmzZmnWrFmSpHHjxik5OdkX5RuqtrY2KOpEeGJ8wqwYmzAzxifMjPEJM2N8ojcMm0KSnZ2tHTt2aNeuXWpsbFRhYaFyc3M77Hf06FG9/fbbuvnmm9tt/+qrryRJe/fu1Z/+9KcOAQcAAABwupx1LpVXHZGzzhXoUgAAPWRYB0ZUVJSWLFmiCRMmqLm5WdOnT1dmZqaWLl0qSZo9e7Ykac2aNRo/frz69+/f7vF5eXlyOp2yWq36zW9+o4SEBKNKBQAAQBgpLqvRgqIKWS0WuT0eFeRlKdeRGuiyAADdiGjpbLGKIDVu3Dht27Yt0GV0izYpmBnjE2bF2ISZMT6Dh7POpZzFm9Tg9ni3RVsten/B1UqKtQWwMuMwPmFmwTI+g+WzZqgzbAoJAAAAYDbVh+tltbQ/BbZaLKo+XB+gigAAPUWAAQAAgLBhT4iR2+Npt83t8cieEBOgigAAPUWAAQAAgLCRFGtTQV6Woq0WxdmiFG21qCAvK2SnjwBAKDFsEU8AAADAjHIdqcpJH6jqw/WyJ8QQXgBAkCDAAAAAQNhJirURXABAkGEKCQAAAAAAMD0CDAAAAAAAYHoEGAAAAAAAwPQIMAAAAAAAgOkRYAAAAAAAANMjwAAAAAAAAKZHgAEAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAAAMD0CDAAAAAAAIDpEWAAAADAb5x1LpVXHZGzzhXoUgAAQSYq0AUAAAAgPBSX1WhBUYWsFovcHo8K8rKU60gNdFkAgCBBBwYAAAAM56xzaUFRhRrcHh13NanB7dH8ogo6MQAAPUaAAQAAAMNVH66X1dL+1NNqsaj6cH2AKgIABBsCDAAAABjOnhAjt8fTbpvb45E9ISZAFQEAgg0BBgAAAAyXFGtTQV6Woq0WxdmiFG21qCAvS0mxtkCXBgAIEiziCQAAAL/IdaQqJ32gqg/Xy54QQ3gBAOgVAgwAAAD4TVKsjeACAHBamEICAAAAAABMjwADAAAAAACYHgEGAAAAAAAwPQIMAAAAAABgegQYAAAAAADA9AgwAAAAAACA6RFgAAAAAAAA0yPAAAAAAAAApkeAAQAAAHyLs86l8qojcta5Al0KAODfogJdAAAAAGAmxWU1WlBUIavFIrfHo4K8LOU6UgNdFgCEPTowAAAAgH9z1rm0oKhCDW6Pjrua1OD2aH5RBZ0YAGACBBgAAADAv1UfrpfV0v4U2WqxqPpwfYAqAgC0IsAAAAAA/s2eECO3x9Num9vjkT0hJkAVAQBaEWAAAAAA/5YUa1NBXpairRbF2aIUbbWoIC9LSbG2QJcGAGGPRTwBAACANnIdqcpJH6jqw/WyJ8QQXgCASRBgAAAAAN+SFGsjuAAAk2EKCQAAAIKKs86l8qojXBkEAMIMHRgAAAAIGsVlNVpQVCGrxSK3x6OCvCzlOlIDXRYAwA/owAAAAIDPGNkd4axzaUFRhRrcHh13NanB7dH8ogo6MQAgTNCBAQAAAJ8wujui+nC9rBaLGvTNZU6tFouqD9ezXgUAhAFDOzBKSko0atQopaena9GiRR3uf/rpp+VwOORwODRmzBhFRkbq0KFDkqRnn31WmZmZGjNmjPLz89XQ0GBkqQAAAOgDf3RH2BNi5PZ42m1zezyyJ8T47DUAAOZlWIDR3NysOXPmaMOGDaqsrNTq1atVWVnZbp958+aprKxMZWVlWrhwoa644golJiaqpqZGL7zwgrZt26bt27erublZhYWFRpUKAACAPmrtjmirtTvCV5JibSrIy1K01aI4W5SirRYV5GXRfQEAYcKwKSSlpaVKT0/XiBEjJElTpkxRcXGxRo8e3en+q1evVn5+vvd2U1OT6uvrZbVadfLkSaWkpBhVKgAAAPrIX90RuY5U5aQPVPXhetkTYggvACCMGBZg1NTUKC0tzXvbbrdr69atne578uRJlZSUaMmSJZKk1NRUPfzwwxo6dKhiYmI0fvx4jR8/vtPHLlu2TMuWLZMk7d+/X7W1tT5+J8YIljoRnhifMCvGJsyM8Sm9da+jw7bG44dUe9z3rzU4Smo8Xm/Ic4cixifMjPGJnjIswGhpaemwLSIiotN9161bp5ycHCUmJkqSDh8+rOLiYu3atUvx8fG6/fbbtXLlSk2dOrXDY2fNmqVZs2ZJksaNG6fk5GQfvgtj1NbWBkWdCE+MT5gVYxNmxvj8hrPORXeEyTA+YWaMT/SGYWtg2O12VVVVeW9XV1d3OQ2ksLCw3fSRN998U8OHD9egQYNktVp16623asuWLUaVCgAAAB9JirXpvLR4wgsAgM8ZFmBkZ2drx44d2rVrlxobG1VYWKjc3NwO+x09elRvv/22br75Zu+2oUOH6sMPP9TJkyfV0tKijRs3KiMjw6hSAQAAEEKcdS6VVx3x6RVQAvEaAID2DJtCEhUVpSVLlmjChAlqbm7W9OnTlZmZqaVLl0qSZs+eLUlas2aNxo8fr/79+3sfe/HFF+u2227TBRdcoKioKJ1//vneaSIAAABAV4rLarSgqEJWi0Vuj0cFeVnKdaQG3WsAADqKaOlssYogNW7cOG3bti3QZXSLeV4wM8YnzIqxCTMLl/Fp9vUtnHUu5SzepAb3N1dDibZa9P6Cq31Wrz9ew9fCZXwiOAXL+AyWz5qhzrAODAAAAISOYOg6qD5cL6vFogZ9Ey5YLRZVH673Wbjgj9cAAHTOsDUwAAAAEBqcdS4tKKpQg9uj464mNbg9ml9UYbr1H+wJMXJ7PO22uT0e2RNiguo1AACdI8AAAADAKbV2HbTV2nVgJkmxNhXkZSnaalGcLUrRVosK8rJ82hnhj9cAAHSOKSQAAAA4pWDqOsh1pConfaCha3X44zUAAB3RgQEAAIBTCraug6RYm85Lize0Pn+8BgCgPTowAAAA0C26DgAAgUaAAQAAgB5JirURXAAAAoYpJAAAAAAAwPQIMAAAAAAAgOkRYAAAAAAAes1Z51J51RE561yBLgVhgjUwAAAAAAC9UlxWowVFFbJaLHJ7PCrIy1KuIzXQZSHE0YEBAAAAAOgxZ51LC4oq1OD26LirSQ1uj+YXVdCJAcMRYAAAAAAAeqz6cL2slvYfJa0Wi6oP1weoIoQLAgwAAAAAQI/ZE2Lk9njabXN7PLInxASoIoQLAgwAAAAAQI8lxdpUkJelaKtFcbYoRVstKsjLUlKsLdClIcSxiCcAAAAAoFdyHanKSR+o6sP1sifEEF7ALwgwAAAAAAC9lhRrI7iAXzGFBAAAAAAAmB4BBgAAAAAAMD0CDAAAAAAAYHoEGAAAAAAAwPQIMAAAAAAAgOkRYAAAAAAAANMjwAAAAAAAAKZHgAEAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAIAg4KxzqbzqiJx1rkCXAgBAQEQFugAAAACcWnFZjRYUVchqscjt8aggL0u5jtRAlwUAgF/RgQEAAGBizjqXFhRVqMHt0XFXkxrcHs0vqqATAwAQdggwAAAATKz6cL2slvanbFaLRdWH6wNUEcIN05cAmEW3U0g8Ho/Ky8u1b98+xcTEKDMzU0OGDPFHbQAAAGHPnhAjt8fTbpvb45E9ISZAFSGcMH0JgJl0GWDs3LlTixcv1ptvvqmRI0dq0KBBamho0GeffaYzzjhDP/7xj3X33XfLYqGJA+blrHOp+nC97AkxSoq1BbocAAB6LSnWpoK8LM3/1odI/q7BaG2nLzXo6xBtflGFctIHMv4ABESXAcajjz6q//iP/9Bvf/tbRUREtLvvq6++0qpVq/TKK6/o7rvvNrxI4HTwjQEAIFTkOlKVkz6QUB5+1Tp9qTW8kL6ZvsQYBBAIXQYYq1ev7vJBgwcP1gMPPGBEPYBP8I0BACDUJMXa+BsGv2L6EgCz6dFlVLds2aLdu3erqanJu+2HP/yhYUUBfeXLbwyYhgIAAMIR05cAmE23AcZdd92lnTt3yuFwKDIyUpIUERFBgAFT89U3BkxDAQAA4YzpSwDMpNsAY9u2baqsrOywDgZgZr74xoBpKAAAAExfAmAe3QYYY8aM0f79+5WcnOyPegCf6es3BixcBQAAAADm0W2AcfDgQY0ePVoXXXSRbLZvPrStXbvW0MIAX+jLNwYsXAUAAIKds87l/V++gAEQ7LoNMJ588kk/lAGYDwtXAQCAvgj0QuCta3m9da9DOYs3sZYXgKDXbYBxxRVX+KMOwJRYuAoAAJyOQC8E3nYtL0lqcHtYywtA0LN0t8OHH36o7OxsxcbGql+/foqMjNSAAQP8URtgCkmxNp2XFs8fewAA0CNtw4PjriZveNA6ncMfWtfyaqt1LS8ACFbdBhj33XefVq9erZEjR6q+vl7Lly/Xfffd54/aAAAAgKBjhvCAtbwAhKJuAwxJSk9PV3NzsyIjI/WjH/1Imzdv7tGTl5SUaNSoUUpPT9eiRYs63P/000/L4XDI4XBozJgxioyM1KFDh/Tpp596tzscDg0YMEDPPfdcb94XAABAWHHWuVRedcSv3/Kjc2YID1rX8oq2fn26H221sJYXgKDX7RoYZ5xxhhobG+VwODR//nwlJyfrxIkT3T5xc3Oz5syZo7/97W+y2+3Kzs5Wbm6uRo8e7d1n3rx5mjdvniRp3bp1evbZZ5WYmKjExESVlZV5nyc1NVW33HLLab5FAACA0Bbo9RbQnlkWAm9dy6vx+CG9v+BqwgsAQa/bAOOVV16Rx+PRkiVL9Oyzz6qqqkpFRUXdPnFpaanS09M1YsQISdKUKVNUXFzcLsBoa/Xq1crPz++wfePGjTrnnHM0bNiwbl8TAAAg3LRdb6FBX3/rz2KNgWeWhcCTYm2qPS7GAoCQ0G2AMWzYMNXX16u2tlZPPPFEj5+4pqZGaWlp3tt2u11bt27tdN+TJ0+qpKRES5Ys6XBfYWFhp8FGq2XLlmnZsmWSpP3796u2trbHNQZSsNSJ8MT4hFkxNmFmgRyfb93r6LCt8fgh1R73fy1ob3CU1Hi8PuD/Fhw/YWaMT/RUtwHGunXr9PDDD6uxsVG7du1SWVmZHn/8ca1du/aUj2tpaemwLSIiosvXyMnJUWJiYrvtjY2NWrt2rRYuXNjl68yaNUuzZs2SJI0bN07JycndvaWAq62tDYo6EZ4YnzArxibMLJDj01nnUs7iTd7LZUpfr3fAlAG04viJ0+Gsc/mlg4jxid7odhHPJ598UqWlpYqPj5ckORwO7d69u9snttvtqqqq8t6urq5WSkpKp/t21WWxYcMGXXDBBRoyZEi3rwcAABCs+rIAZ9vFGuNsUSzWCKDPistqlLN4k6Yu36qcxZu0tqwm0CUBknrQgREVFaUzzzyz10+cnZ2tHTt2aNeuXUpNTVVhYaFWrVrVYb+jR4/q7bff1sqVKzvc19W6GAAAAKHCFwtwmmW9BQDBj3V1YGbddmCMGTNGq1atUnNzs3bs2KH7779fl112WbdPHBUVpSVLlmjChAnKyMjQ5MmTlZmZqaVLl2rp0qXe/dasWaPx48erf//+7R5/8uRJ/e1vf9Ott956Gm8LAADA/Np+UDjualKD26P5RRWn3YlxXlo8HzAA9En14XpZLe0/JlotFlUfrg9QRcA3uu3A+PWvf62nnnpKNptN+fn5mjBhgh577LEePfnEiRM1ceLEdttmz57d7va0adM0bdq0Do8944wz5HQ6e/Q6AAAAwaj1g0Lrt5zSNx8UCCIABII9IUZuj6fdNrfHI3tCTIAqAr7RbYBxxhln6KmnntJTTz3lj3oAAADCBh8UAJhN67o68781tY1QFWbQbYCxbds2/fKXv9Tu3bvV1NTk3V5RUWFoYQAAAKGODwoAzIh1dWBW3QYYd955p55++mmNHTtWFku3S2YAAACgF/igAMCMkmJtHI9gOt0GGIMGDVJubq4/agEAAAhLfFAAAKB73QYYP/3pTzVz5kxdc801stm++cPK1UEAAAAAAIC/dBtgrFixQp988oncbrd3CklERAQBBgAAAACgS846F9Pj4FPdBhjl5eX65z//6Y9aAAAAAAAhoLisRgu+tUBxriM10GUhyHW7Kucll1yiyspKf9QCAAAAAAhyzjqXFhRVqMHt0XFXkxrcHs0vqpCzzhXo0hDkuu3AeO+99/TSSy9p+PDhstlsamlpUUREBJdRBQAACDG0ewPwherD9bJaLGqQx7vNarGo+nA9xxb0SbcBRklJiT/qAAAAQADR7g3AV+wJMXJ7PO22uT0e2RNiAlQRQkW3AcawYcP8UQcAAAACpG27d+s3pvOLKpSTPpBvSwH0WlKsTQV5WZr/rVCU4wn6qtsAAwAAAKGNdm8AvpbrSFVO+kCmpcGnCDAAAADCHO3eAIyQFGsjuIBPdXsVEgBA6HLWuVRedYRVwYEw19ruHW21KM4WpWirhXZvAIDp9LoD4/vf/76sVqvmzJmjG2+80YiaAAB+wIJ9ANqi3Ts4ceUYAOGk1wHGyy+/rNraWn344YdG1AMA8AMW7APQGdq9gwtBNIBw0+sAIyUlRSkpKbrwwguNqAcA4Acs2AcAwY0gGkA46jLAmDx5sv7whz9o7NixioiIaHdfRESEEhMT9cADD+jmm282vEgAgG+xYB8ABDeCaADhqMsA4/nnn5ckrV+/vtP7Dx48qDvvvJMAAwCCENdnB4DgRhANIBx1GWAkJyfrz3/+sz7//HONHTtWEyZMaHf/sGHD9OqrrxpeIADAGCzYBwDBiyAaQDjqMsC499579a9//UuXXXaZHnvsMZWWluqxxx5rtw/rYPgfK00D8CUW7AOA4EUQDSDcdBlgvPPOOyovL1dkZKROnjyp733vex0CDPgXK00DAIDTxZcgoYkgGkA46TLA6NevnyIjIyVJZ5xxhlpaWvxWFDpipWkAAHC6+BIECC8ElghVXQYYn3zyibKysiRJLS0t2rlzp7KystTS0qKIiAhVVFT4rUiE50rTPTnwcnAGAODU+BIE3eF8KrQQWCKUdRlgfPzxx/6sA90It5Wme3Lg5eAMAED3wvFLEPQc51OhhcASoa7LAGPo0KGKiIg45YNbuzFgvHBaabonB14OzgAA9Ey4fQkSTALd+cD5VOghsESo6zLAuOqqq5SXl6ebb75ZQ4cO9W5vbGzUe++9p5deeklXXXWVpk2b5o86ofBZabonB14OzggHgT6xBRAawulLkGBihs4Hf55P8TfNPwgsEeq6DDBKSkr0+9//Xvn5+dq1a5fi4+PV0NCg5uZmjR8/Xg8++KAcDocfS4UUHitN9+TAy8EZoc4MJ7YAQke4fAkSLMzS+eCv8yn+pvkPgSVCXZcBRnR0tO69917de++9crvdOnjwoGJiYhQfH+/H8hCOenLgNdPBmW8U4GtmObEFEFrC4UuQYGGWTlJ/nE/xN83/CCwRyroMMNqyWq1KTk42uhbAqycHXjMcnPlGAUYwy4ktAN8h7EZbZuokNfp8ir9pgUFgiVDVowADCISeHHgDeXDmGwUYxUwntgD6jrAb32amTtLWeox6bf6mAfAlAgzgNPGNAoxithNbAKePsBtdMUMnqT/wNw2ALxFgAKeJbxRgpHA5sQVCHWE3TiVc2vz5mwbAVyyBLgAIJGedS+VVR+Ssc/X6sa3fKERbLYqzRSnaauEbBfhUUqxN56XFM6aAIEbYDXyNv2kAfIEODIQtX8xJ5hsFAMCp0D4PAIDvEGAgLPlyTnK4tH8CAE4PYTcAAL5BgIGwxJxkAIA/EXYDANB3rIGBsMScZAAAAAAILgQYCEsswAkAAAAAwYUpJAhbzEkGAADwDWedi3MqAIYjwEBYY04yAABA3/jiym4A0BNMIQEAAABwWtpe2e24q0kNbo/mF1XIWecKdGkAQhABBgAYwFnnUnnVEU7gAAAhrfXKbm21XtkNAHyNKSQA4GO00gIAwgVXdgPgT4Z2YJSUlGjUqFFKT0/XokWLOtz/9NNPy+FwyOFwaMyYMYqMjNShQ4ckSUeOHNFtt92mc889VxkZGfrggw+MLBUAfIJWWgBAOOHKbgD8ybAOjObmZs2ZM0d/+9vfZLfblZ2drdzcXI0ePdq7z7x58zRv3jxJ0rp16/Tss88qMTFRkjR37lxdd911+uMf/6jGxkadPHnSqFIB+AgrkH/TStugb76Nam2lDdefCQAgtHFlt+DEeRuCkWEBRmlpqdLT0zVixAhJ0pQpU1RcXNwuwGhr9erVys/PlyQdO3ZM77zzjl588UVJUr9+/dSvXz+jSgXgA0yb+BqttACAcMSV3YIL520IVoZNIampqVFaWpr3tt1uV01NTaf7njx5UiUlJcrLy5MkffHFFxo0aJB+9KMf6fzzz9fMmTN14sQJo0oF0ENdLUzJtIlv0EoLhJ/WY104HvMABB/O2xDMDOvAaGlp6bAtIiKi033XrVunnJwc7/SRpqYmffTRR/r1r3+tiy++WHPnztWiRYv085//vMNjly1bpmXLlkmS9u/fr9raWh++C+MES50IT6can4OjpMbj9ao93n77W/c6OuzbePxQh/3CQfYQS4efB7/zvsHPEWYWrsc8BAeOn2jLbOdtjE/0lGEBht1uV1VVlfd2dXW1UlJSOt23sLDQO32k9bF2u10XX3yxJOm2227rdBFQSZo1a5ZmzZolSRo3bpySk5N99RYMU1tbGxR1Ijx1Nj6ddS7lLN6kBvc3UyOirRa9v+BqJcXaur0f8AWOnTCbtse+D+ZeoEuf/4hjH3zOF+sUcPxEW2Y7b2N8ojcMm0KSnZ2tHTt2aNeuXWpsbFRhYaFyc3M77Hf06FG9/fbbuvnmm73bzjrrLKWlpenTTz+VJG3cuLHLtTMAGK+7a7wzbQJAOOru2Aj0VXFZjXIWb9LU5VuVs3iT1pZ1Ph0b6A0znbcxBQ+9ZVgHRlRUlJYsWaIJEyaoublZ06dPV2ZmppYuXSpJmj17tiRpzZo1Gj9+vPr379/u8b/+9a915513qrGxUSNGjNCKFSuMKhVAN3qyMCUrkAMINyzaCyO1Xaeg9cpW84sqlJM+kL+x6DMznLe1LiT61r0O5SzexEKi6BHDAgxJmjhxoiZOnNhuW2tw0WratGmaNm1ah8c6HA5t27bNyPIA9FBrUj//W6tVf/uPHSuQAwgnbY+Nkug+g09xWW4YLZDnbW0DOknehUQJ6NAdQwMMAKHDDEk9AJhN67Gx8fgh1r6AT9Hhg1BGQIfTZdgaGABCT1KsTeelxfOHBQDaaD0mcmyEL5lpnQLA1wjocLrowAAAAABMiO5HhCqm4OF0EWAAAAAAJsX6UghVTMHD6WAKCeAHzjqXyquOcIkoAAAA4N+YgofeogMDMFjrJaLaXr2DS0QhmDjrXKdsX+7ufgAAAMAXCDAAA3ENdwS77gI4AjoAAAD4C1NIAAO1XiKqrdZLRAG+YOT0pLYB3HFXk/ca7a2v1d39AAAAgC/RgQEYiEtEwUhGdz90d412ruEOAAAAf6IDAzAQ13CHUfzR/dBdAEdABwAAAH+iA8NkWAwv9HANd3SlL7/v/uh+aHuN9rZdHm1XDD/V/QAAwBh8ZkC4IsAwERbDC11cwx3f1tffd391P3QXwBHQAQDgX3xmQDhjColJhONieEYuPgiYmS9+3/05PSkp1qbz0uK7fO7u7gcAAL4Rjp8ZgLbowDCJcFsMj+QY4cxXv+90PwAAEF7C7TMD8G0EGCYRTovhtU2OWw++84sqlJM+kAMvwoIvf9+ZngQAQPgIp88MQGeYQmIS4XS1itbkuK3W5BgIB+H0+w4AAHyHcwiEOzowTCRc2sFJjoHw+X0HAAC+xTkEwhkBhsmEQzs4l14EvhYOv+8AAMD3OIdAuCLAQECQHAMAAAAAeoMAIwQ561xBEQyQHAMAAAAAeooAI8RweVIAAAAAQCjiKiQhpO3lSY+7mtTg9mh+UYWcda5AlwYAAAAAQJ8QYIQQLk8KAAAAAAhVBBghhMuTAgAAAABCFQFGCGm9PGm01aI4W5SirRYuTwoAAAAACAks4hliuDwpAAAAACAUEWCEIC5PCgAAAAAINUwhAQAAAAAApkeAAQAAAAAATI8AAwgCzjqXyquOyFnnCnQpAAAAABAQrIEBmFxxWY0WFFXIarHI7fGoIC9LuY7UQJeFIOGsc7GoLwAAAEICAQZgYs46lxYUVajB7VGDPJKk+UUVykkfGLIfRvnA7TuEXwAAAAglBBiAiVUfrpfVYvGGF5JktVhUfbg+JD/c84Hbd8Ix/AIAAEBoYw0MwMTsCTFyezzttrk9HtkTYnr9XGZfR6PtB+7jriY1uD2aX1Rh2nrNrjX8aqs1/AIAAACCEQEGYGJJsTYV5GUp2mpRnC1K0VaLCvKyev0NenFZjXIWb9LU5VuVs3iT1pbVGFTx6Qu2D9xmD4R8GX4BAAAAZsAUEsDkch2pykkfeNrrQgTLVIJg+sAdDFNdWsOv+d+q00z/5gAAAEBvEGAAQSAp1nbaHzyDZR2NYPnAHSyBkNT38AsAAAAwEwIMIMSZrbPhVFcZCYYP3MESCLXqS/gFAAAAmAkBBhDizNTZ0JOpF2b/wG22QAgAAAAIFwQYQBgwQ2dDME29OBUzBUIAAABAOCHAAMJEoDsbgm3qxamYIRACAAAAwg0BBgC/CLWpF4EOhAD4z6nW7gEAAP5jCXQBAMJD69SLaKtFcbYoRVstTL0II846l8qrjshZ5wp0KUCvFJfVKGfxJk1dvlU5izdpbVlNoEsCACBs0YEBwG+YehGeerJ4K2BGobJ2DxAM6HQC0BMEGAD8iqkX4YUPgAhmobR2D2BmPQ26CTkAGDqFpKSkRKNGjVJ6eroWLVrU4f6nn35aDodDDodDY8aMUWRkpA4dOiRJOvvsszV27Fg5HA6NGzfOyDIBAAZp/QDYVusHQBiHKTu+EWpr9wBm1DboPu5qUoPbo/lFFR2OX0znAiAZGGA0Nzdrzpw52rBhgyorK7V69WpVVla222fevHkqKytTWVmZFi5cqCuuuEKJiYne+9966y2VlZVp27ZtRpUJADAQHwD9j5N832HtHsB4PQm6expyAAh9hk0hKS0tVXp6ukaMGCFJmjJlioqLizV69OhO91+9erXy8/ONKgcAEACtHwDnf6s1mA+AxmDKju+xdg9grJ4E3UznAtAqoqWlpcWIJ/7jH/+okpISLV++XJL0yiuvaOvWrVqyZEmHfU+ePCm73a7PP//c24ExfPhwJSQkKCIiQj/+8Y81a9asTl9n2bJlWrZsmSRp//79+vvf/27E2wEAAAAAhKmbbrqJmQEmYFgHRme5SERERKf7rlu3Tjk5Oe2mj7z//vtKSUnRV199pWuvvVbnnnuuLr/88g6PnTVrljfcGDdunJKTk330DoxTW1sbFHUiPDE+YVaMze4561zKWbxJDe5vvqWMtlr0/oKr+ZbSYIxPmFkwjM/uFuhcW1bToZuPK1qFhmAYnzAPwwIMu92uqqoq7+3q6mqlpKR0um9hYWGH6SOt+w4ePFi33HKLSktLOw0wgFDAqtoAfMFsU3Y4tgHoqe6uUsZ0LgCSgQFGdna2duzYoV27dik1NVWFhYVatWpVh/2OHj2qt99+WytXrvRuO3HihDwej+Li4nTixAm98cYbevzxx40qFQionl46DAB6wiwn+RzbAPgal2IHYFiAERUVpSVLlmjChAlqbm7W9OnTlZmZqaVLl0qSZs+eLUlas2aNxo8fr/79+3sf++WXX+qWW26RJDU1NemOO+7QddddZ1SpQMCw4B4AIwT6JJ9jGwAAMIJhAYYkTZw4URMnTmy3rTW4aDVt2jRNmzat3bYRI0aovLzcyNIAU2BVbQChiGMbAAAwgqX7XQAYpSeXDgOAYMOxDQAAGIEAA4Zw1rlUXnVEzjpXoEsxtdYF96KtFsXZohRttQR0wT0A8AWObQAAwAiGTiFBeGLhtt4xy4J7wcQfVzbg6gnhiX933+HYBgAAfI0AAz7Fwm2nJ9AL7gUTfwRkhHDhiX933+PYBgAAfIkpJPCp1oXb2mpduA3oq7YB2XFXkxrcHs0vqvDpVCV/vAbMh393AICZMB0b6BwdGPApFm4LnHBofffHlQ24ekJ44t8dAGAWdAQCXaMDAz7Fwm2BUVxWo5zFmzR1+VblLN6ktWU1gS7JEP4IyAjhwhP/7gAAM6AjEDg1Agz4XK4jVe8vuForZ16s9xdcTWJssHD6Q+ePgIwQLjz589+dtmD/4ucNIJgwHRs4NaaQwBAs3OY/4db67o8rG3D1hPDkj3932oL9i583gGBDRyBwanRgAEEuHP/QJcXadF5avKHBgj9eA+Zj5L97OHVLmQE/bwDBiE5Q4NTowACCXOsfuvnf+paRP3QIJixCG5rvOZD4eQMIVnSCAl0jwABCAH/oEMzCpc0/HLulAomfN4BgxnRsoHNMIQFCBFMeEIzCqc2ftmD/4ucNAEDooQMDgFfrh0ZnnYuTfPhFuLX50y3lX/y8AQAILXRgAJD0dRt/zuJNkqScxZu0tqwmwBUhHIRjmz/dUv7FzxsAgNBBgAGgXRu/pJBu44e50OYPAACAnmIKSRgKh9X+0Tvh1sYPc6HNHwAAAD1BgBFmwmW1f/ROOLbxw1xYbR0AAADdYQpJGAmn1f7RO23b+CWFfBu/s86l8qojjH3gFPg9AQAAZkMHRhhhmgBOpbWNv/H4Ib2/4OqQHRN0IQHd4/cEAACYER0YYYRpAuhOa2gRquEFXUhA9/g9ARCs6BwDQh8BRhhhtX+Eu9YupLZau5AAfI3fEwDBqPVy8FOXb+Vy8EAIYwpJmGG1f4QzupCA7vF7AiDYtO0ca50qPb+oQjnpAznXBUIMHRhhKCnWpvPS4jmgI+zQhQR0j98TAMGGzjEgfNCBASCs0IUEdI/fEwDBxJedY8461ymPfd3dD8BYBBgAwk5SrI2TDqAb/J4ACBatnWPzv3X1pN4ew7q7AhNXaAICjwADnSJdBgAAQLDoa+dYd+tosM4GYA4EGOiAdBkAAADBpi+dY63raLSGE9I362gkxdq6vR+Af7CIJ9ppmy4fdzWpwe3R/KIKrqcNAACAkNXdOhq+XmejvOoI59fAaSDAQDus4gwACDd8mADQ3RWYfHWFpuKyGuUs3qSpy7cqZ/EmrS2rMeLtACGLKSRox5fpMgAAZse0SQCtultHw+h1NgB0jw4MtOOrdBkAYF50HHyNaZMAvi0p1qbz0uK7PPft7v5TodMZ6Ds6MNBBX9NlAIB50XHwDRblA+BPdDoDfUcHBjrVl3QZAHD6jOyOoOOgPT5MAPAnOp2BvqMDAwAAkzC6O4KOg/ZaP0zM/9bPPBx/FgD8g05noG8IMAAAMAF/LO5Gx0FHfJgA4G9JsTaONcBpYgoJAAA+0pfpH/5Y3I325c4xbRIAgOBABwYAAD7Q1+kf/uqOoOMAAAAEKzowAADoI18sjunP7ohg6Tjgcq8AAKAtOjAAAOgjXy2OSXfEN7jcKwAA+DYCDAAA+siX0z9Y3M0/C5oCAIDgwxQSAAD6iMUxfcsfC5oCAIDgQwcGAAA+wPQP3+FyrwAAoDN0YAAA4CPBsjim2dHRAgAAOmNoB0ZJSYnmzp2r5uZmzZw5U//1X//V7v6nn35ar776qiSpqalJH3/8sQ4cOKDExERJUnNzs8aNG6fU1FStX7/eyFLRS846F98yAggqHLeCCx0tAADg2wwLMJqbmzVnzhz97W9/k91uV3Z2tnJzczV69GjvPvPmzdO8efMkSevWrdOzzz7rDS8k6fnnn1dGRoaOHTtmVJk4DawMDyDYcNwKTixoCgAA2jJsCklpaanS09M1YsQI9evXT1OmTFFxcXGX+69evVr5+fne29XV1Xr99dc1c+ZMo0rEaWi7MvxxV5Ma3B7NL6qQs84V6NIAoFMct8zLWedSedUR/i0AAECPGBZg1NTUKC0tzXvbbrerpqam031PnjypkpIS5eXlebc98MADKigokMXCMh1mwsrwAIINxy1zKi6rUc7iTZq6fKtyFm/S2rLOzxEAAABaGTaFpKWlpcO2iIiITvddt26dcnJyvNNH1q9fr8GDB+vCCy/U5s2bT/k6y5Yt07JlyyRJ+/fvV21tbd8K95NgqfPbBkdJb8zO6uSeetXW8mEgVATr+EToO52xyXHLnC4aYtFb9zrabQv2Y0+w14/QxviEmTE+0VOGBRh2u11VVVXe29XV1UpJSel038LCwnbTR95//32tXbtWf/nLX9TQ0KBjx45p6tSpWrlyZYfHzpo1S7NmzZIkjRs3TsnJyT5+J75XW1sbFHV2ZW1ZjeYzlzxkBfv4ROjqy9j01XGLhUB9o7zqiKYu36rjribvtjhblFbOvFjnpcUHrrA+4NgJM2N8wswYn+gNwwKM7Oxs7dixQ7t27VJqaqoKCwu1atWqDvsdPXpUb7/9drtwYuHChVq4cKEkafPmzfrVr37VaXiBwGBleADBxhfHLRYC9R17QozcHk+7bW6PR/aEmABVBAAAgoFhC0xERUVpyZIlmjBhgjIyMjR58mRlZmZq6dKlWrp0qXe/NWvWaPz48erfv79RpcAASbE2nZcWT3gBIGj05bjFQqC+lRRrU0FelqKtFsXZohRttaggL4u/KQAA4JQM68CQpIkTJ2rixIntts2ePbvd7WnTpmnatGldPseVV16pK6+80oDqAADomdaFQBv0TddA60KgfOg+PXTzAQCA3jI0wACAQGCdAvgaUx6MkRRr43cUQMjhPAQwDgEGgJDCOgUwQuuUh28vBMqJKQCgLc5DAGMRYAAIGW3XKWht9Z9fVKGc9IF80ESfmWnKA9/uAYD5cB4CGI8AA0DIYJ0CGM0MUx74dg8AzInzEMB4hl2FBAD8jXUKQlfr1T7C/aofXA0FAMyL8xDAeAQYAEIGl2YMTcVlNcpZvEmSlLN4k9aW1QS4osBp/XavrdZv9wAAgcV5CGA8ppAACClmWqcAfde240CSt+MgXOcT8+0eAJgb5yGAsejAABBykmJtOi8tnpOGEEDHQXt8uwcA5sd5CGAcOjAAAKZFx0FHfLsHAADCFR0YAADTattxIImOg3/j2z0AABCO6MAAAJhaa8dB4/FDen/B1XxoRzvOOhfdKAB6jGMGENwIMAAAppcUa1PtcXGyiXaKy2q0oKhCVotFbo9HBXlZynWkBrosACbFMQMIfkwhAQAAQaftFWqOu5q8V6hx1rkCXRoAE+KYAYQGAgwAANBrzjqXyquOBOzknyvUAOgNjhlAaGAKCQAA6BUztGFzhRoAvcExAwgNdGAAAIAeM0sbdtsr1MTZorhCDYBT4pgBhAY6MAAAQI+1tmE36JtvMlvbsP39QaD1CjVcUQBAT3DMAIIfAQYAAOgxs7VhJ8Xa+BACoMc4ZgDBjSkkAACgx2jDBgAAgUIHBgAA6BXasAEAQCAQYAAAgF6jDRsAAPgbU0gAAAAAAIDpEWAAAAAAAADTI8AAAIQEZ51L5VVH5KxzBboUAAAAGIA1MAAAQa+4rEYLiipktVjk9nhUkJelXEdqoMsCAACAD9GBAQAIas46lxYUVajB7dFxV5Ma3B7NL6qgEwMAACDEEGAAAIJa9eF6WS3t/5xZLRZVH64PUEUAAAAwAgEGACCo2RNi5PZ42m1zezyyJ8QEqCIAAAAYgQADABDUkmJtKsjLUrTVojhblKKtFhXkZSkp1hbo0gAAAOBDLOIJAAh6uY5U5aQPVPXhetkTYggvAAAAQhABBgAgJCTF2gguAAAAQhhTSAAAAAAAgOkRYAAAAAAAANMjwAAAAAAAAKZHgAEAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAAAMD0CDAAAAAAAIDpEWAAAAAAAADTI8AAAAAAAACmR4ABAAAAAABMjwADAAAAAACYHgEGAAAAAAAwvYiWlpaWQBfhKwMHDtTZZ58d6DK6deDAAQ0aNCjQZQCdYnzCrBibMDPGJ8yM8QkzC5bxuXv3bh08eDDQZYS9kAowgsW4ceO0bdu2QJcBdIrxCbNibMLMGJ8wM8YnzIzxid5gCgkAAAAAADA9AgwAAAAAAGB6BBgBMGvWrECXAHSJ8QmzYmzCzBifMDPGJ8yM8YneYA0MAAAAAABgenRgAAAAAAAA0yPA8KOSkhKNGjVK6enpWrRoUaDLQZirqqrSVVddpYyMDGVmZur555+XJB06dEjXXnutRo4cqWuvvVaHDx8OcKUIZ83NzTr//PN14403SmJ8wjyOHDmi2267Teeee64yMjL0wQcfMD5hGs8++6wyMzM1ZswY5efnq6GhgfGJgJk+fboGDx6sMWPGeLedajwuXLhQ6enpGjVqlP76178GomSYGAGGnzQ3N2vOnDnasGGDKisrtXr1alVWVga6LISxqKgoPfPMM/r444/14Ycf6je/+Y0qKyu1aNEiXXPNNdqxY4euueYawjYE1PPPP6+MjAzvbcYnzGLu3Lm67rrr9Mknn6i8vFwZGRmMT5hCTU2NXnjhBW3btk3bt29Xc3OzCgsLGZ8ImGnTpqmkpKTdtq7GY2VlpQoLC/Wvf/1LJSUluvfee9Xc3ByIsmFSBBh+UlpaqvT0dI0YMUL9+vXTlClTVFxcHOiyEMaSk5N1wQUXSJLi4uKUkZGhmpoaFRcX6+6775Yk3X333frzn/8cwCoRzqqrq/X6669r5syZ3m2MT5jBsWPH9M4772jGjBmSpH79+ik+Pp7xCdNoampSfX29mpqadPLkSaWkpDA+ETCXX365EhMT223rajwWFxdrypQpstlsGj58uNLT01VaWurvkmFiBBh+UlNTo7S0NO9tu92umpqaAFYEfGP37t36xz/+oYsvvlhffvmlkpOTJX0dcnz11VcBrg7h6oEHHlBBQYEslm/+VDE+YQZffPGFBg0apB/96Ec6//zzNXPmTJ04cYLxCVNITU3Vww8/rKFDhyo5OVlnnnmmxo8fz/iEqXQ1HvnMhO4QYPhJZxd7iYiICEAlQHt1dXXKy8vTc889pwEDBgS6HECStH79eg0ePFgXXnhhoEsBOmhqatJHH32k//iP/9A//vEP9e/fn3Z8mMbhw4dVXFysXbt2ad++fTpx4oRWrlwZ6LKAHuEzE7pDgOEndrtdVVVV3tvV1dVKSUkJYEWA5Ha7lZeXpzvvvFO33nqrJGnIkCGqra2VJNXW1mrw4MGBLBFh6v3339fatWt19tlna8qUKdq0aZOmTp3K+IQp2O122e12XXzxxZKk2267TR999BHjE6bw5ptvavjw4Ro0aJCsVqtuvfVWbdmyhfEJU+lqPPKZCd0hwPCT7Oxs7dixQ7t27VJjY6MKCwuVm5sb6LIQxlpaWjRjxgxlZGToJz/5iXd7bm6uXnrpJUnSSy+9pJtvvjlQJSKMLVy4UNXV1dq9e7cKCwt19dVXa+XKlYxPmMJZZ52ltLQ0ffrpp5KkjRs3avTo0YxPmMLQoUP14Ycf6uTJk2ppadHGjRuVkZHB+ISpdDUec3NzVVhYKJfLpV27dmnHjh266KKLAlkqTCaipbM+HRjiL3/5ix544AE1Nzdr+vTpeuSRRwJdEsLYe++9p+9973saO3asd42BX/7yl7r44os1efJk7d27V0OHDtX//u//dlh4CfCnzZs361e/+pXWr18vp9PJ+IQplJWVaebMmWpsbNSIESO0YsUKeTwexidM4YknntBrr72mqKgonX/++Vq+fLnq6uoYnwiI/Px8bd68WQcPHtSQIUP005/+VJMmTepyPD711FP6/e9/r6ioKD333HO6/vrrA/wOYCYEGAAAAAAAwPSYQgIAAAAAAEyPAAMAAAAAAJgeAQYAAAAAADA9AgwAAAAAAGB6BBgAAAAAAMD0CDAAADAZp9Mph8Mhh8Ohs846S6mpqXI4HIqNjdW9995r2Otu3rxZW7ZsMez5AQAA+iIq0AUAAID2kpKSVFZWJkl68sknFRsbq4cfftjw1928ebNiY2N12WWXGf5aAAAAvUUHBgAAQWLz5s268cYbJX0dbNx9990aP368zj77bP3pT3/S/PnzNXbsWF133XVyu92SpP/7v//TFVdcoQsvvFATJkxQbW2tJOmFF17Q6NGjlZWVpSlTpmj37t1aunSpnn32WTkcDr377rs6cOCA8vLylJ2drezsbL3//vve177rrrt09dVXa+TIkfrd734nSaqtrdXll18uh8OhMWPG6N133w3ATwkAAIQqOjAAAAhSO3fu1FtvvaXKykpdeumlKioqUkFBgW655Ra9/vrruuGGG3T//feruLhYgwYN0muvvaZHHnlEv//977Vo0SLt2rVLNptNR44cUXx8vGbPnt2u2+OOO+7Qgw8+qO9+97vau3evJkyYoI8//liSVFFRoQ8//FAnTpzQ+eefrxtuuEGrV6/WhAkT9Mgjj6i5uVknT54M5I8HAACEGAIMAACC1PXXXy+r1aqxY8equblZ1113nSRp7Nix2r17tz799FNt375d1157rSSpublZycnJkqSsrCzdeeedmjRpkiZNmtTp87/55puqrKz03j527JiOHz8uSbr55psVExOjmJgYXXXVVSotLVV2dramT58ut9utSZMmyeFwGPfmAQBA2CHAAAAgSNlsNkmSxWKR1WpVRESE93ZTU5NaWlqUmZmpDz74oMNjX3/9db3zzjtau3atfv7zn+tf//pXh308Ho8++OADxcTEdLiv9bXa3r788sv1zjvv6PXXX9ddd92lefPm6Yc//KEv3ioAAABrYAAAEKpGjRqlAwcOeAMMt9utf/3rX/J4PKqqqtJVV12lgoICHTlyRHV1dYqLi/N2WEjS+PHjtWTJEu/t1oVFJam4uFgNDQ1yOp3avHmzsrOztWfPHg0ePFj33HOPZsyYoY8++shv7xUAAIQ+AgwAAEJUv3799Mc//lELFizQeeedJ4fDoS1btqi5uVlTp07V2LFjdf755+vBBx9UfHy8brrpJq1Zs8a7iOcLL7ygbdu2KSsrS6NHj9bSpUu9z33RRRfphhtu0CWXXKLHHntMKSkp2rx5sxwOh84//3wVFRVp7ty5AXz3AAAg1ES0tLS0BLoIAAAQPPx5aVcAAIBWdGAAAAAAAADTowMDAAAAAACYHh0YAAAAAADA9AgwAAAAAACA6RFgAAAAAAAA0yPAAAAAAAAApkeAAQAAAAAATI8AAwAAAAAAmN7/B5c4/ow/kYpmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_pool_simulation_plot(experiments, config_ids, 'a','j','P')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}