Experiment_results_block2_sigma50_short_testruns.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: 2\n",
      "Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (100, 15, 2, 24)\n",
      "Execution Method: local_simulations\n",
      "SimIDs   : [0, 0, 1, 1]\n",
      "SubsetIDs: [0, 0, 1, 1]\n",
      "Ns       : [0, 1, 0, 1]\n",
      "ExpIDs   : [0, 0, 0, 0]\n",
      "Execution Mode: parallelized\n",
      "{'q_sold': 2100, 'ri_sold': 1050, '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'}{'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",
      "\n",
      " R to R swap called \n",
      " R added to pool of   R added to pool of  i  =  1050\n",
      " R removed from pool of  j  =  16380.430836353917\n",
      "i{'q_sold': 2130, 'ri_sold': 4260, '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   =  987\n",
      " R removed from pool of  j  =  15412.112282068469\n",
      " {'q_sold': 2306, 'ri_sold': 4612, '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'}j  =  4260\n",
      " R removed from pool of  i  =  276.27648867690004\n",
      "{'q_sold': 1954, 'ri_sold': 3908, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3908\n",
      " R removed from pool of   R added to pool of i  =  251.44930871995166\n",
      " j  =  4612\n",
      " R removed from pool of  i  =  298.4414207537193\n",
      "{'q_sold': 1954, 'ri_sold': 3908, '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",
      "{'q_sold': 2316, 'ri_sold': 1158, '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  j  =  3908\n",
      " R removed from pool of  i  =  250.8084233700065\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  i  =  1158\n",
      " R removed from pool of  j  =  17755.910291042645\n",
      "{'q_sold': 1908, 'ri_sold': 3816, '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  =  274.29953857162036\n",
      " R added to pool of  j  =  3816\n",
      " R removed from pool of  {'q_sold': 2110, 'ri_sold': 1055, '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 i  =  1055\n",
      " R removed from pool of  j  =  16376.491080523934\n",
      " {'q_sold': 1908, 'ri_sold': 3816, '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'} =  252.268131034798\n",
      "{'q_sold': 1908, 'ri_sold': 954, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  i  =  954\n",
      " R removed from pool of  j  =  14282.334835263202\n",
      " {'q_sold': 2050, 'ri_sold': 4100, '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'}j  =  3816\n",
      " R removed from pool of  i  =  248.8341646041954\n",
      "{'q_sold': 1618, 'ri_sold': 3236, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4100\n",
      " R removed from pool of  i  =  276.6241061765468\n",
      "{'q_sold': 1656, 'ri_sold': 3312, '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'} R added to pool of \n",
      " R to R swap called \n",
      " R added to pool of   j  =  3312\n",
      " R removed from pool of  i  =  221.8232136084698\n",
      "{'q_sold': 1888, 'ri_sold': 3776, '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'}j\n",
      " R to R swap called \n",
      "  R added to pool of  j  =  3776\n",
      " R removed from pool of  i  =  251.1374716318678\n",
      " = {'q_sold': 1798, 'ri_sold': 899, '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'} 3236\n",
      "\n",
      " R removed from pool of  i  =  209.5711915899301\n",
      "{'q_sold': 2064, 'ri_sold': 4128, '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'}{'q_sold': 1820, 'ri_sold': 910, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   i  =  910\n",
      " R removed from pool of  j  =  13550.89993313537\n",
      "{'q_sold': 1718, 'ri_sold': 3436, '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'}j\n",
      " R to R swap called \n",
      "  R added to pool of  =  4128\n",
      " R removed from pool of  i  =  265.4394488526741\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",
      "j  =  3436\n",
      " R removed from pool of  i  =  233.05384487775154\n",
      "{'q_sold': 1956, 'ri_sold': 978, '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  R added to pool of  i  =  978\n",
      " R removed from pool of  j  =  14263.712211642182\n",
      " i  =  1147\n",
      " R removed from pool of  j  =  17604.663051052252\n",
      "{'q_sold': 2014, 'ri_sold': 1007, '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'}{'q_sold': 1716, 'ri_sold': 858, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  {'q_sold': 1892, 'ri_sold': 3784, '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'}i\n",
      "  =  858 R to R swap called \n",
      " R added to pool of  \n",
      "j R removed from pool of  j  =   12185.200918163988\n",
      "{'q_sold': 2024, 'ri_sold': 4048, '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  =  4048\n",
      " 3784\n",
      " R removed from pool of  i  =  249.877077907091\n",
      " R removed from pool of {'q_sold': 1770, 'ri_sold': 3540, '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'} i\n",
      " R to R swap called \n",
      "  =  287.3881009644829\n",
      "{'q_sold': 2076, 'ri_sold': 4152, '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 added to pool of  j  =  3540\n",
      " R removed from pool of  i  =  232.09891529707238\n",
      "{'q_sold': 1880, 'ri_sold': 3760, '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'} R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of \n",
      " j  =  3760\n",
      " R removed from pool of  i  =  244.77940467279404\n",
      "{'q_sold': 1942, 'ri_sold': 3884, '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  R added to pool of  j  =  4152\n",
      " R removed from pool of  i  =  292.32717993087135\n",
      "{'q_sold': 1880, 'ri_sold': 3760, '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'} j  =  3884\n",
      " R removed from pool of \n",
      " R to R swap called \n",
      " R added to pool of  j  =  3760\n",
      " R removed from pool of  i  =  262.6185741957743\n",
      " i  =  250.98681597877294\n",
      "{'q_sold': 2372, 'ri_sold': 4744, '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",
      "{'q_sold': 1998, 'ri_sold': 999, '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'} R added to pool of \n",
      " R to R swap called \n",
      " j  =  4744\n",
      " R removed from pool of  i  =  328.5234109873418\n",
      "{'q_sold': 1790, 'ri_sold': 3580, '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  i  =  999\n",
      " R removed from pool of  j  =  15288.625925970846\n",
      "{'q_sold': 2166, 'ri_sold': 1083, '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'} R added to pool of  j  =  3580\n",
      " R removed from pool of  i  =  245.85677952645347\n",
      "\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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  j  =  3512\n",
      " R removed from pool of  i  =  239.48661292972974\n",
      " {'q_sold': 2042, 'ri_sold': 4084, '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'}i  =  1083\n",
      " R removed from pool of  j  =  16072.022520390572\n",
      "\n",
      "{'q_sold': 1610, 'ri_sold': 3220, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4084\n",
      " R removed from pool of  i  =  276.39832999627106\n",
      " R added to pool of {'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'} j  =  3220\n",
      " R removed from pool of  i  =  219.74878314742818\n",
      "{'q_sold': 2040, 'ri_sold': 1020, '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",
      "\n",
      " R added to pool of  i  =  1020\n",
      " R removed from pool of  j  =  14773.810499069747\n",
      " R to R swap called \n",
      " R added to pool of  i  =  868\n",
      " R removed from pool of  j  =  12715.357723145979\n",
      "{'q_sold': 2148, 'ri_sold': 4296, '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",
      "{'q_sold': 2036, 'ri_sold': 4072, '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  R added to pool of  j  =  4296\n",
      " R removed from pool of  i  =  299.72555034188554\n",
      " {'q_sold': 1978, 'ri_sold': 989, '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'}j  =  4072\n",
      " R removed from pool of  i  =  280.36961121624336\n",
      "\n",
      "{'q_sold': 1940, 'ri_sold': 970, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i  =  970\n",
      " R removed from pool of  j  =  13948.375594681711\n",
      " R added to pool of {'q_sold': 1706, 'ri_sold': 3412, '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'} i  =  989\n",
      " R removed from pool of  j  =  14035.897103475756\n",
      "\n",
      "{'q_sold': 1858, 'ri_sold': 929, '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'} R to R swap called \n",
      "\n",
      " R to R swap called  R added to pool of \n",
      " j  =  3412\n",
      " R removed from pool of  i  =  239.803053969983\n",
      "{'q_sold': 1908, 'ri_sold': 3816, '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'} R added to pool of  i  =  929\n",
      " R removed from pool of  j  =  12827.672224752023\n",
      "\n",
      "{'q_sold': 2212, 'ri_sold': 1106, '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 to R swap called \n",
      " R added to pool of  j  =  3816\n",
      " R removed from pool of  i  =  266.246110284701\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  =  1106\n",
      " R removed from pool of  j  =  14839.457740931539\n",
      "{'q_sold': 1648, 'ri_sold': 3296, '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'} R added to pool of \n",
      " R to R swap called \n",
      "  R added to pool of i  =  1035\n",
      " R removed from pool of  j  =  14671.512511015637\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 j i   =  3296\n",
      " R removed from pool of  i  =  248.6317832726054\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  =  12627.31259549607\n",
      "{'q_sold': 1866, 'ri_sold': 3732, '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'}1033\n",
      " R to R swap called \n",
      "\n",
      " R added to pool of  R removed from pool of  j  =  14216.967759382562\n",
      "{'q_sold': 2124, 'ri_sold': 1062, '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",
      "j R added to pool of   =  3732\n",
      " R removed from pool of  i  =  287.0023133554496\n",
      "{'q_sold': 2208, 'ri_sold': 4416, '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 i j  =  4416\n",
      " R removed from pool of  i  =  336.6813809177838\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",
      " =  1062\n",
      " R removed from pool of  j  =  14190.873963731225\n",
      "{'q_sold': 1834, 'ri_sold': 3668, '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 added to pool of  R to R swap called \n",
      " j  R added to pool of  =  j  =  3668\n",
      " R removed from pool of  i  =  277.539062659489\n",
      "{'q_sold': 1940, 'ri_sold': 970, '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'} 4376\n",
      " R removed from pool of  \n",
      " R to R swap called \n",
      "i  =  330.5491212680936\n",
      "{'q_sold': 2068, 'ri_sold': 1034, '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'} R added to pool of \n",
      " i  =  970\n",
      " R to R swap called \n",
      " R removed from pool of  j  =  12697.37831055699\n",
      "{'q_sold': 2196, 'ri_sold': 4392, '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'} R added to pool of \n",
      " R to R swap called \n",
      " R added to pool of   j  =  4392\n",
      " R removed from pool of  i  =  338.48029477894306\n",
      "{'q_sold': 1906, 'ri_sold': 953, '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'}i\n",
      " R to R swap called \n",
      " R added to pool of   i  =  953\n",
      " R removed from pool of  j  =  12261.891756466706\n",
      "{'q_sold': 1964, 'ri_sold': 982, '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  =  982\n",
      " R removed from pool of  j 1034\n",
      " R removed from pool of  =  12304.94835032802\n",
      " {'q_sold': 1780, 'ri_sold': 3560, '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'}j  =  13555.99765794049\n",
      "{'q_sold': 1762, 'ri_sold': 881, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   j  =  3560\n",
      " R removed from pool of  i  =  286.81115515949205\n",
      "{'q_sold': 2164, 'ri_sold': 4328, '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'}i\n",
      " R to R swap called \n",
      " R added to pool of   j  =  4328\n",
      " R removed from pool of  i  =  345.70483164070174\n",
      "{'q_sold': 2272, 'ri_sold': 4544, '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",
      " 881\n",
      " R removed from pool of  j  =  11248.785447123228\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'q_sold': 1986, 'ri_sold': 993, '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'} R added to pool of  j  =  4544\n",
      " R removed from pool of  i  =  359.4875561541412\n",
      "\n",
      " R to R swap called \n",
      " R added to pool of {'q_sold': 2202, 'ri_sold': 1101, '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'} i  =  993\n",
      " R removed from pool of  j  =  12359.029620028217\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 2074, 'ri_sold': 4148, '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'} R added to pool of \n",
      " R to R swap called \n",
      " R added to pool of   j  =  4148i  =  1101\n",
      " R removed from pool of  j  =  13777.674448347301\n",
      "\n",
      "{'q_sold': 1872, 'ri_sold': 936, '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'} R removed from pool of  i  =  336.26352635142393\n",
      "\n",
      "{'q_sold': 2074, 'ri_sold': 1037, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1037\n",
      " R removed from pool of  j  =  12672.292179759941\n",
      " R added to pool of  i  =  936\n",
      " R removed from pool of  j  =  11394.843843449722\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",
      "{'q_sold': 1994, 'ri_sold': 3988, '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  i  =  1154\n",
      " R removed from pool of  j  =  13695.439977050293\n",
      "{'q_sold': 2314, 'ri_sold': 1157, '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'} R added to pool of \n",
      " R to R swap called \n",
      " j  =  3988\n",
      " R removed from pool of  i  =  330.2645685393363\n",
      "{'q_sold': 1478, 'ri_sold': 2956, '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  i  =  1157\n",
      " R removed from pool of  j  =  13319.13343380799\n",
      "{'q_sold': 2166, 'ri_sold': 1083, '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  j  =  2956\n",
      " R removed from pool of  i  =  242.927717495244\n",
      "{'q_sold': 2018, 'ri_sold': 1009, '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  =  1083\n",
      " R removed from pool of  j  =  12109.277071841643\n",
      " R added to pool of  i  =  1009\n",
      " R removed from pool of  j  =  12152.94943574979\n",
      "{'q_sold': 2020, 'ri_sold': 1010, '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'}{'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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i  =  971\n",
      " R removed from pool of  j  =  10574.262112049852\n",
      "{'q_sold': 1604, 'ri_sold': 3208, '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  R added to pool of  i  =  1010\n",
      " R removed from pool of  j  =  11843.188390859985\n",
      " {'q_sold': 1958, 'ri_sold': 979, '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'}j  =  3208\n",
      " R removed from pool of  i  =  297.13005005801097\n",
      "{'q_sold': 2072, 'ri_sold': 4144, '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",
      "\n",
      " R added to pool of  i  =  979\n",
      " R removed from pool of  j  =  11183.93856679101\n",
      " R to R swap called {'q_sold': 2296, 'ri_sold': 1148, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4144\n",
      " R removed from pool of  i  =  380.52359759411775\n",
      "{'q_sold': 1898, 'ri_sold': 949, '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  =  1148\n",
      " R removed from pool of  j  =  12758.095234021195\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  =  949\n",
      " R removed from pool of  j  =  10260.556020524702\n",
      "{'q_sold': 1900, 'ri_sold': 950, '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  =  9956.86542554712\n",
      "{'q_sold': 2184, 'ri_sold': 4368, '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  i  =  950\n",
      " R removed from pool of  j  =  10027.479080571444\n",
      " R added to pool of  j  =  4368\n",
      " R removed from pool of  i  =  406.2517882466782\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",
      "{'q_sold': 1896, 'ri_sold': 3792, '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'} R added to pool of \n",
      " R to R swap called \n",
      " i  =  1033\n",
      " R removed from pool of  j  =  10636.349335152307\n",
      "{'q_sold': 2104, 'ri_sold': 4208, '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  =  3792\n",
      " R removed from pool of  i  =  349.307706290856\n",
      " R added to pool of {'q_sold': 2020, 'ri_sold': 1010, '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'} j  =  4208\n",
      " R removed from pool of  i  =  411.85985997482203\n",
      "\n",
      "{'q_sold': 2194, 'ri_sold': 1097, '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'} R to R swap called \n",
      "\n",
      " R added to pool of  R to R swap called \n",
      " i  =  1010\n",
      " R removed from pool of  j  =  10872.92118189123\n",
      "{'q_sold': 1860, 'ri_sold': 3720, '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  R added to pool of  i  =  1097\n",
      " R removed from pool of  j  =  11113.894514547777\n",
      " j  =  3720\n",
      " R removed from pool of  i  =  348.4773579160683\n",
      "{'q_sold': 1922, 'ri_sold': 961, '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'}{'q_sold': 1936, 'ri_sold': 3872, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i  =  961\n",
      " R removed from pool of  j  =  10179.955150913098\n",
      "{'q_sold': 1708, 'ri_sold': 854, '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'} R added to pool of \n",
      " R to R swap called \n",
      " R added to pool of   i  =  854\n",
      " R removed from pool of  j  = j  =  3872\n",
      " R removed from pool of  i  =  385.5691509237513\n",
      "{'q_sold': 2060, 'ri_sold': 4120, '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 8842.828204759047 j  =  4120\n",
      " R removed from pool of  i  =  406.27739540231414\n",
      "\n",
      "{'q_sold': 2002, 'ri_sold': 4004, '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'}{'q_sold': 2054, 'ri_sold': 1027, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   i  =  1027\n",
      " R removed from pool of  j  =  10335.534406815772\n",
      "j  =  4004{'q_sold': 1954, 'ri_sold': 3908, '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 removed from pool of  i  =  388.94387509161606\n",
      "\n",
      " R to R swap called \n",
      " R added to pool of {'q_sold': 1718, 'ri_sold': 859, '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'} j  =  3908\n",
      " R removed from pool of  i  =  391.38178970036097\n",
      "{'q_sold': 1916, 'ri_sold': 958, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  i  =  859\n",
      " R removed from pool of  j  =  8791.696523841354\n",
      " i  =  958\n",
      " R removed from pool of  j  =  9499.830097467988\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'}{'q_sold': 1932, 'ri_sold': 3864, '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 \n",
      " i  =  920\n",
      " R removed from pool of  j  =  8916.797774712322\n",
      " R to R swap called \n",
      "{'q_sold': 1968, 'ri_sold': 984, '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'} R added to pool of \n",
      " j  =  3864\n",
      " R removed from pool of  i  =  379.79288359126076\n",
      " R to R swap called \n",
      " R added to pool of {'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'} i  =  984\n",
      " R removed from pool of  j  =  9320.863565045525\n",
      "\n",
      "{'q_sold': 2378, 'ri_sold': 1189, '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'} R to R swap called \n",
      " R to R swap called \n",
      "\n",
      " R added to pool of  i  =  1189\n",
      " R removed from pool of  j  =  10975.011671195272\n",
      "{'q_sold': 2036, 'ri_sold': 4072, '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'} R added to pool of \n",
      " R to R swap called \n",
      " j  =  4164\n",
      " R removed from pool of  i  =  405.3126627821475\n",
      "{'q_sold': 2052, 'ri_sold': 4104, '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  R added to pool of  j  =  4104\n",
      " R removed from pool of  i  =  395.5059969408903\n",
      " j  =  4072\n",
      " R removed from pool of  i  =  445.0635358041618{'q_sold': 1986, 'ri_sold': 3972, '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 \n",
      " j  =  3972\n",
      " R removed from pool of  i  =  379.0919257327914\n",
      "{'q_sold': 1618, 'ri_sold': 809, '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'}{'q_sold': 1996, 'ri_sold': 998, '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",
      "\n",
      " R added to pool of  R to R swap called \n",
      " R added to pool of   i  =  998\n",
      " R removed from pool of  j  =  9071.913089572452\n",
      "i  =  809\n",
      " R removed from pool of  j  =  8431.064632096444\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'}{'q_sold': 2494, 'ri_sold': 1247, '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",
      "\n",
      " R added to pool of  R to R swap called \n",
      "  R added to pool of j  =  4028\n",
      " R removed from pool of  i  =  446.0140986845363\n",
      "{'q_sold': 1962, 'ri_sold': 981, '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 i  =  981\n",
      " R removed from pool of  j  =  8804.396127548185\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  =  1247\n",
      " R removed from pool of  j  =  12667.92877900519\n",
      "\n",
      "{'q_sold': 1994, 'ri_sold': 997, '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'} R added to pool of \n",
      " R to R swap called \n",
      " j  =  4124\n",
      " R removed from pool of  i  =  462.3064211255405\n",
      "{'q_sold': 1768, 'ri_sold': 3536, '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'} R added to pool of \n",
      " R to R swap called \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R added to pool of  j  =  3536\n",
      " R removed from pool of  i  =  392.45040465146303\n",
      " i  =  997\n",
      " R removed from pool of  j  =  9852.466266304487\n",
      "{'q_sold': 2230, 'ri_sold': 4460, '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",
      "{'q_sold': 2152, 'ri_sold': 4304, '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 added to pool of  j  =  4460\n",
      " R removed from pool of  i  =  489.8981241788715\n",
      "{'q_sold': 2160, 'ri_sold': 4320, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4304\n",
      " R removed from pool of  i  =  438.52413021447137\n",
      "{'q_sold': 1820, 'ri_sold': 3640, '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'} R added to pool of \n",
      " R to R swap called \n",
      " j  =  4320 R added to pool of \n",
      " R removed from pool of  i  =  469.17967516509816\n",
      " j  =  3640\n",
      " R removed from pool of  i  =  367.2418448838871\n",
      "{'q_sold': 2282, 'ri_sold': 4564, '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",
      "{'q_sold': 1720, 'ri_sold': 860, '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  =  860\n",
      " R removed from pool of  j R added to pool of  j  =  4564\n",
      " R removed from pool of  i  =  490.07080023316666\n",
      "{'q_sold': 2118, 'ri_sold': 1059, '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",
      "  =  8473.10868212406\n",
      "{'q_sold': 2440, 'ri_sold': 4880, '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  R added to pool of  i  =  1059\n",
      " R removed from pool of  j  =  9796.290658065933\n",
      " j  =  {'q_sold': 1802, 'ri_sold': 3604, '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",
      "4880\n",
      " R removed from pool of  i  =  R added to pool of  j  =  3604 497.5100763300434\n",
      "\n",
      "{'q_sold': 1888, 'ri_sold': 944, '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'} R removed from pool of  i  =  392.697664313484\n",
      "{'q_sold': 2020, 'ri_sold': 4040, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  i  =  944\n",
      " R removed from pool of  j  =  9209.623275624122\n",
      " R added to pool of {'q_sold': 1900, 'ri_sold': 950, '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'} j  =  4040\n",
      " R removed from pool of  i  =  435.9064618877601\n",
      "{'q_sold': 1690, 'ri_sold': 3380, '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",
      "\n",
      " R added to pool of  j  =  3380\n",
      " R removed from pool of  i  =  R to R swap called \n",
      " R added to pool of  361.25364429177716\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'} i  =  950\n",
      " R removed from pool of  j  =  9058.463217631332\n",
      "{'q_sold': 1788, 'ri_sold': 3576, '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",
      "\n",
      " R added to pool of  R to R swap called \n",
      " j  =  3576\n",
      " R removed from pool of  i  =  377.6257281934377\n",
      "{'q_sold': 2096, 'ri_sold': 4192, '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'} R added to pool of  j  =  4028\n",
      " R removed from pool of  i  =  426.47954994416796\n",
      "\n",
      "{'q_sold': 1818, 'ri_sold': 3636, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3636\n",
      " R removed from pool of  i  =  381.2635027207434\n",
      " R added to pool of {'q_sold': 2310, 'ri_sold': 1155, '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'} j  =  4192\n",
      " R removed from pool of  i  =  438.35759582626633\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 1844, 'ri_sold': 3688, '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  i  =  1155\n",
      " R removed from pool of  j  =  10913.583556766156\n",
      "{'q_sold': 1936, 'ri_sold': 3872, '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'} R added to pool of \n",
      " R to R swap called \n",
      " j  =  3688\n",
      " R removed from pool of  i  =  381.85779827437364\n",
      "{'q_sold': 2164, 'ri_sold': 4328, '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  R added to pool of  j  =  3872\n",
      " R removed from pool of  i  =  413.44254485238343\n",
      "{'q_sold': 1754, 'ri_sold': 877, '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'} j  =  4328\n",
      " R removed from pool of  i  =  443.662479440216\n",
      "\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'} R to R swap called \n",
      "\n",
      " R added to pool of  R to R swap called \n",
      " R added to pool of   j  =  4092\n",
      " R removed from pool of  i  =  415.10790289985016\n",
      "{'q_sold': 2262, 'ri_sold': 4524, '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'}i\n",
      " R to R swap called \n",
      "  R added to pool of  =  877\n",
      " R removed from pool of  j  =  8168.291704989155\n",
      "{'q_sold': 1680, 'ri_sold': 840, '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'} j  =  4524\n",
      " R removed from pool of  i  =  454.0774759121705\n",
      "\n",
      " R to R swap called \n",
      " R added to pool of {'q_sold': 2038, 'ri_sold': 1019, '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",
      "i R added to pool of   =  840\n",
      " R removed from pool of  j  =  7666.226105083013\n",
      "{'q_sold': 2052, 'ri_sold': 4104, '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",
      "i  =  1019\n",
      " R removed from pool of  j  =  10082.686330884695\n",
      " R added to pool of {'q_sold': 2158, 'ri_sold': 1079, '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'} j  =  4104\n",
      " R removed from pool of  i  =  451.7491933738347\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 1754, 'ri_sold': 3508, '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  R added to pool of  j  =  3508\n",
      " R removed from pool of  i  =  382.3672789938282\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 i  =  868\n",
      " R removed from pool of  j  =  7918.068383534788\n",
      "{'q_sold': 2106, 'ri_sold': 1053, '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  =  1053\n",
      " R removed from pool of  j  =  9391.89073202468\n",
      " =  {'q_sold': 2060, 'ri_sold': 4120, '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'}1079\n",
      " R removed from pool of  j  =  10407.629075493896\n",
      "\n",
      "{'q_sold': 2052, 'ri_sold': 1026, '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'} R to R swap called \n",
      " R added to pool of  \n",
      " R to R swap called \n",
      " R added to pool of  ij  =  1026\n",
      " R removed from pool of  j  =  9649.221406063065\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",
      " =  4120\n",
      " R removed from pool of  i  =  465.11295995046385\n",
      " R added to pool of  j{'q_sold': 1864, 'ri_sold': 3728, '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'}  =  4140\n",
      " R removed from pool of  i  =  443.2887061270885\n",
      "\n",
      "{'q_sold': 2048, 'ri_sold': 4096, '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'} R to R swap called \n",
      "\n",
      " R added to pool of  R to R swap called \n",
      " j  =  3728\n",
      " R removed from pool of  i  =  416.5579323861748\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'} R added to pool of \n",
      " R to R swap called \n",
      "  R added to pool of j  =  4096\n",
      " R removed from pool of  i  =  434.0087194587104\n",
      "{'q_sold': 1858, 'ri_sold': 3716, '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",
      "i  =  1030\n",
      " R removed from pool of  j  =  9152.577370630344\n",
      "{'q_sold': 2374, 'ri_sold': 4748, '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  R added to pool of  j  =  4748\n",
      " R removed from pool of  i  =  537.4068059124984\n",
      "{'q_sold': 2096, 'ri_sold': 4192, '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 j  =  4192\n",
      " R removed from pool of  i  =  468.94904420292005\n",
      "{'q_sold': 1762, 'ri_sold': 3524, '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",
      " =  3716\n",
      " R removed from pool of  i  =  389.87377849430777\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'} R added to pool of \n",
      " R to R swap called \n",
      " R added to pool of   i  =  1066\n",
      " R removed from pool of  j  =  10078.62031066697\n",
      "{'q_sold': 1806, 'ri_sold': 903, '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'}j\n",
      " R to R swap called \n",
      "  =  3524\n",
      " R removed from pool of  i  =  390.27862764475867\n",
      "{'q_sold': 2158, 'ri_sold': 1079, '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  =  1079\n",
      " R removed from pool of  j  =  9664.859248768771\n",
      " R added to pool of {'q_sold': 2232, 'ri_sold': 1116, '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'} i  =  903\n",
      " R removed from pool of  j  =  8339.51982928079\n",
      "\n",
      "{'q_sold': 1836, 'ri_sold': 3672, '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'} R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of \n",
      " j  =  3672\n",
      " R removed from pool of  i  =  399.98516578041017\n",
      "{'q_sold': 2194, 'ri_sold': 1097, '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'} R added to pool of \n",
      " i  =  1116\n",
      " R removed from pool of  j  =  9744.595979486825\n",
      " R to R swap called \n",
      "{'q_sold': 2084, 'ri_sold': 4168, '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  R added to pool of  j  =  4168\n",
      " R removed from pool of  i  =  480.8651353993919\n",
      "{'q_sold': 2166, 'ri_sold': 4332, '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 i j  =  4332\n",
      " R removed from pool of  i  =  494.1923843671102\n",
      " {'q_sold': 2380, 'ri_sold': 1190, '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'} =  1097\n",
      " R removed from pool of  j  =  9988.731532972539\n",
      "{'q_sold': 1952, 'ri_sold': 976, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  i  =  976\n",
      " R removed from pool of  j  =  8673.792429188383\n",
      " {'q_sold': 1850, 'ri_sold': 925, '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'}i  =  1190\n",
      " R removed from pool of  j  =  10348.20997533563\n",
      "{'q_sold': 1824, 'ri_sold': 912, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i  =   R added to pool of 925\n",
      " R removed from pool of  j  =  8041.443796309992\n",
      "{'q_sold': 1838, 'ri_sold': 919, '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'} i  =  912\n",
      " R removed from pool of  j  =  7741.704177856096\n",
      "\n",
      "{'q_sold': 1986, 'ri_sold': 3972, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i  =  919\n",
      " R removed from pool of  j  =  7822.024341118988\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'} R added to pool of \n",
      " R to R swap called \n",
      " j  =  3972\n",
      " R removed from pool of  i  =  470.28507705708034\n",
      " R added to pool of {'q_sold': 2056, 'ri_sold': 1028, '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'} i  =  1035\n",
      " R removed from pool of  j  =  8615.99639130896\n",
      "\n",
      "{'q_sold': 2166, 'ri_sold': 4332, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  j  =  4332\n",
      " R removed from pool of  i  =  523.4104070162866\n",
      " i  =  1028\n",
      " R removed from pool of  j  =  8627.804932974163\n",
      "{'q_sold': 2292, 'ri_sold': 1146, '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  =  1146\n",
      " R removed from pool of  {'q_sold': 2004, 'ri_sold': 4008, '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  =  9385.160264879116\n",
      "49918.05423203609 \n",
      "{'q_sold': 1948, 'ri_sold': 974, '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': 49918.05423203609}\n",
      "Ri_plus =  1020700.8082816184\n",
      "{'q_sold': 2546, 'ri_sold': 5092, '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",
      "j  =  4008\n",
      " R removed from pool of  i  =  478.808786198264\n",
      "49918.51195880495 R added to pool of  j  =  \n",
      "{'q_sold': 2126, 'ri_sold': 1063, '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': 49918.51195880495}\n",
      "Ri_plus =  1019080.7085600994\n",
      "5092\n",
      "{'q_sold': 1830, 'ri_sold': 915, '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'} R removed from pool of  i  =  625.4112734197406\n",
      "{'q_sold': 1786, 'ri_sold': 893, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   i  =  893\n",
      " R removed from pool of  j  =  7248.947389604524\n",
      "i  =  915\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 removed from pool of  R to R swap called \n",
      " j  =  7621.386148381629\n",
      "{'q_sold': 1956, 'ri_sold': 978, '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  R added to pool of  j  =  4196\n",
      " R removed from pool of  i  =  519.0855677957879\n",
      " {'q_sold': 2144, 'ri_sold': 1072, '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'}i  =  978\n",
      " R removed from pool of  j  =  7973.84965218557\n",
      "{'q_sold': 2096, 'ri_sold': 4192, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R to R swap called  j  =  4192\n",
      " R removed from pool of  i  =  516.8323646151694\n",
      "\n",
      "{'q_sold': 1986, 'ri_sold': 3972, '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'} R added to pool of \n",
      " i  =  1072\n",
      " R removed from pool of  j  =  8612.275889120065\n",
      " R to R swap called \n",
      " R added to pool of {'q_sold': 1832, 'ri_sold': 3664, '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  =  3664\n",
      " R removed from pool of  i  =  459.21019139664713\n",
      "{'q_sold': 1698, 'ri_sold': 3396, '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'}j\n",
      " R to R swap called \n",
      "  =  3972\n",
      " R removed from pool of  i  =  484.2288246653043\n",
      "{'q_sold': 1912, 'ri_sold': 3824, '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'} R added to pool of \n",
      " j  =  3396\n",
      " R removed from pool of  i  =  421.4610095847165\n",
      "{'q_sold': 2172, 'ri_sold': 1086, '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'} R to R swap called \n",
      " R added to pool of \n",
      " R to R swap called \n",
      " R added to pool of   i  =  1086\n",
      " R removed from pool of  j  =  8686.239596570493\n",
      "j{'q_sold': 2044, 'ri_sold': 1022, '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'}  =  3824\n",
      " R removed from pool of  i  =  461.2332070103148\n",
      "{'q_sold': 1752, 'ri_sold': 3504, '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  \n",
      "i  =  1022\n",
      " R removed from pool of  j  =  7985.522297778516\n",
      " R to R swap called \n",
      "{'q_sold': 2036, 'ri_sold': 1018, '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  R added to pool of   j  =  3504\n",
      " R removed from pool of  i  =  418.43773618736304\n",
      "i  =  1018\n",
      " R removed from pool of  j  =  {'q_sold': 2078, 'ri_sold': 1039, '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'}7778.2331494960235\n",
      "\n",
      "{'q_sold': 1824, 'ri_sold': 3648, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  j  i  =  1039\n",
      " R removed from pool of  j  =  8639.723828422371\n",
      "{'q_sold': 2032, 'ri_sold': 4064, '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  =  4064\n",
      " R removed from pool of  i  =  491.78130177408457\n",
      "3648\n",
      " R removed from pool of  i  =  480.2609768952243\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'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  =  481.5972148736473\n",
      "{'q_sold': 1772, 'ri_sold': 886, '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  =  886\n",
      " R removed from pool of  j  =  7369.214753305656\n",
      "{'q_sold': 2114, 'ri_sold': 1057, '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'}{'q_sold': 2072, 'ri_sold': 1036, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  i  =  1057\n",
      " R removed from pool of  j  =  16430.60780720343\n",
      "{'q_sold': 2218, 'ri_sold': 4436, '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  =  4436\n",
      " R removed from pool of  i  =  289.42909032292664\n",
      " R added to pool of  {'q_sold': 2212, 'ri_sold': 4424, '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",
      "i  =  1036\n",
      " R removed from pool of  j  =  16110.31550984038\n",
      " R added to pool of {'q_sold': 1726, 'ri_sold': 3452, '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'} j  =  4424\n",
      " R removed from pool of  i  =  285.63220660213847\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 1814, 'ri_sold': 3628, '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'} R added to pool of  j  =  3452\n",
      " R removed from pool of  i  =  225.31878821959253\n",
      "{'q_sold': 2104, 'ri_sold': 4208, '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",
      "\n",
      " R added to pool of  R to R swap called \n",
      "  R added to pool of j  =  3628\n",
      " R removed from pool of  i  =  232.02488068770617\n",
      "{'q_sold': 2104, 'ri_sold': 4208, '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'} j\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4208\n",
      " R removed from pool of  i  =  266.6537424136186\n",
      " {'q_sold': 2104, 'ri_sold': 4208, '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'} =  4208\n",
      " R removed from pool of  i  =  \n",
      " R to R swap called \n",
      "272.1823974578874 R added to pool of \n",
      " j  =  4208\n",
      " R removed from pool of  i  =  264.0430070486618\n",
      "{'q_sold': 2116, 'ri_sold': 1058, '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",
      "{'q_sold': 2772, 'ri_sold': 1386, '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  R added to pool of \n",
      " R added to pool of  i  =  1386\n",
      " R removed from pool of  j  =  21001.42169684393\n",
      "{'q_sold': 2228, 'ri_sold': 4456, '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'} i  =  1058\n",
      " R removed from pool of  j  =  16616.598883421626\n",
      "\n",
      " R to R swap called {'q_sold': 2192, 'ri_sold': 4384, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   j  =  4384\n",
      " R removed from pool of  i  =  283.16243599832524\n",
      "j  =  4456\n",
      " R removed from pool of {'q_sold': 2132, 'ri_sold': 4264, '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'} i  =  299.92543389857747\n",
      "{'q_sold': 1938, 'ri_sold': 3876, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R to R swap called  j  =  3876\n",
      " R removed from pool of  i  =  258.2866315228166\n",
      "{'q_sold': 2152, 'ri_sold': 1076, '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",
      "\n",
      " R added to pool of  i  =   R added to pool of  j  =  4264\n",
      " R removed from pool of  i  =  1076272.617017784738\n",
      "{'q_sold': 1846, 'ri_sold': 923, '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",
      "\n",
      " R removed from pool of {'q_sold': 1954, 'ri_sold': 3908, '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'} j  =  15913.565161933657\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 1904, 'ri_sold': 3808, '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'} R added to pool of \n",
      " j  =  3908\n",
      " R removed from pool of  i  =  247.4737558241468\n",
      " R to R swap called {'q_sold': 2018, 'ri_sold': 4036, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4036\n",
      " R removed from pool of  i  =  253.2239025279414\n",
      "{'q_sold': 2464, 'ri_sold': 1232, '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'} R added to pool of  j  =  3808\n",
      " R removed from pool of  i  =  261.2600319223711\n",
      "{'q_sold': 1628, 'ri_sold': 3256, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R to R swap called  j  =  3256\n",
      " R removed from pool of  i  =  221.48211198090576\n",
      "\n",
      " R added to pool of {'q_sold': 2138, 'ri_sold': 1069, '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'} i  = \n",
      " {'q_sold': 1678, 'ri_sold': 839, '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'}1232\n",
      " R removed from pool of  j  =  19287.24639099976\n",
      "{'q_sold': 1906, 'ri_sold': 953, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   i  =  953\n",
      " R removed from pool of  j  =  14338.17935506336\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'}i\n",
      " R to R swap called \n",
      "  =  839\n",
      " R removed from pool of  j  =  12200.865497140796\n",
      "{'q_sold': 2014, 'ri_sold': 1007, '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'} R added to pool of \n",
      " R to R swap called \n",
      " R added to pool of   i  =  1007\n",
      " R removed from pool of  j  =  14179.09322170564\n",
      "{'q_sold': 1786, 'ri_sold': 3572, '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'}i\n",
      " R to R swap called \n",
      "  =  1109\n",
      " R removed from pool of  j  =  16084.611148687312\n",
      "{'q_sold': 1858, 'ri_sold': 3716, '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 added to pool of  R to R swap called \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R added to pool of  j  =  3716\n",
      " R removed from pool of  i  =  260.07909116032533\n",
      "{'q_sold': 2136, 'ri_sold': 1068, '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 j  =  3572\n",
      " R removed from pool of  i  =  257.01050759875216\n",
      "{'q_sold': 1854, 'ri_sold': 3708, '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 i  =  1068\n",
      " R removed from pool of  j  =   15047.67147968593\n",
      "{'q_sold': 2044, 'ri_sold': 1022, '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",
      "j  R added to pool of  =  3708\n",
      " R removed from pool of  i  =  264.4060370635707\n",
      " i  =  1022\n",
      " R removed from pool of  j  =  13891.693125572987\n",
      "{'q_sold': 1870, 'ri_sold': 3740, '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'}{'q_sold': 2010, 'ri_sold': 4020, '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  =  4020\n",
      " R removed from pool of  i  =  \n",
      " R to R swap called \n",
      "283.94045035680756 R added to pool of \n",
      "{'q_sold': 2430, 'ri_sold': 4860, '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'} j  =  3740\n",
      " R removed from pool of \n",
      " R to R swap called \n",
      " R added to pool of  j  =   4860\n",
      " R removed from pool of  i  =  339.5582449452486\n",
      "{'q_sold': 2128, 'ri_sold': 1064, '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  =  278.6576980911195\n",
      "{'q_sold': 1596, 'ri_sold': 3192, '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 i  =  1064\n",
      " R removed from pool of  j  =  15038.82475064462\n",
      "{'q_sold': 2038, 'ri_sold': 4076, '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  =  4076j\n",
      " R removed from pool of  i  =  292.27930729603395\n",
      "{'q_sold': 1868, 'ri_sold': 3736, '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  3192\n",
      " R removed from pool of  i  =  235.76524257147685\n",
      "{'q_sold': 2172, 'ri_sold': 1086, '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 j  i  =  1086\n",
      " R removed from pool of  j  =  14494.562088280567\n",
      " = {'q_sold': 1842, 'ri_sold': 3684, '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'} 3736\n",
      " R removed from pool of  i  =  265.32854352286085\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 1978, 'ri_sold': 989, '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  =  989\n",
      " R removed from pool of  j  =  13753.684823674615\n",
      " R added to pool of  {'q_sold': 2102, 'ri_sold': 1051, '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",
      "j R added to pool of  i  =  1051\n",
      " R removed from pool of  j  =  14116.126705390983\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'}  =  3684\n",
      " R removed from pool of  i  =  279.8045237849001\n",
      "\n",
      "{'q_sold': 1760, 'ri_sold': 880, '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 to R swap called \n",
      " R added to pool of  i  =  880\n",
      " R removed from pool of  j  =  11471.559916192666\n",
      "{'q_sold': 1934, 'ri_sold': 3868, '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  =  327.7172305912245\n",
      "{'q_sold': 1906, 'ri_sold': 3812, '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'} R added to pool of  j  =  3868\n",
      " R removed from pool of  i  =  299.6047554863617\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 1870, 'ri_sold': 935, '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'} R added to pool of  j\n",
      " R to R swap called \n",
      " R added to pool of  i  =  935\n",
      " R removed from pool of  j   =  11945.949335420039\n",
      "{'q_sold': 2354, 'ri_sold': 1177, '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  =  1177\n",
      " R removed from pool of  j  =  14529.486637267517\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'}3812\n",
      " R removed from pool of  i  =  284.37295293062925\n",
      "{'q_sold': 1748, 'ri_sold': 874, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R to R swap called \n",
      " i  =  874\n",
      " R removed from pool of  j  =  11600.751356646419\n",
      "{'q_sold': 2184, 'ri_sold': 4368, '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'} R added to pool of \n",
      " j  =  4164\n",
      " R removed from pool of  i  =  341.90779492026195\n",
      "{'q_sold': 2426, 'ri_sold': 1213, '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   R to R swap called i  =  1213\n",
      " R removed from pool of  j  =  14569.971352111199\n",
      "\n",
      "{'q_sold': 2208, 'ri_sold': 4416, '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 added to pool of  R to R swap called  \n",
      "j  R added to pool of  =  4368\n",
      " R removed from pool of  i  =  332.101327186916\n",
      " {'q_sold': 1698, 'ri_sold': 849, '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  ji  =  849\n",
      " R removed from pool of  j  =  11071.237928519608\n",
      " {'q_sold': 1904, 'ri_sold': 3808, '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'} =  4416\n",
      " R removed from pool of  i  =  372.61057608691044\n",
      "{'q_sold': 2254, 'ri_sold': 1127, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of   R added to pool of i  =  1127\n",
      " R removed from pool of  j  =  13199.873851403128\n",
      "{'q_sold': 1860, 'ri_sold': 930, '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",
      "j  =  3808\n",
      " R removed from pool of  i  =  294.7281825977843\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R added to pool of {'q_sold': 2090, 'ri_sold': 4180, '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'} i  =  \n",
      " R to R swap called \n",
      "930\n",
      " R removed from pool of  j  =  10549.099435169832\n",
      "{'q_sold': 2156, 'ri_sold': 1078, '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'} R added to pool of  j  =  4180\n",
      "\n",
      " R removed from pool of  i  =  320.2346686059609\n",
      " R to R swap called {'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",
      "\n",
      " R to R swap called  R added to pool of \n",
      " i  =  1078\n",
      " R removed from pool of  j  =  11858.106690124958\n",
      "{'q_sold': 1990, 'ri_sold': 3980, '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  R added to pool of  j  =  3980\n",
      " R removed from pool of  i  =  365.7572830109857\n",
      " {'q_sold': 1352, 'ri_sold': 2704, '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",
      "j R added to pool of   =  3632\n",
      " R removed from pool of  i  =  275.5000786529854\n",
      " j  =  2704\n",
      " R removed from pool of  i  =  246.20616763131693\n",
      "{'q_sold': 1796, 'ri_sold': 3592, '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'}{'q_sold': 1950, 'ri_sold': 975, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   j  =  3592\n",
      " R removed from pool of  ii  =  975\n",
      " R removed from pool of  j  =  10591.324644349632\n",
      "{'q_sold': 1752, 'ri_sold': 876, '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  =  876\n",
      " R removed from pool of  j  =  9255.903330170317\n",
      "  =  269.98462251923047\n",
      "{'q_sold': 1714, 'ri_sold': 857, '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'}{'q_sold': 2328, 'ri_sold': 4656, '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  i  =  857\n",
      " R removed from pool of  j  =  11290.775171809015\n",
      "\n",
      "{'q_sold': 1912, 'ri_sold': 3824, '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'} R to R swap called \n",
      "\n",
      " R added to pool of  R to R swap called \n",
      " j  =  4656\n",
      " R removed from pool of  i  =  443.5029558867682\n",
      " R added to pool of  j  =  3824\n",
      " R removed from pool of  i  =  293.0091556725092\n",
      "{'q_sold': 2290, 'ri_sold': 1145, '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'}{'q_sold': 2028, 'ri_sold': 4056, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  i  =  1145\n",
      " R removed from pool of  j  =  14734.8580871101\n",
      "{'q_sold': 1714, 'ri_sold': 3428, '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'} j  =  4056\n",
      " R removed from pool of  i  =  381.6475015874021\n",
      "\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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of   R added to pool of j  i  =  1117\n",
      " R removed from pool of  j  =  11742.825065424666\n",
      " = {'q_sold': 2004, 'ri_sold': 1002, '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",
      "3428\n",
      " R removed from pool of  i  =  270.26319036702625\n",
      "{'q_sold': 1804, 'ri_sold': 3608, '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'} R to R swap called \n",
      " R to R swap called \n",
      "\n",
      " R added to pool of   R added to pool of j  =  3608\n",
      " R removed from pool of  i  =  281.8860842594877\n",
      "{'q_sold': 2094, 'ri_sold': 1047, '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'} i  =  1002\n",
      " R removed from pool of  j  =  10210.971488455078\n",
      "{'q_sold': 2040, 'ri_sold': 1020, '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",
      "\n",
      " R added to pool of  R to R swap called \n",
      "  R added to pool of i  =  1047\n",
      " R removed from pool of  j  =  13234.789537873236\n",
      "{'q_sold': 1880, 'ri_sold': 3760, '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 i j  =  3760\n",
      " R removed from pool of  i  =  301.11192729650065\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  =  319.3296649553813\n",
      "{'q_sold': 1924, 'ri_sold': 3848, '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 1020 j  =  3848\n",
      " R removed from pool of  i  =  301.9708398138173\n",
      "{'q_sold': 2024, 'ri_sold': 4048, '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",
      "\n",
      " R to R swap called  R removed from pool of  j  =  10095.151058091666\n",
      "{'q_sold': 2134, 'ri_sold': 1067, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  j  =  4048\n",
      " R removed from pool of  i  =  314.45649937563576\n",
      " i  =  1067\n",
      " R removed from pool of  j  =  10251.5613735097\n",
      "{'q_sold': 1754, 'ri_sold': 3508, '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'}{'q_sold': 2318, 'ri_sold': 1159, '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",
      "\n",
      " R added to pool of  R to R swap called \n",
      " R added to pool of   i  =  1159\n",
      " R removed from pool of  j  =  14715.169300048728\n",
      "j{'q_sold': 2428, 'ri_sold': 4856, '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'}  =  3508\n",
      " R removed from pool of  i  =  368.74216076754965\n",
      "{'q_sold': 1610, 'ri_sold': 805, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called  R added to pool of \n",
      " i  =  805\n",
      " R removed from pool of  j  =  7611.8384522660635\n",
      "{'q_sold': 1970, 'ri_sold': 3940, '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'} R added to pool of \n",
      "  R to R swap called j\n",
      "  =  R added to pool of  4856\n",
      " R removed from pool of  i  =  387.3537300308235\n",
      "{'q_sold': 2118, 'ri_sold': 1059, '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 j i  =  1059\n",
      " R removed from pool of  j  =  13132.874241464073\n",
      " {'q_sold': 2058, 'ri_sold': 1029, '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'} =  3940\n",
      " R removed from pool of  i  =  418.9424887455534\n",
      "{'q_sold': 1876, 'ri_sold': 938, '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",
      "\n",
      " R to R swap called  R to R swap called \n",
      "\n",
      " R added to pool of  R added to pool of   i  =  1029\n",
      " R removed from pool of  j  =  12340.431390162441\n",
      "{'q_sold': 1960, 'ri_sold': 980, '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'}i\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  =  938\n",
      " R removed from pool of  j  =  8758.348183291848\n",
      "{'q_sold': 1902, 'ri_sold': 3804, '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'} R to R swap called \n",
      " R to R swap called \n",
      "\n",
      " R added to pool of  R added to pool of  i  =  980\n",
      " R removed from pool of  j  =  11386.378236819175\n",
      "{'q_sold': 1884, 'ri_sold': 3768, '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 j  =  3768\n",
      " R removed from pool of  i  =  327.63481684215367\n",
      "{'q_sold': 2170, 'ri_sold': 4340, '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",
      " =  3804\n",
      " R removed from pool of  i  =  410.40548092662357\n",
      "{'q_sold': 2072, 'ri_sold': 4144, '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'} R added to pool of \n",
      " R to R swap called \n",
      "  R added to pool of j j  =  4144\n",
      " R removed from pool of  i  =  441.82892940030433\n",
      "{'q_sold': 1862, 'ri_sold': 931, '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",
      " 4340\n",
      " R removed from pool of  R added to pool of  i  =  373.2699573484715\n",
      " {'q_sold': 2180, 'ri_sold': 4360, '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'}i  =  931\n",
      " R removed from pool of  j  =  8673.147282075603\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 to R swap called \n",
      "\n",
      " R added to pool of   R added to pool of  j j =   =  4360\n",
      " R removed from pool of  i  =  370.6405623923056\n",
      "{'q_sold': 2164, 'ri_sold': 1082, '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   4124\n",
      " R removed from pool of  i  =  445.68189094914123\n",
      "i{'q_sold': 2126, 'ri_sold': 4252, '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'}  =  1082\n",
      " R removed from pool of  j  =  12587.604962427751\n",
      "{'q_sold': 1836, 'ri_sold': 918, '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 \n",
      " R to R swap called \n",
      " R added to pool of  j  =  4252\n",
      " R removed from pool of  i  =  453.8170606212225\n",
      " {'q_sold': 1888, 'ri_sold': 944, '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'}i  =  918\n",
      " R removed from pool of  j  =  10353.303802546929\n",
      "\n",
      "{'q_sold': 2150, 'ri_sold': 4300, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  j  =  4300\n",
      " R removed from pool of  i  i  =  944\n",
      " R removed from pool of  j  =  384.4203750088345\n",
      "{'q_sold': 2124, 'ri_sold': 4248, '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'} =  8784.998898022459\n",
      "{'q_sold': 1996, 'ri_sold': 998, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i  =  998\n",
      " R removed from pool of  j  =  9042.55037391116\n",
      " R added to pool of  j  =  4248\n",
      " R removed from pool of  i  =  375.36560863861814\n",
      "{'q_sold': 2024, 'ri_sold': 4048, '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'}{'q_sold': 2256, 'ri_sold': 1128, '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 \n",
      " R to R swap called \n",
      " R added to pool of  i  =  1128\n",
      " R removed from pool of  j  =  12619.672730406048\n",
      "{'q_sold': 1882, 'ri_sold': 941, '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  =  941\n",
      " R removed from pool of  j  =  10201.76545375958\n",
      "{'q_sold': 2110, 'ri_sold': 1055, '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'}j\n",
      " R to R swap called \n",
      " R added to pool of   =  4048\n",
      " R removed from pool of  i  =  450.13445015391335\n",
      "{'q_sold': 1960, 'ri_sold': 980, '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 i  =  980\n",
      " R removed from pool of  j  =  8750.104160678922\n",
      "{'q_sold': 1958, 'ri_sold': 979, '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  =  1055\n",
      " R removed from pool of  j  =  11101.90208702744\n",
      "{'q_sold': 2220, 'ri_sold': 4440, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   i  =  979\n",
      " R removed from pool of  j j  =  8513.611996480962\n",
      "{'q_sold': 2166, 'ri_sold': 4332, '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  =  4332\n",
      " R removed from pool of  i  =  501.34881748235784\n",
      "{'q_sold': 2164, 'ri_sold': 4328, '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'}4440\n",
      " R to R swap called \n",
      " R added to pool of  j  =  4328\n",
      " R removed from pool of  i  =  494.2620313675143\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 removed from pool of  i  =  425.8909225168172\n",
      "{'q_sold': 2006, 'ri_sold': 4012, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i  =  1131\n",
      " R removed from pool of  j  =  9819.983237530803\n",
      "{'q_sold': 2138, 'ri_sold': 4276, '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 to R swap called  R added to pool of \n",
      " j R added to pool of   =  4276\n",
      " R removed from pool of   j  =  4012\n",
      " R removed from pool of  i  = i  =  496.68561407900415\n",
      "{'q_sold': 1938, 'ri_sold': 3876, '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'} 380.27665788843296\n",
      "{'q_sold': 2066, 'ri_sold': 4132, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  j  =  4132\n",
      " R removed from pool of  i  =  387.2030078775715\n",
      "{'q_sold': 1974, 'ri_sold': 3948, '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'} j  =  3876\n",
      " R removed from pool of  i  =  444.6049319289159\n",
      "{'q_sold': 2020, 'ri_sold': 1010, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called  R added to pool of \n",
      " R added to pool of  j  =  3948\n",
      " R removed from pool of  i  =  365.81319236569107\n",
      "{'q_sold': 2120, 'ri_sold': 1060, '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'} i  =  1010\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R removed from pool of  j  =  \n",
      " R to R swap called \n",
      " R added to pool of 8739.096268105204\n",
      "{'q_sold': 1838, 'ri_sold': 919, '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",
      " i  =  1060\n",
      " R removed from pool of  j  =  11322.26008508529\n",
      "{'q_sold': 2420, 'ri_sold': 4840, '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'} R added to pool of \n",
      " i  =  919\n",
      " R removed from pool of  j  =  7751.1020993178245\n",
      "{'q_sold': 1800, 'ri_sold': 3600, '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'} R to R swap called \n",
      "\n",
      " R added to pool of  j  =  4840\n",
      " R removed from pool of  i  =  457.23650848818943\n",
      "{'q_sold': 1758, 'ri_sold': 3516, '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'} R to R swap called \n",
      "\n",
      " R to R swap called \n",
      " R added to pool of  j  =  3516\n",
      " R removed from pool of  i  =  328.2921996067744\n",
      "{'q_sold': 2232, 'ri_sold': 4464, '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'} R added to pool of \n",
      " j  =  3600\n",
      " R removed from pool of  i  =  429.59346755361184\n",
      " R to R swap called {'q_sold': 2160, 'ri_sold': 1080, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  i R added to pool of   j  =  4464\n",
      " R removed from pool of  =  1080\n",
      " R removed from pool of  j  =  8974.157884217682\n",
      "{'q_sold': 2126, 'ri_sold': 4252, '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",
      " i  =  412.2019060649909\n",
      "{'q_sold': 2126, 'ri_sold': 1063, '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'} R added to pool of \n",
      " R to R swap called \n",
      " j  R added to pool of  =  i  =  1063\n",
      " R removed from pool of  j  =  11400.657887523412\n",
      "{'q_sold': 1998, 'ri_sold': 3996, '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  =  3996\n",
      " R removed from pool of  i  =  376.4470876301639\n",
      "{'q_sold': 1822, 'ri_sold': 3644, '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 4252 j  =  3644\n",
      " R removed from pool of  i  =  339.63704980537295\n",
      "\n",
      "{'q_sold': 2016, 'ri_sold': 4032, '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'} R removed from pool of  i  =  515.5043796226382\n",
      "{'q_sold': 1866, 'ri_sold': 3732, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called  R added to pool of \n",
      " R added to pool of   j  =  4032\n",
      " R removed from pool of  i  =  371.807073027594\n",
      "{'q_sold': 2196, 'ri_sold': 4392, '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'}j\n",
      " R to R swap called \n",
      "  =  R added to pool of  3732\n",
      " R removed from pool of  i  =  446.8202487071976\n",
      " {'q_sold': 1972, 'ri_sold': 986, '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'}j  =  4392\n",
      " R removed from pool of  i  =  400.30763422045857\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 1836, 'ri_sold': 3672, '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  i  =  986\n",
      " R removed from pool of  j  R added to pool of  =  8177.916902416153\n",
      " j  = {'q_sold': 1936, 'ri_sold': 968, '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 3672 i  =  968\n",
      " R removed from pool of  j  =  7828.080622386769\n",
      "\n",
      "{'q_sold': 2084, 'ri_sold': 4168, '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'} R removed from pool of  i  =  330.9853229012806\n",
      "{'q_sold': 1888, 'ri_sold': 3776, '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 to R swap called \n",
      " R added to pool of  \n",
      "j R added to pool of   j  =  3776\n",
      " R removed from pool of  i  =  336.90251007210463\n",
      "{'q_sold': 1836, 'ri_sold': 918, '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  4168\n",
      " R removed from pool of  i  =  518.4018798968755\n",
      " {'q_sold': 2050, 'ri_sold': 4100, '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'}i  =  918\n",
      " R removed from pool of  j  =  10198.36135943199\n",
      "{'q_sold': 2050, 'ri_sold': 1025, '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",
      "\n",
      " R added to pool of  R to R swap called \n",
      "  R added to pool of i j  =  4100\n",
      "  R removed from pool of  i =   1025\n",
      " R removed from pool of  j  =  11057.217683499795\n",
      "{'q_sold': 1900, 'ri_sold': 950, '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 503.28713441756554\n",
      "\n",
      "{'q_sold': 1906, 'ri_sold': 3812, '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'} R added to pool of \n",
      "  R to R swap called i\n",
      "  R added to pool of  =   j950 \n",
      " =  R removed from pool of   j3812\n",
      " R removed from pool of  i  =  462.12076424318366\n",
      "{'q_sold': 2048, 'ri_sold': 1024, '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",
      "  =  9950.956258870312\n",
      "{'q_sold': 1712, 'ri_sold': 3424, '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'} R added to pool of  i  =  1024\n",
      " R removed from pool of  j  =  8385.869595220429\n",
      "{'q_sold': 1720, 'ri_sold': 3440, '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",
      "\n",
      " R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of  j  =  3440\n",
      " R removed from pool of  i  =  423.34659735579044\n",
      " R added to pool of  j  =  3424\n",
      " R removed from pool of  i  =  329.89967823424377\n",
      "{'q_sold': 1958, 'ri_sold': 3916, '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'}{'q_sold': 1858, 'ri_sold': 3716, '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",
      "\n",
      " R added to pool of  R to R swap called  j  =  3916\n",
      " R removed from pool of  i  =  373.40989076625556\n",
      "{'q_sold': 2064, 'ri_sold': 4128, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of  j  j  =  4128\n",
      " R removed from pool of  i  =  =   3716\n",
      " R removed from pool of  i  =  452.16473994846456\n",
      "{'q_sold': 2296, 'ri_sold': 4592, '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",
      "389.19734426843934 R added to pool of \n",
      " {'q_sold': 2172, 'ri_sold': 1086, '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'}j  =  4592\n",
      " R removed from pool of  i  =  551.503241620725\n",
      "\n",
      "{'q_sold': 1970, 'ri_sold': 985, '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'} R to R swap called \n",
      "\n",
      " R added to pool of  R to R swap called \n",
      " i  =  1086\n",
      " R removed from pool of  j  =  11400.785717214225\n",
      "{'q_sold': 1998, 'ri_sold': 999, '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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " R added to pool of  R added to pool of   i  =  985\n",
      " R removed from pool of  j  =  8155.44211499847\n",
      "i{'q_sold': 1800, 'ri_sold': 3600, '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'}  =  999\n",
      " R removed from pool of  j  =  10171.334838328883\n",
      "\n",
      " R to R swap called \n",
      "{'q_sold': 1998, 'ri_sold': 999, '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'} R added to pool of  j  =  3600\n",
      " R removed from pool of  i  =  437.91887447284535\n",
      "{'q_sold': 1944, 'ri_sold': 3888, '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",
      "\n",
      " R added to pool of  R to R swap called \n",
      " R added to pool of   ji   =  3888 = \n",
      " R removed from pool of  i   = 999\n",
      " R removed from pool of  j  =  9882.04289524327\n",
      "{'q_sold': 2066, 'ri_sold': 4132, '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",
      "467.4129744260572 R added to pool of \n",
      " {'q_sold': 1836, 'ri_sold': 918, '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'}j\n",
      "  R to R swap called  = \n",
      " 4132\n",
      " R added to pool of  R removed from pool of   ii  =   =  421.19726801407523\n",
      " {'q_sold': 2104, 'ri_sold': 4208, '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'}918\n",
      " R removed from pool of  j  =  7591.534292154363\n",
      "{'q_sold': 2308, 'ri_sold': 4616, '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 \n",
      "  R to R swap called j\n",
      "  =  4616\n",
      " R added to pool of  j  =  4208\n",
      " R removed from pool of  i  =  423.788129159715\n",
      "{'q_sold': 1980, 'ri_sold': 990, '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'} R removed from pool of \n",
      " R to R swap called \n",
      " R added to pool of  i  =  560.7961213656235\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",
      "i  =  990\n",
      " R removed from pool of  j R added to pool of   j  =  4164\n",
      " R removed from pool of  i  =  498.9477136514615\n",
      "{'q_sold': 2140, 'ri_sold': 4280, '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",
      "9751.180855654413\n",
      " R added to pool of  j  =  4280\n",
      " R removed from pool of  i  =  506.1272010898683\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",
      "{'q_sold': 1920, 'ri_sold': 3840, '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'} R to R swap called \n",
      " R to R swap called \n",
      " R added to pool of \n",
      " j  =  R added to pool of  3840 j  =  4092\n",
      " R removed from pool of  i  =  477.6453494480811\n",
      "\n",
      "{'q_sold': 2004, 'ri_sold': 4008, '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'} R removed from pool of  i  =  393.2102358657867\n",
      "\n",
      "{'q_sold': 2020, 'ri_sold': 1010, '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'} R to R swap called \n",
      "\n",
      " R added to pool of  R to R swap called  \n",
      " R added to pool of j i  =  1010\n",
      " R removed from pool of  j  =  9777.867648140178\n",
      "{'q_sold': 1846, 'ri_sold': 3692, '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  =  3692\n",
      " R removed from pool of  i  =  384.7646909772884\n",
      "{'q_sold': 2006, 'ri_sold': 4012, '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",
      "4008\n",
      " R removed from pool of  i  =  462.0270149856806\n",
      " R added to pool of  j  =  4012\n",
      " R removed from pool of  i  =  413.4210245301947\n",
      "{'q_sold': 2292, 'ri_sold': 1146, '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'}{'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 \n",
      " i  =  1146\n",
      " R removed from pool of   R to R swap called j\n",
      "  R added to pool of  =   j11007.245709695853\n",
      " {'q_sold': 2192, 'ri_sold': 4384, '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'} =  4200\n",
      " R removed from pool of  i  =  478.0998621052131\n",
      "\n",
      " R to R swap called \n",
      " R added to pool of {'q_sold': 2432, 'ri_sold': 1216, '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",
      "j R to R swap called  \n",
      " =  R added to pool of   i  =  1216\n",
      " R removed from pool of  4384\n",
      " R removed from pool of  i  =  460.87848256365396\n",
      "{'q_sold': 1848, 'ri_sold': 3696, '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'}j\n",
      " R to R swap called   =  10577.733354651718\n",
      "\n",
      "{'q_sold': 2176, 'ri_sold': 4352, '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  R added to pool of  j  =  4352\n",
      " R removed from pool of  i  =  505.09378408198245\n",
      "{'q_sold': 2038, 'ri_sold': 1019, '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  =  1019\n",
      " R removed from pool of  j  =  8720.526036826079\n",
      "49988.93057023108j\n",
      "{'q_sold': 2438, 'ri_sold': 1219, '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': 49988.93057023108}\n",
      "Ri_plus =  1013377.4901604957\n",
      "{'q_sold': 1840, 'ri_sold': 3680, '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",
      " =  3696\n",
      " R removed from pool of  i  =  383.9590298032854\n",
      "49902.21438502438 R added to pool of \n",
      " j  =  3680\n",
      " R removed from pool of  i  =  433.36154034617357\n",
      "{'q_sold': 1882, 'ri_sold': 941, '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'}{'q_sold': 1834, 'ri_sold': 917, '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': 49902.21438502438}\n",
      " R to R swap called \n",
      " R added to pool of \n",
      " iRi_plus =    = 1009047.6827975422\n",
      " 941\n",
      " R removed from pool of {'q_sold': 1576, 'ri_sold': 3152, '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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  R to R swap called j  =  7937.513933142647\n",
      "{'q_sold': 1896, 'ri_sold': 3792, '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 \n",
      " j  =  3792\n",
      " R removed from pool of  i  =  452.46307165105827\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  R added to pool of  j   =  3700\n",
      " R removed from pool of  i  =  436.3368736162083\n",
      "{'q_sold': 2032, 'ri_sold': 1016, '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'}j\n",
      " R to R swap called \n",
      " R added to pool of   i  =  1016\n",
      " R removed from pool of  j  =  8549.945514939958\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  3152\n",
      " R removed from pool of  i  =  324.1741827757796\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",
      " i  =  1035\n",
      " R removed from pool of  j  =  8479.089081697632\n",
      "{'q_sold': 2192, 'ri_sold': 4384, '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'} R added to pool of  \n",
      "j R to R swap called \n",
      "  R added to pool of  =   4752\n",
      " R removed from pool of  i  =  j 483.1194096643012 =  4384\n",
      " R removed from pool of  i  =  538.6088921338087\n",
      "\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'}{'q_sold': 1822, 'ri_sold': 911, '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 \n",
      " i  =  911\n",
      " R removed from pool of  j  =  8906.230895603308\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'} R to R swap called \n",
      "\n",
      " R added to pool of  R to R swap called  \n",
      "i  =  943\n",
      " R removed from pool of  j  =  7635.689649582142\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'} R added to pool of \n",
      " j  =  4200\n",
      " R removed from pool of  i  R to R swap called  =  432.5658040003618\n",
      "{'q_sold': 2368, 'ri_sold': 4736, '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",
      "\n",
      " R to R swap called \n",
      " R added to pool of  R added to pool of   i  =  j 868\n",
      " =  R removed from pool of  4736\n",
      " R removed from pool of  i  =  481.43969441310037\n",
      "{'q_sold': 2236, 'ri_sold': 1118, '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  =  1118\n",
      "j  =  6866.862375645316\n",
      "{'q_sold': 1908, 'ri_sold': 954, '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 removed from pool of  j  =  10898.843648846028\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'} R added to pool of \n",
      " R to R swap called \n",
      " R added to pool of   j  =  3316\n",
      " R removed from pool of  i  =  343.9409976460738\n",
      "{'q_sold': 2272, 'ri_sold': 4544, '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'}i\n",
      " R to R swap called \n",
      " R added to pool of   j  =  4544\n",
      " R removed from pool of  i  =  465.909218137851\n",
      " =  954\n",
      " R removed from pool of  j  =  7374.94506237458\n",
      "{'q_sold': 1978, 'ri_sold': 3956, '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  =  3956\n",
      " R removed from pool of  i  =  400.6189540621126\n",
      "{'q_sold': 2248, 'ri_sold': 1124, '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  =  1124\n",
      " R removed from pool of  j  =  10985.37516407203\n",
      "Total execution time: 3.00s\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": {
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       "</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",
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       "      <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",
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       "      <td>2000000</td>\n",
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       "      <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>1050</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>1065</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>j</td>\n",
       "      <td>977</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>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>i</td>\n",
       "      <td>1158</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>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                    j   \n",
       "4  1000000  1000000000000  ...  1000000000000                    i   \n",
       "\n",
       "   trade_random_size  trade_random_direction purchased_asset_id  simulation  \\\n",
       "0               1000            test_q_for_r                N/A           0   \n",
       "1               1050            test_r_for_r                  j           0   \n",
       "2               1065            test_r_for_r                  i           0   \n",
       "3                977            test_r_for_r                  i           0   \n",
       "4               1158            test_r_for_r                  j           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": {
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       "      <td>98</td>\n",
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       "      <td>1000000000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>test_r_for_r</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
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       "  </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",
       "399   1191  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "400   1194  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "401   1197  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "402   1200  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "403   1203  2000000  1000000   10000  2000000  1000000   10000  1000000   \n",
       "\n",
       "      UNI_ji        UNI_Sij  ...              C  asset_random_choice  \\\n",
       "399  1000000  1000000000000  ...  1000000000000                    i   \n",
       "400  1000000  1000000000000  ...  1000000000000                    j   \n",
       "401  1000000  1000000000000  ...  1000000000000                    i   \n",
       "402  1000000  1000000000000  ...  1000000000000                    i   \n",
       "403  1000000  1000000000000  ...  1000000000000                    i   \n",
       "\n",
       "     trade_random_size  trade_random_direction purchased_asset_id  simulation  \\\n",
       "399               1035            test_r_for_r                  j           1   \n",
       "400               1096            test_r_for_r                  i           1   \n",
       "401                943            test_r_for_r                  j           1   \n",
       "402                868            test_r_for_r                  j           1   \n",
       "403                954            test_r_for_r                  j           1   \n",
       "\n",
       "     subset  run  substep timestep  \n",
       "399       1    2        3       96  \n",
       "400       1    2        3       97  \n",
       "401       1    2        3       98  \n",
       "402       1    2        3       99  \n",
       "403       1    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": {
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       "      <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",
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       "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, 1050, 1065,  977, 1158,  954, 1025,  828,  944,  899,  910,\n",
       "        859,  978,  858, 1012, 1038,  940, 1186,  895,  878, 1021,  868,\n",
       "       1018,  970,  853, 1035, 1033, 1062,  917, 1098,  953,  982,  890,\n",
       "       1082, 1136, 1101,  936,  997,  739, 1009, 1010,  979, 1148,  920,\n",
       "       1092,  948,  930,  961,  854, 1001,  966, 1041, 1026,  993,  809,\n",
       "       1247, 1076,  860, 1220,  950,  894, 1048,  922, 1023, 1131, 1019,\n",
       "       1079, 1024,  929, 1066,  903,  918, 1097,  976,  925,  919, 1083,\n",
       "       1002, 1063,  915,  956,  876, 1039, 1016, 1006,  886,  987, 1153,\n",
       "       1077, 1055, 1032, 1147, 1007,  946,  885,  971,  999,  805, 1020,\n",
       "       1074,  989, 1106,  824,  962,  933, 1104, 1094, 1034,  881, 1037,\n",
       "       1154, 1157,  802, 1036,  949, 1052,  968, 1030, 1027,  958,  984,\n",
       "       1189,  998,  981, 1031,  884, 1115, 1080, 1141, 1059,  901,  845,\n",
       "        909, 1155,  877,  840, 1053,  932, 1187, 1116, 1042, 1190,  912,\n",
       "       1028, 1146,  974, 1273,  893, 1049, 1072,  916,  849, 1086, 1022,\n",
       "        863, 1386, 1114,  969,  952,  814, 1069,  839,  927, 1005, 1215,\n",
       "       1064,  934, 1051, 1087,  874, 1045,  908,  898,  857, 1145,  902,\n",
       "       1047, 1159, 1214, 1029,  980,  942, 1085, 1090, 1075, 1128,  941,\n",
       "       1110, 1003, 1060, 1210,  879,  911, 1008,  856,  990,  960,  923,\n",
       "       1096,  924,  788, 1188, 1184, 1118,  829, 1124, 1057, 1109,  907,\n",
       "       1058, 1232, 1068,  935,  798,  921,  880,  967, 1177, 1213, 1127,\n",
       "       1078,  995,  676,  975, 1164, 1014, 1117, 1067,  985,  938,  951,\n",
       "        931,  900,  986,  972, 1070, 1216, 1088, 1219,  943], 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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YP2cjR45csOhhQC/Mb5aVuc2yMre7ce7h1WzMvlezeKfbynS/ubNbq2riyeBtbE1/0R5a9T8HAAA4G2urKye+V7NYhw8fzg033HCnkwN7rbWWG264IYcPHz7jx6jAYPDmFRjjkQQGAACcjfFoJRtb+ztgHopjx47lmmuuyWc+85lsbW1lNBotekindfjw4Rw7duyMr5fAYPDmmeI1FRgAAHBWxqPKRAXGvjAej/OgBz0oSbK+vp4jR44seETdEbExeCowAABgd9ZWR5aQ0DsRG4M3mZW6qcAAAICzszaqE38YhL6I2Bi8+S/aNRUYAABwVqY9MCQw6JeIjcHTAwMAAHZHE0/2goiNwZtsbmelktHK/t8nGQAA9qPxaMUSEnongcHgTba2VV8AAMAurK2WJST0TtTG4E02t+1AAgAAu6AHBntB1MbgTba2c0gFBgAAnLU1PTDYA6I2Bm9jc9sOJAAAsAvjVT0w6J+ojcGbbG1nrAIDAADO2ni0ksnWdlpThUF/RG0M3saWCgwAANiNtdF0R7/NbQkM+iNqY/A08QQAgN2Zf5/WyJM+idoYvMlWs40qAADswokExqYKDPojamPwJptblpAAAMAuzHvKTVRg0CNRG4O3oQIDAAB25ZAlJOyB3qO2qhpV1bur6o2nOPfQqvrjqjpeVS/YcfwhVfWeHT83VdXzZudeUlWf2nHucX2/B5bbtAdGLXoYAABwYI1Xp9+nbaVKn1b34DWem+RDSc47xbnPJfnRJE/eebC19uEkj0imCZAkn0ryhh2X/EJr7aU9jJUB2tjaVoEBAAC7oIkne6HXqK2qjiV5fJJXnOp8a+361to7k2zcztM8JslftdY+3sMQwS4kAACwS/Pv03pg0Ke+o7aXJXlhkt3M4qcmee1Jx55TVe+tqldW1T138dyQiQoMAADYlbUTFRh2IaE/vS0hqaonJLm+tXZlVT36LJ9jLckTk7x4x+GXJ/mpJG32788l+cFTPPaSJJckydGjR7O+vn42Q1i4gzrug+TWyWY2j9/qs95jPm+WmfnNsjK3WVbm9u7ddONNSZJPX/eZrK/esuDRsNMyze8+e2A8KskTZ002Dyc5r6pe01p7+p14jscm+bPW2nXzAztvV9UvJ/mq5qCz6y5LclmSXHzxxe3IkSNn8RYWa319PQdx3AfNVntf7nHe3XzWe8jcZpmZ3ywrc5tlZW534763rCW5Oufd4545cuSCRQ+HmWWb373VzbfWXtxaO9ZauzDTZSBvv5PJiyR5Wk5aPlJVOz/970ny/l0NlMHTAwMAAHZnviRbDwz6tBe7kHyFqnp2krTWLq2q+yZ5V6Y7lGzPtkp9WGvtpqq6a5K/m+SHT3qKn62qR2S6hORjpzgPd8rGVtMDAwAAduFEE0/bqNKjPUlgtNauSHLF7PalO45/Osmx0zzm5iTnn+L4M3oZJIPUWstkSwUGAADsxvwPgpp40idRG4M2/wV7SAUGAACctfGJXUhUYNAfURuDNl+jNx7VgkcCAAAH1/z7tB4Y9EkCg0HbmK3RW7OEBAAAztqaCgz2gKiNQTtRgWEJCQAAnLUTS0g08aRHojYGbaICAwAAdk0TT/aCqI1Bm1dg2EYVAADO3oltVC0hoUeiNgZtvkZPBQYAAJy9E008LSGhR6I2Bm3+C3YsgQEAAGetqjIelSae9ErUxqBtWEICAACdGI9WJDDolaiNQTuuAgMAADoxTWBo4kl/RG0M2oldSFRgAADAroxHK5p40itRG4M2zxAfksAAAIBdObS6kg1NPOmRqI1B08QTAAC6MR6VCgx6JWpj0DTxBACAbmjiSd9EbQzabRUYteCRAADAwTYerWSyqYkn/ZHAYNAmKjAAAKAT41UVGPRL1MagndiFRA8MAADYlbVRSWDQK1Ebg6YHBgAAdEMPDPomamPQ7EICAADdWFtdyWRLDwz6I2pj0Da2tlOVrK5o4gkAALsxbeKpAoP+SGAwaMe3tjMeraRKAgMAAHZjzRISeiaBwaBtbLYcsnwEAAB2bayJJz0TuTFok62tjDXwBACAXRuPVrJhCQk9ErkxaBubzRaqAADQgbEmnvRM5MagTba2M17V/wIAAHZLDwz6JoHBoE22tlVgAABAB9ZWJTDol8iNQZtsTnchAQAAdmc8Ktuo0iuRG4O2sbWdQ5p4AgDAro1HK9ncbtne1geDfojcGDQVGAAA0I359+qNbVUY9EPkxqBtbG1nTQUGAADs2ry33IadSOiJyI1BU4EBAADdGI+mu/tt6INBT0RuDNpkq6nAAACADoxX5xUYEhj0Q+TGoE02t2yjCgAAHZh/r55IYNATkRuDtqECAwAAOjH/Xm0rVfoicmPQpj0watHDAACAA2+siSc9k8Bg0OxCAgAA3bgtgaECg36I3Bg0u5AAAEA35pXNemDQF5EbgzZRgQEAAJ2YN/G0jSp9EbkxWK21aQJDBQYAAOzabduo6oFBP3qP3KpqVFXvrqo3nuLcQ6vqj6vqeFW94KRzH6uq91XVe6rqXTuO36uq3lpVV83+vWff74HltLnd0lokMAAAoANremDQs72I3J6b5EOnOfe5JD+a5KWnOf/trbVHtNYu3nHsRUne1lq7KMnbZvfhTpv/YrWEBAAAdm/eW+64JST0pNfIraqOJXl8klec6nxr7frW2juTbNyJp31SklfNbr8qyZN3M0aGa74/tSaeAACwe2ur0yaeKjDoS9+R28uSvDDJ2czgluQtVXVlVV2y4/h9WmvrSTL79967HiWDNFGBAQAAnbGNKn1b7euJq+oJSa5vrV1ZVY8+i6d4VGvt2qq6d5K3VtVftNbecSde/5IklyTJ0aNHs76+fhZDWLyDOu6DYP2m40mSm790k895AXzmLDPzm2VlbrOszO1ufP6LkyTJZ274fNbXRwseDXPLNL97S2AkeVSSJ1bV45IcTnJeVb2mtfb0M3lwa+3a2b/XV9Ubknxzknckua6qjrTW1qvqSJLrT/P4y5JcliQXX3xxO3LkyO7f0R5bX1/PQRz3QXHr+MtJknuffy+f8x4zt1lm5jfLytxmWZnb3Vm92/Ek789dzz3PZ7pPLNv87q12vrX24tbasdbahUmemuTtZ5q8qKpzqurc+e0kfy/J+2enL0/yzNntZyb5nU4HzmDogQEAAN05sQuJJp70pM8KjFOqqmcnSWvt0qq6b5J3JTkvyXZVPS/Jw5JckOQNVTUf46+11t48e4qfSfK6qvqhJJ9I8pS9fQcsC7uQAABAd+bfq/XAoC97ksBorV2R5IrZ7Ut3HP90kmOneMhNSb7+NM91Q5LHdD5IBuf4iQqMWvBIAADg4Jt/r56owKAn/vTMYKnAAACA7oxWKlUqMOiPyI3BmmeG1/TAAACAXauqjEcrmWy1RQ+FJSVyY7BUYAAAQLfWRisqMOiNyI3BsgsJAAB0azwqCQx6I3JjsCYqMAAAoFNjFRj0SOTGYOmBAQAA3VpbXclkUw8M+iFyY7A2Zs2FVGAAAEA31kYrJyqdoWsiNwZrsrmVRA8MAADoyni0ko1NCQz6IXJjsFRgAABAt8armnjSH5EbgzUvbRuPasEjAQCA5TC2hIQeSWAwWJp4AgBAt+xCQp9EbgzWZGs741GlSgUGAAB0YW20cmKpNnRNAoPB2tjcVn0BAAAdWltVgUF/RG8M1mRrO2MNPAEAoDPjUZ1Yqg1dE70xWBtbKjAAAKBLmnjSJ9Ebg3V8cztjCQwAAOjMmiae9Ej0xmBtbLUcsoQEAAA6Mx6tZGNTE0/6IXpjsCabWyowAACgQ+PVUoFBb0RvDNZkcztrKjAAAKAzemDQJ9Ebg7Wx1TIe1aKHAQAAS8M2qvRJAoPBUoEBAADdWhut2EaV3ojeGKzJ1nbWVkeLHgYAACyN8Wgl2y3Z2tbIk+5JYDBYk83trFlCAgAAnZk3ybeMhD5IYDBYG1uWkAAAQJfmPeY08qQPojcGa7K1bRtVAADo0PwPhBv6YNAD0RuDtbG5nTUJDAAA6MxtS0j0wKB7ojcGa7K1nbElJAAA0Jk1PTDokeiNwZqowAAAgE7N/0B43BISeiB6Y7AmmngCAECn5rv8qcCgD6I3Bmtjq6nAAACADtlGlT6J3hikre2Wre1mFxIAAOiQBAZ9Er0xSPNfqJaQAABAd+YJjMmmXUjonuiNQZo3FRrP1ugBAAC7t7aqBwb9kcBgkOa/UA+pwAAAgM6sjUZJJDDoh+iNQZqcqMDwPwEAAOjKeFaBMbGNKj0QvTFIemAAAED3TvTAUIFBD0RvDJIKDAAA6N7aiV1INPGke6I3BmmiAgMAADpnG1X6JHpjkOYVGGsqMAAAoDPzXf4kMOhD79FbVY2q6t1V9cZTnHtoVf1xVR2vqhfsOH7/qvr9qvpQVX2gqp6749xLqupTVfWe2c/j+n4PLJ95SZsKDAAA6M549v1aE0/6sLoHr/HcJB9Kct4pzn0uyY8mefJJxzeTPL+19mdVdW6SK6vqra21D87O/0Jr7aV9DZjlpwcGAAB0Tw8M+tRr9FZVx5I8PskrTnW+tXZ9a+2dSTZOOr7eWvuz2e0vZpoAOdrnWBkWu5AAAED3TuxCogKDHvQdvb0syQuTnPXsraoLk3xDkj/dcfg5VfXeqnplVd1zVyNkkI6fqMCoBY8EAACWx2ilMlopPTDoRW9LSKrqCUmub61dWVWPPsvnuFuS1yd5Xmvtptnhlyf5qSRt9u/PJfnBUzz2kiSXJMnRo0ezvr5+NkNYuIM67v3uMzd8Lkly0+dvyHrdvODRDJO5zTIzv1lW5jbLytzu1upK8vmbbvK57hPL9N+hzx4Yj0ryxFmTzcNJzquq17TWnn4mD66qcabJi19trf3W/Hhr7bod1/xykq9qDjq77rIklyXJxRdf3I4cOXLWb2RR1tfXcxDHfRDc9dqtJMn97nufHDn/nAWPZnjMbZaZ+c2yMrdZVuZ299ZW35u1w3f1ue4Dyza/e1tC0lp7cWvtWGvtwiRPTfL2O5G8qCT/PsmHWms/f9K5nZ/+9yR5f0dDZkD0wAAAgH6sjVYsIaEXe7ELyVeoqmcnSWvt0qq6b5J3ZbpDyXZVPS/Jw5L8zSTPSPK+qnrP7KE/1lp7U5KfrapHZLqE5GNJfngvx89ymGzZhQQAAPowHq1kY9MuJHRvTxIYrbUrklwxu33pjuOfTnLsFA/5gySn7K7YWntG9yNkaOZdkVVgAABAt9ZWVWDQD9EbgzSvwFhTgQEAAJ0ajyrHJTDogeiNQTpRgSGBAQAAnZouIZHAoHuiNwZpY2s7qyuVlZVTrlQCAADOkiUk9EUCg0GabG5r4AkAAD0Yj1aysaWJJ90TwTFIG1tNA08AAOjBeFQnes5Bl0RwDNJxFRgAANCLaQWGBAbdE8ExSBtb2zmkAgMAADp3SA8MeiKCY5CmPTA08AQAgK6NRysndv2DLklgMEgbW9t6YAAAQA808aQvIjgGyS4kAADQDxUY9EUExyBNVGAAAEAv1lZLDwx6IYJjkFRgAABAP+xCQl9EcAySXUgAAKAfemDQFxEcgzTZUoEBAAB9WFtdyUQFBj0QwTFIG5staxIYAADQuXkTz9ZUYdAtERyDNNnaztgSEgAA6NzaqJIkm9sSGHRLBMcgTTa3VWAAAEAP5ku1NfKkayI4Bmm6jWotehgAALB0TiQwNlVg0C0JDAZpY0sFBgAA9GG+VFsjT7omgmOQJpt2IQEAgD7Me2BYQkLXRHAM0sbWdtY08QQAgM7Nv2dLYNA1ERyDs73dsrHVVGAAAEAP5t+zJ5sSGHRLBMfgbGxPf5GqwAAAgO6dSGCowKBjIjgGZ54J1sQTAAC6t3ZiG1W7kNAtERyDM/9FqgIDAAC6d2IbVRUYdEwEx+DMKzD0wAAAgO6N57uQ6IFBx0RwDM48E6wCAwAAujde1QODfojgGJzjJyowasEjAQCA5aMHBn2RwGBw5ktIDqnAAACAzs0rnW2jStdEcAyOJSQAANAfTTzpiwiOwZmvxdPEEwAAujdfqq0HBl0TwTE4827IaxIYAADQuTUVGPREBMfgHJ9XYFhCAgAAnTuxhEQPDDomgmNwVGAAAEB/5n8otAsJXRPBMTgTTTwBAKA38z8U6oFB10RwDM6JXUhUYAAAQOdONPG0hISOieAYnPkvUj0wAACge1WV8ag08aRzIjgGZzJbi6cCAwAA+jEerUhg0DkRHIMz0cQTAAB6NU1gaOJJt3qP4KpqVFXvrqo3nuLcQ6vqj6vqeFW94KRz31VVH66qq6vqRTuO36uq3lpVV83+vWff74HlsqGJJwAA9Go8WtHEk87tRQT33CQfOs25zyX50SQv3XmwqkZJfinJY5M8LMnTquphs9MvSvK21tpFSd42uw9n7EQPjFlzIQAAoFtro8qGJp50rNcERlUdS/L4JK841fnW2vWttXcm2Tjp1Dcnubq19pHW2iTJryd50uzck5K8anb7VUme3PW4WW4bW9tZqWTVEhIAAOjF2qoeGHSv7wjuZUlemOTOztyjST654/41s2NJcp/W2nqSzP699y7HyMBMNrczlrwAAIDeWEJCH1b7euKqekKS61trV1bVo+/sw09x7E51gKmqS5JckiRHjx7N+vr6nRzC/nBQx72fff6mL2a8Uj7bBfP5s8zMb5aVuc2yMrd70LbyxS/f4rPdB5bpv0FvCYwkj0ryxKp6XJLDSc6rqte01p5+Bo+9Jsn9d9w/luTa2e3rqupIa229qo4kuf5UT9BauyzJZUly8cUXtyNHjpzt+1iY9fX1HMRx73fjQ5/NofHIZ7tA5jbLzPxmWZnbLCtzux93PfyRrKyOfbYLtmzzu7c6+tbai1trx1prFyZ5apK3n2HyIknemeSiqnpQVa3NHn/57NzlSZ45u/3MJL/T4bAZgI2tbTuQAABAj9ZGpQcGneuzAuOUqurZSdJau7Sq7pvkXUnOS7JdVc9L8rDW2k1V9Zwk/yXJKMkrW2sfmD3FzyR5XVX9UJJPJHnKXr8HDjY9MAAAoF/jkSaedG9PEhittSuSXDG7femO45/OdHnIqR7zpiRvOsXxG5I8po9xMgwbW00FBgAA9Gg8WsmXJ1uLHgZLRhTH4BxXgQEAAL1aW13JxqYKDLolimNw9MAAAIB+rdlGlR6I4hicyeZ21kan2qkXAADowlgTT3oggcHgqMAAAIB+jUeWkNA9URyDM9nSAwMAAPo0Xl3JZKstehgsmTsdxVXVSlWd18dgYC9Ml5BIYAAAQF/WbKNKD84oiquqX6uq86rqnCQfTPLhqvpn/Q4N+jHZ2s7YEhIAAOiNHhj04UyjuIe11m5K8uQkb0rygCTP6GtQ0KfJ5nYOqcAAAIDerK2qwKB7ZxrFjatqnGkC43daaxtJLGjiQNrQAwMAAHo1Hq1kY6tle1vYSHfONIr7f5N8LMk5Sd5RVQ9MclNfg4I+TTbtQgIAAH2a/8FwY1sVBt05oyiutfaLrbWjrbXHtamPJ/n2nscGvdjYahIYAADQo3nT/A07kdCh1ds7WVX/9A4e//MdjgX2xGTTEhIAAOjTeFRJko3N7eTQggfD0rjdBEaSc2f/PiTJ30py+ez+dyd5R1+Dgr601jLZsoQEAAD6NN/1TyNPunS7CYzW2k8mSVW9Jck3tta+OLv/kiS/0fvooGPzEra1WUYYAADo3rzieSKBQYfO9M/QD0gy2XF/kuTCzkcDPZtngFVgAABAfw6t6oFB9+5oCcncryT571X1hky3T/2eJK/ubVTQk8nmNIGhBwYAAPTnRAXGpgoMunNGCYzW2k9X1ZuTfNvs0LNaa+/ub1jQDxUYAADQvxPbqFpCQofOtAIjSd6TZH3+mKp6QGvtE30MCvpyXAUGAAD0br4LiR4YdOmMEhhV9U+S/Isk1yXZSlKZLiX5m/0NDbo3zwAfUoEBAAC9WZtXYFhCQofOtALjuUke0lq7oc/BQN/mGWAVGAAA0J+xJp704EyjuE8mubHPgcBe2Nicb6MqgQEAAH3RA4M+nGkFxkeSXFFVv5vk+Pxga+3nexkV9GSytZXktowwAADQvfkfDPXAoEtnmsD4xOxnbfYDB9JEBQYAAPRubXXWxFMPDDp0ptuo/mTfA4G9MDmxjWoteCQAALC8LCGhD2e6C8nvZ7rryFdorf2dzkcEPZp3QV4bjRY8EgAAWF4SGPThTJeQvGDH7cNJ/kGSze6HA/06sQuJCgwAAOjN+EQPDLuQ0J0zXUJy5UmH/rCq/r8exgO9mmeA9cAAAID+zL9vb+iBQYfOdAnJvXbcXUnyTUnu28uIoEfHZ79AxxIYAADQm3nFsyUkdOlMl5DsrMDYTPLRJD/U/XCgX/NfoIdsowoAAL1Z0wODHtxuAqOqHtBa+0Rr7UF7NSDo00QFBgAA9G60UqmyjSrduqMo7rfnN6rq9f0OBfp3ogeGCgwAAOhNVWU8WtHEk07dURS3c6uGr+1zILAXVGAAAMDeWButWEJCp+4oimunuQ0H0jwDPB7ZRhUAAPo0HpUEBp26oyaeX19VN2VaiXGX2e3M7rfW2nm9jg46NtncztpoJVUSGAAA0KexCgw6drsJjNbaaK8GAnthY2tb/wsAANgD49FKJpsK+emOSI5BmWxuWz4CAAB74NCqCgy6JYHBoEw2VWAAAMBemFZgSGDQHZEcg2IJCQAA7I3xqiaedEskx6Ac39q2hSoAAOyB8WglEwkMOtR7JFdVo6p6d1W98RTnqqp+saqurqr3VtU3zo4/pKres+Pnpqp63uzcS6rqUzvOPa7v98Dy2JjtQgIAAPTLLiR07Y62Ue3Cc5N8KMmptlx9bJKLZj/fkuTlSb6ltfbhJI9IpgmQJJ9K8oYdj/uF1tpLexwzS2piCQkAAOyJtdFKbtnYWvQwWCK9RnJVdSzJ45O84jSXPCnJq9vUnyS5R1UdOemaxyT5q9bax3scKgOxsaUCAwAA9sJ4pAcG3eo7kntZkhcmOd2sPZrkkzvuXzM7ttNTk7z2pGPPmS05eWVV3bOLgTIM021UJTAAAKBva6t2IaFbvS0hqaonJLm+tXZlVT36dJed4ljb8RxrSZ6Y5MU7zr88yU/NrvupJD+X5AdP8fqXJLkkSY4ePZr19fU7/yb2gYM67v3qy7ccz3mHV32u+4D/Biwz85tlZW6zrMztfmxtTHLz8YnPd8GW6fPvswfGo5I8cdZk83CS86rqNa21p++45pok999x/1iSa3fcf2ySP2utXTc/sPN2Vf1ykq9qDjq77rIklyXJxRdf3I4cOXllyv63vr6egzju/aytXJ273fUuPtcFM7dZZuY3y8rcZlmZ2/0575zr0j57q893gZZtfvdWS99ae3Fr7Vhr7cJMl4G8/aTkRZJcnuT7Z7uRfGuSG1trO9NDT8tJy0dO6pHxPUne3/3oWVYbW9s5pIknAAD0bjxaycZmu+ML4QztxS4kX6Gqnp0krbVLk7wpyeOSXJ3k5iTP2nHdXZP83SQ/fNJT/GxVPSLTJSQfO8V5OK1pD4xTrVwCAAC6NF7VxJNu7UkCo7V2RZIrZrcv3XG8JfmR0zzm5iTnn+L4M3oZJIOwYRtVAADYE+PRSiYSGHRIJMeg2IUEAAD2xtpoRQUGnRLJMSgTFRgAALAn1lZXsrGlBwbdEckxKJPN7aypwAAAgN6NRyvZ2m7Z2pbEoBsiOQZFDwwAANgb86XblpHQFZEcg7G5tZ3tFj0wAABgD8x3/9PIk66I5BiM+fo7FRgAANC/+ffujU0JDLohkmMwJrNfnCowAACgf7ctIdEDg26I5BiMeemaCgwAAOifHhh0TSTHYJxIYMzW4gEAAP2Z/+FQDwy6IoHBYMzX3qnAAACA/s3/cDjRA4OOiOQYjHnmVw8MAADonyUkdE0kx2DMM79rEhgAANA7CQy6JpJjME5UYFhCAgAAvZsnMCabdiGhGyI5BmPeA+OQCgwAAOjd2uq0B4YKDLoikmMwVGAAAMDesYSEronkGIyNLT0wAABgr8x3/5PAoCsiOQZj3sTTLiQAANC/+ffu47ZRpSMiOQZj/otzzRISAADo3dqJJSSaeNINkRyDMf/FeUgCAwAAeqcHBl0TyTEYlpAAAMDeGY/sQkK3RHIMxokmniowAACgd/Pd/yZ6YNARkRyDcVsFRi14JAAAsPz0wKBrEhgMxkQFBgAA7Bk9MOiaSI7BOFGBsWLaAwBA30YrldFKSWDQGZEcg7GxtZ3xqLKyYgkJAADshfGo9MCgMxIYDMZkc9sOJAAAsIfGo5UTS7lht0RzDMbG1rb+FwAAsIfWRiuWkNAZ0RyDMdlSgQEAAHtpPFrJxqZdSOiGaI7BmGy2E1s5AQAA/RuvauJJd0RzDMbEEhIAANhTemDQJdEcg7Gxua0CAwAA9pAeGHRJNMdgTLa2M161hSoAAOyVtdUV26jSGQkMBmNjSwUGAADspfFoJRtbmnjSDdEcg3F80y4kAACwl8aj0gODzojmGIwNTTwBAGBPjfXAoEOiOQZjooknAADsKU086ZJojsFQgQEAAHtrPFrJxqYeGHRDNMdgTPTAAACAPbW2qgKD7ojmGIyNraYCAwAA9tB4tJLjtlGlI71Hc1U1qqp3V9UbT3GuquoXq+rqqnpvVX3jjnMfq6r3VdV7qupdO47fq6reWlVXzf69Z9/vgeVgFxIAANhba6ulAoPO7EU099wkHzrNuccmuWj2c0mSl590/ttba49orV2849iLkryttXZRkrfN7sMd2tjaziEVGAAAsGfsQkKXeo3mqupYkscnecVpLnlSkle3qT9Jco+qOnIHT/ukJK+a3X5Vkid3MVaW37QHRi16GAAAMBjTBIYmnnSj7z9HvyzJC5OcLuV2NMknd9y/ZnYsSVqSt1TVlVV1yY5r7tNaW0+S2b/37nTELC27kAAAwN4aj1YyUYFBR1b7euKqekKS61trV1bVo0932SmOzdNzj2qtXVtV907y1qr6i9baO+7E61+S6bKUHD16NOvr62c++H3koI57v9luLZvbLcdvvtlnuk/478AyM79ZVuY2y8rc7s/xW76cjc3tXHvttalSDb0IyzS/e0tgJHlUkidW1eOSHE5yXlW9prX29B3XXJPk/jvuH0tybZK01ub/Xl9Vb0jyzUnekeS6qjrSWlufLTe5/lQv3lq7LMllSXLxxRe3I0fuaGXK/rO+vp6DOO796NaNrSTJve55ns90HzC3WWbmN8vK3GZZmdv9Ov8eX0rLp3Pv+9w3qxrq77llm9+9zaDW2otba8daaxcmeWqSt5+UvEiSy5N8/2w3km9NcuMsMXFOVZ2bJFV1TpK/l+T9Ox7zzNntZyb5nb7eA8tjXra25pcmAADsmfkugJaR0IU+KzBOqaqenSSttUuTvCnJ45JcneTmJM+aXXafJG+YlRitJvm11tqbZ+d+JsnrquqHknwiyVP2bvQcVJPZ3tN6YAAAwN6ZJzA2NluytuDBcODtSQKjtXZFkitmty/dcbwl+ZFTXP+RJF9/mue6Iclj+hgny2tDBQYAAOy58aoKDLojmmMQ5hUYYwkMAADYM2ujaePODQkMOiCaYxBOVGBYQgIAAHvmxBISCQw6IJpjEI6rwAAAgD0ngUGXRHMMwsZWS5IcUoEBAAB7Zl4BPdlsCx4Jy0A0xyDogQEAAHtvzTaqdEg0xyDogQEAAHvPEhK6JJpjEG6rwKgFjwQAAIZj/v17Y1MCg92TwGAQJiowAABgz41XLSGhO6I5BmFegbGmBwYAAOyZtRNLSDTxZPdEcwyCHhgAALD39MCgS6I5BsEuJAAAsPfmf0CUwKALojkGQQUGAADsvXkTz+OaeNIB0RyDcFwFBgAA7Lk1S0jokGiOQZg3DTqkAgMAAPbMiR4YKjDogGiOQdADAwAA9t541S4kdEc0xyBsbG1ntFIZrdSihwIAAIMx74ExsYSEDkhgMAiTre0TvzwBAIC9MV7RA4PuSGAwCJPN7RMNhAAAgL2xslIZj0oCg06I6BiEyda2LVQBAGABxqOVEz3pYDdEdAzChgoMAABYiPFoRRNPOiGiYxAmW9snOiADAAB7Zzxa0cSTTojoGISNLRUYAACwCGujyoYlJHRARMcgTDa3M5bAAACAPTdeXdHEk06I6BiEyVbTxBMAABZADwy6IqJjECabW5aQAADAAqzpgUFHRHQMwmTTNqoAALAI41XbqNINER2DsGEJCQAALMTaqPTAoBMiOgZh2sSzFj0MAAAYnGkPDAkMdk8Cg0HY2NrO2upo0cMAAIDBGY9WMtHEkw5IYDAIx1VgAADAQoxHK9nQA4MOSGAwCBtb2zmkBwYAAOy5tVU9MOiGiI5BmGxtZ2wbVQAA2HNremDQEREdg7CxuZ01CQwAANhz45FtVOmGiI5BmGxtZ2wJCQAA7LnxqiaedENEx9JrrWVjq6nAAACABbCEhK6I6Fh6k9kvyzUVGAAAsOfGI0086YaIjqW3MStXU4EBAAB7b6wCg46I6Fh684ZB41EteCQAADA80wRGS2v6YLA7EhgsvY0TS0hGCx4JAAAMz3wp94ZGnuxS7wmMqhpV1bur6o2nOFdV9YtVdXVVvbeqvnF2/P5V9ftV9aGq+kBVPXfHY15SVZ+qqvfMfh7X93vgYFOBAQAAizNfyj2xjIRdWt2D13hukg8lOe8U5x6b5KLZz7ckefns380kz2+t/VlVnZvkyqp6a2vtg7PH/UJr7aX9D51loIknAAAszvwPiRub28mhBQ+GA63XiK6qjiV5fJJXnOaSJyV5dZv6kyT3qKojrbX11tqfJUlr7YuZJkCO9jlWlte8AkMTTwAA2HvjE0tIVGCwO31HdC9L8sIkp5upR5N8csf9a3JSoqKqLkzyDUn+dMfh58yWnLyyqu7Z2WhZShsqMAAAYGHGlpDQkd6WkFTVE5Jc31q7sqoefbrLTnHsRGeXqrpbktcneV5r7abZ4Zcn+anZdT+V5OeS/OApXv+SJJckydGjR7O+vn52b2TBDuq495Nrr/tSkuSLN34h6+t+ae4X5jbLzPxmWZnbLCtzu183f3Eayl27fl1Gtx5e8GiGZ5nmd589MB6V5ImzJpuHk5xXVa9prT19xzXXJLn/jvvHklybJFU1zjR58auttd+aX9Bau25+u6p+OclXNQedXXdZksuS5OKLL25Hjhzp5E3tpfX19RzEce83H735s0mS+977ghw5cv6CR0NibrPczG+WlbnNsjK3+/c1n0mSj+Ue51+QI/c5d9HDGZRlm9+91dS31l7cWjvWWrswyVOTvP2k5EWSXJ7k+2e7kXxrkhtba+tVVUn+fZIPtdZ+fucDqmrnp/89Sd7f13tgOdy2C4klJAAAsNfmS7nn38vhbO3FLiRfoaqenSSttUuTvCnJ45JcneTmJM+aXfaoJM9I8r6qes/s2I+11t6U5Ger6hGZLiH5WJIf3quxczDN95s+pAcGAADsufkuJHpgsFt7ksBorV2R5IrZ7Ut3HG9JfuQU1/9BTt0fI621Z/QySJaWCgwAAFic+W6AGyow2CURHUvPLiQAALA4t22j2u7gSrh9IjqW3m0VGKcs6gEAAHo0r4TesISEXZLAYOlNVGAAAMDC6IFBV0R0LL15BcaaHhgAALDn1lRg0BERHUtPDwwAAFictVUJDLohomPp2YUEAAAWZ/49fGIXEnZJRMfSm2xtpypZXdHEEwAA9tqJBIZdSNglCQyW3mRrO2ujlVRJYAAAwF470QNDBQa7JIHB0ptsbmvgCQAACzJenf4hUQ8MdktUx9Lb2NrWwBMAABZkbBcSOiKqY+lNNrc18AQAgAWZ96LTA4PdEtWx9Da2mgoMAABYkKrK2uqKCgx2TVTH0ptWYGjgCQAAi7I2WrGNKrsmgcHSm2xtZ211tOhhAADAYI1HpQKDXZPAYOlNdyFRgQEAAIsyHllCwu5JYLD07EICAACLNR6tZLKpiSe7I6pj6dmFBAAAFksTT7ogqmPpqcAAAIDF0gODLojqWHrHVWAAAMBCqcCgC6I6lp4KDAAAWKzxaCXHbaPKLonqWHqTre2sqcAAAICFsQsJXRDVsfQ2NpsEBgAALNDaaCUbW3YhYXdEdSy9ydZ2xqu16GEAAMBgaeJJFyQwWHobm9tZG40WPQwAABis8WglEz0w2CUJDJbecRUYAACwUGO7kNABCQyWWmstG1vbOaQHBgAALMwhPTDogKiOpba53dLatGQNAABYDEtI6IKojqU2L1NbWzXVAQBgUcarmniye6I6lto8y6sCAwAAFmc8WslEAoNdEtWx1CYqMAAAYOHWRpp4snuiOpbavAJjTQUGAAAszFgTTzogqmOpzX9JqsAAAIDFGY9WsrXdsrUticHZE9Wx1PTAAACAxZv/QdEyEnZDVMdSO7GERAUGAAAszHhUSaKRJ7siqmOpzX9Bzn9hAgAAe+9EBcamBAZnTwKDpaYCAwAAFm++pFsjT3ZDVMdSm6+xOySBAQAAC3NbAkMFBmdPVMdS08QTAAAWTw8MuiCqY6nNM7yWkAAAwOKsqcCgA71HdVU1qqp3V9UbT3GuquoXq+rqqnpvVX3jjnPfVVUfnp170Y7j96qqt1bVVbN/79n3e+Dguq2JpwQGAAAsym1NPPXA4OztRVT33CQfOs25xya5aPZzSZKXJ9OkR5Jfmp1/WJKnVdXDZo95UZK3tdYuSvK22X04pRNNPCUwAABgYeZ/UJxsbS14JBxkq30+eVUdS/L4JD+d5J+e4pInJXl1a60l+ZOqukdVHUlyYZKrW2sfmT3Pr8+u/eDs30fPHv+qJFck+ef9vYvF+MvrvpjrPntLbqybFj2UA+2Tn78liSUkAACwSCcSGCow2IVeExhJXpbkhUnOPc35o0k+ueP+NbNjpzr+LbPb92mtrSdJa229qu7d5YD3iyf/0h/m5onsZBdWKrnr2mjRwwAAgMFaW5028dQDg93oLYFRVU9Icn1r7cqqevTpLjvFsXY7x+/M61+S6bKUHD16NOvr63fm4Qv3E3/vgdnelp3swgV3G+dLn/9svrTogfAVDtr/JuHOML9ZVuY2y8rc7t+Nn/9ykuS6z9yQ9XM3FzyaYVmm+d1nBcajkjyxqh6X5HCS86rqNa21p++45pok999x/1iSa5OsneZ4klxXVUdm1RdHklx/qhdvrV2W5LIkufjii9uRI0e6eE975qlHjmR9fT0HbdxwJsxtlpn5zbIyt1lW5vbe+EJuSvLhnHv3u/u899Cyze/eGgO01l7cWjvWWrswyVOTvP2k5EWSXJ7k+2e7kXxrkhtny0PemeSiqnpQVa3NHn/5jsc8c3b7mUl+p6/3AAAAwO7d1sRTlTlnr+8eGF+lqp6dJK21S5O8Kcnjklyd5OYkz5qd26yq5yT5L0lGSV7ZWvvA7Cl+JsnrquqHknwiyVP29h0AAABwZxw6sY2qHhicvT1JYLTWrsh0t5B54mJ+vCX5kdM85k2ZJjhOPn5Dksf0MU4AAAC6d1sFhgQGZ8/ekgAAAPRqPLILCbsngQEAAECvxrMlJBNLSNgFCQwAAAB6tTZbQrKhiSe7IIEBAABAr8YnEhgqMDh7EhgAAAD0arRSWSkJDHZHAgMAAIDera2u2IWEXZHAAAAAoHfj0YomnuyKBAYAAAC9WxutWELCrkhgAAAA0LvxaCUbm3Yh4exJYAAAANC78WqpwGBXJDAAAADo3XikiSe7I4EBAABA7/TAYLckMAAAAOjd2upKNrb0wODsSWAAAADQO9uoslsSGAAAAPRuPCo9MNiV1UUPAAAAgOU3Hq3kxls2ctOtG4seymAcX7KKFwkMAAAAeneX8Sj/7arP5m++5C2LHspg/OA33zc/cf+jix5GZyQwAAAA6N0/+86H5JsfdK9FD2NQHnDO1qKH0CkJDAAAAHp30X3OzUX3OXfRwxiU9fX1RQ+hU5p4AgAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPtetdYWPYbeVdVnknx80eM4Cxck+eyiBwE9MLdZZuY3y8rcZlmZ2yyzgzq/H9ha+5qTDw4igXFQVdW7WmsXL3oc0DVzm2VmfrOszG2WlbnNMlu2+W0JCQAAALDvSWAAAAAA+54Exv522aIHAD0xt1lm5jfLytxmWZnbLLOlmt96YAAAAAD7ngoMAAAAYN+TwAAAAAD2PQmMfaiqvquqPlxVV1fVixY9HtiNqrp/Vf1+VX2oqj5QVc+dHb9XVb21qq6a/XvPRY8VzkZVjarq3VX1xtl9c5sDr6ruUVW/WVV/Mfv9/T+a2yyLqvo/Zt9J3l9Vr62qw+Y3B1FVvbKqrq+q9+84dtq5XFUvnsWYH66q71zMqHdHAmOfqapRkl9K8tgkD0vytKp62GJHBbuymeT5rbX/Icm3JvmR2Zx+UZK3tdYuSvK22X04iJ6b5EM77pvbLIN/neTNrbWHJvn6TOe4uc2BV1VHk/xokotba1+XZJTkqTG/OZj+Y5LvOunYKefy7Pv3U5M8fPaYfzeLPQ8UCYz955uTXN1a+0hrbZLk15M8acFjgrPWWltvrf3Z7PYXM/0SfDTTef2q2WWvSvLkhQwQdqGqjiV5fJJX7DhsbnOgVdV5Sf6nJP8+SVprk9baF2JuszxWk9ylqlaT3DXJtTG/OYBaa+9I8rmTDp9uLj8pya+31o631j6a5OpMY88DRQJj/zma5JM77l8zOwYHXlVdmOQbkvxpkvu01taTaZIjyb0XODQ4Wy9L8sIk2zuOmdscdF+b5DNJ/sNsedQrquqcmNssgdbap5K8NMknkqwnubG19paY3yyP083lpYgzJTD2nzrFMXvdcuBV1d2SvD7J81prNy16PLBbVfWEJNe31q5c9FigY6tJvjHJy1tr35Dky1FOz5KY9QN4UpIHJblfknOq6umLHRXsiaWIMyUw9p9rktx/x/1jmZa1wYFVVeNMkxe/2lr7rdnh66rqyOz8kSTXL2p8cJYeleSJVfWxTJf7/Z2qek3MbQ6+a5Jc01r709n938w0oWFuswy+I8lHW2ufaa1tJPmtJI+M+c3yON1cXoo4UwJj/3lnkouq6kFVtZZpo5XLFzwmOGtVVZmuo/5Qa+3nd5y6PMkzZ7efmeR39npssButtRe31o611i7M9Hf121trT4+5zQHXWvt0kk9W1UNmhx6T5IMxt1kOn0jyrVV119l3lMdk2p/L/GZZnG4uX57kqVV1qKoelOSiJP99AePblWrtwFWNLL2qelym66pHSV7ZWvvpxY4Izl5VfVuS/5bkfbmtT8CPZdoH43VJHpDpl4mntNZObkIEB0JVPTrJC1prT6iq82Nuc8BV1SMybU67luQjSZ6V6R++zG0OvKr6ySTfm+lOae9O8r8muVvMbw6YqnptkkcnuSDJdUn+RZLfzmnmclX9eJIfzHTuP6+19nt7P+rdkcAAAAAA9j1LSAAAAIB9TwIDAAAA2PckMAAAAIB9TwIDAAAA2PckMAAAAIB9TwIDAFiYqtqqqvdU1Z9X1Z9V1SNnxy+sqvef5XN+rKou6HakAMCirS56AADAoN3SWntEklTVdyb5v5L8zwsdEQCwL6nAAAD2i/OSfP7kg1V1uKr+Q1W9r6reXVXfPjs+qqqXzo6/t6r+yUmPu0tVvbmq/nFVnVNVvzur9Hh/VX3vHr0nAKAjKjAAgEW6S1W9J8nhJEeS/J1TXPMjSdJa+xtV9dAkb6mqByd5VpIHJfmG1tpmVd1rx2PuluTXk7y6tfbqqvoHSa5trT0+Sarq7r29IwCgFyowAIBFuqW19ojW2kOTfFeSV1dVnXTNtyX5lSRprf1Fko8neXCS70hyaWttc3buczse8ztJ/kNr7dWz++9L8h1V9X9X1d9urd3Y31sCAPoggQEA7AuttT9OckGSrznp1MkJjZ3H22nO/WGSx86TIa21v0zyTZkmMv6vqvqJ3Y8YANhLEhgAwL4wWx4ySnLDSafekeT7Ztc8OMkDknw4yVuSPLuqVmfndi4h+YnZ8/y72bn7Jbm5tfaaJC9N8o39vRMAoA96YAAAizTvgZFMKyqe2VrbOmkVyb9LcmlVvS/JZpIfaK0dr6pXZLqU5L1VtZHkl5P82x2Pe16SV1bVzyZ5W5L/p6q2k2wk+d96fE8AQA+qtdNVXgIAAADsD5aQAAAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPueBAYAAACw70lgAAAAAPve/w/1WKbj6D6ICAAAAABJRU5ErkJggg==\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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YhN/vV7t27ZSVlaWamhr5fL5Eh9SoSCSiEydOyO/3q1u3bk16DltIAAAAAACwiaqqKqWkpMgwjESHclmGYSglJUVVVVVNfg4FDAAAAAAAbKSlFy+irjROChgAAAAAAMA0bdu2rff966+/rh//+MfNfl0KGAAAAAAAoMWjgAEAAAAAAFo87kICAAAAAIAN/aLkc+09ctbU1+yTkaznvtf3so+5cOGCcnNzY9+fPHlSY8eObfZ7U8AAAAAAAACmufbaa7V79+7Y96+//ro+/vjjZr8uBQwAAAAAAGzo2dG95PP5Eh2GaZiBAQAAAAAAWjwKGAAAAAAAoMVjCwkg6cCJc6qsCiU6DMeoPHVBJ2pPJzoMwBKsb9gVaxt2xdo2h2FIN6W1k9fN38ghnT1bf3DoI488okceeaTZr0sBA473WflpjXnxg0SHAQAAALRqj3w7S3PHXv7uFEBzUMCA4x07Uy1JKhzWXZ2vuzbB0ThE8Lzka5PoKABrsL5hV6xt2BVr2xRLtpTq2NnqRIcBm6OAAcerDtVKksbenKF+ndonOBpnCAQCSk9PT3QYgCVY37Ar1jbsirVtjjd3HFDN179XA1ZhgxIcryZc94M2ycN/DgAAAMDV8Lpdsd+rkXiRSCTRITTJlcZJxgbHC35dKfZRwAAAAACuis/jUpACRotwzTXX6MSJEy2+iBGJRHTixAldc801TX4OW0jgeNEftBQwAAAAgKvjc7tifxhEYmVmZsrv9+vYsWMKh8Nyu92JDumSrrnmGmVmZjb58RQw4HixDgxu+QQAAABcFa/HpfPnQ4kOA5K8Xq+6desmyX4zXsjY4HhsIQEAAACax+dmCwmsR8YGx2MLCQAAANA8Po+hmnDLnrmA1o+MDY5XzRYSAAAAoFm8zMBAHFiesYXDYQ0cOFBjxoxpcG7v3r0aOnSokpKStHDhwtjxzz//XLm5ubF/ycnJWrx4sSRp7ty56tSpU+xcSUmJ1ZcAmwuGauV1GzIMI9GhAAAAAK2Sj9uoIg4sH+K5ZMkS5eTkqLKyssG5jh076oUXXtDatWvrHe/Vq5d2794tqa4A0qlTJ40bNy52/oknntDs2bOtDBsOEgzVsn0EAAAAaAavhw4MWM/SrM3v92vjxo2aNm1ao+dTU1M1ePBgeb3eS77Gli1b1KNHD3Xt2tWqMOFwwXCY7SMAAABAM9CBgXiwNGubNWuWFixYIJfr6t+muLhYEydOrHds6dKlGjBggKZOnapTp041N0w4HB0YAAAAQPP4PC6GeMJylm0h2bBhg1JTU5WXl6dt27Zd1WsEg0GtX79e8+bNix2bOXOm5syZI8MwNGfOHD311FN67bXXGjy3qKhIRUVFkqTDhw8rEAhcVQyJ1lrjbk1OnzknjxHhs44zPm/YGesbdsXahl2xtpuv+sI5BUNhPssWyE7/m1hWwNi+fbvWr1+vkpISVVVVqbKyUpMmTdKqVaua/BqbNm3SoEGDlJaWFjv2119Pnz690eGgklRYWKjCwkJJUn5+vtLT06/yShInEAi0yrhbG7fvkK7xBfms44i1DTtjfcOuWNuwK9a2OTq2P6tw5IhS026U28Vw/JbCbuvbsr75efPmye/3q6ysTMXFxRo+fPgVFS8kafXq1Q22j/x19WjNmjXq16+fKfHCudhCAgAAADSP11NXtGAOBqxk+V1I/tayZcskSTNmzNDhw4eVn5+vyspKuVwuLV68WHv27FFycrLOnz+vf/u3f9PLL79c7/lPP/20du/eLcMwlJWV1eA8cKWqKWAAAAAAzRIdih8M1+oarzvB0cCu4lLAKCgoUEFBgaS6wkXUjTfeKL/f3+hz2rRpoxMnTjQ4/sYbb1gSI5wrGKpVkpsfsgAAAMDViv5BsIZbqcJC/NkZjlcdqlWSl/8UAAAAgKvl/asODMAqZG1wvGCoNtbyBgAAAODKRX+frglxK1VYh6wNjhcMMwMDAAAAaA6vhw4MWI+sDY7HXUgAAACA5okN8WQGBixE1gbHC4bZQgIAAAA0h4/bqCIOyNrgeHRgAAAAAM3DEE/EA1kbHI8CBgAAANA8F4d4UsCAdcja4HgM8QQAAACahyGeiAeyNjhaJBJRMFSrJGZgAAAAAFeNIZ6IB7I2OFpNuO4+1XRgAAAAAFcv+vt09PdrwApkbXC0aIsbBQwAAADg6l0c4hlOcCSwM7I2OFq0xY3bqAIAAABXL9aBEaIDA9Yha4OjxQoYHneCIwEAAABaL6/bkMQQT1iLAgYc7WIBg/8UAAAAgKvFEE/EA1kbHC26R48CBgAAAHD1Lg7xpIAB65C1wdGqmYEBAAAANFt0iCcFDFiJrA2OdnELiZHgSAAAAIDWy+MyZBhsIYG1KGDA0S7ehYQhngAAAMDVMgxDXrdLwTB3IYF1KGDA0aJTkpmBAQAAADSP123QgQFLkbXB0bgLCQAAAGAOn9vFDAxYiqwNjhZkiCcAAABgCgoYsBpZGxyNLSQAAACAOXweF1tIYCmyNjha9DaqSRQwAAAAgGapG+JJAQPWIWuDozEDAwAAADCH100HBqxF1gZHYwYGAAAAYA6fhxkYsBZZGxyNGRgAAACAObxuQzXhSKLDgI2RtcHR2EICAAAAmMPncak6FE50GLAxsjY4WjBUK8OQPC4j0aEAAAAArZrP42aIJyxFAQOOFgzXyud2yTAoYAAAAADN4XMbDPGEpShgwNGCoVq2jwAAAAAmqBviyQwMWIfMDY5WHarlDiQAAACACbxu7kICa5G5wdHowAAAAADM4XW72EICS5G5wdGCYQoYAAAAgBnqtpBQwIB1yNzgaMFQmC0kAAAAgAl8bmZgwFpkbnA0tpAAAAAA5vB52EICa5G5wdFqwhEKGAAAAIAJvG6DLSSwFJkbHK06FFYSBQwAAACg2bxul0K1EdXWso0E1rA8cwuHwxo4cKDGjBnT4NzevXs1dOhQJSUlaeHChfXOZWVlqX///srNzVV+fn7s+MmTJzVy5Ej17NlTI0eO1KlTp6y+BNhY3RYSd6LDAAAAAFq9aGdzkC4MWMTyAsaSJUuUk5PT6LmOHTvqhRde0OzZsxs9/95772n37t36+OOPY8fmz5+vESNGqLS0VCNGjND8+fMtiRvOEAzVMsQTAAAAMEH092q2kcAqlmZufr9fGzdu1LRp0xo9n5qaqsGDB8vr9Tb5NdetW6cpU6ZIkqZMmaK1a9eaESocKhiuZQsJAAAAYALv1wUMBnnCKpZmbrNmzdKCBQvkcl352xiGoVGjRikvL09FRUWx40eOHFF6erokKT09XUePHjUtXjgPdyEBAAAAzBH9vZpbqcIqHqteeMOGDUpNTVVeXp62bdt2xc/fvn27MjIydPToUY0cOVK9e/fWsGHDmvz8oqKiWOHj8OHDCgQCVxxDS9Ba424tLgRDCgWr+JwTgM8cdsb6hl2xtmFXrG1znDtTKUnyBw4rcj4pwdEgyk7r27ICxvbt27V+/XqVlJSoqqpKlZWVmjRpklatWtWk52dkZEiq22Yybtw47dy5U8OGDVNaWpoCgYDS09MVCASUmpra6PMLCwtVWFgoScrPz491bbQm0euEdcKRP6tDu7Z8znHG2oadsb5hV6xt2BVr2zypRyOS9qtDx+uVnto20eFA9lvflvXOz5s3T36/X2VlZSouLtbw4cObXLw4d+6czpw5E/v697//vfr16ydJGjt2rFasWCFJWrFihe655x5rLgCOwBYSAAAAwBw+tyGJIZ6wjmUdGJeybNkySdKMGTN0+PBh5efnq7KyUi6XS4sXL9aePXt0/PhxjRs3TpIUCoX04IMP6s4775QkPfPMMxo/frxeffVVdenSRW+//Xa8LwE2EgxTwAAAAADMwBBPWC0uBYyCggIVFBRIqitcRN14443y+/0NHp+cnKxPP/200ddKSUnRli1bLIkTzlJbG1FNOMJtVAEAAAATXBziSQED1iBzg2MFv/7BSgcGAAAA0Hx0YMBqZG5wrGgBI4kCBgAAANBs0T8MBunAgEXI3OBY0cowHRgAAABA80W3ZteEIwmOBHZF5gbHihUwmIEBAAAANBtbSGA1Mjc4Fh0YAAAAgHkY4gmrkbnBsRjiCQAAAJjH6zYkMQMD1iFzg2OxhQQAAAAwT2yIJ1tIYBEyNzhWNVtIAAAAANNcHOJJAQPWIHODYzEDAwAAADAPQzxhNTI3OFZ0b14SBQwAAACg2RjiCauRucGxLs7AcCc4EgAAAKD187iiQzwjCY4EdkUBA47FFhIAAADAPIZhyOs22EICy5C5wbGC4bAkChgAAACAWXweF1tIYBkyNzgWHRgAAACAuXxuFx0YsAyZGxzr4gwM/jMAAAAAzOBz04EB65C5wbGq6cAAAAAATOX1uGJ3+wPMRuYGx+I2qgAAAIC5vGwhgYXI3OBYbCEBAAAAzMUWEliJzA2OFQzVyuMy5Pr6ftUAAAAAmofbqMJKFDDgWMFQLfMvAAAAABP5PG7VhCOJDgM2RfYGxwqGa9k+AgAAAJjI56EDA9Yhe4NjBUO18lLAAAAAAEzjdbtUHQ4nOgzYFNkbHIstJAAAAIC5kjwu1YTYQgJrkL3BsarDFDAAAAAAM3m5CwksRPYGxwqGmIEBAAAAmMnncSlIAQMWIXuDY7GFBAAAADAXHRiwEtkbHIsCBgAAAGAur9vFXUhgGbI3OFZ1qFZJFDAAAAAA0yR5XKoJM8QT1iB7g2MFQ2E6MAAAAAATed0GW0hgGbI3OFYwzBBPAAAAwExsIYGVyN7gWMzAAAAAAMzl87gUqo2otpZtJDAf2RscKximgAEAAACYyft1hzO3UoUVyN7gWEGGeAIAAACmiv5+zRwMWIHsDY4VDDEDAwAAADBTtAODO5HACmRvcCy2kAAAAADmim0hYZAnLED2BsdiiCcAAABgLh9bSGAhsjc4Uihcq9qI5HO7Ex0KAAAAYBtetyFJqqYDAxaggAFHik5FpgMDAAAAMA9DPGEly7O3cDisgQMHasyYMQ3O7d27V0OHDlVSUpIWLlwYO37w4EHdcccdysnJUd++fbVkyZLYublz56pTp07Kzc1Vbm6uSkpKrL4E2FB0Tx4FDAAAAMA8F4d4UsCA+TxWv8GSJUuUk5OjysrKBuc6duyoF154QWvXrq0flMejRYsWadCgQTpz5ozy8vI0cuRI9enTR5L0xBNPaPbs2VaHDhujgAEAAACYjyGesJKl2Zvf79fGjRs1bdq0Rs+npqZq8ODB8nq99Y6np6dr0KBBkqR27dopJydH5eXlVoYKh4luIUniNqoAAACAaaJ/IAzSgQELWNqBMWvWLC1YsEBnzpy56tcoKyvTJ598oltvvTV2bOnSpVq5cqXy8/O1aNEiXXfddQ2eV1RUpKKiIknS4cOHFQgErjqGRGqtcbd05aeqJEnnz1byGScInzvsjPUNu2Jtw65Y2+aprDgrSTpy7IQCbWoSHA0ke61vywoYGzZsUGpqqvLy8rRt27areo2zZ8/q/vvv1+LFi5WcnCxJmjlzpubMmSPDMDRnzhw99dRTeu211xo8t7CwUIWFhZKk/Px8paenX/W1JEogEGiVcbcGp426LU2p13fkM04A1jbsjPUNu2Jtw65Y2+Y6UXta0j61Te6g9PS0RIfjeHZb35b1z2/fvl3r169XVlaWJkyYoK1bt2rSpElNfn5NTY3uv/9+PfTQQ7rvvvtix9PS0uR2u+VyuTR9+nTt3LnTivBhc7EZGGwhAQAAAEzDEE9YybLsbd68efL7/SorK1NxcbGGDx+uVatWNem5kUhEjz76qHJycvTkk0/WO/fX7S9r1qxRv379TI0bzsAQTwAAAMB8XrchiSGesIbldyH5W8uWLZMkzZgxQ4cPH1Z+fr4qKyvlcrm0ePFi7dmzR3/605/0xhtvqH///srNzZUkPf/88xo9erSefvpp7d69W4ZhKCsrSy+//HK8LwE2QAEDAAAAMB9DPGGluBQwCgoKVFBQIKmucBF14403yu/3N3j87bffrkgk0uhrvfHGG5bECGepDlPAAAAAAMzmYwsJLET2BkdiBgYAAABgvugMDLaQwApkb3Ck6A/UJDowAAAAANNEO5zpwIAVyN7gSMzAAAAAAMxHBwasRPYGRwoyAwMAAAAwXewuJOHGZxoCzUH2BkdiBgYAAABgPsMw5HUbbCGBJcje4EhsIQEAAACs4XO72EICS5C9wZHYQgIAAABYw+dx0YEBS5C9wZGq2UICAAAAWMLrpoABa5C9wZGCoVr53C4ZhpHoUAAAAABb8XlcsT8YAmaigAFHCoZq2T4CAAAAWKCuA4O7kMB8ZHBwpGA4zPYRAAAAwAJet6FgKJzoMGBDZHBwJDowAAAAAGvUDfGkAwPmI4ODI1HAAAAAAKzBEE9YhQwOjhQM17KFBAAAALBAktul6hoKGDAfGRwciQ4MAAAAwBpej0tBOjBgATI4OFI1BQwAAADAEj63S0FuowoLkMHBkYIhtpAAAAAAVqgb4kkBA+Yjg4MjBUO1SvKy/AEAAACzMcQTViGDgyMxxBMAAACwhtfNDAxYgwwOjsQQTwAAAMAaPo9LNaFIosOADZHBwZEY4gkAAABYw+c26MCAJcjg4EhsIQEAAACswRBPWIUMDo7EFhIAAADAGgzxhFXI4OBIFDAAAAAAa9QVMCKqrWUOBsxFBgdHCoYpYAAAAABWiP6eXVNLFwbMRQYHx4lEInUdGMzAAAAAAEwX/T27JkwHBsxFBgfHif4gpYABAAAAmC/agREM0YEBc5HBwXGit3RiCwkAAABgPm+sA4MCBsxFBgfHiVaCKWAAAAAA5vO6DUl0YMB8ZHBwHAoYAAAAgHViW0jowIDJyODgOLECBjMwAAAAANP52EICi5DBwXGC4bAkOjAAAAAAKzDEE1Yhg4PjVH/9gzSJAgYAAABgOoZ4wipkcHAcZmAAAAAA1okWMKrpwIDJyODgOBdnYLgTHAkAAABgP9E/FNaEIwmOBHZDAQOOE52GTAcGAAAAYL7YEE86MGAyMjg4DltIAAAAAOt4PYYkbqMK81mewYXDYQ0cOFBjxoxpcG7v3r0aOnSokpKStHDhwnrnNm/erF69eik7O1vz58+PHT958qRGjhypnj17auTIkTp16pTVlwCb4TaqAAAAgHW4jSqsYnkGt2TJEuXk5DR6rmPHjnrhhRc0e/bsesfD4bAee+wxbdq0SXv27NHq1au1Z88eSdL8+fM1YsQIlZaWasSIEfWKG0BTsIUEAAAAsA5DPGEVSzM4v9+vjRs3atq0aY2eT01N1eDBg+X1eusd37lzp7Kzs9W9e3f5fD5NmDBB69atkyStW7dOU6ZMkSRNmTJFa9eutfISYEPcRhUAAACwTpKHDgxYw2Pli8+aNUsLFizQmTNnruh55eXl6ty5c+z7zMxM7dixQ5J05MgRpaenS5LS09N19OjRRl+jqKhIRUVFkqTDhw8rEAhczSUkXGuNuyU7dqJu29GpE8fkqfYlOBrnYm3DzljfsCvWNuyKtW2u0xdCkqQTJysUCPD7dqLZaX1bVsDYsGGDUlNTlZeXp23btl3RcyORhrfbMQzjil6jsLBQhYWFkqT8/PxY0aM1CQQCrTLulu7aL6okSZ0z0nXdt/iBmgisbdgZ6xt2xdqGXbG2zdeuOiTpT7rmW235bBPMbuvbsh767du3a/369crKytKECRO0detWTZo0qUnPzczM1MGDB2Pf+/1+ZWRkSJLS0tJiFaRAIKDU1FTzg4etMQMDAAAAsM7FIZ4N/zANNIdlGdy8efPk9/tVVlam4uJiDR8+XKtWrWrScwcPHqzS0lJ99dVXCgaDKi4u1tixYyVJY8eO1YoVKyRJK1as0D333GPVJcCmuI0qAAAAYB2v++vbqDLEEyazdAZGY5YtWyZJmjFjhg4fPqz8/HxVVlbK5XJp8eLF2rNnj5KTk7V06VJ997vfVTgc1tSpU9W3b19J0jPPPKPx48fr1VdfVZcuXfT222/H+xLQytWEa2UYksd1ZduSAAAAAHwzwzDkc7tinc+AWeJSwCgoKFBBQYGkusJF1I033ii/39/oc0aPHq3Ro0c3OJ6SkqItW7ZYEiecIRiqlc/tuuK5KgAAAACaxus2VEMHBkxGDz0cpzpUy/YRAAAAwEJeOjBgAbI4OE4wXBsbLAQAAADAfD6PSzUUMGAysjg4TpAODAAAAMBSXrdLwRB3IYG5yOLgOBQwAAAAAGv5PGwhgfnI4uA4wVCtkthCAgAAAFiGIZ6wAlkcHCcYpgMDAAAAsBJDPGEFsjg4DltIAAAAAGsxxBNWIIuD41SHwhQwAAAAAAv53C4F2UICk5HFwXHqOjDciQ4DAAAAsC2fhwIGzEcBA44TDNfKxxBPAAAAwDI+ZmDAAmRxcJxgqFZJbCEBAAAALONlCwksQBYHx2GIJwAAAGAthnjCCmRxcBy2kAAAAADW8rpdqglHEh0GbIYsDo5DBwYAAABgLZ/HYAYGTHfFWVxtba0qKyutiAWICwoYAAAAgLV8bpdqmIEBkzUpi3vwwQdVWVmpc+fOqU+fPurVq5f+5V/+xerYAEsEwxQwAAAAACvVbSGhgAFzNSmL27Nnj5KTk7V27VqNHj1aBw4c0BtvvGF1bIDpamsjqglHmIEBAAAAWMjn4TaqMF+TsriamhrV1NRo7dq1uueee+T1emUYhtWxAaaL/hClAwMAAACwTnSIZyTCIE+Yp0lZ3A9/+ENlZWXp3LlzGjZsmPbv36/k5GSrYwNMFy1gJFHAAAAAACwT/YMhXRgwU5OyuMcff1zl5eUqKSmRYRjq2rWr3nvvPatjA0wXDNGBAQAAAFgtumWbW6nCTJ7LnfzVr3512Sc/+eSTpgYDWC1WwGAGBgAAAGAZr7tu5EBNqFZKSnAwsI3LFjDOnDkjSfr888/1hz/8QWPHjpUkvfPOOxo2bJj10QEmowMDAAAAsJ7P45bEFhKY67IFjOeee06SNGrUKO3atUvt2rWTJM2dO1cPPPCA9dEBJmOIJwAAAGC9aAdG9A+IgBmalMUdOHBAPp8v9r3P51NZWZlVMQGWYQsJAAAAYD2GeMIKl+3AiHr44Yd1yy23aNy4cTIMQ2vWrNHkyZOtjg0wXTVbSAAAAADLXRziSQED5mlSAePZZ5/VnXfeqQ8++ECStHz5cg0cONDSwAArMAMDAAAAsJ43WsAIcRcSmKdJBQxJys3NVXp6ukKhkKS6bSVdunSxLDDACtEWtiQKGAAAAIBlvLEtJOEERwI7aVIB48UXX9Q//dM/KS0tTW63W5FIRIZh6E9/+pPV8QGmujgDw53gSAAAAAD7im4hCdKBARM1qYCxZMkSff7550pJSbE6HsBSbCEBAAAArOfzfH0XEmZgwERNyuI6d+6s9u3bWx0LYLloCxsFDAAAAMA60Y7nGm6jChM1qQOje/fuKigo0N13362kpKTY8SeffNKywAArRDswovelBgAAAGA+79cdGNyFBGZqUgGjS5cu6tKli4LBoILBoNUxAZZhCwkAAABgvehdSNhCAjM1qYDx3HPPWR0HEBfVXxcwkhjiCQAAAFjm4hBPChgwT5MKGHfccYcMo2HL/datW00PCLBStAJMBwYAAABgnejv2zVh7kIC8zSpgLFw4cLY11VVVfrtb38rj6dJTwVaFLaQAAAAANa72IERTnAksJMmVSHy8vLqfX/bbbfpv/23/2ZJQICVgqFauV2G3C6GeAIAAABW8dKBAQs0qYBx8uTJ2Ne1tbX64x//qMOHD1sWFGCVYKg2Vg0GAAAAYI3oXf8Y4gkzNSmTy8vLi/0bOnSoFi1apFdffbVJbxAOhzVw4ECNGTOmwblIJKLHH39c2dnZGjBggHbt2iVJ+vzzz5Wbmxv7l5ycrMWLF0uS5s6dq06dOsXOlZSUNPFSgbofoGwfAQAAAKzFEE9Y4bIdGAcOHFCXLl301VdfXfUbLFmyRDk5OaqsrGxwbtOmTSotLVVpaal27NihmTNnaseOHerVq5d2794tqa4A0qlTJ40bNy72vCeeeEKzZ8++6pjgXMEQBQwAAADAaoZhyOs2VEMHBkx02Uzu3nvvjX19//33X/GL+/1+bdy4UdOmTWv0/Lp16zR58mQZhqEhQ4aooqJCgUCg3mO2bNmiHj16qGvXrlf8/sDfYgsJAAAAEB9et4sODJjqsh0YkcjFgStffvnlFb/4rFmztGDBAp05c6bR8+Xl5ercuXPs+8zMTJWXlys9PT12rLi4WBMnTqz3vKVLl2rlypXKz8/XokWLdN111zV47aKiIhUVFUmSDh8+3KAw0lq01rhbqtNnz8mtWj7XFoD/DWBnrG/YFWsbdsXatobHJVWcOcvnm2B2+vwvW8AwDKPRr5tiw4YNSk1NVV5enrZt29boY/66QNLY+wSDQa1fv17z5s2LHZs5c6bmzJkjwzA0Z84cPfXUU3rttdcavE5hYaEKCwslSfn5+fWKIq1FIBBolXG3ZC5vua69JsznmmCsbdgZ6xt2xdqGXbG2rZPk/Yu8Sdfw+SaQ3db3ZQsYn376qZKTkxWJRHThwgUlJydLqis8GIbR6FyLqO3bt2v9+vUqKSlRVVWVKisrNWnSJK1atSr2mMzMTB08eDD2vd/vV0ZGRuz7TZs2adCgQUpLS4sd++uvp0+f3uhwUOBS2EICAAAAxIfP7VIwxG1UYZ7LZnLhcFiVlZU6c+aMQqGQKisrY99frnghSfPmzZPf71dZWZmKi4s1fPjwesULSRo7dqxWrlypSCSijz76SO3bt69XHVq9enWD7SN/3f6yZs0a9evXr8kXCwRDtUpiiCcAAABgOYZ4wmyX7cCwwrJlyyRJM2bM0OjRo1VSUqLs7Gy1adNGy5cvjz3u/Pnz+rd/+ze9/PLL9Z7/9NNPa/fu3TIMQ1lZWQ3OA5cTDFPAAAAAAOKBIZ4wW1wKGAUFBSooKJBUV7iIMgxDL730UqPPadOmjU6cONHg+BtvvGFJjHCG6lBY7a6Je90OAAAAcByfx0UHBkzFn6LhKMzAAAAAAOLD53YpSAEDJiKTg6MEQ7XysYUEAAAAsJzPwxYSmItMDo5SE45QwAAAAADiwOehAwPmIpODozDEEwAAAIgPhnjCbGRycBRmYAAAAADx4XMzxBPmIpODozADAwAAAIgPr8elmnAk0WHARsjk4CjBMAUMAAAAIB58bCGBycjk4Bjh2ojCtRH53O5EhwIAAADYns9jsIUEpqKAAceIVn/pwAAAAACsxxBPmI1MDo5BAQMAAACIH4Z4wmxkcnCM6nBYEgUMAAAAIB4Y4gmzkcnBMaIdGEncRhUAAACwnNftUjBcq0iEIgbMQSYHx2ALCQAAABA/SV//3k0XBsxCJgfHCIYpYAAAAADx4nUbki7+Hg40F5kcHCPWgcEWEgAAAMBy0d+7a7gTCUxCJgfHYAsJAAAAED/e2BYSChgwB5kcHIMCBgAAABA/3q87MKrpwIBJyOTgGNXMwAAAAADiJokODJiMTA6OwQwMAAAAIH6iHRgM8YRZyOTgGNECRhIdGAAAAIDlLg7x5DaqMAeZHByDGRgAAABA/ESHeNKBAbOQycExgszAAAAAAOLG6zYkXfxDItBcZHJwDGZgAAAAAPHDEE+YjUwOjsEWEgAAACB+okM8KWDALGRycAy2kAAAAADxE/29my0kMAuZHByjmi0kAAAAQNxwG1WYjUwOjhEM1crndskwjESHAgAAANhe9A+HdGDALBQw4BjBUC3bRwAAAIA48cWGeEYSHAnsgmwOjhEMh9k+AgAAAMQJQzxhNrI5OAYdGAAAAED8eN11W7fZQgKzkM3BMShgAAAAAPETuwsJHRgwCdkcHCMYrmULCQAAABAnXhdDPGEusjk4Bh0YAAAAQPy4XIa8boMZGDAN2Rwco5oCBgAAABBXXreLAgZMQzYHx6ADAwAAAIgvr9vFFhKYhmwOjhEMMQMDAAAAiCefx6VgOJLoMGATZHNwjGCYDgwAAAAgnrxugw4MmMbybC4cDmvgwIEaM2ZMg3ORSESPP/64srOzNWDAAO3atSt2LisrS/3791dubq7y8/Njx0+ePKmRI0eqZ8+eGjlypE6dOmX1JcAm6MAAAAAA4svHDAyYyPJsbsmSJcrJyWn03KZNm1RaWqrS0lIVFRVp5syZ9c6/99572r17tz7++OPYsfnz52vEiBEqLS3ViBEjNH/+fEvjh30wAwMAAACIL4Z4wkyWZnN+v18bN27UtGnTGj2/bt06TZ48WYZhaMiQIaqoqFAgELjsa65bt05TpkyRJE2ZMkVr1641O2zYFFtIAAAAgPjyeRjiCfN4rHzxWbNmacGCBTpz5kyj58vLy9W5c+fY95mZmSovL1d6eroMw9CoUaNkGIZ++MMfqrCwUJJ05MgRpaenS5LS09N19OjRRl+7qKhIRUVFkqTDhw9/Y2GkpWqtcbdEVcGQQtVVfKYtBP87wM5Y37Ar1jbsirVtHaM2rLPnL/AZJ5CdPnvLChgbNmxQamqq8vLytG3btkYfE4k0nEZrGIYkafv27crIyNDRo0c1cuRI9e7dW8OGDWvy+xcWFsaKHvn5+bGiR2sSCARaZdwtVU3tn9QhuS2faQvA2oadsb5hV6xt2BVr21ptrimT4RKfcYLYbX1b1k+/fft2rV+/XllZWZowYYK2bt2qSZMm1XtMZmamDh48GPve7/crIyNDkmL/NzU1VePGjdPOnTslSWlpabEKUiAQUGpqqlWXAJsJhmuVxBYSAAAAIG6SvC5Vs4UEJrEsm5s3b578fr/KyspUXFys4cOHa9WqVfUeM3bsWK1cuVKRSEQfffSR2rdvr/T0dJ07dy627eTcuXP6/e9/r379+sWes2LFCknSihUrdM8991h1CbCRSCTCEE8AAAAgzhjiCTNZOgOjMcuWLZMkzZgxQ6NHj1ZJSYmys7PVpk0bLV++XFLdnItx48ZJkkKhkB588EHdeeedkqRnnnlG48eP16uvvqouXbro7bffjvcloBWqCddtV+I2qgAAAED8eN0GQzxhmrgUMAoKClRQUCCprnARZRiGXnrppQaP7969uz799NNGXyslJUVbtmyxJE7YV/Drqi8dGAAAAED8+Dzu2B8TgeYim4MjRKu+FDAAAACA+PG6DbaQwDRkc3AEChgAAABA/PncLraQwDRkc3CEWAGDGRgAAABA3Pg8DPGEecjm4AjBcFgSHRgAAABAPHnpwICJyObgCNF7TydRwAAAAADipq4DgyGeMAfZHByBGRgAAABA/HndLgXDtYpEKGKg+cjm4AgXZ2C4ExwJAAAA4Bw+tyFJdGHAFBQw4AjRH5h0YAAAAADxE/39m0GeMAPZHByBIZ4AAABA/HndFDBgHrI5OAK3UQUAAADiL/oHRO5EAjOQzcERqhniCQAAAMRdtAMjSAcGTEA2B0cIchtVAAAAIO6iHdB0YMAMZHNwhGjFlw4MAAAAIH4uDvHkLiRoPrI5OAIzMAAAAID4Y4gnzEQ2B0cIMgMDAAAAiLvo79/VbCGBCcjm4AgUMAAAAID487oNSXRgwBxkc3CEYLhWhiF5XEaiQwEAAAAcgyGeMBMFDDhCMFQrn9slw6CAAQAAAMTLxSGeFDDQfBQw4AjVoVq2jwAAAABxxhBPmImMDo4QDFPAAAAAAOItWsBgiCfMQEYHRwiGapXELVQBAACAuEqKbSGJJDgS2AEZHRwhyBYSAAAAIO68DPGEicjo4AgUMAAAAID4Y4gnzERGB0cIhmtjt3ACAAAAEB9ed91dAClgwAxkdHAEOjAAAACA+GOIJ8xERgdHqA6FKWAAAAAAcebjNqowERkdHKGuA8Od6DAAAAAAR3G5DHlcBgUMmIICBhyBGRgAAABAYvg8Lu5CAlOQ0cERgqHa2D2oAQAAAMSP1+1STTiS6DBgA2R0cASGeAIAAACJ4XUbDPGEKcjo4AhsIQEAAAASw+dxMQMDpiCjgyPQgQEAAAAkRt0WEgoYaD4yOjgCBQwAAAAgMXxuhnjCHGR0cIRgmAIGAAAAkAhsIYFZyOhge7W1EdWEI8zAAAAAABLA63YxxBOmIKOD7QW/rvbSgQEAAADEn48ZGDAJGR1sL1rASKKAAQAAAMSdz+NSMBRJdBiwAcszunA4rIEDB2rMmDENzkUiET3++OPKzs7WgAEDtGvXLknSwYMHdccddygnJ0d9+/bVkiVLYs+ZO3euOnXqpNzcXOXm5qqkpMTqS0ArFx0YRAcGAAAAEH9et6FgOJzoMGADHqvfYMmSJcrJyVFlZWWDc5s2bVJpaalKS0u1Y8cOzZw5Uzt27JDH49GiRYs0aNAgnTlzRnl5eRo5cqT69OkjSXriiSc0e/Zsq0OHTcQKGMzAAAAAAOKubognHRhoPkszOr/fr40bN2ratGmNnl+3bp0mT54swzA0ZMgQVVRUKBAIKD09XYMGDZIktWvXTjk5OSovL7cyVNgYHRgAAABA4ni5jSpMYmlGN2vWLC1YsEAuV+NvU15ers6dO8e+z8zMbFCoKCsr0yeffKJbb701dmzp0qUaMGCApk6dqlOnTlkTPGwjOgPDSwcGAAAAEHfcRhVmsWwLyYYNG5Samqq8vDxt27at0cdEIg3biAzDiH199uxZ3X///Vq8eLGSk5MlSTNnztScOXNkGIbmzJmjp556Sq+99lqD1ykqKlJRUZEk6fDhwwoEAiZcVfy11rhbkkNHz0uSzp05rUDA+IZHI15Y27Az1jfsirUNu2JtWytUXaWqYIjPOUHs9LlbVsDYvn271q9fr5KSElVVVamyslKTJk3SqlWrYo/JzMzUwYMHY9/7/X5lZGRIkmpqanT//ffroYce0n333Rd7TFpaWuzr6dOnNzocVJIKCwtVWFgoScrPz1d6erqp1xcP0e00aJ5DwbounbQbUpSenprgaCCxtmFvrG/YFWsbdsXatl77dicUjlTyOSeA3da3ZT318+bNk9/vV1lZmYqLizV8+PB6xQtJGjt2rFauXKlIJKKPPvpI7du3V3p6uiKRiB599FHl5OToySefrPecv64erVmzRv369bPqEmAT0f12SWwhAQAAAOKOIZ4wi+V3Iflby5YtkyTNmDFDo0ePVklJibKzs9WmTRstX75cUl33xhtvvKH+/fsrNzdXkvT8889r9OjRevrpp7V7924ZhqGsrCy9/PLL8b4EtDLRGRgM8QQAAADiz+t2xX4nB5ojLgWMgoICFRQUSKorXEQZhqGXXnqpweNvv/32RudjSNIbb7xhSYywL+5CAgAAACSOz1N3F5JIJFJv5iFwpcjoYHsUMAAAAIDE8bnrihahWraRoHnI6GB7wXBYkuRjBgYAAAAQd96vfw+P/mERuFpkdLA9OjAAAACAxIn+Hl7DHAw0ExkdbI8CBgAAAJA4sQ4MChhoJjI62F517Daq7gRHAgAAADhP9A+JbCFBc1HAgO1xG1UAAAAgcaKz6GrCDPFE85DRwfbYQgIAAAAkDkM8YRYyOtheMFQrt8uQ28U9pwEAAIB4Y4gnzEIBA7YXDNVyC1UAAAAgQbzuuj8kMsQTzUVWB9sLhmvZPgIAAAAkiI8tJDAJWR1sLxiigAEAAAAkCltIYBayOtgeW0gAAACAxGGIJ8xCVgfbYwsJAAAAkDh0YMAsZHWwPbaQAAAAAIkT68AIRxIcCVo7sjrYXjBcqyS2kAAAAAAJwRBPmIWsDrZHBwYAAACQOGwhgVnI6mB7FDAAAACAxPG6DUkUMNB8ZHWwPYZ4AgAAAIkT/V2cLSRoLrI62B63UQUAAAAS5+IQTwoYaB6yOtgeW0gAAACAxGGIJ8xCVgfbq6aAAQAAACSMy2XI4zKYgYFmI6uD7QXDtUqigAEAAAAkjNftUk04kugw0MqR1cH2mIEBAAAAJJbP42ILCZqNrA62xwwMAAAAILG8boMhnmg2sjrYHrdRBQAAABLL66YDA81HVgdbC9dGFK6NyOd2JzoUAAAAwLF8HhdDPNFsFDBga9EqLx0YAAAAQOL43BQw0HxkdbA1ChgAAABA4rGFBGYgq4OtVYfDkihgAAAAAInk87gU5DaqaCayOthatMqbxG1UAQAAgITxug0FQ+FEh4FWjqwOtsYWEgAAACDxkjxu1YTowEDzkNXB1qL3mqaAAQAAACSO123EfjcHrhZZHWwt1oHBFhIAAAAgYbxuFwUMNBtZHWyNLSQAAABA4vk8LtVwFxI0E1kdbI0CBgAAAJB4PrdLNXRgoJnI6mBr1czAAAAAABKu7jaqFDDQPGR1sDVmYAAAAACJ53W7uAsJmo2sDrYWLWAk0YEBAAAAJAxDPGEGy7O6cDisgQMHasyYMQ3ORSIRPf7448rOztaAAQO0a9eu2LnNmzerV69eys7O1vz582PHT548qZEjR6pnz54aOXKkTp06ZfUloBVjBgYAAACQeGwhgRksz+qWLFminJycRs9t2rRJpaWlKi0tVVFRkWbOnCmprujx2GOPadOmTdqzZ49Wr16tPXv2SJLmz5+vESNGqLS0VCNGjKhX3AD+VpAZGAAAAEDC+dyGasK1ikTYRoKr57Hyxf1+vzZu3Khnn31Wv/rVrxqcX7dunSZPnizDMDRkyBBVVFQoEAiorKxM2dnZ6t69uyRpwoQJWrdunfr06aN169Zp27ZtkqQpU6aooKBAv/zlL628jIS4bf5Wna0KyuX6c6JDadWqaqJbSNwJjgQAAABwLq/bpUhECtVG5HUbiQ4HrZSlBYxZs2ZpwYIFOnPmTKPny8vL1blz59j3mZmZKi8vb/T4jh07JElHjhxRenq6JCk9PV1Hjx5t9LWLiopUVFQkSTp8+LACgYAp1xQv3+nWTjVhqpNmSGvnVdXp4wpU8oOyJWlt/00CV4L1DbtibcOuWNvWq7pwTpJ0sPyQrvXyx8V4stP6tqyAsWHDBqWmpiovLy/WMfG3GmsfMgzjksevRGFhoQoLCyVJ+fn5saJHazH/++kKBAKtLm6gKVjbsDPWN+yKtQ27Ym3HR0qHKknl6nh9qjq08SU6HMew2/q2bDDA9u3btX79emVlZWnChAnaunWrJk2aVO8xmZmZOnjwYOx7v9+vjIyMSx6XpLS0tFgFKRAIKDU11apLAAAAAACYIDqTjkGeaA7LChjz5s2T3+9XWVmZiouLNXz4cK1atareY8aOHauVK1cqEonoo48+Uvv27ZWenq7BgwertLRUX331lYLBoIqLizV27NjYc1asWCFJWrFihe655x6rLgEAAAAAYAKfuy71ZJs8msPSGRiNWbZsmSRpxowZGj16tEpKSpSdna02bdpo+fLldUF5PFq6dKm++93vKhwOa+rUqerbt68k6ZlnntH48eP16quvqkuXLnr77bfjfQkAAAAAgCvg9dSNBAiG6MDA1YtLAaOgoEAFBQWS6goXUYZh6KWXXmr0OaNHj9bo0aMbHE9JSdGWLVssiRMAAAAAYD6fu25wZw1bSNAMlm0hAQAAAABAUuzWqXRgoDkoYAAAAAAALMUQT5iBAgYAAAAAwFKxIZ50YKAZKGAAAAAAACzlpQMDJqCAAQAAAACw1MXbqFLAwNWjgAEAAAAAsJT36wJGMBRJcCRozShgAAAAAAAsxRBPmIECBgAAAADAUgzxhBkoYAAAAAAALOX1GJLowEDzUMAAAAAAAFiKIZ4wAwUMAAAAAIClYrdRZQsJmoECBgAAAADAUtEODLaQoDkoYAAAAAAALOWNDfHkNqq4ehQwAAAAAACWcrsMuV2GguFwokNBK0YBAwAAAABgOZ/bpZowHRi4ehQwAAAAAACW87oNhniiWShgAAAAAAAs53W7GOKJZqGAAQAAAACwnM/jUg0dGGgGChgAAAAAAMv56MBAM1HAAAAAAABYzudxqYYCBpqBAgYAAAAAwHJet0vBEHchwdWjgAEAAAAAsJzXbbCFBM1CAQMAAAAAYDmGeKK5KGAAAAAAACzn8zDEE83jSXQAAAAAAAD787ld+vTgaU1+bUeiQ3EEl2FoeLdvaXJ6eqJDMQ0FDAAAAACA5UbkpOmr4+d04MT5RIfiCC7DUGX6NYkOw1QUMAAAAAAAlps0pKsmDema6DAcJRAIJDoEUzEDAwAAAAAAtHgUMAAAAAAAQItHAQMAAAAAALR4FDAAAAAAAECLRwEDAAAAAAC0eBQwAAAAAABAi0cBAwAAAAAAtHgUMAAAAAAAQItHAQMAAAAAALR4FDAAAAAAAECLZ1kBo6qqSrfccotuvvlm9e3bV88991yDx5w6dUrjxo3TgAEDdMstt+izzz6TJH3++efKzc2N/UtOTtbixYslSXPnzlWnTp1i50pKSqy6BAAAAAAA0EJ4rHrhpKQkbd26VW3btlVNTY1uv/123XXXXRoyZEjsMc8//7xyc3O1Zs0a7d27V4899pi2bNmiXr16affu3ZKkcDisTp06ady4cbHnPfHEE5o9e7ZVoQMAAAAAgBbGsg4MwzDUtm1bSVJNTY1qampkGEa9x+zZs0cjRoyQJPXu3VtlZWU6cuRIvcds2bJFPXr0UNeuXa0KFQAAAAAAtHCWdWBIdd0TeXl5+q//+i899thjuvXWW+udv/nmm/V//+//1e23366dO3dq//798vv9SktLiz2muLhYEydOrPe8pUuXauXKlcrPz9eiRYt03XXXNXjvoqIiFRUVSZIOHz6sQCBgwRVar7XGDXwT1jbsjPUNu2Jtw65Y27AzO61vIxKJRKx+k4qKCo0bN04vvvii+vXrFzteWVmpn/70p/rkk0/Uv39/7d27V6+88opuvvlmSVIwGFRGRob+8pe/xIoaR44c0fXXXy/DMDRnzhwFAgG99tprl33//Px8ffzxx9ZdoEUCgYDS09MTHQZgOtY27Iz1DbtibcOuWNuws9a6vi+Vw1vagRHVoUMHFRQUaPPmzfUKGMnJyVq+fLkkKRKJqFu3burWrVvs/KZNmzRo0KB6HRl//fX06dM1ZsyYOFwBAAAAAABIJMtmYBw7dkwVFRWSpAsXLujdd99V79696z2moqJCwWBQkvTKK69o2LBhSk5Ojp1fvXp1g+0jf93+smbNmnoFEQAAAAAAYE+WbSH505/+pClTpigcDqu2tlbjx4/Xz372My1btkySNGPGDP3nf/6nJk+eLLfbrT59+ujVV1+NzbM4f/68OnfurC+//FLt27ePve7DDz+s3bt3yzAMZWVl6eWXX/7Glpjrr79eWVlZVlympY4dO6Ybbrgh0WEApmNtw85Y37Ar1jbsirUNO2ut67usrEzHjx9vcDwuMzBwdVrr7A7gm7C2YWesb9gVaxt2xdqGndltfVu2hQQAAAAAAMAsFDAAAAAAAECLRwGjBSssLEx0CIAlWNuwM9Y37Iq1DbtibcPO7La+mYEBAAAAAABaPDowAAAAAABAi0cBowXavHmzevXqpezsbM2fPz/R4QDNcvDgQd1xxx3KyclR3759tWTJEknSyZMnNXLkSPXs2VMjR47UqVOnEhwpcHXC4bAGDhyoMWPGSGJtwx4qKir093//9+rdu7dycnL0n//5n6xt2Mb/+l//S3379lW/fv00ceJEVVVVsb7RKk2dOlWpqanq169f7Njl1vK8efOUnZ2tXr166Xe/+10iQm42ChgtTDgc1mOPPaZNmzZpz549Wr16tfbs2ZPosICr5vF4tGjRIv2///f/9NFHH+mll17Snj17NH/+fI0YMUKlpaUaMWIExTq0WkuWLFFOTk7se9Y27OCnP/2p7rzzTu3du1effvqpcnJyWNuwhfLycr3wwgv6+OOP9dlnnykcDqu4uJj1jVbpkUce0ebNm+sdu9Ra3rNnj4qLi/WXv/xFmzdv1o9+9COFw+FEhN0sFDBamJ07dyo7O1vdu3eXz+fThAkTtG7dukSHBVy19PR0DRo0SJLUrl075eTkqLy8XOvWrdOUKVMkSVOmTNHatWsTGCVwdfx+vzZu3Khp06bFjrG20dpVVlbqP/7jP/Too49Kknw+nzp06MDahm2EQiFduHBBoVBI58+fV0ZGBusbrdKwYcPUsWPHescutZbXrVunCRMmKCkpSd26dVN2drZ27twZ75CbjQJGC1NeXq7OnTvHvs/MzFR5eXkCIwLMU1ZWpk8++US33nqrjhw5ovT0dEl1RY6jR48mODrgys2aNUsLFiyQy3Xx/52yttHaffnll7rhhhv0gx/8QAMHDtS0adN07tw51jZsoVOnTpo9e7a6dOmi9PR0tW/fXqNGjWJ9wzYutZbtkmdSwGhhGrspjGEYCYgEMNfZs2d1//33a/HixUpOTk50OECzbdiwQampqcrLy0t0KICpQqGQdu3apZkzZ+qTTz7Rt771LdrpYRunTp3SunXr9NVXX+nQoUM6d+6cVq1aleiwAMvZJc+kgNHCZGZm6uDBg7Hv/X6/MjIyEhgR0Hw1NTW6//779dBDD+m+++6TJKWlpSkQCEiSAoGAUlNTExkicMW2b9+u9evXKysrSxMmTNDWrVs1adIk1jZavczMTGVmZurWW2+VJP393/+9du3axdqGLbz77rvq1q2bbrjhBnm9Xt1333368MMPWd+wjUutZbvkmRQwWpjBgwertLRUX331lYLBoIqLizV27NhEhwVctUgkokcffVQ5OTl68sknY8fHjh2rFStWSJJWrFihe+65J1EhAldl3rx58vv9KisrU3FxsYYPH65Vq1axttHq3XjjjercubM+//xzSdKWLVvUp08f1jZsoUuXLvroo490/vx5RSIRbdmyRTk5Oaxv2Mal1vLYsWNVXFys6upqffXVVyotLdUtt9ySyFCvihFprJcECVVSUqJZs2YpHA5r6tSpevbZZxMdEnDVPvjgA33nO99R//79Y3MCnn/+ed16660aP368Dhw4oC5duujtt99uMIQIaC22bdumhQsXasOGDTpx4gRrG63e7t27NW3aNAWDQXXv3l3Lly9XbW0taxu28Nxzz+mtt96Sx+PRwIED9corr+js2bOsb7Q6EydO1LZt23T8+HGlpaXpn/7pn3Tvvfdeci3/4he/0GuvvSaPx6PFixfrrrvuSvAVXDkKGAAAAAAAoMVjCwkAAAAAAGjxKGAAAAAAAIAWjwIGAAAAAABo8ShgAAAAAACAFo8CBgAAAAAAaPEoYAAAgIRxu93Kzc3VzTffrEGDBunDDz+UJJWVlalfv35X9ZpZWVk6fvy4mWECAIAWwJPoAAAAgHNde+212r17tyTpd7/7nf7hH/5B//7v/57YoAAAQItEBwYAAGgRKisrdd111zU4XlVVpR/84Afq37+/Bg4cqPfee0+SFA6HNXv2bPXv318DBgzQiy++WO95Fy5c0J133ql//dd/1blz53T33Xfr5ptvVr9+/fTWW2/F5ZoAAIB56MAAAAAJc+HCBeXm5qqqqkqBQEBbt25t8JiXXnpJkvTnP/9Ze/fu1ahRo7Rv3z4tX75cX331lT755BN5PB6dPHky9pyzZ89qwoQJmjx5siZPnqzf/va3ysjI0MaNGyVJp0+fjs8FAgAA09CBAQAAEia6hWTv3r3avHmzJk+erEgkUu8xH3zwgR5++GFJUu/evdW1a1ft27dP7777rmbMmCGPp+7vMR07dow955577tEPfvADTZ48WZLUv39/vfvuu/of/+N/6P3331f79u3jdIUAAMAsFDAAAECLMHToUB0/flzHjh2rd/xvCxp/fdwwjEbP3Xbbbdq0aVPsuTfddJP++Mc/qn///vqHf/gH/fznPzc3eAAAYDkKGAAAoEXYu3evwuGwUlJS6h0fNmyY3nzzTUnSvn37dODAAfXq1UujRo3SsmXLFAqFJKneFpKf//znSklJ0Y9+9CNJ0qFDh9SmTRtNmjRJs2fP1q5du+J0VQAAwCzMwAAAAAkTnYEh1XVUrFixQm63u95jfvSjH2nGjBnq37+/PB6PXn/9dSUlJWnatGnat2+fBgwYIK/Xq+nTp+vHP/5x7HmLFy/W1KlT9fTTT2vEiBH67//9v8vlcsnr9ep//+//Hc/LBAAAJjAil+rLBAAAAAAAaCHYQgIAAAAAAFo8ChgAAAAAAKDFo4ABAAAAAABaPAoYAAAAAACgxaOAAQAAAAAAWjwKGAAAAAAAoMWjgAEAAAAAAFo8ChgAAAAAAKDF+//EdUktkEybGQAAAABJRU5ErkJggg==\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"
    }
   ],
   "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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      "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"
    }
   ],
   "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"
    }
   ],
   "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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      "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"
    }
   ],
   "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"
    }
   ],
   "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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      "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"
    }
   ],
   "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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qlmsfeeSRrFixomV8eXn5Jut9+umns++++653bN999019ff3bWv+WuOOOO/KFL3whM2fOzB577LHJcddcc00qKyvTrVu3dO/ePS+99NJ66wIAdhylbV0AAGzP+vfvn0996lP57ne/m+SNIOHxxx/fYFx5eXn233//1NXVveU5nnzyyZx99tmZPXt2Dj/88LRv3z5VVVVpbm5uGVNSUrLJ6/v06ZMnn3xyvWNPPfVUjj322Ldcy5aYNWtWzj777Nx999055JBDNjnugQceyDe/+c3Mnj07AwcOTLt27bLbbrutty4AYMdhBwYAbEWPPvporrnmmixfvjzJGzsubrnllgwbNixJctZZZ+Vb3/pW/vCHP6S5uTl/+9vf8uSTT+bd7353dt1113zzm9/MP/7xjzQ1NeWRRx7Zol+/+sorr6SkpCQ9e/ZM8sajGY888sgW1zxixIj89a9/zY9//OOsXbs2t956axYvXrzF771Yu3ZtXnvttTQ1NaWpqWm9nSH/7p577sno0aNz22235d3vfveb3vfvf/97SktL07Nnz6xduzZf+9rXsnr16i1eFwCwfRFgAMBW1LVr18ydOzdDhw7NLrvskmHDhuXggw/ONddck+SNd1VceumlOeWUU9K1a9eccMIJeeGFF9K+ffv84he/yMKFC7P//vtnjz32yFlnnZWXXnpps3MOGDAgF198cQ4//PDstdde+dOf/pT3vve9W1xzjx49ctddd+Waa65Jjx49cvXVV+euu+5qebTjG9/4Rj784Q9v8vqvf/3r6dy5cyZMmJAf/ehH6dy5c77+9a8neWMnR5cuXfLUU08lSa644oq89NJLGTFiRLp06ZIuXbps8t7Dhw/Phz/84Rx00EHZd999s9NOO73pozAAwPatpNk+TAAAAKDg7MAAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwStu6gHfCHnvskf3226+ty9isNWvWpEOHDm1dBqxHX1JUepOi0psUkb6kqLaV3ly6dGlWrFjR1mW8ZWvWrMny5cvz2muvtXUpW2SnnXZKWVnZJntihwgw9ttvv8yfP7+ty9ishoaG9O7du63LgPXoS4pKb1JUepMi0pcU1bbSm0OGDGnrEt6W5cuXp2vXrtlvv/1SUlLS1uW8qebm5qxcuTLLly/P/vvvv9ExHiEBAACA7dBrr72WHj16FD68SJKSkpL06NHjTXeLCDAAAABgO7UthBf/tLlaBRgAAABAq3jmmWcyatSoHHDAARkwYEBGjBiR+++/PyNHjnzL99oh3oEBAAAAvLOam5vzsY99LGPGjMm0adOSJAsXLszf//73/OxnP3vL97MDAwAAAEiSrHz59SxatiorX379f32ve++9Nx06dMj48eNbjlVVVaW8vDwHH3zwW76fHRgAAABApi+sz+dvezgd2rXLmnXrcvWJg1JTtffbvt8jjzySwYMHb7X67MAAAACAHdzKl1/P5297OK+tWZe/v742r61Zl0tue3ir7MTYWgQYAAAAsINb/uI/0qHd+hFBh3btsvzFf7ztew4cODB/+MMf/reltRBgAAAAwA6ubLfOWbNu3XrH1qxbl7LdOr/te37wgx/M66+/nu9973stx37/+9/nySeffFv3E2AAAADADq5Hl065+sRB2alDu3TtVJqdOrTL1ScOSo8und72PUtKSnL77bfn17/+dQ444IAMHDgwl19+efr06ZOSkpK3fD8v8QQAAABSU7V33luxR5a/+I+U7db5fxVe/FOfPn3yk5/8ZL1jf/jDH7L77ru/5XsJMAAAAIAkb+zE2BrBxabMnz8/p5xySiZMmPCWrxVgAAAAAO+IIUOG5K9//evbutY7MAAAAIDCE2AAAADAdqq5ubmtS9him6tVgAEAAADboZ122ikrV67cJkKM5ubmrFy5MjvttNMmx3gHBgAAAGyHysrKsnz58jz//PNtXcoW2WmnnVJWVrbJ8wIMAAAA2A516NAh+++/f1uXsdV4hAQAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAovFYNMGbNmpV+/fqloqIiEyZM2OB8c3NzLrjgglRUVGTQoEFZsGBBy7lVq1Zl5MiR6d+/fyorKzNnzpyWcxMnTky/fv0ycODAXHLJJa25BAAAAKAASlvrxk1NTTnvvPPy61//OmVlZamurk5NTU0GDBjQMmbmzJmpq6tLXV1d5s6dm3POOSdz585NktTW1ubYY4/Nz372szQ2NubVV19Nktx7772ZPn16Hn744XTq1CnPPfdcay0BAAAAKIhW24Exb968VFRUpG/fvunYsWNGjRqV6dOnrzdm+vTpOf3001NSUpJhw4Zl1apVaWhoyOrVq3P//ffnzDPPTJJ07Ngx3bt3T5L813/9V77whS+kU6dOSZI999yztZYAAAAAFESrBRj19fUpLy9v+VxWVpb6+votGvPEE0+kZ8+eOeOMM3LYYYflrLPOyiuvvJIk+etf/5oHHnggQ4cOzZFHHpnf//73rbUEAAAAoCBa7RGS5ubmDY6VlJRs0Zi1a9dmwYIFmThxYoYOHZra2tpMmDAhV1xxRdauXZsXX3wxDz30UH7/+9/npJNOyhNPPLHBvSdPnpzJkycnSZ555pk0NDRsxdW1nm2lTnYs+pKi0psUld6kiPQlRaU32VKtFmCUlZVl2bJlLZ+XL1+ePn36bNGYkpKSlJWVZejQoUmSkSNHtrwEtKysLB//+MdTUlKSd7/73WnXrl1WrFiRnj17rnfvcePGZdy4cUmSIUOGpHfv3q2yzq2poaFhm6iTHYu+pKj0JkWlNykifUlR6U3eilZ7hKS6ujp1dXVZsmRJGhsbM23atNTU1Kw3pqamJjfddFOam5vz0EMPpVu3bundu3d69eqV8vLyPPbYY0mS2bNnt7z884QTTsg999yT5I3HSRobG7PHHnu01jIAAACAAmi1HRilpaWZNGlShg8fnqampowdOzYDBw7MDTfckCQZP358RowYkRkzZqSioiI777xzpkyZ0nL9xIkTM3r06DQ2NqZv374t58aOHZuxY8fm4IMPTseOHTN16tQNHh8BAAAAti8lzRt7EcV2ZsiQIZk/f35bl7FZtk9RRPqSotKbFJXepIj0JUW1rfTmtvIz5fau1R4hAQAAANhaBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4bVqgDFr1qz069cvFRUVmTBhwgbnm5ubc8EFF6SioiKDBg3KggULWs6tWrUqI0eOTP/+/VNZWZk5c+YkSS6//PLsvffeqaqqSlVVVWbMmNGaSwAAAAAKoLS1btzU1JTzzjsvv/71r1NWVpbq6urU1NRkwIABLWNmzpyZurq61NXVZe7cuTnnnHMyd+7cJEltbW2OPfbY/OxnP0tjY2NeffXVlusuuuiifO5zn2ut0gEAAICCabUdGPPmzUtFRUX69u2bjh07ZtSoUZk+ffp6Y6ZPn57TTz89JSUlGTZsWFatWpWGhoasXr06999/f84888wkSceOHdO9e/fWKhUAAAAouFbbgVFfX5/y8vKWz2VlZS27K95sTH19fUpLS9OzZ8+cccYZWbRoUQYPHpzrrrsuu+yyS5Jk0qRJuemmmzJkyJBcc8012W233TaYf/LkyZk8eXKS5JlnnklDQ0NrLHOr21bqZMeiLykqvUlR6U2KSF9SVHqTLdVqAUZzc/MGx0pKSrZozNq1a7NgwYJMnDgxQ4cOTW1tbSZMmJArrrgi55xzTr785S+npKQkX/7yl3PxxRfnxhtv3OA+48aNy7hx45IkQ4YMSe/evbfSylpPQ0PDNlEnOxZ9SVHpTYpKb1JE+pKi0pu8Fa32CElZWVmWLVvW8nn58uXp06fPFo0pKytLWVlZhg4dmiQZOXJkyws+99prr7Rv3z7t2rXL2WefnXnz5rXWEgAAAICCaLUAo7q6OnV1dVmyZEkaGxszbdq01NTUrDempqYmN910U5qbm/PQQw+lW7du6d27d3r16pXy8vI89thjSZLZs2e3vPzzX7cX3X777Tn44INbawkAAABAQbTaIySlpaWZNGlShg8fnqampowdOzYDBw7MDTfckCQZP358RowYkRkzZqSioiI777xzpkyZ0nL9xIkTM3r06DQ2NqZv374t5y655JIsXLgwJSUl2W+//fLd7363tZYAAAAAFERJ88ZeRLGdGTJkSObPn9/WZWyW578oIn1JUelNikpvUkT6kqLaVnpzW/mZcnvXao+QAAAAAGwtAgwAAACg8AQYAAAAQOEJMAAAAIDCE2AAAAAAhSfAAAAAAApPgAEAAAAUngADAAAAKDwBBgAAAFB4AgwAAACg8AQYAAAAQOEJMAAAAIDCE2AAAAAAhSfAAAAAAApPgAEAAAAUngADAAAAKDwBBgAAAFB4AgwAAACg8AQYAAAAQOEJMAAAAIDCE2AAAAAAhSfAAAAAAApPgAEAAAAUngADAAAAKDwBBgAAAFB4AgwAAACg8AQYAAAAQOEJMAAAAIDCE2AAAAAAhSfAAAAAAApPgAEAAAAUngADAAAAKDwBBgAAAFB4AgwAAACg8AQYAAAAQOEJMAAAAIDCE2AAAAAAhSfAAAAAAApPgAEAAAAUngADAAAAKDwBBgAAAFB4AgwAAACg8AQYAAAAQOEJMAAAAIDCE2AAAAAAhSfAAAAAAApPgAEAAAAUngADAAAAKDwBBgAAAFB4rRpgzJo1K/369UtFRUUmTJiwwfnm5uZccMEFqaioyKBBg7JgwYKWc6tWrcrIkSPTv3//VFZWZs6cOetd+61vfSslJSVZsWJFay4BAAAAKIBWCzCamppy3nnnZebMmVm8eHFuueWWLF68eL0xM2fOTF1dXerq6jJ58uScc845Ledqa2tz7LHH5tFHH82iRYtSWVnZcm7ZsmX59a9/nX322ae1ygcAAAAKpNUCjHnz5qWioiJ9+/ZNx44dM2rUqEyfPn29MdOnT8/pp5+ekpKSDBs2LKtWrUpDQ0NWr16d+++/P2eeeWaSpGPHjunevXvLdRdddFGuvvrqlJSUtFb5AAAAQIGUttaN6+vrU15e3vK5rKwsc+fO3eyY+vr6lJaWpmfPnjnjjDOyaNGiDB48ONddd1122WWX3Hnnndl7771z6KGHvun8kydPzuTJk5MkzzzzTBoaGrbi6lrPtlInOxZ9SVHpTYpKb1JE+pKi0ptsqVYLMJqbmzc49u87JjY1Zu3atVmwYEEmTpyYoUOHpra2NhMmTMgXv/jFXHnllfnVr3612fnHjRuXcePGJUmGDBmS3r17v82VvHMaGhq2iTrZsehLikpvUlR6kyLSlxSV3uStaLVHSMrKyrJs2bKWz8uXL0+fPn22aExZWVnKysoydOjQJMnIkSOzYMGCPP7441myZEkOPfTQ7Lffflm+fHne9a535ZlnnmmtZQAAAAAF0GoBRnV1derq6rJkyZI0NjZm2rRpqampWW9MTU1NbrrppjQ3N+ehhx5Kt27d0rt37/Tq1Svl5eV57LHHkiSzZ8/OgAEDcsghh+S5557L0qVLs3Tp0pSVlWXBggXp1atXay0DAAAAKIBWe4SktLQ0kyZNyvDhw9PU1JSxY8dm4MCBueGGG5Ik48ePz4gRIzJjxoxUVFRk5513zpQpU1qunzhxYkaPHp3Gxsb07dt3vXMAAADAjqXVAowkGTFiREaMGLHesfHjx7f8c0lJSb7zne9s9NqqqqrMnz//Te+/dOnS/3WNAAAAQPG12iMkAAAAAFuLAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFV7olg1588cU8/fTT6dy5c/bbb7+0ayf3AAAAAN45mwwwXnrppXznO9/JLbfcksbGxvTs2TOvvfZann322QwbNiznnntuPvCBD7yTtQIAAAA7qE0GGCNHjszpp5+eBx54IN27d1/v3B/+8If88Ic/zBNPPJEzzzyztWsEAAAAdnCbDDB+/etfb/KiwYMHZ/Dgwa1SEAAAAMC/26J3YNTX1+fJJ5/M2rVrW44dccQRrVYUAAAAwL/abIDx+c9/PrfeemsGDBiQ9u3bJ0lKSkoEGAAAAMA7ZrMBxh133JHHHnssnTp1eifqAQAAANjAZn8fat++fbNmzZp3ohYAAACAjdrsDoydd945VVVVOeqoo9bbhXH99de3amEAAAAA/7TZAKOmpiY1NTXvRC0AAAAAG7XZAGPMmDHvRB0AAAAAm7TZAKOuri5f/OIXs3jx4rz22mstx5944olWLQwAAADgnzb7Es8zzjgj55xzTkpLS3Pvvffm9NNPz2mnnfZO1AYAAACQZAsCjH/84x856qij0tzcnH333TeXX3557rnnnneiNgAAAIAkW/AIyU477ZR169blwAMPzKRJk7L33nvnueeeeydqAwAAAEiyBTswrr322rz66qu5/vrr84c//CE/+tGPMnXq1HeiNgAAAIAkW7ADo7q6OklSUlKSKVOmtHpBAAAAAP9uszsw5syZkwEDBqSysjJJsmjRopx77rmtXhgAAADAP202wLjwwgvzy1/+Mj169EiSHHroobn//vtbvTAAAACAf9psgJEk5eXl631u3759qxQDAAAAsDGbfQdGeXl5HnzwwZSUlKSxsTHXX399y+MkAAAAAO+Eze7AuOGGG/Kd73wn9fX1KSsry8KFC/Od73znnahth7Ly5dfX+3Nj5xctW/W2z2+Ne5hjx5zjX//cltdhju1vjn/9c1tehzm2vzn+9c9teR3m2L7m+Nc/t+V1mGP7m+Nf/4TN2ewOjD322CM333zzO1HLDmv6wvp8/raHc++5VXnvN+/J1ScOSk3V3huc79CuXdasW/eWz2+Ne5hjx51jU325ra3DHNvfHG/3v5lFW4c5tr85/H1ujiLO4e9zcxR1jjfrTfh3Jc3Nzc1vNmDJkiWZOHFili5dmrVr17Ycv/POO1u9uK1lyJAhmT9/fluXsVErX3497/3mPXltzbrMqX1XDr9uQXbq0C6/+/wH06NLp/XO/9NbOf/vc7yde5hjx55jY325La7DHNvfHG/nv5lFXIc5tr85/H1ujiLO4e9zcxR1jk31ZtEU+WfKHclmHyE54YQTst9+++X888/PxRdf3PLF1rH8xX+kQ7v1/zV0aNcuy1/8x1Y5bw5zmMMc5jCHOcxhDnOYwxzb0hywKZt9hGSnnXbKBRdc8E7UskMq261z1qxbt96xNevWpWy3zlvlvDnMYQ5zmMMc5jCHOcxhDnNsS3PApmx2B0ZtbW2++tWvZs6cOVmwYEHL15aYNWtW+vXrl4qKikyYMGGD883NzbngggtSUVGRQYMGrXffVatWZeTIkenfv38qKyszZ86cJMmXv/zlDBo0KFVVVTnmmGPy9NNPb+laC6lHl065+sRB2anDG/8qdurQLlefOKhl69S/nu/aqfQtn98a9zDHjj3HxvpyW1yHOba/OTbWm9viOsyx/c3xdnqziOswx/Y1x8b6cltchzm2vzk21ZuwMZt9B8YXv/jF/PCHP8wBBxyQdv9vm09JSUnuueeeN71xU1NTDjrooPz6179OWVlZqqurc8stt2TAgAEtY2bMmJGJEydmxowZmTt3bmprazN37twkyZgxY/If//EfOeuss9LY2JhXX3013bt3z+rVq7PrrrsmSa6//vosXrw4N9xww5vWsi08r7Ty5dfT+PcX0rHr7hv9H+7Kl1/P8hf/kbLdOr+t81vjHubYMed4s77cltZhju1vjv/NfzOLtA5zbH9z+PvcHEWcw9/n5ijqHJvrzaLYFn6m3BFsNsDo379/Hn744XTs2PEt3XjOnDm5/PLL88tf/jJJctVVVyV5IxD5p09/+tN5//vfn5NPPjlJ0q9fv9x3333ZZZddcuihh+aJJ55ISUnJJue46qqr8tRTT+W//uu/3rSWbaXZGhoa0rt377YuA9ajLykqvUlR6U2KSF9SVNtKb24rP1Nu7zb7CMmhhx6aVatWveUb19fXp7y8vOVzWVlZ6uvrt2jME088kZ49e+aMM87IYYcdlrPOOiuvvPJKy7hLL7005eXlufnmm/O1r33tLdcGAAAAbFs2+xLPZ599Nv379091dXU6dfr/23o292tUN7ax4993U2xqzNq1a7NgwYJMnDgxQ4cOTW1tbSZMmJArrrgiSXLllVfmyiuvzFVXXZVJkyblq1/96gb3mTx5ciZPnpwkeeaZZ9LQ0LC5pRbCtlInOxZ9SVHpTYpKb1JE+pKi0ptsqc0GGBsLB7ZEWVlZli1b1vJ5+fLl6dOnzxaNKSkpSVlZWYYOHZokGTly5EZfAnrKKafkuOOO22iN48aNy7hx45K8sd1nW9iWtK1sn2LHoi8pKr1JUelNikhfUlR6k7diswHGkUce+bZuXF1dnbq6uixZsiR77713pk2blh//+MfrjampqcmkSZMyatSozJ07N926dWtp3vLy8jz22GPp169fZs+e3fLyz7q6uhx44IFJ3tgF0r9//7dVHwAAALDt2GyA8bZvXFqaSZMmZfjw4WlqasrYsWMzcODAlt8YMn78+IwYMSIzZsxIRUVFdt5550yZMqXl+okTJ2b06NFpbGxM3759W8594QtfyGOPPZZ27dpl33333exvIAEAAAC2fZv9LSTbg23ljbG2T1FE+pKi0psUld6kiPQlRbWt9Oa28jPl9m6zv4UEAAAAoK295QBjzJgxOeecc/LII4+0Rj0AAAAAG3jLAcZnPvOZfOhDH8oPf/jD1qgHAAAAYANv+SWe1dXVqa6uzoknntga9QAAAABsYJMBxoUXXphrr702xx9/fEpKStY7V1JSkt133z2f/vSnM2zYsFYvEgAAANixbTLAOO2005Ikn/vc5zZ6fsWKFRk7dmwWL17cOpUBAAAA/D+bDDD22WefLF68OEceeeR6x//85z9nzz33TM+ePdOxY8dWLxAAAABgky/xPP/88/P8889vcHz58uWpra1Nkhx//PGtVxkAAADA/7PJAONPf/rTBrsvkmT48OF5+OGHW7UoAAAAgH+1yQBjzZo1m7zozc4BAAAAbG2bDDAOPPDAzJgxY4PjM2fOTN++fVu1KAAAAIB/tcmXeH7729/ORz7ykfzkJz/J4MGDkyTz58/PnDlzctddd71jBQIAAABscgfGQQcd1PIejKVLl2bp0qU58sgj8/DDD+eggw56J2sEAAAAdnCb3IHR3NycTp065Ywzztjkxc3NzSkpKWmVwgAAAAD+aZM7MD7wgQ9k4sSJeeqpp9Y73tjYmHvuuSdjxozJ1KlTW71AAAAAgE3uwJg1a1ZuvPHGnHzyyVmyZEm6d++ef/zjH1m3bl2OOeaYXHTRRamqqnoHSwUAAAB2VJsMMHbaaaece+65Offcc7NmzZqsWLEinTt3Tvfu3d/B8gAAAADeJMD4Vx06dEjv3r1buxYAAACAjdrkOzAAAAAAikKAAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACF16oBxqxZs9KvX79UVFRkwoQJG5xvbm7OBRdckIqKigwaNCgLFixoObdq1aqMHDky/fv3T2VlZebMmZMk+c///M/0798/gwYNysc+9rGsWrWqNZcAAAAAFECrBRhNTU0577zzMnPmzCxevDi33HJLFi9evN6YmTNnpq6uLnV1dZk8eXLOOeeclnO1tbU59thj8+ijj2bRokWprKxMkhx99NF55JFH8vDDD+eggw7KVVdd1VpLAAAAAAqi1QKMefPmpaKiIn379k3Hjh0zatSoTJ8+fb0x06dPz+mnn56SkpIMGzYsq1atSkNDQ1avXp37778/Z555ZpKkY8eO6d69e5LkmGOOSWlpaZJk2LBhWb58eWstAQAAACiI0ta6cX19fcrLy1s+l5WVZe7cuZsdU19fn9LS0vTs2TNnnHFGFi1alMGDB+e6667LLrvsst71N954Yz75yU9udP7Jkydn8uTJSZJnnnkmDQ0NW2tprWpbqZMdi76kqPQmRaU3KSJ9SVHpTbZUqwUYzc3NGxwrKSnZojFr167NggULMnHixAwdOjS1tbWZMGFCrrjiipZxV155ZUpLSzN69OiNzj9u3LiMGzcuSTJkyJD07t37f7Ocd0RDQ8M2USc7Fn1JUelNikpvUkT6kqLSm7wVrfYISVlZWZYtW9byefny5enTp88WjSkrK0tZWVmGDh2aJBk5cuR6L/icOnVq7rrrrtx8880bhCIAAADA9qfVAozq6urU1dVlyZIlaWxszLRp01JTU7PemJqamtx0001pbm7OQw89lG7duqV3797p1atXysvL89hjjyVJZs+enQEDBiR54zebfPOb38ydd96ZnXfeubXKBwAAAAqk1R4hKS0tzaRJkzJ8+PA0NTVl7NixGThwYG644YYkyfjx4zNixIjMmDEjFRUV2XnnnTNlypSW6ydOnJjRo0ensbExffv2bTn3mc98Jq+//nqOPvroJG+8yPOf9wQAAAC2T60WYCTJiBEjMmLEiPWOjR8/vuWfS0pK8p3vfGej11ZVVWX+/PkbHP/b3/62dYsEAAAACq/VHiEBAAAA2FoEGAAAAEDhCTAAAACAwhNgAAAAAIUnwAAAAAAKT4ABAAAAFJ4AAwAAACg8AQYAAABQeAIMAAAAoPAEGAAAAEDhCTAAAACAwhNgAAAAAIUnwAAAAAAKT4ABAAAAFJ4AAwAAACg8AQYAAABQeAIMAAAAoPAEGAAAAEDhCTAAAACAwhNgAAAAAIUnwAAAAAAKT4ABAAAAFJ4AAwAAACg8AQYAAABQeAIMAAAAoPAEGAAAAEDhCTAAAACAwhNgAAAAAIUnwAAAAAAKT4ABAAAAFJ4AAwAAACg8AQYAAABQeAIMAAAAoPAEGAAAAEDhCTAAAACAwhNgAAAAAIUnwAAAAAAKT4ABAAAAFJ4AAwAAACg8AQYAAABQeAIMAAAAoPAEGAAAAEDhCTAAAACAwhNgAAAAAIUnwAAAAAAKT4ABAAAAFJ4AAwAAACg8AQYAAABQeAIMAAAAoPBaNcCYNWtW+vXrl4qKikyYMGGD883NzbngggtSUVGRQYMGZcGCBS3nVq1alZEjR6Z///6prKzMnDlzkiQ//elPM3DgwLRr1y7z589vzfIBAACAgmi1AKOpqSnnnXdeZs6cmcWLF+eWW27J4sWL1xszc+bM1NXVpa6uLpMnT84555zTcq62tjbHHntsHn300SxatCiVlZVJkoMPPjg///nPc8QRR7RW6QAAAEDBlLbWjefNm5eKior07ds3STJq1KhMnz49AwYMaBkzffr0nH766SkpKcmwYcOyatWqNDQ0ZJdddsn999+fH/zgB0mSjh07pmPHjknSEmQAAAAAO45W24FRX1+f8vLyls9lZWWpr6/fojFPPPFEevbsmTPOOCOHHXZYzjrrrLzyyiutVSoAAABQcK22A6O5uXmDYyUlJVs0Zu3atVmwYEEmTpyYoUOHpra2NhMmTMgVV1yxxfNPnjw5kydPTpI888wzaWhoeIsraBvbSp3sWPQlRaU3KSq9SRHpS4pKb7KlWi3AKCsry7Jly1o+L1++PH369NmiMSUlJSkrK8vQoUOTJCNHjtzoS0DfzLhx4zJu3LgkyZAhQ9K7d++3u5R3TENDwzZRJzsWfUlR6U2KSm9SRPqSotKbvBWt9ghJdXV16urqsmTJkjQ2NmbatGmpqalZb0xNTU1uuummNDc356GHHkq3bt3Su3fv9OrVK+Xl5XnssceSJLNnz17v3RkAAADAjqXVdmCUlpZm0qRJGT58eJqamjJ27NgMHDgwN9xwQ5Jk/PjxGTFiRGbMmJGKiorsvPPOmTJlSsv1EydOzOjRo9PY2Ji+ffu2nLv99ttz/vnn5/nnn89xxx2Xqqqq/PKXv2ytZQAAAAAFUNK8sRdRbGeGDBmS+fPnt3UZm2X7FEWkLykqvUlR6U2KSF9SVNtKb24rP1Nu71rtERIAAACArUWAAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABReqwYYs2bNSr9+/VJRUZEJEyZscL65uTkXXHBBKioqMmjQoCxYsKDl3KpVqzJy5Mj0798/lZWVmTNnTpLkhRdeyNFHH50DDzwwRx99dF588cXWXAIAAABQAK0WYDQ1NeW8887LzJkzs3jx4txyyy1ZvHjxemNmzpyZurq61NXVZfLkyTnnnHNaztXW1ubYY4/No48+mkWLFqWysjJJMmHChBx11FGpq6vLUUcdtdFgBAAAANi+tFqAMW/evFRUVKRv377p2LFjRo0alenTp683Zvr06Tn99NNTUlKSYcOGZdWqVWloaMjq1atz//3358wzz0ySdOzYMd27d2+5ZsyYMUmSMWPG5I477mitJQAAAAAFUdpaN66vr095eXnL57KyssydO3ezY+rr61NaWpqePXvmjDPOyKJFizJ48OBcd9112WWXXfLss8+md+/eSZLevXvnueee2+j8kydPzuTJk5MkzzzzTBoaGrb2ElvFtlInOxZ9SVHpTYpKb1JE+pKi0ptsqVYLMJqbmzc4VlJSskVj1q5dmwULFmTixIkZOnRoamtrM2HChFxxxRVbPP+4ceMybty4JMmQIUNaQo8ia2ho2CbqZMeiLykqvUlR6U2KSF9SVHqTt6LVHiEpKyvLsmXLWj4vX748ffr02aIxZWVlKSsry9ChQ5MkI0eObHnB51577dWS0DU0NGTPPfdsrSUAAAAABdFqAUZ1dXXq6uqyZMmSNDY2Ztq0aampqVlvTE1NTW666aY0NzfnoYceSrdu3dK7d+/06tUr5eXleeyxx5Iks2fPzoABA1qumTp1apJk6tSp+ehHP9paSwAAAAAKotUeISktLc2kSZMyfPjwNDU1ZezYsRk4cGBuuOGGJMn48eMzYsSIzJgxIxUVFdl5550zZcqUlusnTpyY0aNHp7GxMX379m0594UvfCEnnXRS/vu//zv77LNPfvrTn7bWEgAAAICCKGne2IsotjNDhgzJ/Pnz27qMzfL8F0WkLykqvUlR6U2KSF9SVNtKb24rP1Nu71rtERIAAACArUWAAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGAAAAUHgCDAAAAKDwBBgAAABA4QkwAAAAgMITYAAAAACFV9Lc3Nzc1kW0tj322CP77bdfW5exWc8//3x69uzZ1mXAevQlRaU3KSq9SRHpS4pqW+nNpUuXZsWKFW1dxg5vhwgwthVDhgzJ/Pnz27oMWI++pKj0JkWlNykifUlR6U3eCo+QAAAAAIUnwAAAAAAKT4BRIOPGjWvrEmAD+pKi0psUld6kiPQlRaU3eSu8AwMAAAAoPDswAAAAgMITYBTArFmz0q9fv1RUVGTChAltXQ47sGXLluUDH/hAKisrM3DgwFx33XVJkhdeeCFHH310DjzwwBx99NF58cUX27hSdkRNTU057LDD8pGPfCSJvqQYVq1alZEjR6Z///6prKzMnDlz9CaF8O1vfzsDBw7MwQcfnJNPPjmvvfaa3qRNjB07NnvuuWcOPvjglmNv1otXXXVVKioq0q9fv/zyl79si5IpMAFGG2tqasp5552XmTNnZvHixbnllluyePHiti6LHVRpaWmuueaa/OUvf8lDDz2U73znO1m8eHEmTJiQo446KnV1dTnqqKMEbbSJ6667LpWVlS2f9SVFUFtbm2OPPTaPPvpoFi1alMrKSr1Jm6uvr8/111+f+fPn55FHHklTU1OmTZumN2kTn/rUpzJr1qz1jm2qFxcvXpxp06blz3/+c2bNmpVzzz03TU1NbVE2BSXAaGPz5s1LRUVF+vbtm44dO2bUqFGZPn16W5fFDqp3795517velSTp2rVrKisrU19fn+nTp2fMmDFJkjFjxuSOO+5owyrZES1fvjx33313zjrrrJZj+pK2tnr16tx///0588wzkyQdO3ZM9+7d9SaFsHbt2vzjH//I2rVr8+qrr6ZPnz56kzZxxBFHZPfdd1/v2KZ6cfr06Rk1alQ6deqU/fffPxUVFZk3b947XTIFJsBoY/X19SkvL2/5XFZWlvr6+jasCN6wdOnS/PGPf8zQoUPz7LPPpnfv3kneCDmee+65Nq6OHc2FF16Yq6++Ou3a/f+/tvQlbe2JJ55Iz549c8YZZ+Swww7LWWedlVdeeUVv0ub23nvvfO5zn8s+++yT3r17p1u3bjnmmGP0JoWxqV70sxGbI8BoYxv7JTAlJSVtUAn8fy+//HJOPPHEXHvttdl1113buhx2cHfddVf23HPPDB48uK1LgfWsXbs2CxYsyDnnnJM//vGP2WWXXWzJpxBefPHFTJ8+PUuWLMnTTz+dV155JT/60Y/auizYLD8bsTkCjDZWVlaWZcuWtXxevnx5+vTp04YVsaNbs2ZNTjzxxIwePTof//jHkyR77bVXGhoakiQNDQ3Zc88927JEdjC/+93vcuedd2a//fbLqFGjcs899+TUU0/Vl7S5srKylJWVZejQoUmSkSNHZsGCBXqTNveb3/wm+++/f3r27JkOHTrk4x//eB588EG9SWFsqhf9bMTmCDDaWHV1derq6rJkyZI0NjZm2rRpqampaeuy2EE1NzfnzDPPTGVlZT772c+2HK+pqcnUqVOTJFOnTs1HP/rRtiqRHdBVV12V5cuXZ+nSpZk2bVo++MEP5kc/+pG+pM316tUr5eXleeyxx5Iks2fPzoABA/QmbW6fffbJQw89lFdffTXNzc2ZPXt2Kisr9SaFsalerKmpybRp0/L6669nyZIlqaury7vf/e62LJWCKWne2D4d3lEzZszIhRdemKampowdOzaXXnppW5fEDuq3v/1t/uM//iOHHHJIy7sGvvGNb2To0KE56aST8tRTT2WfffbJT3/60w1exgTvhPvuuy/f+ta3ctddd2XlypX6kja3cOHCnHXWWWlsbEzfvn0zZcqUrFu3Tm/S5i677LLceuutKS0tzWGHHZbvf//7efnll/Um77iTTz459913X1asWJG99torX/3qV3PCCSdsshevvPLK3HjjjSktLc21116bD3/4w228AopEgAEAAAAUnkdIAAAAgMITYAAAAACFJ8AAAAAACk+AAQAAABSeAAMAAAAoPAEGABTYypUrU1VVlaqqqvTq1St77713qqqq0qVLl5x77rmtNu99992XBx98sNXuDwDwVpW2dQEAwKb16NEjCxcuTJJcfvnl6dKlSz73uc+1+rz33XdfunTpkve85z2tPhcAwJawAwMAtkH33XdfPvKRjyR5I9gYM2ZMjjnmmOy33375+c9/nksuuSSHHHJIjj322KxZsyZJ8oc//CFHHnlkBg8enOHDh6ehoSFJcv3112fAgAEZNGhQRo0alaVLl+aGG27It7/97VRVVeWBBx7I888/nxNPPDHV1dWprq7O7373u5a5TzvttHzwgx/MgQcemO9973tJkoaGhhxxxBGpqqrKwQcfnAceeKANvksAwPbEDgwA2A48/vjjuffee7N48eIcfvjhue2223L11VfnYx/7WO6+++4cd9xxOf/88zN9+vT07Nkzt956ay699NLceOONmTBhQpYsWZJOnTpl1apV6d69e8aPH7/ebo9TTjklF110Ud73vvflqaeeyvDhw/OXv/wlSfLwww/noYceyiuvvJLDDjssxx13XG655ZYMHz48l156aZqamvLqq6+25bcHANgOCDAAYDvw4Q9/OB06dMghhxySpqamHHvssUmSQw45JEuXLs1jjz2WRx55JEcffXSSpKmpKb17906SDBo0KKNHj84JJ5yQE044YaP3/81vfpPFixe3fF69enX+/ve/J0k++tGPpnPnzuncuXM+8IEPZN68eamurs7YsWOzZs2anHDCCamqqmq9xQMAOwQBBgBsBzp16pQkadeuXTp06JCSkpKWz2vXrk1zc3MGDhyYOXPmbHDt3Xffnfvvvz933nlnrrjiivz5z3/eYMy6desyZ86cdO7ceYNz/5zrXz8fccQRuf/++3P33XfntNNOy3/+53/m9NNP3xpLBQB2UN6BAQA7gH79+uX5559vCTDWrFmTP//5z1m3bl2WLVuWD3zgA7n66quzatWqvPzyy+natWvLDoskOeaYYzJp0qSWz/98sWiSTJ8+Pa+99lpWrlyZ++67L9XV1XnyySez55575uyzz86ZZ56ZBQsWvGNrBQC2TwIMANgBdOzYMT/72c/y+c9/Poceemiqqqry4IMPpqmpKaeeemoOOeSQHHbYYbnooovSvXv3HH/88bn99ttbXuJ5/fXXZ/78+Rk0aFAGDBiQG264oeXe7373u3Pcccdl2LBh+fKXv5w+ffrkvvvuS1VVVQ477LDcdtttqa2tbcPVAwDbg5Lm5ubmti4CANg2vZO/2hUA2LHZgQEAAAAUnh0YAAAAQOHZgQEAAAAUngADAAAAKDwBBgAAAFB4AgwAAACg8AQYAAAAQOEJMAAAAIDC+7/sF9BITLmt6AAAAABJRU5ErkJggg==\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"
    }
   ],
   "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"
    }
   ],
   "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": 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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"
    }
   ],
   "source": [
    "param_pool_simulation_plot(experiments, config_ids, 'a','j','P')"
   ]
  }
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