experiments/notebooks/2_epoch_rewards.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Notebook: Epoch Rewards\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [Experiment Summary](#Experiment-Summary)\n",
    "* [Experiment Assumptions](#Experiment-Assumptions)\n",
    "* [Experiment Setup](#Experiment-Setup)\n",
    "* [Analysis 1: ...](#Analysis-1:-...)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Summary \n",
    "\n",
    "The purpose of this notebook is to replicate and simulate Celo epoch rewards\n",
    "\n",
    "# Experiment Assumptions\n",
    "\n",
    "See [assumptions document](../../ASSUMPTIONS.md) for further details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment Setup\n",
    "\n",
    "We begin with several experiment-notebook-level preparatory setup operations:\n",
    "\n",
    "* Import relevant dependencies\n",
    "* Import relevant experiment templates\n",
    "* Create copies of experiments\n",
    "* Configure and customize experiments \n",
    "\n",
    "Analysis-specific setup operations are handled in their respective notebook sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the setup module:\n",
    "# * sets up the Python path\n",
    "# * runs shared notebook-configuration methods, such as loading IPython modules\n",
    "import setup\n",
    "\n",
    "# External dependencies\n",
    "import copy\n",
    "import logging\n",
    "import pandas as pd\n",
    "import plotly.express as px\n",
    "from pprint import pprint\n",
    "import importlib as imp\n",
    "\n",
    "# Project dependencies\n",
    "import model.constants as constants\n",
    "from experiments.run import run\n",
    "import experiments.visualizations as visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import experiment templates\n",
    "import experiments.default_experiment as default_experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a simulation for each analysis\n",
    "simulation_1 = copy.deepcopy(default_experiment.experiment.simulations[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display code of default experiment to view and check simulation config (Monte Carlo Runs and Timesteps)\n",
    "# Set runs to number of ETH price / staked samples\n",
    "#simulation.runs = 50\n",
    "# Run single timestep, set unit of time `dt` to multiple epochs\n",
    "# (see 0_README.ipynb for further details)\n",
    "#simulation.timesteps = 1\n",
    "pprint(\"Simulation configuration\")\n",
    "pprint(f\"Monte Carlo runs: {simulation_1.runs}\")\n",
    "pprint(f\"Timesteps in blocks: {simulation_1.timesteps}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pprint(simulation_1.model.initial_state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pprint(simulation_1.model.params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Possible state and params update\n",
    "#simulation_1.model.initial_state.update({})\n",
    "\n",
    "#simulation_1.model.params.update({})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis 1: Naive Celo supply propagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the naive propagation of Celo epoch rewards into the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Experiment execution\n",
    "df, exceptions = run(simulation_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post-processing and Analysing Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that we had no exceptions for the single simulation we executed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exceptions[0]['exception'] == None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can simply display the Pandas DataFrame to inspect the results. This DataFrame already has some default post-processing applied (see [experiments/post_processing.py](../post_processing.py)). For example, parameters that change in the parameter grid (if there are any) are attached as columns to the end of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have the results post-processed and in a Pandas DataFrame, we can use Plotly for plotting our results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the celo floating supply for each subset (each parameter grid combination)\n",
    "#px.line(df, x='timestep', y='floating_supply_celo', color='run', facet_col='subset', title='Floating supply CELO')\n",
    "visualizations.plot_celo_floating_supply(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the celo reserve balance for each subset (each parameter grid combination)\n",
    "visualizations.plot_reserve_balance(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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