{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "esd-csv-python.ipynb", "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "UDD7Cijbr2qz" }, "source": [ "# Plotting Empty Set Dollar(ESD) Epoch Bonding Data\n", "\n", "We look at how ESD unlocked amounts trend with upcoming epochs\n", "\n", "Import dependencies:" ] }, { "cell_type": "code", "metadata": { "id": "_k-2YOVHhwJF" }, "source": [ "import csv\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import requests\n", "from datetime import datetime, timezone" ], "execution_count": 76, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Na6PYGY_sNNk" }, "source": [ "Define the epoch start, period and offsets:" ] }, { "cell_type": "code", "metadata": { "id": "wXh98ogYsWmt" }, "source": [ "epoch_period = 28800\n", "epoch_start = 1602201600\n", "epoch_offset = 105" ], "execution_count": 77, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "vT_unbLhtDqi" }, "source": [ "Next, we define some key global variables to use with the Covalent APIs:" ] }, { "cell_type": "code", "metadata": { "id": "tEH0NhIEsYbL" }, "source": [ "API_KEY = 'u473736hfdf-347yfd-fdu34hg'\n", "\n", "starting_block = 1\n", "\n", "topic_address = '0x93530ac0ee8c50e696e13c5ac62355d0c0ba4bd943620d5bda1eb08b64ae7512'\n", "\n", "pool_sender_address = '0x4082D11E506e3250009A991061ACd2176077C88f'\n", "\n", "dao_sender_address = '0x443D2f2755DB5942601fa062Cc248aAA153313D3'" ], "execution_count": 78, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "bp4p_i7Fveeu" }, "source": [ "Function for retrieving the latest block number:" ] }, { "cell_type": "code", "metadata": { "id": "QREMxX0uu2o4" }, "source": [ "def get_latest_block_num():\n", " covalent_block_api = 'https://api.covalenthq.com/v1/1/block_v2/latest'\n", "\n", " payload = {\n", " \"key\": API_KEY,\n", " \"page-size\": 9999\n", " }\n", "\n", " block_request = requests.get(covalent_block_api, params=payload)\n", " latest_block_number = block_request.json()['data']['items'][0]['height']\n", " return(latest_block_number)\n", "\n", "\n" ], "execution_count": 79, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Kt2c1M_-0SFa" }, "source": [ "Function for getting the unbonded events for both the POOL and DAO:" ] }, { "cell_type": "code", "metadata": { "id": "gbuLtIgb0MNz" }, "source": [ "def get_unbonded_events(lockup_epochs, type):\n", " \n", " latest_block = get_latest_block_num()\n", " \n", " covalent_topic_api = f'https://api.covalenthq.com/v1/1/events/topics/{topic_address}/'\n", "\n", " sender_address = pool_sender_address if type == 'POOL' else dao_sender_address\n", "\n", " payload = {\n", " \"starting-block\": starting_block,\n", " \"ending-block\": latest_block,\n", " \"sender-address\": sender_address,\n", " \"key\": API_KEY,\n", " \"page-size\": 9999\n", " }\n", "\n", " r = requests.get(covalent_topic_api, params=payload)\n", " data = r.json()\n", " \n", " # Filter out the json data\n", " filtered_data = []\n", "\n", " for item in data[\"data\"][\"items\"]:\n", " epoch_block = int(item[\"decoded\"][\"params\"][1][\"value\"])\n", " endtimestamp = datetime.fromtimestamp(\n", " (epoch_start + (epoch_period * (epoch_block - epoch_offset)) + epoch_period * lockup_epochs), timezone.utc\n", " ).strftime('%Y-%m-%d %H:%M')\n", "\n", " filtered_data.append({\n", " \"address\": item[\"decoded\"][\"params\"][0][\"value\"],\n", " \"startEpoch\": epoch_block,\n", " \"endTimestamp\": endtimestamp,\n", " \"amount\": int(item[\"decoded\"][\"params\"][2][\"value\"]) / 1e18,\n", " \"type\": type\n", " })\n", "\n", " return(filtered_data)" ], "execution_count": 111, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "wjfB97Gc85lP" }, "source": [ "Function for writing a list of dictionaries to a CSV given the data and headers:" ] }, { "cell_type": "code", "metadata": { "id": "ThwCn6tP9DNh" }, "source": [ "def write_csv(data, filename, headers):\n", " with open(filename, 'w', newline='') as csvfile:\n", " writer = csv.DictWriter(csvfile, fieldnames=headers)\n", " writer.writeheader()\n", "\n", " for item in data:\n", " writer.writerow(item)\n", " \n", " print('Data written to CSV')\n" ], "execution_count": 112, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "UkrbdxjW8pf2" }, "source": [ "Now the `main` function which calls for all the events:" ] }, { "cell_type": "code", "metadata": { "id": "__qyoCjQ8wX_", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "87225db6-cf7a-4972-e370-4968aea246a5" }, "source": [ "def main():\n", " now_utc = datetime.utcnow().strftime('%Y-%m-%d %H:%M')\n", "\n", " # Get unbonded events for next POOL lockup epochs:\n", " pool_unbond_evt_items = get_unbonded_events(4,'POOL')\n", "\n", " # Filter pool unbond events by those in the future from this point\n", " filtered_pool_evts = [d for d in pool_unbond_evt_items if d['endTimestamp'] >= now_utc]\n", "\n", " # Get unbonded events for next DAO lockup epochs:\n", " dao_unbond_evt_items = get_unbonded_events(14, 'DAO')\n", "\n", " # Filter dao unbond events by those in the future from this point\n", " filtered_dao_evts = [d for d in dao_unbond_evt_items if d['endTimestamp'] >= now_utc]\n", "\n", " # Combine events and reverse sort by endTimestamp\n", " combined_evts = sorted(filtered_pool_evts + filtered_dao_evts, key = lambda i: i['endTimestamp'])\n", "\n", " # Write data to CSV\n", " csv_headers = ['address', 'startEpoch', 'endTimestamp', 'amount', 'type']\n", " write_csv(combined_evts, 'data.csv', csv_headers)\n", "\n", "\n", "main()" ], "execution_count": 113, "outputs": [ { "output_type": "stream", "text": [ "Data written to CSV\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "4CCewqm_bfg3" }, "source": [ "The above function generates the `data.csv`. The next few steps create a bar chart using that data. \n", "\n", "## Bar Plots\n", "\n", "First we import the data as a dataframe. " ] }, { "cell_type": "code", "metadata": { "id": "qDLZFoqJBbZM" }, "source": [ "df = pd.read_csv('data.csv')" ], "execution_count": 114, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "g7q5PMQvbvbd" }, "source": [ "Next, we filter out the data frame by `type` and plot the `'POOL'` and `'DAO'` data separately:" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "wr3OxXC_RCGu", "outputId": "d08a4607-544f-466b-9c31-8c71c7125065" }, "source": [ "pool_df = df[df['type'] == 'POOL']\n", "dao_df = df[df['type'] == 'DAO']\n", "\n", "pool_df = pool_df.groupby('endTimestamp')['amount'].sum()\n", "dao_df = dao_df.groupby('endTimestamp')['amount'].sum()\n", "\n", "fig, ax1 = plt.subplots(figsize=(16,9))\n", "\n", "ax1 = pool_df.plot(kind='bar', color=\"indigo\", fontsize=13)\n", "ax1.set_title(\"Upcoming POOL Unlocks\", fontsize=22)\n", "ax1.set_ylabel(\"ESD Locked\", fontsize=15)\n", "\n", "\n", "fig, ax2 = plt.subplots(figsize=(16,9))\n", "ax2 = dao_df.plot(kind='bar', color=\"indigo\", fontsize=13)\n", "ax2.set_title(\"Upcoming DAO Unlocks\", fontsize=22)\n", "ax2.set_ylabel(\"ESD Locked\", fontsize=15)\n", "\n", "plt.show()" ], "execution_count": 115, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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