From 2290e97219ed40aacdd5e8ed938e15e4336b8a7e Mon Sep 17 00:00:00 2001 From: Johnnatan Messias Date: Mon, 13 May 2024 10:23:21 +0200 Subject: [PATCH] Added code used by Kris --- notebooks/2.1-inscriptions-eda-zksync.ipynb | 12133 ++++++++++++++++++ 1 file changed, 12133 insertions(+) create mode 100644 notebooks/2.1-inscriptions-eda-zksync.ipynb diff --git a/notebooks/2.1-inscriptions-eda-zksync.ipynb b/notebooks/2.1-inscriptions-eda-zksync.ipynb new file mode 100644 index 0000000..d68cbd3 --- /dev/null +++ b/notebooks/2.1-inscriptions-eda-zksync.ipynb @@ -0,0 +1,12133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Project Inscriptions -- Exploratory Data Analysis\n", + "\n", + "**Johnnatan Messias**, April 2024\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from tqdm.notebook import tqdm\n", + "import polars as pl\n", + "import json\n", + "import plotly.graph_objects as go" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "code_dir = os.path.realpath(os.path.join(os.getcwd(), \"..\", \"src\"))\n", + "\n", + "sys.path.append(code_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "ename": "ImportError", + "evalue": "cannot import name 'get_plotly_layout' from 'plot_utils' (/Users/kris/Library/Python/3.9/lib/python/site-packages/plot_utils/__init__.py)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mplot_utils\u001b[39;00m \u001b[39mimport\u001b[39;00m get_plotly_layout\n\u001b[1;32m 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mplot_utils\u001b[39;00m \u001b[39mimport\u001b[39;00m colors\n\u001b[1;32m 3\u001b[0m width, height \u001b[39m=\u001b[39m \u001b[39m800\u001b[39m, \u001b[39m450\u001b[39m\n", + "\u001b[0;31mImportError\u001b[0m: cannot import name 'get_plotly_layout' from 'plot_utils' (/Users/kris/Library/Python/3.9/lib/python/site-packages/plot_utils/__init__.py)" + ] + } + ], + "source": [ + "from plot_utils import get_plotly_layout\n", + "from plot_utils import colors\n", + "width, height = 800, 450" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "matplotlib.rcParams['pdf.fonttype'] = 42\n", + "matplotlib.rcParams['ps.fonttype'] = 42\n", + "\n", + "width, height = 800, 450\n", + "layout = go.Layout(\n", + " template='simple_white',\n", + " font=dict(size=18, family='Clear Sans'),\n", + " margin=go.layout.Margin(\n", + " l=10, # left margin\n", + " r=10, # right margin\n", + " b=10, # bottom margin\n", + " t=10 # top margin\n", + " ),\n", + " width=width,\n", + " height=height,\n", + " xaxis=dict(minor_ticks=\"inside\", showgrid=True,\n", + " griddash='dash', minor_griddash=\"dot\"),\n", + " yaxis=dict(minor_ticks=\"inside\", showgrid=True,\n", + " griddash='dash', minor_griddash=\"dot\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "colors = {'red': '#ee443a', 'blue': '#42bbf1', 'dark_blue': '#1a4fec',\n", + " 'green': '#50be61', 'grey': '#b7b7b7', 'orange': '#f28222', 'purple': '#6e18ee', 'brown': '#a65628', 'pink': '#ef4793',\n", + " 'yellow': '#f8c94c', 'black': '#000000', 'white': '#ffffff', 'light_blue': '#a6cee3', 'light_green': '#b2df8a',\n", + " 'light_grey': '#999999', 'light_orange': '#fdbf6f', 'light_purple': '#cab2d6', 'light_brown': '#ffff99', 'light_pink': '#1f78b4',\n", + " 'light_yellow': '#fb9a99', 'light_black': '#e31a1c', 'light_white': '#33a02c', 'gold': '#ff7f00', 'silver': '#b2df8a'}\n", + "styles = ['-', '--', ':', '-.']\n", + "percentiles = [.01, .05, .1, .2, .25, .50, .75, .8, .9, .95, .99]\n", + "linestyles = ['dotted', 'dotted', 'solid', 'dashdot', 'dashed', 'solid']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Existing dataset dir\n", + "data_dir = '../data/'\n", + "\n", + "# Existing plots dir\n", + "plots_dir = data_dir+'/plots/'\n", + "os.makedirs(data_dir, exist_ok=True)\n", + "os.makedirs(plots_dir, exist_ok=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inscriptions_tag = '0x646174613a'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exploratory Data Analysis\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_input_data(data):\n", + " response = dict()\n", + " if data is None:\n", + " response = None\n", + " else:\n", + " if data.startswith('data:,{'):\n", + " response['type'] = 'json'\n", + " response['data'] = ','.join(data.split(',')[1:])\n", + " elif data.startswith('data:image/png;'):\n", + " response['type'] = 'image/png'\n", + " response['data'] = ','.join(data.split(',')[1:])\n", + " elif data.startswith('data:application/json,'):\n", + " response['type'] = 'application/json'\n", + " response['data'] = ','.join(data.split(',')[1:])\n", + " elif data.startswith('data:application/json,'):\n", + " response['type'] = 'application/json'\n", + " response['data'] = ','.join(data.split(',')[1:])\n", + " else:\n", + " response['type'] = 'unknown'\n", + " response['data'] = data\n", + " return response" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 17060492 inscriptions in our dataset.\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "shape: (68, 4)\n", + "┌──────┬───────────┬──────────┬─────────┐\n", + "│ tick ┆ p ┆ op ┆ count │\n", + "│ --- ┆ --- ┆ --- ┆ --- │\n", + "│ str ┆ str ┆ str ┆ u32 │\n", + "╞══════╪═══════════╪══════════╪═════════╡\n", + "│ zkzk ┆ zrc-20 ┆ transfer ┆ 171 │\n", + "│ zkzk ┆ zrc-20 ┆ mint ┆ 1219956 │\n", + "│ zkzk ┆ zrc-20 ┆ deploy ┆ 312 │\n", + "│ zkzk ┆ zks-20 ┆ mint ┆ 1 │\n", + "│ zkzk ┆ zks-20 ┆ deploy ┆ 1 │\n", + "│ … ┆ … ┆ … ┆ … │\n", + "│ $L2 ┆ layer2-20 ┆ claim ┆ 5651395 │\n", + "│ $L2 ┆ layer2-20 ┆ Others ┆ 4 │\n", + "│ $L2 ┆ Others ┆ mint ┆ 21 │\n", + "│ $L2 ┆ Others ┆ claim ┆ 63 │\n", + "│ $L2 ┆ Others ┆ Others ┆ 323 │\n", + "└──────┴───────────┴──────────┴─────────┘" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = inscriptions_parsed.filter(pl.col('p').is_not_null() & (pl.col('p') != ''))\n", + "\n", + "value_counts = data.group_by('p').count().sort('count', descending=True)\n", + "top_protocols = value_counts.head(6)['p']\n", + "\n", + "value_counts = data.group_by('tick').count().sort('count', descending=True)\n", + "top_ticks = value_counts.head(6)['tick']\n", + "\n", + "value_counts = data.group_by('op').count().sort('count', descending=True)\n", + "top_operations = value_counts.head(6)['op']\n", + "\n", + "data = data.with_columns(pl.when(pl.col('p').is_in(top_protocols)).then('p').otherwise(pl.lit('Others')).alias('p'))\n", + "data = data.with_columns(pl.when(pl.col('tick').is_in(top_ticks)).then('tick').otherwise(pl.lit('Others')).alias('tick'))\n", + "data = data.with_columns(pl.when(pl.col('op').is_in(top_operations)).then('op').otherwise(pl.lit('Others')).alias('op'))\n", + "\n", + "value_counts = data.group_by('tick', 'p', 'op').count().sort('tick', 'p', 'op', descending=True)\n", + "\n", + "\n", + "value_counts" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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22657261… ┆ 8d3ef78964… ┆ ┆ ┆ ┆ 103140cd29… ┆ │\n", + "└──────────────┴──────────────┴──────────────┴─────────────┴───┴──────┴──────┴─────────────┴───────┘" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#data = inscriptions_parsed.filter((pl.col('p')=='zrc-20') & (pl.col('tick')=='sync') & (pl.col('op') == 'transfer'))\n", + "data = inscriptions_parsed.filter((pl.col('p')=='era-20') & (pl.col('op') == 'buy'))\n", + "\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inscriptions_parsed.filter(pl.col('price').is_not_null()).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What are the top deployed inscriptions on zkSync?\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inscriptions_parsed['op'].value_counts().sort('count', descending=True).head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = inscriptions_parsed[['p']].to_pandas().copy()\n", + "top_protocols = data['p'].value_counts().iloc[:5].index\n", + "data.loc[~data['p'].isin(top_protocols), 'p'] = 'Others'\n", + "\n", + "print(data['p'].value_counts(normalize=False))\n", + "data = data['p'].value_counts(normalize=True)*100\n", + "\n", + "fig = go.Figure(layout=get_plotly_layout(width=width, height=height))\n", + "\n", + "fig.add_trace(go.Bar(x=data.index, y=data.values,\n", + " marker_color=colors['blue'], textposition='auto', text=data.values.round(2), name='Protocols'))\n", + "fig.update_layout(yaxis_title=\"Percentage\",\n", + " xaxis_title=\"Protocol\", yaxis_ticksuffix=\"%\")\n", + "\n", + "fig.update_traces(\n", + " texttemplate='%{text:,.4}', textfont_size=18)\n", + "fig.update_yaxes(range=[0, 100])\n", + "\n", + "fig.write_image(plots_dir+\"top-15-protocols-zksync.pdf\")\n", + "\n", + "fig.show('png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = inscriptions_parsed[['op']].to_pandas().copy()\n", + "top_protocols = data['op'].value_counts().iloc[:5].index\n", + "data.loc[~data['op'].isin(top_protocols), 'op'] = 'Others'\n", + "\n", + "print(data['op'].value_counts(normalize=False))\n", + "data = data['op'].value_counts(normalize=True)*100\n", + "\n", + "fig = go.Figure(layout=get_plotly_layout(width=width, height=height))\n", + "\n", + "fig.add_trace(go.Bar(x=data.index, y=data.values,\n", + " marker_color=colors['blue'], textposition='auto', text=data.values.round(2), name='Operation'))\n", + "fig.update_layout(yaxis_title=\"Percentage\",\n", + " xaxis_title=\"Operation\", yaxis_ticksuffix=\"%\")\n", + "\n", + "fig.update_traces(\n", + " texttemplate='%{text:,.4}', textfont_size=18)\n", + "fig.update_yaxes(range=[0, 100])\n", + "\n", + "fig.write_image(plots_dir+\"top-15-operation-zksync.pdf\")\n", + "\n", + "fig.show('png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = inscriptions_parsed[['tick']].to_pandas().copy()\n", + "top_protocols = data['tick'].value_counts().iloc[:5].index\n", + "data.loc[~data['tick'].isin(top_protocols), 'tick'] = 'Others'\n", + "\n", + "print(data['tick'].value_counts(normalize=False))\n", + "data = data['tick'].value_counts(normalize=True)*100\n", + "\n", + "fig = go.Figure(layout=get_plotly_layout(width=width, height=height))\n", + "\n", + "fig.add_trace(go.Bar(x=data.index, y=data.values,\n", + " marker_color=colors['blue'], textposition='auto', text=data.values.round(2), name='Protocols'))\n", + "fig.update_layout(yaxis_title=\"Percentage\",\n", + " xaxis_title=\"Tick\", yaxis_ticksuffix=\"%\")\n", + "\n", + "fig.update_traces(\n", + " texttemplate='%{text:,.4}', textfont_size=18)\n", + "fig.update_yaxes(range=[0, 100])\n", + "\n", + "fig.write_image(plots_dir+\"top-15-tick-zksync.pdf\")\n", + "\n", + "fig.show('png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inscriptions_parsed.filter(pl.col('tick').eq('bgnt') & pl.col('op').eq('list'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inscriptions_parsed.filter(pl.col('p').eq('era-20') & pl.col('op').eq('buy'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = inscriptions_parsed.filter(pl.col('op').eq('deploy')).to_pandas().copy()\n", + "top_protocols = data['p'].value_counts().iloc[:5].index\n", + "data.loc[~data['p'].isin(top_protocols), 'p'] = 'Others'\n", + "\n", + "print(data['p'].value_counts(normalize=False))\n", + "data = data['p'].value_counts(normalize=True)*100\n", + "\n", + "\n", + "fig = go.Figure(layout=get_plotly_layout(width=width, height=height))\n", + "\n", + "fig.add_trace(go.Bar(x=data.index, y=data.values,\n", + " marker_color=colors['blue'], textposition='auto', text=data.values.round(2), name='Protocols'))\n", + "fig.update_layout(yaxis_title=\"Percentage\",\n", + " xaxis_title=\"Protocols deployed\", yaxis_ticksuffix=\"%\")\n", + "\n", + "fig.update_traces(\n", + " texttemplate='%{text:,.4}', textfont_size=18)\n", + "fig.update_yaxes(range=[0, 100])\n", + "\n", + "fig.write_image(plots_dir+\"top-15-deployed-protocols-zksync.pdf\")\n", + "\n", + "fig.show('png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = inscriptions_parsed.filter(pl.col('op').eq('deploy')).to_pandas().copy()\n", + "top_protocols = data['tick'].value_counts().iloc[:5].index\n", + "data.loc[~data['tick'].isin(top_protocols), 'tick'] = 'Others'\n", + "\n", + "print(data['tick'].value_counts(normalize=False))\n", + "data = data['tick'].value_counts(normalize=True)*100\n", + "\n", + "\n", + "fig = go.Figure(layout=get_plotly_layout(width=width, height=height))\n", + "\n", + "fig.add_trace(go.Bar(x=data.index, y=data.values,\n", + " marker_color=colors['blue'], textposition='auto', text=data.values.round(2), name='Ticks'))\n", + "fig.update_layout(yaxis_title=\"Percentage\",\n", + " xaxis_title=\"Ticks deployed\", yaxis_ticksuffix=\"%\")\n", + "\n", + "fig.update_traces(\n", + " texttemplate='%{text:,.4}', textfont_size=18)\n", + "fig.update_yaxes(range=[0, 100])\n", + "\n", + "fig.write_image(plots_dir+\"ticks-zksync.pdf\")\n", + "\n", + "fig.show('png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Loading all txs inscriptions dataframe from a file\n", + "all_txs_df = pl.scan_parquet(\n", + " data_dir+'inscriptions_all_txs_df.parquet').collect(streaming=True)\n", + "print(\"There are {} txs in our dataset.\".format(\n", + " all_txs_df.shape[0]))\n", + "all_txs_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"There are {} unique transactions in our dataset.\".format(\n", + " all_txs_df['tx_hash'].n_unique()))\n", + "print(\"There are {} unique blocks in our dataset.\".format(\n", + " all_txs_df['block_number'].n_unique()))\n", + "print(\"The average number of txs per block is {} txs.\".format(round(\n", + " all_txs_df['tx_hash'].n_unique()/all_txs_df['block_number'].n_unique(), 2)))\n", + "\n", + "print(\"The minimum and max number of txs per block are: {} and {}.\".format(\n", + " all_txs_df['block_number'].min(), all_txs_df['block_number'].max()))\n", + "\n", + "print(\"The minimum timestamp is {} and the maximum timestamp is {}.\".format(\n", + " all_txs_df['timestamp'].min(), all_txs_df['timestamp'].max()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Number of inscriptions in comparison to the total number of transactions per day on zkSync Era\n", + "\n", + "all_txs_per_day = (all_txs_df[['block_number', 'timestamp']]\n", + " .sort('timestamp').group_by_dynamic(\"timestamp\", every=\"1d\")\n", + " .agg(pl.len()))\n", + "\n", + "inscriptions_txs_per_day = (all_txs_df.filter(pl.col('is_inscription').eq(True))[['block_number', 'timestamp']]\n", + " .sort('timestamp')\n", + " .group_by_dynamic(\"timestamp\", every=\"1d\")\n", + " .agg(pl.len()))\n", + "\n", + "\n", + "fig = go.Figure(layout=get_plotly_layout(width=width, height=height))\n", + "fig.add_trace(go.Scatter(\n", + " x=all_txs_per_day['timestamp'], y=all_txs_per_day['len'], line=dict(color=colors['blue'], width=3, dash='solid'), mode='lines', name='Transactions'))\n", + "fig.add_trace(go.Scatter(\n", + " x=inscriptions_txs_per_day['timestamp'], y=inscriptions_txs_per_day['len'], line=dict(color=colors['red'], width=3, dash='solid'), mode='lines', name='Inscripted transactions'))\n", + "# fig.add_trace(go.Scatter(\n", + "# x=self_txs_per_day['timestamp'], y=self_txs_per_day['len'], line=dict(color=colors['green'], width=3, dash='solid'), mode='lines', name='Self-transfer transactions'))\n", + "\n", + "fig.update_layout(yaxis_title=\"Number of transactions\",\n", + " xaxis_title=\"Date\", legend=dict(xanchor='center',\n", + " x=0.5, y=1.02, orientation='h'))\n", + "\n", + "fig.write_image(plots_dir+'fraction-of-inscriptions-zksync.pdf')\n", + "fig.show('png')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Distribution of the percentage of inscriptions in comparison to the total number of transactions per week on zkSync\n", + "\n", + "all_txs_per_day = (all_txs_df[['block_number', 'timestamp']]\n", + " .sort('timestamp').group_by_dynamic(\"timestamp\", every=\"6h\")\n", + " .agg(pl.len()))\n", + "\n", + "inscriptions_txs_per_day = (all_txs_df.filter(pl.col('is_inscription').eq(True))[['block_number', 'timestamp']]\n", + " .sort('timestamp')\n", + " .group_by_dynamic(\"timestamp\", every=\"6h\")\n", + " .agg(pl.len()))\n", + "\n", + "data = inscriptions_txs_per_day.join(all_txs_per_day, on='timestamp', how='left', suffix='_all_txs').with_columns(\n", + " pl.col('len').truediv(pl.col('len_all_txs')).alias('fraction'))\n", + "\n", + "\n", + "fig = go.Figure(layout=get_plotly_layout(width=width, height=height))\n", + "fig.add_trace(go.Scatter(\n", + " x=data['timestamp'], y=data['fraction']*100, line=dict(color=colors['blue'], width=3, dash='solid'), mode='lines', name='Transactions'))\n", + "\n", + "fig.update_xaxes(range=['2023-11-09', '2024-03-24'])\n", + "\n", + "fig.update_yaxes(range=[0, 100])\n", + "\n", + "fig.update_layout(yaxis_title=\"Percentage\",\n", + " xaxis_title=\"Date\", yaxis_ticksuffix=\"%\", legend=dict(xanchor='center',\n", + " x=0.5, y=1.02, orientation='h'))\n", + "\n", + "fig.write_image(plots_dir+'fraction-of-inscriptions-zksync.pdf')\n", + "fig.show('png')" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "inscription_buysell = inscriptions_parsed.filter((pl.col('op')=='buy') | (pl.col('op')=='sell'))" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (2, 17)
block_numbertx_hashtx_input_dataissuerreceivertimestampgas_usedgas_effective_pricefeestx_statusdecoded_input_datapoptickamttxprice
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21964532"0x115725014c9b…"0x646174613a2c…"0xacac7716be22…"0xaa97f900599d…2023-12-20 10:39:456533411900000000.0001241"{"p":"zrc-20",…"zrc-20""sell"nullnullnullnull
" + ], + "text/plain": [ + "shape: (2, 17)\n", + "┌──────────────┬──────────────┬──────────────┬─────────────┬───┬──────┬──────┬─────────────┬───────┐\n", + "│ block_number ┆ tx_hash ┆ tx_input_dat ┆ issuer ┆ … ┆ tick ┆ amt ┆ tx ┆ price │\n", + "│ --- ┆ --- ┆ a ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", + "│ i64 ┆ str ┆ --- ┆ str ┆ ┆ str ┆ str ┆ str ┆ str │\n", + "│ ┆ ┆ str ┆ ┆ ┆ ┆ ┆ ┆ │\n", + "╞══════════════╪══════════════╪══════════════╪═════════════╪═══╪══════╪══════╪═════════════╪═══════╡\n", + "│ 22233365 ┆ 0x4a9c0858e3 ┆ 0x646174613a ┆ 0xcd5446c94 ┆ … ┆ null ┆ null ┆ 57d7eb724a7 ┆ null │\n", + "│ ┆ d4ad62331c66 ┆ 2c7b2270223a ┆ 6f45eba0121 ┆ ┆ ┆ ┆ 8a290bd06ee ┆ │\n", + "│ ┆ 311c63bc… ┆ 22657261… ┆ 65a1eace31… ┆ ┆ ┆ ┆ 832d536407… ┆ │\n", + "│ 21964532 ┆ 0x115725014c ┆ 0x646174613a ┆ 0xacac7716b ┆ … ┆ null ┆ null ┆ null ┆ null │\n", + "│ ┆ 9b5a450d7efb ┆ 2c7b2270223a ┆ e221a9585bc ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ f45fee38… ┆ 227a7263… ┆ acd5cc1e5c… ┆ ┆ ┆ ┆ ┆ │\n", + "└──────────────┴──────────────┴──────────────┴─────────────┴───┴──────┴──────┴─────────────┴───────┘" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inscription_buysell.unique('p')\n", + "#only zrc-20 and era-20" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 1204 merged buy and sell.\n", + "Out of total 1309 of buy and sell.\n" + ] + }, + { + "data": { + "text/html": [ + "
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block_numbertx_hashtx_input_dataissuerreceivertimestampgas_usedgas_effective_pricefeestx_statusdecoded_input_datapoptickamttxpriceblock_number_righttx_input_data_rightissuer_rightreceiver_righttimestamp_rightgas_used_rightgas_effective_price_rightfees_righttx_status_rightdecoded_input_data_rightp_rightop_righttick_rightamt_righttx_rightprice_right
i64strstrstrstrdatetime[μs]i64i64f64i64strstrstrstrstrstrstri64strstrstrdatetime[μs]i64i64f64i64strstrstrstrstrstrstr
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22892003"0x70a3781ced6c…"0x646174613a2c…"0x055c851d9d3a…"0xc84567f12d0e…2023-12-31 18:57:131909181500000000.0000291"{"p":"era-20",…"era-20""buy"nullnull"0x57d7eb724a78…null22233218"0x646174613a2c…"0xf4f837133a7b…"0xf4f837133a7b…2023-12-23 15:55:162713861500000000.0000411"{"p":"era-20",…"era-20""list""bgnt""5"null"10000000000000…
22901583"0x20db3f736cb4…"0x646174613a2c…"0x055c851d9d3a…"0xf4f837133a7b…2023-12-31 21:41:352112011500000000.0000321"{"p":"era-20",…"era-20""buy"nullnull"0x57d7eb724a78…null22233218"0x646174613a2c…"0xf4f837133a7b…"0xf4f837133a7b…2023-12-23 15:55:162713861500000000.0000411"{"p":"era-20",…"era-20""list""bgnt""5"null"10000000000000…
22895362"0xf5919bbfcbc0…"0x646174613a2c…"0x055c851d9d3a…"0x055c851d9d3a…2023-12-31 19:55:032006891500000000.000031"{"p":"era-20",…"era-20""buy"nullnull"0x05388d7a8e00…null22890920"0x646174613a2c…"0x055c851d9d3a…"0x055c851d9d3a…2023-12-31 18:38:321960511500000000.0000291"{"p":"era-20",…"era-20""list""bgnt""5"null"0.000000000000…
22895909"0x5a0412b4eaa1…"0x646174613a2c…"0x055c851d9d3a…"0x055c851d9d3a…2023-12-31 20:04:281987591500000000.000031"{"p":"era-20",…"era-20""buy"nullnull"0x05388d7a8e00…null22890920"0x646174613a2c…"0x055c851d9d3a…"0x055c851d9d3a…2023-12-31 18:38:321960511500000000.0000291"{"p":"era-20",…"era-20""list""bgnt""5"null"0.000000000000…
" + ], + "text/plain": [ + "shape: (5, 33)\n", + "┌───────────┬───────────┬───────────┬───────────┬───┬───────────┬───────────┬──────────┬───────────┐\n", + "│ block_num ┆ tx_hash ┆ tx_input_ ┆ issuer ┆ … ┆ tick_righ ┆ amt_right ┆ tx_right ┆ price_rig │\n", + "│ ber ┆ --- ┆ data ┆ --- ┆ ┆ t ┆ --- ┆ --- ┆ ht │\n", + "│ --- ┆ str ┆ --- ┆ str ┆ ┆ --- ┆ str ┆ str ┆ --- │\n", + "│ i64 ┆ ┆ str ┆ ┆ ┆ str ┆ ┆ ┆ str │\n", + "╞═══════════╪═══════════╪═══════════╪═══════════╪═══╪═══════════╪═══════════╪══════════╪═══════════╡\n", + "│ 24378005 ┆ 0x1babe31 ┆ 0x6461746 ┆ 0x0f63ae8 ┆ … ┆ bgnt ┆ 5 ┆ null ┆ 100000000 │\n", + "│ ┆ 683efd9bd ┆ 13a2c7b22 ┆ 3d78594a3 ┆ ┆ ┆ ┆ ┆ 00000 │\n", + "│ ┆ 05990ebfa ┆ 70223a226 ┆ e6774d598 ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ 19c76… ┆ 57261… ┆ f0c62… ┆ ┆ ┆ ┆ ┆ │\n", + "│ 22892003 ┆ 0x70a3781 ┆ 0x6461746 ┆ 0x055c851 ┆ … ┆ bgnt ┆ 5 ┆ null ┆ 100000000 │\n", + "│ ┆ ced6c77f9 ┆ 13a2c7b22 ┆ d9d3aab85 ┆ ┆ ┆ ┆ ┆ 00000 │\n", + "│ ┆ 898278655 ┆ 70223a226 ┆ bd682bedb ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ 76ee2… ┆ 57261… ┆ b8261… ┆ ┆ ┆ ┆ ┆ │\n", + "│ 22901583 ┆ 0x20db3f7 ┆ 0x6461746 ┆ 0x055c851 ┆ … ┆ bgnt ┆ 5 ┆ null ┆ 100000000 │\n", + "│ ┆ 36cb4094d ┆ 13a2c7b22 ┆ d9d3aab85 ┆ ┆ ┆ ┆ ┆ 00000 │\n", + "│ ┆ a2dd084b1 ┆ 70223a226 ┆ bd682bedb ┆ ┆ ┆ ┆ ┆ │\n", + "│ ┆ 67e5c… ┆ 57261… ┆ b8261… ┆ ┆ ┆ ┆ ┆ │\n", + "│ 22895362 ┆ 0xf5919bb ┆ 0x6461746 ┆ 0x055c851 ┆ … ┆ bgnt ┆ 5 ┆ null ┆ 0.0000000 │\n", + "│ ┆ fcbc00918 ┆ 13a2c7b22 ┆ d9d3aab85 ┆ ┆ ┆ ┆ ┆ 000000000 │\n", + "│ ┆ 9fda33c23 ┆ 70223a226 ┆ bd682bedb ┆ ┆ ┆ ┆ ┆ 01 │\n", + "│ ┆ f1adf… ┆ 57261… ┆ b8261… ┆ ┆ ┆ ┆ ┆ │\n", + "│ 22895909 ┆ 0x5a0412b ┆ 0x6461746 ┆ 0x055c851 ┆ … ┆ bgnt ┆ 5 ┆ null ┆ 0.0000000 │\n", + "│ ┆ 4eaa1375e ┆ 13a2c7b22 ┆ d9d3aab85 ┆ ┆ ┆ ┆ ┆ 000000000 │\n", + "│ ┆ 90e84c7d5 ┆ 70223a226 ┆ bd682bedb ┆ ┆ ┆ ┆ ┆ 01 │\n", + "│ ┆ 8a7e8… ┆ 57261… ┆ b8261… ┆ ┆ ┆ ┆ ┆ │\n", + "└───────────┴───────────┴───────────┴───────────┴───┴───────────┴───────────┴──────────┴───────────┘" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_df = inscription_buysell.join(inscriptions_parsed, how='inner', left_on='tx', right_on = 'tx_hash' )\n", + "\n", + "print(\"There are {} merged buy and sell.\".format(merged_df.shape[0]))\n", + "print(\"Out of total {} of buy and sell.\".format(inscription_buysell.shape[0]))\n", + "merged_df.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "merged_df = merged_df.with_columns(pl.col('amt_right').cast(pl.Int32))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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block_numbertx_hashtx_input_dataissuerreceivertimestampgas_usedgas_effective_pricefeestx_statusdecoded_input_datapoptickamttxpriceblock_number_righttx_input_data_rightissuer_rightreceiver_righttimestamp_rightgas_used_rightgas_effective_price_rightfees_righttx_status_rightdecoded_input_data_rightp_rightop_righttick_rightamt_righttx_rightprice_right
i64strstrstrstrdatetime[μs]i64i64f64i64strstrstrstrstrstrstri64strstrstrdatetime[μs]i64i64f64i64strstrstrstri32strf32
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"rgb(36,36,36)" + }, + "yaxis": { + "automargin": true, + "gridcolor": "rgb(232,232,232)", + "linecolor": "rgb(36,36,36)", + "showgrid": false, + "showline": true, + "ticks": "outside", + "title": { + "standoff": 15 + }, + "zeroline": false, + "zerolinecolor": "rgb(36,36,36)" + } + } + }, + "width": 800, + "xaxis": { + "griddash": "dash", + "minor": { + "griddash": "dot", + "ticks": "inside" + }, + "showgrid": true, + "title": { + "text": "Transaction Timestamp " + } + }, + "yaxis": { + "griddash": "dash", + "minor": { + "griddash": "dot", + "ticks": "inside" + }, + "showgrid": true, + "title": { + "text": "Price (USD)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#fig = go.Figure()\n", + "fig = go.Figure(layout=layout)\n", + "\n", + "\n", + "temp_df = data_f.filter((pl.col('price_1token_USD')<1))\n", + "\n", + "fig.add_trace(go.Scatter(x=temp_df['timestamp'], y=temp_df['price_1token_USD'], mode='markers', name='Price'))\n", + "\n", + "# Update layout\n", + "fig.update_layout(xaxis_title='Transaction Timestamp ',\n", + " yaxis_title='Price (USD)')\n", + "\n", + "fig.write_image(plots_dir+\"buybgnt_blue.pdf\",\n", + " width=1540, height=380, scale=1)\n", + "\n", + "fig.show()\n", + "\n", + "#1) number of sales thorouhg time\n", + "#2 )box plot through time -> seaborn\n", + "#3) top receiveers, top issuers - 800\n", + "#4) check how did mint vs you did list\n", + "#5) gas fee ->\n", + "\n", + "#add to conculasions - gas comparison with BTC and ETH -> that memecoins at this price are only feasible at L2s\n", + "#profibaikliy of insction on btc and eth vs l2\n", + "#yahooo fiuannce pytthon api" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "buy_bgnt = data_f.with_columns((pl.col('timestamp').cast(pl.Date)).alias('date'))\n", + "buy_bgnt = buy_bgnt.with_columns((pl.col('price_1token_USD')).alias('value_USD'))\n", + "buy_bgnt = buy_bgnt.filter((pl.col('value_USD')<1))\n", + "data = buy_bgnt.group_by('date').agg(pl.col(\"value_USD\").sum())\n", + "data = data.sort(pl.col(\"date\"))\n", + "data\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#a65628", + "width": 2 + }, + "mode": "lines", + "name": "Median Price", + "type": "scatter", + "x": [ + "2023-12-31", + "2024-01-01", + "2024-01-02", + "2024-01-03", + "2024-01-05", + "2024-01-15", + "2024-01-18", + "2024-01-19", + "2024-01-20", + "2024-01-21", + "2024-01-22", + "2024-01-23", + "2024-01-24", + "2024-01-25", + "2024-01-26", + "2024-01-27", + "2024-01-28", + "2024-01-29", + "2024-01-30", + "2024-01-31", + "2024-02-01", + "2024-02-02", + "2024-02-03", + 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"automargin": true, + "gridcolor": "rgb(232,232,232)", + "linecolor": "rgb(36,36,36)", + "showgrid": false, + "showline": true, + "ticks": "outside", + "title": { + "standoff": 15 + }, + "zeroline": false, + "zerolinecolor": "rgb(36,36,36)" + } + } + }, + "width": 800, + "xaxis": { + "griddash": "dash", + "minor": { + "griddash": "dot", + "ticks": "inside" + }, + "showgrid": true, + "title": { + "text": "Transaction Date" + } + }, + "yaxis": { + "griddash": "dash", + "minor": { + "griddash": "dot", + "ticks": "inside" + }, + "showgrid": true, + "title": { + "text": "Price (USD)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure(layout=layout)\n", + "\n", + "\n", + "#data = transfer_sync.group_by('date').agg(pl.col(\"value_USD\").sum())\n", + "#data = data.sort(pl.col(\"date\"))\n", + "#fig.add_trace(go.Scatter(x=data['date'], y=data['value_USD'], line=dict(\n", + "# color=colors['light_purple'], width=4), mode='lines', name='Total Volume'))\n", + "\n", + "buy_bgnt = buy_bgnt.filter((pl.col('value_USD')>1e-10))\n", + "\n", + "data = buy_bgnt.group_by('date').agg(pl.col(\"value_USD\").median())\n", + "data = data.sort(pl.col(\"date\"))\n", + "fig.add_trace(go.Scatter(x=data['date'], y=data['value_USD'], line=dict(\n", + " color=colors['brown'], width=2), mode='lines', name='Median Price'))\n", + "\n", + "data = buy_bgnt\n", + "data = data.sort(pl.col(\"date\"))\n", + "fig.add_trace(go.Box(x=data['date'], y=data['value_USD'],\n", + " name='Transaction Price', marker_color=colors['green'], line=dict(width=1.5), whiskerwidth=0.5, fillcolor=colors['white']))\n", + "\n", + "# Update layout\n", + "fig.update_layout(xaxis_title='Transaction Date',\n", + " yaxis_title='Price (USD)', legend=dict(xanchor='center',\n", + " x=0.5, y=1.02, orientation='h'))\n", + "\n", + "#fig.update_yaxes(type='log', range=[-10, 1], tickvals=[1, 0.1, 0.01, 0.001, 0.0001])\n", + "#fig.update_yaxes(type='log', range=[-11, 2])\n", + "\n", + "fig.write_image(plots_dir+\"buybgnt.pdf\",\n", + " width=1540, height=380, scale=1)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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It has been renamed to `map_elements`.\n", + "\n" + ] + } + ], + "source": [ + "def calculate_value(tx_hash):\n", + " tx_data = w3.eth.get_transaction_receipt(tx_hash)\n", + " return int(tx_data['logs'][0]['data'].hex(), 16) / 1e18\n", + "\n", + "# Apply the function to create a new column 'value' in the DataFrame\n", + "transfer_sync = transfer_sync.with_columns(\n", + " pl.col('tx_hash').apply(calculate_value).alias('value')\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/y_/27jchs_x56gfpv07wkchjxj40000gn/T/ipykernel_1638/64109395.py:8: DeprecationWarning:\n", + "\n", + "`apply` is deprecated. It has been renamed to `map_elements`.\n", + "\n" + ] + } + ], + "source": [ + "def update_amt(amt_str):\n", + " if (amt_str == '1e6'):\n", + " return '10000000'\n", + " return (amt_str)\n", + "\n", + "# Apply the function to create a new column 'value' in the DataFrame\n", + "transfer_sync = transfer_sync.with_columns(\n", + " pl.col('amt').apply(update_amt).alias('amt')\n", + ")\n", + "\n", + "transfer_sync = transfer_sync.with_columns(pl.col('amt').cast(pl.Int32))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "transfer_sync = transfer_sync.with_columns((pl.col('value')*4000 / pl.col('amt')).alias('value_USD'))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Timestamp" + } + }, + "yaxis": { + "griddash": "dash", + "minor": { + "griddash": "dot", + "ticks": "inside" + }, + "range": [ + -0.01, + 0.8 + ], + "showgrid": true, + "title": { + "text": "Price (USD)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure(layout=layout)\n", + "\n", + "\n", + "temp_df = transfer_sync.filter((pl.col('value_USD')<1))\n", + "\n", + "fig.add_trace(go.Scatter(x=temp_df['timestamp'], y=temp_df['value_USD'], mode='markers', name='Price'))\n", + "\n", + "# Update layout\n", + "fig.update_layout(xaxis_title='Transaction Timestamp',\n", + " yaxis_title='Price (USD)')\n", + "\n", + "fig.update_layout(yaxis=dict(range=[-0.01, 0.8]))\n", + "\n", + "fig.write_image(plots_dir+\"transfersync_blue.pdf\",\n", + " width=1540, height=380, scale=1)\n", + "\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (1,)
value_USD
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0.406307
" + ], + "text/plain": [ + "shape: (1,)\n", + "Series: 'value_USD' [f64]\n", + "[\n", + "\t0.406307\n", + "]" + ] + }, + "execution_count": 199, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = transfer_sync.select(pl.std(\"value_USD\"))\n", + "df[\"value_USD\"]\n", + "\n", + "df = transfer_sync.select(pl.median(\"value_USD\"))\n", + "df[\"value_USD\"]\n", + "\n", + "df = transfer_sync.select(pl.mean(\"value_USD\"))\n", + "df[\"value_USD\"]\n", + "\n", + "df = transfer_sync.select(pl.min(\"value_USD\"))\n", + "df[\"value_USD\"]\n", + "\n", + "df = transfer_sync.select(pl.max(\"value_USD\"))\n", + "df[\"value_USD\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = transfer_sync.group_by('date').agg(pl.col(\"value_USD\").sum())\n", + "data = data.sort(pl.col(\"date\"))\n", + "data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [ + { 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"linecolor": "rgb(36,36,36)", + "showgrid": false, + "showline": true, + "ticks": "outside" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "rgb(232,232,232)", + "linecolor": "rgb(36,36,36)", + "showgrid": false, + "showline": true, + "ticks": "outside", + "title": { + "standoff": 15 + }, + "zeroline": false, + "zerolinecolor": "rgb(36,36,36)" + }, + "yaxis": { + "automargin": true, + "gridcolor": "rgb(232,232,232)", + "linecolor": "rgb(36,36,36)", + "showgrid": false, + "showline": true, + "ticks": "outside", + "title": { + "standoff": 15 + }, + "zeroline": false, + "zerolinecolor": "rgb(36,36,36)" + } + } + }, + "width": 800, + "xaxis": { + "griddash": "dash", + "minor": { + "griddash": "dot", + "ticks": "inside" + }, + "showgrid": true, + "title": { + "text": "Transaction Date" + } + }, + "yaxis": { + "griddash": "dash", + "minor": { + "griddash": "dot", + "ticks": "inside" + }, + "range": [ + -0.01, + 0.9 + ], + "showgrid": true, + "title": { + "text": "Price (USD)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = go.Figure(layout=layout)\n", + "\n", + "\n", + "#data = transfer_sync.group_by('date').agg(pl.col(\"value_USD\").sum())\n", + "#data = data.sort(pl.col(\"date\"))\n", + "#fig.add_trace(go.Scatter(x=data['date'], y=data['value_USD'], line=dict(\n", + "# color=colors['light_purple'], width=4), mode='lines', name='Total Volume'))\n", + "\n", + "data = transfer_sync.group_by('date').agg(pl.col(\"value_USD\").median())\n", + "data = data.sort(pl.col(\"date\"))\n", + "fig.add_trace(go.Scatter(x=data['date'], y=data['value_USD'], line=dict(\n", + " color=colors['brown'], width=2), mode='lines', name='Median Price'))\n", + "\n", + "data = transfer_sync\n", + "data = data.sort(pl.col(\"date\"))\n", + "fig.add_trace(go.Box(x=data['date'], y=data['value_USD'],\n", + " name='Transaction Price', marker_color=colors['green'], line=dict(width=1.5), whiskerwidth=0.5, fillcolor=colors['white']))\n", + "\n", + "# Update layout\n", + "fig.update_layout(xaxis_title='Transaction Date',\n", + " yaxis_title='Price (USD)', legend=dict(xanchor='center',\n", + " x=0.5, y=1.02, orientation='h'))\n", + "\n", + "fig.update_layout(yaxis=dict(range=[-0.01, 0.9]))\n", + "#fig.update_yaxes(type='log', range=[-11, 2])\n", + "\n", + "\n", + "fig.write_image(plots_dir+\"transfersync.pdf\",\n", + " width=770, height=380, scale=1) #1540\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from plot_utils import get_plotly_layout\n", + "from plot_utils import colors\n", + "width, height = 800, 450\n", + "fig = go.Figure(layout=get_plotly_layout(width=width, height=height))\n", + "\n", + "\n", + "data = votes_cost_df.groupby('proposalId').tx_fee_usd.sum()\n", + "fig.add_trace(go.Scatter(x=data.index, y=data, line=dict(\n", + " color=colors['light_purple'], width=4), mode='lines', name='total cost'))\n", + "\n", + "data = votes_cost_df.groupby('proposalId').tx_fee_usd.median()\n", + "fig.add_trace(go.Scatter(x=data.index, y=data, line=dict(\n", + " color=colors['brown'], width=2), mode='lines', name='median cost'))\n", + "\n", + "\n", + "data = votes_cost_df.query(\n", + " 'proposalId not in @proposals_defeated_df.proposalId')\n", + "fig.add_trace(go.Box(x=data.proposalId, y=data.tx_fee_usd,\n", + " name='Executed', marker_color=colors['green'], line=dict(width=1.5), whiskerwidth=0.5, fillcolor=colors['white']))\n", + "\n", + "data = votes_cost_df.query(\n", + " 'proposalId in @proposals_defeated_df.proposalId')\n", + "fig.add_trace(go.Box(x=data.proposalId, y=data.tx_fee_usd,\n", + " name='Defeated', marker_color=colors['red'], line=dict(width=1.5), whiskerwidth=0.5, fillcolor=colors['white']))\n", + "\n", + "data = votes_cost_df.query(\n", + " 'proposalId in @proposals_canceled_df.proposalId')\n", + "fig.add_trace(go.Box(x=data.proposalId, y=data.tx_fee_usd,\n", + " name='Cancelled', marker_color=colors['blue'], line=dict(width=1.5), whiskerwidth=0.5, fillcolor=colors['white']))\n", + "\n", + "fig.update_layout(yaxis_title=\"Voting cost (in USD)\", xaxis_title=\"Proposal ID\", template='simple_white',\n", + " font=dict(size=18, family='Clear Sans'), xaxis=dict(tickmode='linear', tick0=0, dtick=10),\n", + " legend=dict(xanchor='center', x=0.5, y=1.15, orientation='h'))\n", + "\n", + "\n", + "fig.update_xaxes(minor_ticks=\"inside\", showgrid=True,\n", + " griddash='dash', minor_griddash=\"dot\")\n", + "fig.update_yaxes(minor_ticks=\"inside\", showgrid=True,\n", + " griddash='dash', minor_griddash=\"dot\")\n", + "fig.update_yaxes(type='log')\n", + "\n", + "fig.write_image(plots_dir+\"voting_cost_per_proposal.pdf\",\n", + " width=1540, height=380, scale=1)\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}