1176 lines
233 KiB
Plaintext
1176 lines
233 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "09975f67",
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"metadata": {},
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"source": [
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"## Z Token Lockdrop Distribution"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "a98eb34e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from decimal import Decimal, ROUND_DOWN, getcontext\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import urbitob\n",
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"import json\n",
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"from collections import defaultdict\n",
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"\n",
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"# Set matplotlib style\n",
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"plt.style.use('seaborn-v0_8')\n",
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"sns.set_palette(\"husl\")\n",
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"\n",
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"# Configure matplotlib for high-quality plots\n",
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"plt.rcParams['figure.figsize'] = (12, 8)\n",
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"plt.rcParams['font.size'] = 11\n",
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"plt.rcParams['axes.titlesize'] = 14\n",
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"plt.rcParams['axes.labelsize'] = 12\n",
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"plt.rcParams['xtick.labelsize'] = 10\n",
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"plt.rcParams['ytick.labelsize'] = 10\n",
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"plt.rcParams['legend.fontsize'] = 10\n",
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"\n",
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"# Fix font warnings by using DejaVu Sans which supports Unicode subscripts\n",
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"# or fall back to a safe font\n",
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"try:\n",
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" plt.rcParams['font.family'] = 'DejaVu Sans'\n",
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"except:\n",
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" plt.rcParams['font.family'] = 'sans-serif'\n",
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" plt.rcParams['font.sans-serif'] = ['DejaVu Sans', 'Liberation Sans', 'Arial', 'Helvetica']\n",
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"\n",
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"# Disable Unicode minus to avoid font issues\n",
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"plt.rcParams['axes.unicode_minus'] = False\n",
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"\n",
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"getcontext().prec = 28\n",
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"getcontext().rounding = ROUND_DOWN"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5aedd1ba",
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"metadata": {},
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"source": [
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"### Constants"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "a7e0a069-488c-4138-8e8c-30f7ecdb3eb1",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"================================================================================\n",
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"📊 $Z LOCKDROP DISTRIBUTION - CORE CONSTANTS\n",
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"================================================================================\n",
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"\n",
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"🔒 LOCKDROP ALLOCATION\n",
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" Parameter Value\n",
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"Total Supply (1 $Z per Urbit ID) 4,294,967,296\n",
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" Lockdrop Allocation % 30.0%\n",
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" Lockdrop Allocation ($Z) 1,288,490,188.8\n",
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"\n",
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"⭐ URBIT POINTS DISTRIBUTION\n",
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"Point Type Count Allocation %\n",
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" Galaxies 256 0.39%\n",
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" Stars 65,280 99.61%\n",
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" Planets 4,294,901,760 0%\n",
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"\n",
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"⏱️ LOCKDROP PARAMETERS\n",
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" Parameter Value\n",
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" Block Duration 2 seconds\n",
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"Max Point Lock Duration (5 yrs) 157,788,000 seconds\n",
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" Total Blocks 78,894,000\n",
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" Star Allocation % 0.996093\n",
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" Galaxy Allocation % 0.003906\n",
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"\n",
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"================================================================================\n"
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]
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}
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],
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"source": [
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"# Constants\n",
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"\n",
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"TOKEN_PRECISION = Decimal('0.00000001') # 8 decimal precision\n",
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"\n",
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"# 1 $Z per Urbit ID\n",
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"total_supply = 4294967296\n",
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"\n",
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"# 30%\n",
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"lockdrop_allocation_percent = Decimal('0.3')\n",
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"lockdrop_allocation = total_supply * lockdrop_allocation_percent\n",
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"\n",
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"# points\n",
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"num_galaxies = pow(2, 8)\n",
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"num_stars = pow(2, 16) - pow(2, 8)\n",
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"num_planets = pow(2, 32) - pow(2, 16)\n",
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"\n",
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"# allocation distribution between galaxies and stars (equal distribution amongst these 2^16 points)\n",
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"star_allocation_percent = Decimal(num_stars / pow(2, 16))\n",
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"galaxy_allocation_percent = 1 - star_allocation_percent\n",
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"\n",
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"# lockdrop duration\n",
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"# 5 years, including 1 leap year\n",
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"lockdrop_duration_seconds = 5 * 365.25 * 24 * 60 *60\n",
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"\n",
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"# approximation\n",
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"block_duration_seconds = 2\n",
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"lockdrop_duration_blocks = int(lockdrop_duration_seconds / block_duration_seconds)\n",
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"\n",
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"####################################################\n",
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"\n",
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"# Display constants as tables\n",
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"print(\"=\" * 80)\n",
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"print(\"📊 $Z LOCKDROP DISTRIBUTION - CORE CONSTANTS\")\n",
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"print(\"=\" * 80)\n",
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"\n",
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"# Lockdrop Allocation Table\n",
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"lockdrop_df = pd.DataFrame({\n",
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" 'Parameter': ['Total Supply (1 $Z per Urbit ID)', 'Lockdrop Allocation %', 'Lockdrop Allocation ($Z)'],\n",
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" 'Value': [f\"{total_supply:,}\", f\"{lockdrop_allocation_percent:.1%}\", f\"{lockdrop_allocation:,.1f}\"]\n",
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"})\n",
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"print(\"\\n🔒 LOCKDROP ALLOCATION\")\n",
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"print(lockdrop_df.to_string(index=False))\n",
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"\n",
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"# Points Distribution Table\n",
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"points_df = pd.DataFrame({\n",
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" 'Point Type': ['Galaxies', 'Stars', 'Planets'],\n",
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" 'Count': [f\"{num_galaxies:,}\", f\"{num_stars:,}\", f\"{num_planets:,}\"],\n",
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" 'Allocation %': ['0.39%', '99.61%', '0%']\n",
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"})\n",
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"print(\"\\n⭐ URBIT POINTS DISTRIBUTION\")\n",
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"print(points_df.to_string(index=False))\n",
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"\n",
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"# Lockdrop Parameters Table\n",
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"params_df = pd.DataFrame({\n",
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" 'Parameter': ['Block Duration', 'Max Point Lock Duration (5 yrs)', 'Total Blocks', 'Star Allocation %', 'Galaxy Allocation %'],\n",
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" 'Value': [f\"{block_duration_seconds} seconds\", f\"{lockdrop_duration_seconds:,.0f} seconds\",\n",
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" f\"{lockdrop_duration_blocks:,}\", f\"{star_allocation_percent:.6f}\", f\"{galaxy_allocation_percent:.6f}\"]\n",
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"})\n",
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"print(\"\\n⏱️ LOCKDROP PARAMETERS\")\n",
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"print(params_df.to_string(index=False))\n",
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"print(\"\\n\" + \"=\"*80)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b5056dec",
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"metadata": {},
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"source": [
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"### Calculations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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||
"id": "933a4722-a007-4ca2-ae9f-142a73dc94fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Calculations\n",
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"\n",
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"lockdrop_allocation_stars = lockdrop_allocation * star_allocation_percent\n",
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"lockdrop_allocation_galaxies = lockdrop_allocation * galaxy_allocation_percent\n",
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"\n",
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"assert (lockdrop_allocation_stars + lockdrop_allocation_galaxies) == lockdrop_allocation, \"point allocation doesn't add up\"\n",
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"\n",
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"# Max allocation a star can get\n",
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"max_allocation_per_star = lockdrop_allocation_stars / num_stars\n",
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"max_allocation_per_galaxy = lockdrop_allocation_galaxies / num_galaxies\n",
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"\n",
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"# Quanta calculation\n",
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"z_available_per_star_per_block = max_allocation_per_star / lockdrop_duration_blocks\n",
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"z_available_per_galaxy_per_block = max_allocation_per_galaxy / lockdrop_duration_blocks\n",
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"\n",
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"# Round down z_available_per_star_per_block to 6 decimals\n",
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"adjusted_z_per_star_per_block = z_available_per_star_per_block.quantize(Decimal('0.000001'), rounding=ROUND_DOWN)\n",
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"adjusted_z_per_galaxy_per_block = z_available_per_galaxy_per_block.quantize(Decimal('0.000001'), rounding=ROUND_DOWN)\n",
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"\n",
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"# Adjusted max allocation a star can get\n",
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"adjusted_max_allocation_per_star = adjusted_z_per_star_per_block * lockdrop_duration_blocks\n",
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"adjusted_max_allocation_per_galaxy = adjusted_z_per_galaxy_per_block * lockdrop_duration_blocks\n",
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"\n",
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"rounding_error_per_star = max_allocation_per_star - adjusted_max_allocation_per_star\n",
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"\n",
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"# Total rounding error from all stars, this goes to bonus pool\n",
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"# Rounding error from bonus pool calculation goes to Zenith foundation\n",
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"# total_rounding_error = rounding_error_per_star * num_stars\n",
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"total_rounding_error_stars = lockdrop_allocation_stars - (adjusted_max_allocation_per_star * num_stars)\n",
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"percentage_rounding_error_stars = total_rounding_error_stars / total_supply * 100"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "pn6wmtugfl9",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"================================================================================\n",
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"🔢 ALLOCATION CALCULATIONS\n",
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"================================================================================\n",
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"\n",
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"💰 RAW PARTICIPANT ALLOCATIONS\n",
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"Point Type Total Allocation ($Z) Max Per Point ($Z)\n",
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" Stars 1,283,457,024.0 19,660.800000\n",
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" Galaxies 5,033,164.8 19,660.800000\n",
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"\n",
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"⚙️ QUANTA CALCULATION\n",
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"Point Type Raw Z per Block Adjusted Z per Block (q)\n",
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" Stars 0.000249205262757 0.000249\n",
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" Galaxies 0.000249205262757 0.000249\n",
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"\n",
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"🎯 ADJUSTED PARTICIPANT ALLOCATIONS\n",
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" Metric Value Note\n",
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" Adjusted Max per Star 19,644.606000 $Z Final allocation\n",
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"Rounding Error per Star 16.194000000 $Z Per star loss\n",
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" Total Rounding Error 1,057,144.320000 $Z Goes to bonus pool\n",
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" Rounding Error % 0.02461355% Of total supply\n",
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"\n",
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"================================================================================\n"
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]
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}
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],
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"source": [
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"print(\"=\" * 80)\n",
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"print(\"🔢 ALLOCATION CALCULATIONS\")\n",
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"print(\"=\" * 80)\n",
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"\n",
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"# Raw Allocations Table\n",
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"raw_allocations_df = pd.DataFrame({\n",
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" 'Point Type': ['Stars', 'Galaxies'],\n",
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" 'Total Allocation ($Z)': [f\"{lockdrop_allocation_stars:,.1f}\", f\"{lockdrop_allocation_galaxies:,.1f}\"],\n",
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" 'Max Per Point ($Z)': [f\"{max_allocation_per_star:,.6f}\", f\"{max_allocation_per_galaxy:,.6f}\"]\n",
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"})\n",
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"print(\"\\n💰 RAW PARTICIPANT ALLOCATIONS\")\n",
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"print(raw_allocations_df.to_string(index=False))\n",
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"\n",
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"# Quanta Calculations Table\n",
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"quanta_df = pd.DataFrame({\n",
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" 'Point Type': ['Stars', 'Galaxies'],\n",
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" 'Raw Z per Block': [f\"{z_available_per_star_per_block:.15f}\", f\"{z_available_per_galaxy_per_block:.15f}\"],\n",
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" 'Adjusted Z per Block (q)': [f\"{adjusted_z_per_star_per_block:.6f}\", f\"{adjusted_z_per_galaxy_per_block:.6f}\"]\n",
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"})\n",
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"print(\"\\n⚙️ QUANTA CALCULATION\")\n",
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"print(quanta_df.to_string(index=False))\n",
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"\n",
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"# Adjusted Allocations Table\n",
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"adjusted_allocations_df = pd.DataFrame({\n",
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" 'Metric': ['Adjusted Max per Star', 'Rounding Error per Star', 'Total Rounding Error', 'Rounding Error %'],\n",
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" 'Value': [f\"{adjusted_max_allocation_per_star:,.6f} $Z\", f\"{rounding_error_per_star:.9f} $Z\",\n",
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" f\"{total_rounding_error_stars:,.6f} $Z\", f\"{percentage_rounding_error_stars:.8f}%\"],\n",
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" 'Note': ['Final allocation', 'Per star loss', 'Goes to bonus pool', 'Of total supply']\n",
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"})\n",
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"print(\"\\n🎯 ADJUSTED PARTICIPANT ALLOCATIONS\")\n",
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"print(adjusted_allocations_df.to_string(index=False))\n",
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"print(\"\\n\" + \"=\"*80)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "590a3951",
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"metadata": {},
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"source": [
|
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"### Allocations By Lockup Period"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "fea549ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Penalty adjusted allocations - CALCULATIONS ONLY\n",
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"penalty_5_years = Decimal('0')\n",
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"penalty_4_years = Decimal('0.2')\n",
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"penalty_3_years = Decimal('0.4')\n",
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"penalty_2_years = Decimal('0.6')\n",
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"penalty_1_years = Decimal('0.8')\n",
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"\n",
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"allocation_per_star_5_years = adjusted_max_allocation_per_star * (1 - penalty_5_years)\n",
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"z_per_star_per_block_5_years = allocation_per_star_5_years / lockdrop_duration_blocks\n",
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"\n",
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"allocation_per_star_4_years = adjusted_max_allocation_per_star * (1 - penalty_4_years)\n",
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"z_per_star_per_block_4_years = allocation_per_star_4_years / (lockdrop_duration_blocks * Decimal('0.8'))\n",
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"\n",
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"allocation_per_star_3_years = adjusted_max_allocation_per_star * (1 - penalty_3_years)\n",
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"z_per_star_per_block_3_years = allocation_per_star_3_years / (lockdrop_duration_blocks * Decimal('0.6'))\n",
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"\n",
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"allocation_per_star_2_years = adjusted_max_allocation_per_star * (1 - penalty_2_years)\n",
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"z_per_star_per_block_2_years = allocation_per_star_2_years / (lockdrop_duration_blocks * Decimal('0.4'))\n",
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"\n",
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"allocation_per_star_1_years = adjusted_max_allocation_per_star * (1 - penalty_1_years)\n",
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"z_per_star_per_block_1_years = allocation_per_star_1_years / (lockdrop_duration_blocks * Decimal('0.2'))"
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]
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},
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{
|
||
"cell_type": "code",
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||
"execution_count": 6,
|
||
"id": "yoxhqva82n",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
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"================================================================================\n",
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||
"⚖️ PENALTY SYSTEM ANALYSIS\n",
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"================================================================================\n",
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"\n",
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"📊 PENALTY ADJUSTED ALLOCATIONS (Before Bonus Distribution)\n",
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||
"Lock Period Penalty Rate Total Allocation ($Z) Z per Block vs Max Allocation\n",
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||
" 5 Years 0.0% 19,644.606000 0.000249000000 100%\n",
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||
" 4 Years 20.0% 15,715.684800 0.000249000000 80%\n",
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||
" 3 Years 40.0% 11,786.763600 0.000249000000 60%\n",
|
||
" 2 Years 60.0% 7,857.842400 0.000249000000 40%\n",
|
||
" 1 Year 80.0% 3,928.921200 0.000249000000 20%\n",
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||
"\n",
|
||
"================================================================================\n"
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||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"⚖️ PENALTY SYSTEM ANALYSIS\")\n",
|
||
"print(\"=\" * 80)\n",
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"\n",
|
||
"# Create comprehensive penalty analysis table\n",
|
||
"penalty_analysis_df = pd.DataFrame({\n",
|
||
" 'Lock Period': ['5 Years', '4 Years', '3 Years', '2 Years', '1 Year'],\n",
|
||
" 'Penalty Rate': [f\"{penalty_5_years:.1%}\", f\"{penalty_4_years:.1%}\", f\"{penalty_3_years:.1%}\",\n",
|
||
" f\"{penalty_2_years:.1%}\", f\"{penalty_1_years:.1%}\"],\n",
|
||
" 'Total Allocation ($Z)': [f\"{allocation_per_star_5_years:,.6f}\", f\"{allocation_per_star_4_years:,.6f}\",\n",
|
||
" f\"{allocation_per_star_3_years:,.6f}\", f\"{allocation_per_star_2_years:,.6f}\",\n",
|
||
" f\"{allocation_per_star_1_years:,.6f}\"],\n",
|
||
" 'Z per Block': [f\"{z_per_star_per_block_5_years:.12f}\", f\"{z_per_star_per_block_4_years:.12f}\",\n",
|
||
" f\"{z_per_star_per_block_3_years:.12f}\", f\"{z_per_star_per_block_2_years:.12f}\",\n",
|
||
" f\"{z_per_star_per_block_1_years:.12f}\"],\n",
|
||
" 'vs Max Allocation': ['100%', '80%', '60%', '40%', '20%']\n",
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"})\n",
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"\n",
|
||
"print(\"\\n📊 PENALTY ADJUSTED ALLOCATIONS (Before Bonus Distribution)\")\n",
|
||
"print(penalty_analysis_df.to_string(index=False))\n",
|
||
"print(\"\\n\" + \"=\"*80)"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cba99011",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Simulation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "66f88dd9",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Load data from mock watcher events\n",
|
||
"\n",
|
||
"# Helper methods\n",
|
||
"def load_watcher_events(file_path):\n",
|
||
" \"\"\"Load and parse watcher events from JSON file\"\"\"\n",
|
||
" with open(file_path, 'r') as f:\n",
|
||
" data = json.load(f)\n",
|
||
" return data['data']['eventsInRange']\n",
|
||
"\n",
|
||
"def analyze_lockdrop_events(events):\n",
|
||
" \"\"\"Analyze lockdrop events and return participation statistics\"\"\"\n",
|
||
" # Initialize counters\n",
|
||
" lock_duration_counts = {\n",
|
||
" 'star': defaultdict(int),\n",
|
||
" 'galaxy': defaultdict(int)\n",
|
||
" }\n",
|
||
"\n",
|
||
" # Process events\n",
|
||
" for event_data in events:\n",
|
||
" if event_data['event']['__typename'] == 'PointLockedEvent':\n",
|
||
" point = event_data['event']['point']\n",
|
||
" lock_period = event_data['event']['lock_period']\n",
|
||
"\n",
|
||
" # Determine if it's a galaxy or star\n",
|
||
" point_num = urbitob.patp_to_num(point)\n",
|
||
" point_type = \"galaxy\" if point_num < num_galaxies else \"star\"\n",
|
||
"\n",
|
||
" # Count by lock period\n",
|
||
" lock_duration_counts[point_type][lock_period] += 1\n",
|
||
"\n",
|
||
" # Extract counts for each year and point type\n",
|
||
" result = {}\n",
|
||
" for years in [1, 2, 3, 4, 5]:\n",
|
||
" result[f'stars_{years}_years'] = lock_duration_counts['star'][years]\n",
|
||
" result[f'galaxies_{years}_years'] = lock_duration_counts['galaxy'][years]\n",
|
||
"\n",
|
||
" return result"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "26573d6b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"📁 WATCHER EVENTS DATA SUMMARY\n",
|
||
"================================================================================\n",
|
||
"✅ Successfully loaded 40200 PointLockedEvent records from watcher file\n",
|
||
"\n",
|
||
"🔢 LOCK DURATION DISTRIBUTION\n",
|
||
"Lock Period Stars Galaxies Total\n",
|
||
" 1 Year 8075 39 8114\n",
|
||
" 2 Years 8056 43 8099\n",
|
||
" 3 Years 7870 36 7906\n",
|
||
" 4 Years 8112 33 8145\n",
|
||
" 5 Years 7887 49 7936\n",
|
||
" Total 40000 200 40200\n",
|
||
"\n",
|
||
"📊 PARTICIPATION RATES\n",
|
||
" Stars: 61.2% (40,000/65,280)\n",
|
||
" Galaxies: 78.1% (200/256)\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"================================================================================\n",
|
||
"📈 DYNAMIC LOCKDROP ANALYSIS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"🎯 PARTICIPATION SUMMARY\n",
|
||
" Point Type Count Participation Rate\n",
|
||
" Stars (Total) 40,000 61.2%\n",
|
||
" Stars (1Y) 8,075 12.3%\n",
|
||
" Stars (2Y) 8,056 12.3%\n",
|
||
" Stars (3Y) 7,870 12.0%\n",
|
||
" Stars (4Y) 8,112 12.4%\n",
|
||
" Stars (5Y) 7,887 12.0%\n",
|
||
"Galaxies (Total) 200 78.1%\n",
|
||
" Galaxies (5Y) 49 19.1%\n",
|
||
"\n",
|
||
"💰 RAW PARTICIPANT ALLOCATIONS\n",
|
||
"Point Type Total Allocation ($Z) Max Per Point (Raw) Adjusted Max Per Point\n",
|
||
" Stars 1,283,457,024.0 32,086.425600 32,030.964000\n",
|
||
" Galaxies 5,033,164.8 25,165.824000 25,088.292000\n",
|
||
"\n",
|
||
"⚙️ QUANTA CALCULATION\n",
|
||
"Point Type Raw Z per Block Adjusted Z per Block (q)\n",
|
||
" Stars 0.000406702988820 0.000406\n",
|
||
" Galaxies 0.000318982736329 0.000318\n",
|
||
"\n",
|
||
"🔄 ROUNDING ERROR ANALYSIS\n",
|
||
"Point Type Rounding Error per Point Total Rounding Error % of Supply Destination\n",
|
||
" Stars 55.461600000 $Z 2,218,464.000000 $Z 0.05165264% Bonus Pool\n",
|
||
" Galaxies 77.532000000 $Z 15,506.400000 $Z 0.00036103% Bonus Pool\n",
|
||
"\n",
|
||
"================================================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Load events from watcher file\n",
|
||
"watcher_events_path = './generated/watcher-events.json'\n",
|
||
"events = load_watcher_events(watcher_events_path)\n",
|
||
"lock_stats = analyze_lockdrop_events(events)\n",
|
||
"\n",
|
||
"# Extract individual counts\n",
|
||
"stars_1_years = Decimal(lock_stats['stars_1_years'])\n",
|
||
"stars_2_years = Decimal(lock_stats['stars_2_years'])\n",
|
||
"stars_3_years = Decimal(lock_stats['stars_3_years'])\n",
|
||
"stars_4_years = Decimal(lock_stats['stars_4_years'])\n",
|
||
"stars_5_years = Decimal(lock_stats['stars_5_years'])\n",
|
||
"\n",
|
||
"galaxies_1_years = Decimal(lock_stats['galaxies_1_years'])\n",
|
||
"galaxies_2_years = Decimal(lock_stats['galaxies_2_years'])\n",
|
||
"galaxies_3_years = Decimal(lock_stats['galaxies_3_years'])\n",
|
||
"galaxies_4_years = Decimal(lock_stats['galaxies_4_years'])\n",
|
||
"galaxies_5_years = Decimal(lock_stats['galaxies_5_years'])\n",
|
||
"\n",
|
||
"total_stars_locked = stars_1_years + stars_2_years + stars_3_years + stars_4_years + stars_5_years\n",
|
||
"total_galaxies_locked = galaxies_1_years + galaxies_2_years + galaxies_3_years + galaxies_4_years + galaxies_5_years\n",
|
||
"\n",
|
||
"star_participation_rate = Decimal(total_stars_locked) / num_stars\n",
|
||
"galaxy_participation_rate = Decimal(total_galaxies_locked) / num_galaxies\n",
|
||
"\n",
|
||
"# Display loaded data summary\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"📁 WATCHER EVENTS DATA SUMMARY\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"\n",
|
||
"if events:\n",
|
||
" total_events = len([e for e in events if e['event']['__typename'] == 'PointLockedEvent'])\n",
|
||
" print(f\"✅ Successfully loaded {total_events} PointLockedEvent records from watcher file\")\n",
|
||
"else:\n",
|
||
" print(\"⚠️ Using hardcoded fallback values\")\n",
|
||
"\n",
|
||
"lock_summary_df = pd.DataFrame({\n",
|
||
" 'Lock Period': ['1 Year', '2 Years', '3 Years', '4 Years', '5 Years', 'Total'],\n",
|
||
" 'Stars': [stars_1_years, stars_2_years, stars_3_years, stars_4_years, stars_5_years, total_stars_locked],\n",
|
||
" 'Galaxies': [galaxies_1_years, galaxies_2_years, galaxies_3_years, galaxies_4_years, galaxies_5_years, total_galaxies_locked],\n",
|
||
" 'Total': [stars_1_years + galaxies_1_years, stars_2_years + galaxies_2_years,\n",
|
||
" stars_3_years + galaxies_3_years, stars_4_years + galaxies_4_years,\n",
|
||
" stars_5_years + galaxies_5_years, total_stars_locked + total_galaxies_locked]\n",
|
||
"})\n",
|
||
"\n",
|
||
"print(\"\\n🔢 LOCK DURATION DISTRIBUTION\")\n",
|
||
"print(lock_summary_df.to_string(index=False))\n",
|
||
"print(f\"\\n📊 PARTICIPATION RATES\")\n",
|
||
"print(f\" Stars: {star_participation_rate:.1%} ({total_stars_locked:,}/{num_stars:,})\")\n",
|
||
"print(f\" Galaxies: {galaxy_participation_rate:.1%} ({total_galaxies_locked:,}/{num_galaxies:,})\")\n",
|
||
"print(\"\\n\" + \"=\"*80)\n",
|
||
"\n",
|
||
"# Continue with existing calculations...\n",
|
||
"# Max allocation a star can get\n",
|
||
"max_allocation_per_star = lockdrop_allocation_stars / total_stars_locked\n",
|
||
"max_allocation_per_galaxy = lockdrop_allocation_galaxies / total_galaxies_locked\n",
|
||
"\n",
|
||
"# Quanta calculation\n",
|
||
"z_available_per_star_per_block = max_allocation_per_star / lockdrop_duration_blocks\n",
|
||
"z_available_per_galaxy_per_block = max_allocation_per_galaxy / lockdrop_duration_blocks\n",
|
||
"\n",
|
||
"# Round down z_available_per_star_per_block to 6 decimals\n",
|
||
"adjusted_z_per_star_per_block = z_available_per_star_per_block.quantize(Decimal('0.000001'), rounding=ROUND_DOWN)\n",
|
||
"adjusted_z_per_galaxy_per_block = z_available_per_galaxy_per_block.quantize(Decimal('0.000001'), rounding=ROUND_DOWN)\n",
|
||
"\n",
|
||
"# Adjusted max allocation a star can get\n",
|
||
"adjusted_max_allocation_per_star = adjusted_z_per_star_per_block * lockdrop_duration_blocks\n",
|
||
"adjusted_max_allocation_per_galaxy = adjusted_z_per_galaxy_per_block * lockdrop_duration_blocks\n",
|
||
"\n",
|
||
"# Total rounding error from all stars, this goes to bonus pool\n",
|
||
"# Rounding error from bonus pool calculation goes to Zenith foundation\n",
|
||
"rounding_error_per_star = max_allocation_per_star - adjusted_max_allocation_per_star\n",
|
||
"total_rounding_error_stars = lockdrop_allocation_stars - (adjusted_max_allocation_per_star * total_stars_locked)\n",
|
||
"percentage_rounding_error_stars = total_rounding_error_stars / total_supply * 100\n",
|
||
"\n",
|
||
"# Total rounding error from all galaxies, this goes to bonus pool\n",
|
||
"# Rounding error from bonus pool calculation goes to Zenith foundation\n",
|
||
"rounding_error_per_galaxy = max_allocation_per_galaxy - adjusted_max_allocation_per_galaxy\n",
|
||
"total_rounding_error_galaxies = lockdrop_allocation_galaxies - (adjusted_max_allocation_per_galaxy * total_galaxies_locked)\n",
|
||
"percentage_rounding_error_galaxies = total_rounding_error_galaxies / total_supply * 100\n",
|
||
"\n",
|
||
"####################################################\n",
|
||
"\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"📈 DYNAMIC LOCKDROP ANALYSIS\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"\n",
|
||
"# Participation Summary Table\n",
|
||
"participation_summary_df = pd.DataFrame({\n",
|
||
" 'Point Type': ['Stars (Total)', 'Stars (1Y)', 'Stars (2Y)', 'Stars (3Y)', 'Stars (4Y)', 'Stars (5Y)', 'Galaxies (Total)', 'Galaxies (5Y)'],\n",
|
||
" 'Count': [f\"{total_stars_locked:,}\", f\"{stars_1_years:,}\", f\"{stars_2_years:,}\", f\"{stars_3_years:,}\", f\"{stars_4_years:,}\", f\"{stars_5_years:,}\", f\"{total_galaxies_locked:,}\", f\"{galaxies_5_years:,}\"],\n",
|
||
" 'Participation Rate': [f\"{star_participation_rate:.1%}\", f\"{stars_1_years/num_stars:.1%}\", f\"{stars_2_years/num_stars:.1%}\", f\"{stars_3_years/num_stars:.1%}\", f\"{stars_4_years/num_stars:.1%}\",\n",
|
||
" f\"{stars_5_years/num_stars:.1%}\", f\"{galaxy_participation_rate:.1%}\", f\"{galaxies_5_years/num_galaxies:.1%}\"]\n",
|
||
"})\n",
|
||
"print(\"\\n🎯 PARTICIPATION SUMMARY\")\n",
|
||
"print(participation_summary_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Raw Allocations Table\n",
|
||
"raw_allocations_df = pd.DataFrame({\n",
|
||
" 'Point Type': ['Stars', 'Galaxies'],\n",
|
||
" 'Total Allocation ($Z)': [f\"{lockdrop_allocation_stars:,.1f}\", f\"{lockdrop_allocation_galaxies:,.1f}\"],\n",
|
||
" 'Max Per Point (Raw)': [f\"{max_allocation_per_star:,.6f}\", f\"{max_allocation_per_galaxy:,.6f}\"],\n",
|
||
" 'Adjusted Max Per Point': [f\"{adjusted_max_allocation_per_star:,.6f}\", f\"{adjusted_max_allocation_per_galaxy:,.6f}\"]\n",
|
||
"})\n",
|
||
"print(\"\\n💰 RAW PARTICIPANT ALLOCATIONS\")\n",
|
||
"print(raw_allocations_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Quanta Analysis Table\n",
|
||
"quanta_analysis_df = pd.DataFrame({\n",
|
||
" 'Point Type': ['Stars', 'Galaxies'],\n",
|
||
" 'Raw Z per Block': [f\"{z_available_per_star_per_block:.15f}\", f\"{z_available_per_galaxy_per_block:.15f}\"],\n",
|
||
" 'Adjusted Z per Block (q)': [f\"{adjusted_z_per_star_per_block:.6f}\", f\"{adjusted_z_per_galaxy_per_block:.6f}\"]\n",
|
||
"})\n",
|
||
"print(\"\\n⚙️ QUANTA CALCULATION\")\n",
|
||
"print(quanta_analysis_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Rounding Error Analysis Table\n",
|
||
"rounding_analysis_df = pd.DataFrame({\n",
|
||
" 'Point Type': ['Stars', 'Galaxies'],\n",
|
||
" 'Rounding Error per Point': [f\"{rounding_error_per_star:.9f} $Z\", f\"{rounding_error_per_galaxy:.9f} $Z\"],\n",
|
||
" 'Total Rounding Error': [f\"{total_rounding_error_stars:,.6f} $Z\", f\"{total_rounding_error_galaxies:,.6f} $Z\"],\n",
|
||
" '% of Supply': [f\"{percentage_rounding_error_stars:.8f}%\", f\"{percentage_rounding_error_galaxies:.8f}%\"],\n",
|
||
" 'Destination': [\"Bonus Pool\", \"Bonus Pool\"]\n",
|
||
"})\n",
|
||
"print(\"\\n🔄 ROUNDING ERROR ANALYSIS\")\n",
|
||
"print(rounding_analysis_df.to_string(index=False))\n",
|
||
"print(\"\\n\" + \"=\"*80)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "8d5c0132",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"🎁 BONUS POOL CALCULATIONS\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"⏱️ LOCK PERIOD DISTRIBUTION\n",
|
||
"Lock Period Stars Galaxies Total Participants\n",
|
||
" 1 Year 8,075 39 8,114\n",
|
||
" 2 Years 8,056 43 8,099\n",
|
||
" 3 Years 7,870 36 7,906\n",
|
||
" 4 Years 8,112 33 8,145\n",
|
||
" 5 Years 7,887 49 7,936\n",
|
||
"\n",
|
||
"⭐ STAR BONUS POOL ANALYSIS\n",
|
||
" Component Value\n",
|
||
" Penalty Pool 514,545,405.696000 $Z\n",
|
||
" Rounding Error Bonus 2,218,464.000000 $Z\n",
|
||
" Total Star Bonus Pool 516,763,869.696000 $Z\n",
|
||
" Recipients (5Y Stars) 7,887\n",
|
||
" Bonus per 5Y Star 65,520.967376 $Z\n",
|
||
"Final 5Y Star Allocation 97,551.931376 $Z\n",
|
||
"\n",
|
||
"🌌 GALAXY BONUS POOL ANALYSIS\n",
|
||
" Component Value\n",
|
||
" Penalty Pool 1,956,886.776000 $Z\n",
|
||
" Rounding Error Bonus 15,506.400000 $Z\n",
|
||
" Total Galaxy Bonus Pool 1,972,393.176000 $Z\n",
|
||
" Recipients (5Y Galaxies) 49\n",
|
||
" Bonus per 5Y Galaxy 40,252.921959 $Z\n",
|
||
"Final 5Y Galaxy Allocation 65,341.213959 $Z\n",
|
||
"\n",
|
||
"💡 Star Bonus Pool Sources:\n",
|
||
" • 1Y Stars: 206,920,027.44 $Z\n",
|
||
" • 2Y Stars: 154,824,867.59 $Z\n",
|
||
" • 3Y Stars: 100,833,474.67 $Z\n",
|
||
" • 4Y Stars: 51,967,035.99 $Z\n",
|
||
"\n",
|
||
"💡 Galaxy Bonus Pool Sources:\n",
|
||
" • 1Y Galaxies: 782,754.71 $Z\n",
|
||
" • 2Y Galaxies: 647,277.93 $Z\n",
|
||
" • 3Y Galaxies: 361,271.40 $Z\n",
|
||
" • 4Y Galaxies: 165,582.72 $Z\n",
|
||
"\n",
|
||
"================================================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Bonus pool calculations\n",
|
||
"\n",
|
||
"# Stars\n",
|
||
"star_bonus_pool = (adjusted_max_allocation_per_star * (\n",
|
||
" stars_4_years * penalty_4_years +\n",
|
||
" stars_3_years * penalty_3_years +\n",
|
||
" stars_2_years * penalty_2_years +\n",
|
||
" stars_1_years * penalty_1_years\n",
|
||
")).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"star_bonus_pool_with_rounding_bonus = star_bonus_pool + total_rounding_error_stars\n",
|
||
"\n",
|
||
"# Handle division by zero if no 5-year stars\n",
|
||
"if stars_5_years > 0:\n",
|
||
" bonus_per_star_5_years = star_bonus_pool_with_rounding_bonus / stars_5_years\n",
|
||
" allocation_per_star_5_years_with_bonus = adjusted_max_allocation_per_star + bonus_per_star_5_years\n",
|
||
"else:\n",
|
||
" bonus_per_star_5_years = Decimal('0')\n",
|
||
" allocation_per_star_5_years_with_bonus = adjusted_max_allocation_per_star\n",
|
||
"\n",
|
||
"# Galaxies\n",
|
||
"galaxy_bonus_pool = (adjusted_max_allocation_per_galaxy * (\n",
|
||
" galaxies_4_years * penalty_4_years +\n",
|
||
" galaxies_3_years * penalty_3_years +\n",
|
||
" galaxies_2_years * penalty_2_years +\n",
|
||
" galaxies_1_years * penalty_1_years\n",
|
||
")).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"galaxy_bonus_pool_with_rounding_bonus = galaxy_bonus_pool + total_rounding_error_galaxies\n",
|
||
"\n",
|
||
"# Handle division by zero if no 5-year galaxies\n",
|
||
"if galaxies_5_years > 0:\n",
|
||
" bonus_per_galaxy_5_years = galaxy_bonus_pool_with_rounding_bonus / galaxies_5_years\n",
|
||
" allocation_per_galaxy_5_years_with_bonus = adjusted_max_allocation_per_galaxy + bonus_per_galaxy_5_years\n",
|
||
"else:\n",
|
||
" bonus_per_galaxy_5_years = Decimal('0')\n",
|
||
" allocation_per_galaxy_5_years_with_bonus = adjusted_max_allocation_per_galaxy\n",
|
||
"\n",
|
||
"####################################################\n",
|
||
"\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"🎁 BONUS POOL CALCULATIONS\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"\n",
|
||
"# Lock Period Distribution Table\n",
|
||
"lock_period_df = pd.DataFrame({\n",
|
||
" 'Lock Period': ['1 Year', '2 Years', '3 Years', '4 Years', '5 Years'],\n",
|
||
" 'Stars': [f\"{stars_1_years:,}\", f\"{stars_2_years:,}\", f\"{stars_3_years:,}\", f\"{stars_4_years:,}\", f\"{stars_5_years:,}\"],\n",
|
||
" 'Galaxies': [f\"{galaxies_1_years:,}\", f\"{galaxies_2_years:,}\", f\"{galaxies_3_years:,}\", f\"{galaxies_4_years:,}\", f\"{galaxies_5_years:,}\"],\n",
|
||
" 'Total Participants': [f\"{stars_1_years + galaxies_1_years:,}\", f\"{stars_2_years + galaxies_2_years:,}\",\n",
|
||
" f\"{stars_3_years + galaxies_3_years:,}\", f\"{stars_4_years + galaxies_4_years:,}\",\n",
|
||
" f\"{stars_5_years + galaxies_5_years:,}\"]\n",
|
||
"})\n",
|
||
"print(\"\\n⏱️ LOCK PERIOD DISTRIBUTION\")\n",
|
||
"print(lock_period_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Star Bonus Pool Analysis\n",
|
||
"star_bonus_components = []\n",
|
||
"for years in [1, 2, 3, 4]:\n",
|
||
" star_count = eval(f\"stars_{years}_years\")\n",
|
||
" penalty = eval(f\"penalty_{years}_years\")\n",
|
||
" if star_count > 0:\n",
|
||
" component_amount = adjusted_max_allocation_per_star * star_count * penalty\n",
|
||
" star_bonus_components.append(f\"{years}Y Stars: {component_amount:,.2f} $Z\")\n",
|
||
"\n",
|
||
"star_bonus_df = pd.DataFrame({\n",
|
||
" 'Component': ['Penalty Pool', 'Rounding Error Bonus', 'Total Star Bonus Pool',\n",
|
||
" 'Recipients (5Y Stars)', 'Bonus per 5Y Star', 'Final 5Y Star Allocation'],\n",
|
||
" 'Value': [f\"{star_bonus_pool:,.6f} $Z\", f\"{total_rounding_error_stars:,.6f} $Z\",\n",
|
||
" f\"{star_bonus_pool_with_rounding_bonus:,.6f} $Z\", f\"{stars_5_years:,}\",\n",
|
||
" f\"{bonus_per_star_5_years:,.6f} $Z\", f\"{allocation_per_star_5_years_with_bonus:,.6f} $Z\"]\n",
|
||
"})\n",
|
||
"print(\"\\n⭐ STAR BONUS POOL ANALYSIS\")\n",
|
||
"print(star_bonus_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Galaxy Bonus Pool Analysis\n",
|
||
"galaxy_bonus_components = []\n",
|
||
"for years in [1, 2, 3, 4]:\n",
|
||
" galaxy_count = eval(f\"galaxies_{years}_years\")\n",
|
||
" penalty = eval(f\"penalty_{years}_years\")\n",
|
||
" if galaxy_count > 0:\n",
|
||
" component_amount = adjusted_max_allocation_per_galaxy * galaxy_count * penalty\n",
|
||
" galaxy_bonus_components.append(f\"{years}Y Galaxies: {component_amount:,.2f} $Z\")\n",
|
||
"\n",
|
||
"galaxy_bonus_df = pd.DataFrame({\n",
|
||
" 'Component': ['Penalty Pool', 'Rounding Error Bonus', 'Total Galaxy Bonus Pool',\n",
|
||
" 'Recipients (5Y Galaxies)', 'Bonus per 5Y Galaxy', 'Final 5Y Galaxy Allocation'],\n",
|
||
" 'Value': [f\"{galaxy_bonus_pool:,.6f} $Z\", f\"{total_rounding_error_galaxies:,.6f} $Z\",\n",
|
||
" f\"{galaxy_bonus_pool_with_rounding_bonus:,.6f} $Z\", f\"{galaxies_5_years:,}\",\n",
|
||
" f\"{bonus_per_galaxy_5_years:,.6f} $Z\", f\"{allocation_per_galaxy_5_years_with_bonus:,.6f} $Z\"]\n",
|
||
"})\n",
|
||
"print(\"\\n🌌 GALAXY BONUS POOL ANALYSIS\")\n",
|
||
"print(galaxy_bonus_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Show bonus pool sources if there are penalties\n",
|
||
"if star_bonus_components:\n",
|
||
" print(f\"\\n💡 Star Bonus Pool Sources:\")\n",
|
||
" for component in star_bonus_components:\n",
|
||
" print(f\" • {component}\")\n",
|
||
"\n",
|
||
"if galaxy_bonus_components:\n",
|
||
" print(f\"\\n💡 Galaxy Bonus Pool Sources:\")\n",
|
||
" for component in galaxy_bonus_components:\n",
|
||
" print(f\" • {component}\")\n",
|
||
"\n",
|
||
"print(\"\\n\" + \"=\"*80)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "579634a2",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Final allocations - CALCULATIONS ONLY\n",
|
||
"\n",
|
||
"allocation_per_star_5_years = (allocation_per_star_5_years_with_bonus).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"allocation_per_star_4_years = (adjusted_max_allocation_per_star * (1 - penalty_4_years)).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"allocation_per_star_3_years = (adjusted_max_allocation_per_star * (1 - penalty_3_years)).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"allocation_per_star_2_years = (adjusted_max_allocation_per_star * (1 - penalty_2_years)).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"allocation_per_star_1_years = (adjusted_max_allocation_per_star * (1 - penalty_1_years)).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"\n",
|
||
"z_per_star_per_block_5_years = (allocation_per_star_5_years / lockdrop_duration_blocks).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"z_per_star_per_block_4_years = (allocation_per_star_4_years / (lockdrop_duration_blocks * Decimal('0.8'))).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"z_per_star_per_block_3_years = (allocation_per_star_3_years / (lockdrop_duration_blocks * Decimal('0.6'))).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"z_per_star_per_block_2_years = (allocation_per_star_2_years / (lockdrop_duration_blocks * Decimal('0.4'))).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"z_per_star_per_block_1_years = (allocation_per_star_1_years / (lockdrop_duration_blocks * Decimal('0.2'))).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"\n",
|
||
"# Sanity check\n",
|
||
"total_stars_allocation = allocation_per_star_5_years * stars_5_years + allocation_per_star_4_years * stars_4_years + allocation_per_star_3_years * stars_3_years + allocation_per_star_2_years * stars_2_years + allocation_per_star_1_years * stars_1_years\n",
|
||
"final_rounding_error_stars = lockdrop_allocation_stars - total_stars_allocation\n",
|
||
"\n",
|
||
"# Galaxies\n",
|
||
"allocation_per_galaxy_5_years = (allocation_per_galaxy_5_years_with_bonus).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"allocation_per_galaxy_4_years = (adjusted_max_allocation_per_galaxy * (1 - penalty_4_years)).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"allocation_per_galaxy_3_years = (adjusted_max_allocation_per_galaxy * (1 - penalty_3_years)).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"allocation_per_galaxy_2_years = (adjusted_max_allocation_per_galaxy * (1 - penalty_2_years)).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"allocation_per_galaxy_1_years = (adjusted_max_allocation_per_galaxy * (1 - penalty_1_years)).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"\n",
|
||
"z_per_galaxy_per_block_5_years = (allocation_per_galaxy_5_years / lockdrop_duration_blocks).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"z_per_galaxy_per_block_4_years = (allocation_per_galaxy_4_years / (lockdrop_duration_blocks * Decimal('0.8'))).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"z_per_galaxy_per_block_3_years = (allocation_per_galaxy_3_years / (lockdrop_duration_blocks * Decimal('0.6'))).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"z_per_galaxy_per_block_2_years = (allocation_per_galaxy_2_years / (lockdrop_duration_blocks * Decimal('0.4'))).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"z_per_galaxy_per_block_1_years = (allocation_per_galaxy_1_years / (lockdrop_duration_blocks * Decimal('0.2'))).quantize(TOKEN_PRECISION, rounding=ROUND_DOWN)\n",
|
||
"\n",
|
||
"# Sanity check\n",
|
||
"total_galaxies_allocation = allocation_per_galaxy_5_years * galaxies_5_years + allocation_per_galaxy_4_years * galaxies_4_years + allocation_per_galaxy_3_years * galaxies_3_years + allocation_per_galaxy_2_years * galaxies_2_years + allocation_per_galaxy_1_years * galaxies_1_years\n",
|
||
"final_rounding_error_galaxies = lockdrop_allocation_galaxies - total_galaxies_allocation\n",
|
||
"final_rounding_error = final_rounding_error_stars + final_rounding_error_galaxies"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "x2y2tguimzn",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"✅ FINAL ALLOCATIONS & VERIFICATION\n",
|
||
"================================================================================\n",
|
||
"\n",
|
||
"⭐ FINAL STAR ALLOCATIONS\n",
|
||
"Lock Period Penalty Final Allocation ($Z) Z per Block Participants\n",
|
||
" 5 Years 0.0% 97551.931376180000 0.001236490000 7,887\n",
|
||
" 4 Years 20.0% 25624.771200000000 0.000406000000 8,112\n",
|
||
" 3 Years 40.0% 19218.578400000000 0.000406000000 7,870\n",
|
||
" 2 Years 60.0% 12812.385600000000 0.000406000000 8,056\n",
|
||
" 1 Year 80.0% 6406.192800000000 0.000406000000 8,075\n",
|
||
"\n",
|
||
"🌌 FINAL GALAXY ALLOCATIONS\n",
|
||
"Lock Period Penalty Final Allocation ($Z) Z per Block Participants\n",
|
||
" 5 Years 0.0% 65341.213959180000 0.000828210000 49\n",
|
||
" 4 Years 20.0% 20070.633600000000 0.000318000000 33\n",
|
||
" 3 Years 40.0% 15052.975200000000 0.000318000000 36\n",
|
||
" 2 Years 60.0% 10035.316800000000 0.000318000000 43\n",
|
||
" 1 Year 80.0% 5017.658400000000 0.000318000000 39\n",
|
||
"\n",
|
||
"🔍 ALLOCATION VERIFICATION\n",
|
||
" Category Calculated Total Expected Total Rounding Error\n",
|
||
" Star Allocations 1283457023.999931660000 $Z 1283457024.000000000000 $Z 0.000068340000 $Z\n",
|
||
"Galaxy Allocations 5033164.799999820000 $Z 5033164.800000000000 $Z 0.000000180000 $Z\n",
|
||
" Combined 1288490188.799931480000 $Z 1288490188.800000000000 $Z 0.000068520000 $Z\n",
|
||
"\n",
|
||
"🎯 LOCKDROP SIMULATION INSIGHTS\n",
|
||
" Insight Value\n",
|
||
" Total Participants 40,200\n",
|
||
" Star 5Y Bonus Multiplier 3.05x\n",
|
||
"Lowest vs Base Allocation 0.20x\n",
|
||
" Total Bonus Distributed 518.7M $Z\n",
|
||
" Final Rounding Error 0.000068520000 $Z\n",
|
||
"\n",
|
||
"================================================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"✅ FINAL ALLOCATIONS & VERIFICATION\")\n",
|
||
"print(\"=\" * 80)\n",
|
||
"\n",
|
||
"# Star Final Allocations Table\n",
|
||
"star_final_df = pd.DataFrame({\n",
|
||
" 'Lock Period': ['5 Years', '4 Years', '3 Years', '2 Years', '1 Year'],\n",
|
||
" 'Penalty': [f\"{penalty_5_years:.1%}\", f\"{penalty_4_years:.1%}\", f\"{penalty_3_years:.1%}\",\n",
|
||
" f\"{penalty_2_years:.1%}\", f\"{penalty_1_years:.1%}\"],\n",
|
||
" 'Final Allocation ($Z)': [f\"{allocation_per_star_5_years:.12f}\", f\"{allocation_per_star_4_years:.12f}\",\n",
|
||
" f\"{allocation_per_star_3_years:.12f}\", f\"{allocation_per_star_2_years:.12f}\",\n",
|
||
" f\"{allocation_per_star_1_years:.12f}\"],\n",
|
||
" 'Z per Block': [f\"{z_per_star_per_block_5_years:.12f}\", f\"{z_per_star_per_block_4_years:.12f}\",\n",
|
||
" f\"{z_per_star_per_block_3_years:.12f}\", f\"{z_per_star_per_block_2_years:.12f}\",\n",
|
||
" f\"{z_per_star_per_block_1_years:.12f}\"],\n",
|
||
" 'Participants': [f\"{stars_5_years:,}\", f\"{stars_4_years:,}\", f\"{stars_3_years:,}\",\n",
|
||
" f\"{stars_2_years:,}\", f\"{stars_1_years:,}\"]\n",
|
||
"})\n",
|
||
"print(\"\\n⭐ FINAL STAR ALLOCATIONS\")\n",
|
||
"print(star_final_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Galaxy Final Allocations Table\n",
|
||
"galaxy_final_df = pd.DataFrame({\n",
|
||
" 'Lock Period': ['5 Years', '4 Years', '3 Years', '2 Years', '1 Year'],\n",
|
||
" 'Penalty': [f\"{penalty_5_years:.1%}\", f\"{penalty_4_years:.1%}\", f\"{penalty_3_years:.1%}\",\n",
|
||
" f\"{penalty_2_years:.1%}\", f\"{penalty_1_years:.1%}\"],\n",
|
||
" 'Final Allocation ($Z)': [f\"{allocation_per_galaxy_5_years:.12f}\", f\"{allocation_per_galaxy_4_years:.12f}\",\n",
|
||
" f\"{allocation_per_galaxy_3_years:.12f}\", f\"{allocation_per_galaxy_2_years:.12f}\",\n",
|
||
" f\"{allocation_per_galaxy_1_years:.12f}\"],\n",
|
||
" 'Z per Block': [f\"{z_per_galaxy_per_block_5_years:.12f}\", f\"{z_per_galaxy_per_block_4_years:.12f}\",\n",
|
||
" f\"{z_per_galaxy_per_block_3_years:.12f}\", f\"{z_per_galaxy_per_block_2_years:.12f}\",\n",
|
||
" f\"{z_per_galaxy_per_block_1_years:.12f}\"],\n",
|
||
" 'Participants': [f\"{galaxies_5_years:,}\", f\"{galaxies_4_years:,}\", f\"{galaxies_3_years:,}\",\n",
|
||
" f\"{galaxies_2_years:,}\", f\"{galaxies_1_years:,}\"]\n",
|
||
"})\n",
|
||
"print(\"\\n🌌 FINAL GALAXY ALLOCATIONS\")\n",
|
||
"print(galaxy_final_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Verification and Rounding Error Analysis\n",
|
||
"verification_df = pd.DataFrame({\n",
|
||
" 'Category': ['Star Allocations', 'Galaxy Allocations', 'Combined'],\n",
|
||
" 'Calculated Total': [f\"{total_stars_allocation:.12f} $Z\", f\"{total_galaxies_allocation:.12f} $Z\",\n",
|
||
" f\"{total_stars_allocation + total_galaxies_allocation:.12f} $Z\"],\n",
|
||
" 'Expected Total': [f\"{lockdrop_allocation_stars:.12f} $Z\", f\"{lockdrop_allocation_galaxies:.12f} $Z\",\n",
|
||
" f\"{lockdrop_allocation:.12f} $Z\"],\n",
|
||
" 'Rounding Error': [f\"{final_rounding_error_stars:.12f} $Z\", f\"{final_rounding_error_galaxies:.12f} $Z\",\n",
|
||
" f\"{final_rounding_error:.12f} $Z\"]\n",
|
||
"})\n",
|
||
"print(\"\\n🔍 ALLOCATION VERIFICATION\")\n",
|
||
"print(verification_df.to_string(index=False))\n",
|
||
"\n",
|
||
"# Final Summary Table with dynamic data insights\n",
|
||
"bonus_multiplier = float(allocation_per_star_5_years)/float(adjusted_max_allocation_per_star) if stars_5_years > 0 else 1.0\n",
|
||
"lowest_year_allocation = min([float(allocation_per_star_1_years), float(allocation_per_star_2_years),\n",
|
||
" float(allocation_per_star_3_years), float(allocation_per_star_4_years)])\n",
|
||
"allocation_ratio = lowest_year_allocation/float(adjusted_max_allocation_per_star)\n",
|
||
"\n",
|
||
"final_summary_df = pd.DataFrame({\n",
|
||
" 'Insight': ['Total Participants', 'Star 5Y Bonus Multiplier', 'Lowest vs Base Allocation',\n",
|
||
" 'Total Bonus Distributed', 'Final Rounding Error'],\n",
|
||
" 'Value': [f\"{total_stars_locked + total_galaxies_locked:,}\",\n",
|
||
" f\"{bonus_multiplier:.2f}x\" if stars_5_years > 0 else \"N/A (no 5Y stars)\",\n",
|
||
" f\"{allocation_ratio:.2f}x\",\n",
|
||
" f\"{float(star_bonus_pool_with_rounding_bonus + galaxy_bonus_pool_with_rounding_bonus)/1e6:.1f}M $Z\",\n",
|
||
" f\"{float(final_rounding_error):.12f} $Z\"]\n",
|
||
"})\n",
|
||
"print(\"\\n🎯 LOCKDROP SIMULATION INSIGHTS\")\n",
|
||
"print(final_summary_df.to_string(index=False))\n",
|
||
"print(\"\\n\" + \"=\"*80)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "e7jh8pl6qtt",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"💾 JSON OUTPUT GENERATED\n",
|
||
"✅ Final allocations saved to: lockdrop_allocations_notebook.json\n",
|
||
"================================================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Generate data for tests\n",
|
||
"\n",
|
||
"# Generate JSON output with final allocations per point\n",
|
||
"allocations_output_file = 'lockdrop_allocations_notebook.json'\n",
|
||
"\n",
|
||
"# Create final allocations data structure (multiply by 10^8 to convert $Z to $sZ)\n",
|
||
"final_allocations = {\n",
|
||
" \"stars\": {\n",
|
||
" \"1_years\": int(allocation_per_star_1_years * Decimal('1e8')),\n",
|
||
" \"2_years\": int(allocation_per_star_2_years * Decimal('1e8')),\n",
|
||
" \"3_years\": int(allocation_per_star_3_years * Decimal('1e8')),\n",
|
||
" \"4_years\": int(allocation_per_star_4_years * Decimal('1e8')),\n",
|
||
" \"5_years\": int(allocation_per_star_5_years * Decimal('1e8'))\n",
|
||
" },\n",
|
||
" \"galaxies\": {\n",
|
||
" \"1_years\": int(allocation_per_galaxy_1_years * Decimal('1e8')),\n",
|
||
" \"2_years\": int(allocation_per_galaxy_2_years * Decimal('1e8')),\n",
|
||
" \"3_years\": int(allocation_per_galaxy_3_years * Decimal('1e8')),\n",
|
||
" \"4_years\": int(allocation_per_galaxy_4_years * Decimal('1e8')),\n",
|
||
" \"5_years\": int(allocation_per_galaxy_5_years * Decimal('1e8'))\n",
|
||
" },\n",
|
||
" \"total\": int((total_stars_allocation + total_galaxies_allocation) * Decimal('1e8'))\n",
|
||
"}\n",
|
||
"\n",
|
||
"# Save to JSON file\n",
|
||
"with open(allocations_output_file, 'w') as f:\n",
|
||
" json.dump(final_allocations, f, indent=2)\n",
|
||
"\n",
|
||
"print(\"=\" * 80)\n",
|
||
"print(\"💾 JSON OUTPUT GENERATED\")\n",
|
||
"print(f\"✅ Final allocations saved to: {allocations_output_file}\")\n",
|
||
"print(\"=\" * 80)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "2jvm7qnecba",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 2000x1000 with 6 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create comprehensive visualization for dynamic lockdrop simulation\n",
|
||
"\n",
|
||
"fig = plt.figure(figsize=(20, 10))\n",
|
||
"gs = fig.add_gridspec(2, 3, hspace=0.35, wspace=0.35) # 2x3 grid\n",
|
||
"\n",
|
||
"fig.suptitle('Lockdrop Simulation Analysis', fontsize=18, fontweight='bold', y=0.98)\n",
|
||
"\n",
|
||
"# 1. Star Participation Distribution by Lock Period\n",
|
||
"ax1 = fig.add_subplot(gs[0, 0])\n",
|
||
"star_years = [1, 2, 3, 4, 5]\n",
|
||
"star_counts = [stars_1_years, stars_2_years, stars_3_years, stars_4_years, stars_5_years]\n",
|
||
"\n",
|
||
"bars1 = ax1.bar(range(len(star_years)), star_counts,\n",
|
||
" color=['#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728'],\n",
|
||
" alpha=0.8)\n",
|
||
"ax1.set_xlabel('Lock Period (Years)')\n",
|
||
"ax1.set_ylabel('Number of Stars')\n",
|
||
"ax1.set_title('Star Participation by Lock Period', fontweight='bold')\n",
|
||
"ax1.set_xticks(range(len(star_years)))\n",
|
||
"ax1.set_xticklabels(star_years)\n",
|
||
"\n",
|
||
"# Add value labels\n",
|
||
"for i, count in enumerate(star_counts):\n",
|
||
" height = bars1[i].get_height()\n",
|
||
" ax1.text(bars1[i].get_x() + bars1[i].get_width()/2., height + height*0.01,\n",
|
||
" f'{count:,}', ha='center', va='bottom', fontweight='bold', fontsize=9)\n",
|
||
"\n",
|
||
"# 2. Galaxy Participation Distribution by Lock Period\n",
|
||
"ax2 = fig.add_subplot(gs[0, 1])\n",
|
||
"galaxy_years = [1, 2, 3, 4, 5]\n",
|
||
"galaxy_counts = [galaxies_1_years, galaxies_2_years, galaxies_3_years, galaxies_4_years, galaxies_5_years]\n",
|
||
"\n",
|
||
"bars2 = ax2.bar(range(len(galaxy_years)), galaxy_counts,\n",
|
||
" color=['#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2'],\n",
|
||
" alpha=0.8)\n",
|
||
"ax2.set_xlabel('Lock Period (Years)')\n",
|
||
"ax2.set_ylabel('Number of Galaxies')\n",
|
||
"ax2.set_title('Galaxy Participation by Lock Period', fontweight='bold')\n",
|
||
"ax2.set_xticks(range(len(galaxy_years)))\n",
|
||
"ax2.set_xticklabels(galaxy_years)\n",
|
||
"\n",
|
||
"# Add value labels\n",
|
||
"for i, count in enumerate(galaxy_counts):\n",
|
||
" height = bars2[i].get_height()\n",
|
||
" ax2.text(bars2[i].get_x() + bars2[i].get_width()/2., height + height*0.01,\n",
|
||
" f'{count:,}', ha='center', va='bottom', fontweight='bold', fontsize=9)\n",
|
||
"\n",
|
||
"# 3. Star Allocations by Lock Period\n",
|
||
"ax3 = fig.add_subplot(gs[0, 2])\n",
|
||
"star_allocations = [\n",
|
||
" float(allocation_per_star_1_years),\n",
|
||
" float(allocation_per_star_2_years),\n",
|
||
" float(allocation_per_star_3_years),\n",
|
||
" float(allocation_per_star_4_years),\n",
|
||
" float(allocation_per_star_5_years)\n",
|
||
"]\n",
|
||
"\n",
|
||
"bars3 = ax3.bar(range(len(star_years)), star_allocations,\n",
|
||
" color=['#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728'],\n",
|
||
" alpha=0.8)\n",
|
||
"ax3.set_xlabel('Lock Period (Years)')\n",
|
||
"ax3.set_ylabel('Allocation per Star ($Z)')\n",
|
||
"ax3.set_title('Star Allocations by Lock Period', fontweight='bold')\n",
|
||
"ax3.set_xticks(range(len(star_years)))\n",
|
||
"ax3.set_xticklabels(star_years)\n",
|
||
"\n",
|
||
"# Add value labels\n",
|
||
"for i, allocation in enumerate(star_allocations):\n",
|
||
" height = bars3[i].get_height()\n",
|
||
" ax3.text(bars3[i].get_x() + bars3[i].get_width()/2., height + height*0.01,\n",
|
||
" f'{allocation:,.0f}', ha='center', va='bottom', fontweight='bold', fontsize=9)\n",
|
||
"\n",
|
||
"# 4. Galaxy Allocations by Lock Period\n",
|
||
"ax4 = fig.add_subplot(gs[1, 0])\n",
|
||
"galaxy_allocations = [\n",
|
||
" float(allocation_per_galaxy_1_years),\n",
|
||
" float(allocation_per_galaxy_2_years),\n",
|
||
" float(allocation_per_galaxy_3_years),\n",
|
||
" float(allocation_per_galaxy_4_years),\n",
|
||
" float(allocation_per_galaxy_5_years)\n",
|
||
"]\n",
|
||
"\n",
|
||
"bars4 = ax4.bar(range(len(galaxy_years)), galaxy_allocations,\n",
|
||
" color=['#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2'],\n",
|
||
" alpha=0.8)\n",
|
||
"ax4.set_xlabel('Lock Period (Years)')\n",
|
||
"ax4.set_ylabel('Allocation per Galaxy ($Z)')\n",
|
||
"ax4.set_title('Galaxy Allocations by Lock Period', fontweight='bold')\n",
|
||
"ax4.set_xticks(range(len(galaxy_years)))\n",
|
||
"ax4.set_xticklabels(galaxy_years)\n",
|
||
"\n",
|
||
"# Add value labels\n",
|
||
"for i, allocation in enumerate(galaxy_allocations):\n",
|
||
" height = bars4[i].get_height()\n",
|
||
" ax4.text(bars4[i].get_x() + bars4[i].get_width()/2., height + height*0.01,\n",
|
||
" f'{allocation:,.0f}', ha='center', va='bottom', fontweight='bold', fontsize=9)\n",
|
||
"\n",
|
||
"# 5. 5-Year Bonus Impact (Stars and Galaxies)\n",
|
||
"ax5 = fig.add_subplot(gs[1, 1])\n",
|
||
"categories = ['Star\\nBase', 'Star\\n5Y Bonus', 'Galaxy\\nBase', 'Galaxy\\n5Y Bonus']\n",
|
||
"star_5y_base = float(adjusted_max_allocation_per_star)\n",
|
||
"star_5y_final = float(allocation_per_star_5_years)\n",
|
||
"galaxy_5y_base = float(adjusted_max_allocation_per_galaxy)\n",
|
||
"galaxy_5y_final = float(allocation_per_galaxy_5_years)\n",
|
||
"\n",
|
||
"star_efficiency_gain = (star_5y_final / star_5y_base - 1) * 100\n",
|
||
"galaxy_efficiency_gain = (galaxy_5y_final / galaxy_5y_base - 1) * 100\n",
|
||
"\n",
|
||
"bonus_values = [star_5y_base, star_5y_final, galaxy_5y_base, galaxy_5y_final]\n",
|
||
"colors_bonus = ['lightcoral', 'lightgreen', 'lightblue', 'lightcyan']\n",
|
||
"\n",
|
||
"bars5 = ax5.bar(range(len(categories)), bonus_values, color=colors_bonus, alpha=0.8)\n",
|
||
"ax5.set_ylabel('Allocation ($Z)')\n",
|
||
"ax5.set_title('5-Year Bonus Impact', fontweight='bold')\n",
|
||
"ax5.set_xticks(range(len(categories)))\n",
|
||
"ax5.set_xticklabels(categories)\n",
|
||
"\n",
|
||
"# Add value labels\n",
|
||
"labels = [\n",
|
||
" f'{star_5y_base:,.0f}',\n",
|
||
" f'{star_5y_final:,.0f}\\n(+{star_efficiency_gain:.1f}%)',\n",
|
||
" f'{galaxy_5y_base:,.0f}',\n",
|
||
" f'{galaxy_5y_final:,.0f}\\n(+{galaxy_efficiency_gain:.1f}%)'\n",
|
||
"]\n",
|
||
"\n",
|
||
"for i, (bar, label) in enumerate(zip(bars5, labels)):\n",
|
||
" height = bar.get_height()\n",
|
||
" ax5.text(bar.get_x() + bar.get_width()/2., height + height*0.01,\n",
|
||
" label, ha='center', va='bottom', fontweight='bold', fontsize=8)\n",
|
||
"\n",
|
||
"# Hide the 6th subplot area (bottom right)\n",
|
||
"ax6 = fig.add_subplot(gs[1, 2])\n",
|
||
"ax6.axis('off')\n",
|
||
"\n",
|
||
"# Manual layout adjustment with more space at top for title\n",
|
||
"plt.subplots_adjust(bottom=0.08, top=0.88, left=0.05, right=0.98)\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
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"kernelspec": {
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||
"display_name": "venv",
|
||
"language": "python",
|
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"name": "python3"
|
||
},
|
||
"language_info": {
|
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"codemirror_mode": {
|
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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||
"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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