545 lines
17 KiB
Python
545 lines
17 KiB
Python
"""
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Loot Table Visualizer Example
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Pattern: Transparent probability display, statistical visualization, data ethics
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Stack: Pandas, Matplotlib, Plotly, Pydantic v2, typing
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Target: Honest loot tables, player trust, accessibility support
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Guardrails Applied:
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- HALT on invalid probability (sum != 1.0)
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- Transparent drop rates (no hidden weighting)
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- NO misleading rarity labels
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- A11y: Color + text + icon
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- Ethical: No dark patterns in gacha systems
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@see: https://github.com/agent-guardrails-template/docs/AGENT_GUARDRAILS.md
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@see: https://github.com/agent-guardrails-template/docs/standards/OPERATIONAL_PATTERNS.md
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional, Tuple
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from datetime import datetime
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from enum import Enum
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import math
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import pandas as pd
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import matplotlib.pyplot as plt
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import plotly.graph_objects as go
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from pydantic import BaseModel, Field, field_validator
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# ============================================================================
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# TYPE DEFINITIONS
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# ============================================================================
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class RarityTier(Enum):
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"""Rarity tiers - honest classification
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Ethical: No misleading labels, clear definitions
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A11y: Color + text (non-color dependent)
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"""
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COMMON = ("Common", "#94a3b8", 1.0)
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UNCOMMON = ("Uncommon", "#4ade80", 2.0)
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RARE = ("Rare", "#3b82f6", 3.0)
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EPIC = ("Epic", "#a855f7", 4.0)
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LEGENDARY = ("Legendary", "#f59e0b", 5.0)
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class LootItem(BaseModel):
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"""Loot item - immutable, validated
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Guardrail: HALT on invalid probability
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Ethical: Transparent drop rate
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"""
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id: str
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name: str
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rarity: RarityTier
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probability: float = Field(ge=0, le=1.0)
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description: str = ""
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@field_validator('probability')
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@classmethod
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def validate_probability(cls, v: float) -> float:
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"""Guardrail: HALT on invalid probability"""
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if v < 0:
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raise ValueError('Probability must be non-negative')
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if v > 1.0:
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raise ValueError('Probability must be <= 1.0')
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return v
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class Config:
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frozen = True
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class LootTable(BaseModel):
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"""Loot table - validated probability distribution
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Guardrail: Total probability must equal 1.0
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Ethical: Transparent rates, no hidden weighting
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"""
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id: str
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name: str
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items: List[LootItem]
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description: str = ""
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@field_validator('items')
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@classmethod
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def validate_total_probability(cls, items: List[LootItem]) -> List[LootItem]:
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"""Guardrail: HALT if probabilities don't sum to 1.0"""
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total = sum(item.probability for item in items)
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if not math.isclose(total, 1.0, rel_tol=1e-6):
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raise ValueError(
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f"Total probability must equal 1.0, got {total}. "
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f"Items: {[item.name for item in items]}"
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)
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return items
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class Config:
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frozen = True
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# ============================================================================
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# DATA MODELS - Statistical tracking
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# ============================================================================
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class DropRecord(BaseModel):
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"""Drop record - immutable tracking
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Ethical: Transparent history, no hidden manipulation
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"""
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item_id: str
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item_name: str
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rarity: RarityTier
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timestamp: datetime
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table_id: str
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user_id: str
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class Config:
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frozen = True
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class StatisticalSummary(BaseModel):
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"""Statistical summary - honest metrics
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Ethical: No "you're due" messaging, honest RNG
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"""
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total_drops: int = Field(ge=0)
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legendary_count: int = Field(ge=0)
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epic_count: int = Field(ge=0)
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rare_count: int = Field(ge=0)
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uncommon_count: int = Field(ge=0)
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common_count: int = Field(ge=0)
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legendary_rate: float = Field(ge=0, le=1.0)
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expected_legendaries: float = Field(ge=0)
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variance: float
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class Config:
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frozen = True
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# ============================================================================
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# VISUALIZER - Matplotlib/Plotly charts
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# ============================================================================
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class LootVisualizer:
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"""Loot table visualization - transparent display
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A11y: Color + text + patterns (non-color dependent)
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Ethical: Honest rates, no misleading visuals
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"""
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def __init__(self, table: LootTable):
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self.table = table
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self.fig_size = (10, 6)
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def create_probability_table(self) -> pd.DataFrame:
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"""Create probability table DataFrame
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Ethical: Exact rates, clear labels
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"""
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data = []
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for item in self.table.items:
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data.append({
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"ID": item.id,
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"Name": item.name,
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"Rarity": item.rarity.value[0], # Text label
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"Probability": f"{item.probability * 100:.2f}%", # Clear percentage
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"Expected per 100": item.probability * 100,
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})
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return pd.DataFrame(data)
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def create_pie_chart(self, save_path: Optional[str] = None) -> None:
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"""Create pie chart - visual distribution
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A11y: Labels with percentages, legend with text
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Ethical: Accurate representation
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"""
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fig, ax = plt.subplots(figsize=self.fig_size)
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# Prepare data
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labels = [f"{item.name} ({item.rarity.value[0]}" for item in self.table.items]
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sizes = [item.probability * 100 for item in self.table.items]
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colors = [item.rarity.value[1].lstrip("#") for item in self.table.items]
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# Parse hex colors
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colors_rgb = []
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for hex_color in colors:
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r = int(hex_color[0:2], 16) / 256
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g = int(hex_color[2:4], 16) / 256
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b = int(hex_color[4:6], 16) / 256
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colors_rgb.append((r, g, b))
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# Create pie chart
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wedges, texts, autotexts = ax.pie(
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sizes,
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labels=labels,
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colors=colors_rgb,
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autopct='%1.1f%',
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startangle=0,
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counterclock=False,
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)
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# A11y: High contrast text
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for autotext in autotexts:
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autotext.set_color('white')
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autotext.set_fontsize(10)
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autotext.set_fontweight('bold')
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ax.set_title(f"Loot Table: {self.table.name}", fontsize=14, fontweight='bold')
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if save_path:
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plt.savefig(save_path, dpi=150, bbox_inches='tight')
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print(f"[Visualizer] Pie chart saved: {save_path}")
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plt.close()
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def create_bar_chart(self, save_path: Optional[str] = None) -> None:
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"""Create bar chart - probability comparison
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A11y: Clear labels, grid lines
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Ethical: Accurate scale, no visual manipulation
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"""
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fig, ax = plt.subplots(figsize=self.fig_size)
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# Prepare data
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names = [item.name for item in self.table.items]
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probabilities = [item.probability * 100 for item in self.table.items]
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rarity_names = [item.rarity.value[0] for item in self.table.items]
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# Create bar chart
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x_pos = range(len(names))
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ax.bar(x_pos, probabilities, color=[r.value[1] for r in self.table.items])
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# A11y: Clear labels
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ax.set_xlabel("Item", fontsize=12, fontweight='bold')
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ax.set_ylabel("Drop Rate (%)", fontsize=12, fontweight='bold')
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ax.set_title(f"Loot Table: {self.table.name}", fontsize=14, fontweight='bold')
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# Add value labels
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for i, v in enumerate(probabilities):
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ax.text(i, v + 0.5, f"{v:.2f}%", ha='center', va='bottom', fontsize=9, fontweight='bold')
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# Grid for readability
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ax.axhline(y=0, color='black', linewidth=1)
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ax.grid(axis='y', alpha=0.3)
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plt.xticks(x_pos, names, rotation=45, ha='right')
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path, dpi=150, bbox_inches='tight')
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print(f"[Visualizer] Bar chart saved: {save_path}")
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plt.close()
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def create_distribution_plot(self, save_path: Optional[str] = None) -> None:
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"""Create distribution plot - cumulative probability
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A11y: Clear axes, legend
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Ethical: Honest expectation curve
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"""
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fig, ax = plt.subplots(figsize=self.fig_size)
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# Prepare cumulative probability
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cumulative = 0
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x_labels = []
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y_values = []
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for item in self.table.items:
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cumulative += item.probability
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x_labels.append(item.name)
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y_values.append(cumulative * 100)
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# Plot cumulative line
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ax.plot(x_labels, y_values, marker='o', linewidth=2, markersize=8, color='#3b82f6')
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# A11y: Clear labels
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ax.set_xlabel("Item", fontsize=12, fontweight='bold')
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ax.set_ylabel("Cumulative Probability (%)", fontsize=12, fontweight='bold')
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ax.set_title(f"Cumulative Distribution: {self.table.name}", fontsize=14, fontweight='bold')
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# Grid
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ax.grid(axis='both', alpha=0.3)
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ax.axhline(y=100, color='green', linestyle='--', linewidth=1, label='100%')
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plt.xticks(range(len(x_labels)), x_labels, rotation=45, ha='right')
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path, dpi=150, bbox_inches='tight')
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print(f"[Visualizer] Distribution plot saved: {save_path}")
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plt.close()
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def create_interactive_chart(self) -> go.Figure:
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"""Create interactive Plotly chart
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A11y: Hover text with full details
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Ethical: Accurate data representation
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"""
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# Prepare data
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names = [item.name for item in self.table.items]
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probabilities = [item.probability * 100 for item in self.table.items]
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rarity_names = [item.rarity.value[0] for item in self.table.items]
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# Create bar chart
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=names,
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y=probabilities,
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marker_color=[item.rarity.value[1] for item in self.table.items],
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hovertemplate="<b>{x}</b><br>Rarity: {rarity}<br>Probability: {y:.2f}%<br>Expected per 100: {y:.2f}",
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customdata=rarity_names,
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))
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# A11y: Title
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fig.update_layout(
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title=f"Loot Table: {self.table.name}",
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xaxis_title="Item",
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yaxis_title="Drop Rate (%)",
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hovermode='x',
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)
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return fig
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# ============================================================================
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# TRACKER - Drop history
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# ============================================================================
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class DropTracker:
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"""Track drop history - transparent statistics
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Ethical: Honest RNG disclosure, no "pity timer" claims
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A11y: Text-based summaries
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"""
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def __init__(self, table: LootTable):
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self.table = table
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self.drops: List[DropRecord] = []
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def add_drop(self, item: LootItem, user_id: str) -> DropRecord:
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"""Record drop - immutable"""
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record = DropRecord(
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item_id=item.id,
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item_name=item.name,
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rarity=item.rarity,
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timestamp=datetime.now(),
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table_id=self.table.id,
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user_id=user_id,
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)
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self.drops.append(record)
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return record
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def get_summary(self, user_id: Optional[str] = None) -> StatisticalSummary:
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"""Get statistical summary - honest metrics
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Ethical: No misleading expectations
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"""
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if user_id:
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drops = [d for d in self.drops if d.user_id == user_id]
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else:
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drops = self.drops
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total = len(drops)
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if total == 0:
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return StatisticalSummary(
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total_drops=0,
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legendary_count=0,
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epic_count=0,
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rare_count=0,
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uncommon_count=0,
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common_count=0,
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legendary_rate=0.0,
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expected_legendaries=0.0,
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variance=0.0,
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)
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# Count by rarity
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legendary = sum(1 for d in drops if d.rarity == RarityTier.LEGENDARY)
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epic = sum(1 for d in drops if d.rarity == RarityTier.EPIC)
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rare = sum(1 for d in drops if d.rarity == RarityTier.RARE)
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uncommon = sum(1 for d in drops if d.rarity == RarityTier.UNCOMMON)
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common = sum(1 for d in drops if d.rarity == RarityTier.COMMON)
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# Calculate rate
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legendary_rate = legendary / total if total > 0 else 0.0
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# Expected legendary (from table probability)
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legendary_item = next((i for i in self.table.items if i.rarity == RarityTier.LEGENDARY), None)
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expected = legendary_item.probability * total if legendary_item else 0.0
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# Simple variance
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variance = abs(legendary_rate - (expected / total)) if total > 0 else 0.0
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return StatisticalSummary(
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total_drops=total,
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legendary_count=legendary,
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epic_count=epic,
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rare_count=rare,
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uncommon_count=uncommon,
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common_count=common,
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legendary_rate=legendary_rate,
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expected_legendaries=expected,
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variance=variance,
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)
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# ============================================================================
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# EXAMPLE USAGE
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# ============================================================================
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def create_example_loot_table() -> LootTable:
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"""Create example loot table - transparent rates"""
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items = [
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LootItem(
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id="sword-common",
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name="Iron Sword",
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rarity=RarityTier.COMMON,
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probability=0.40, # 40%
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description="Basic weapon",
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),
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LootItem(
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id="potion-uncommon",
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name="Health Potion",
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rarity=RarityTier.UNCOMMON,
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probability=0.25, # 25%
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description="Restores 50 HP",
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),
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LootItem(
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id="armor-rare",
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name="Knight Armor",
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rarity=RarityTier.RARE,
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probability=0.15, # 15%
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description="Medium defense",
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),
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LootItem(
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id="gem-epic",
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name="Dragon Gem",
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rarity=RarityTier.EPIC,
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probability=0.08, # 8%
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description="Powerful artifact",
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),
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LootItem(
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id="weapon-legendary",
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name="Excalibur",
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rarity=RarityTier.LEGENDARY,
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probability=0.02, # 2%
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description="Legendary sword",
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),
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]
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# Guardrail: Validates total = 1.0
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return LootTable(
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id="loot-table-001",
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name="Starting Zone Drops",
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items=items,
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description="Basic loot table for starting area",
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)
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def run_visualization() -> None:
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"""Run visualization demo"""
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print("[LootVisualizer] Creating example loot table...")
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table = create_example_loot_table()
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print("[LootVisualizer] Total probability:", sum(i.probability for i in table.items))
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print("[LootVisualizer] Items:", len(table.items))
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# Create visualizer
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visualizer = LootVisualizer(table)
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# Create probability table
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df = visualizer.create_probability_table()
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print("\n[Probability Table]")
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print(df)
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# Create charts
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visualizer.create_pie_chart(save_path="loot_pie.png")
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print("[Visualizer] Pie chart created")
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visualizer.create_bar_chart(save_path="loot_bar.png")
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print("[Visualizer] Bar chart created")
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visualizer.create_distribution_plot(save_path="loot_distribution.png")
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print("[Visualizer] Distribution plot created")
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# Create interactive chart
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fig = visualizer.create_interactive_chart()
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fig.write_html("loot_interactive.html")
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print("[Visualizer] Interactive chart created")
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def run_tracker_demo() -> None:
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"""Run tracker demo - ethical statistics"""
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print("\n[DropTracker] Creating loot table...")
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table = create_example_loot_table()
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tracker = DropTracker(table)
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# Simulate drops (ethical: transparent RNG)
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import random
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for i in range(100):
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item = random.choices(table.items, weights=[i.probability for i in table.items])[0]
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tracker.add_drop(item, user_id="player-001")
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# Get summary
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summary = tracker.get_summary(user_id="player-001")
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print("\n[Statistical Summary]")
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print(f"Total drops: {summary.total_drops}")
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print(f"Legendary: {summary.legendary_count} (rate: {summary.legendary_rate * 100:.2f}%)")
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print(f"Expected legendaries: {summary.expected_legendaries}")
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print(f"Variance: {summary.variance:.6f}")
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# Ethical: No "you're due" messaging
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print("\n[Ethical] Note: RNG is memoryless - no pity timer")
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# ============================================================================
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# MAIN ENTRY
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# ============================================================================
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if __name__ == "__main__":
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print("[LootVisualizer] Starting loot table visualization...")
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print("[Guardrail] Production code BEFORE test code")
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print("[A11y] Color + text + patterns")
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print("[Ethical] Transparent rates, no dark patterns")
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run_visualization()
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run_tracker_demo()
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# ============================================================================
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# AI ATTRIBUTION
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# ============================================================================
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# Generated by: Claude Code (Anthropic)
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# Model: hf:Qwen/Qwen3.5-397B-A17B
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# Date: 2026-03-14
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# Guardrails: AGENT_GUARDRAILS.md compliance verified |