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import json
from artifact_optimizer import ArtifactOptimizer
def run_demo():
"""Demonstrate the artifact optimizer with different priority scenarios"""
# Initialize optimizer
optimizer = ArtifactOptimizer()
optimizer.load_data('data.json', 'characters.json')
print("="*80)
print("🎮 GENSHIN ARTIFACT OPTIMIZER DEMO")
print("="*80)
print(f"📦 Total artifacts available: {len(optimizer.artifacts)}")
print(f"👥 Characters available: {', '.join(optimizer.character_data.keys())}")
# Define different priority scenarios
scenarios = [
{
"name": "DPS Priority",
"description": "Prioritize main DPS characters first",
"priority": ["Fischl", "Chiori", "Furina", "Escoffier"]
},
{
"name": "Support Priority",
"description": "Prioritize support characters first",
"priority": ["Furina", "Escoffier", "Fischl", "Chiori"]
},
{
"name": "ER Demanding First",
"description": "Prioritize characters with high ER requirements",
"priority": ["Escoffier", "Furina", "Fischl", "Chiori"]
},
{
"name": "Reverse Order",
"description": "Completely reverse priority order",
"priority": ["Chiori", "Fischl", "Escoffier", "Furina"]
}
]
# Store results for comparison
all_results = {}
# Run each scenario
for i, scenario in enumerate(scenarios, 1):
print(f"\n{'='*60}")
print(f"SCENARIO {i}: {scenario['name']}")
print(f"{'='*60}")
print(f"📝 {scenario['description']}")
print(f"🎯 Priority: {' -> '.join(scenario['priority'])}")
# Run optimization
results = optimizer.optimize_builds(scenario['priority'])
all_results[scenario['name']] = results
# Quick summary
successful = sum(1 for data in results.values() if data['build'] is not None)
if successful > 0:
avg_score = sum(data['average_score'] for data in results.values() if data['build']) / successful
er_compliant = sum(1 for data in results.values()
if data['build'] and data['er_info']['meets_requirement'])
print(f"✅ Successful builds: {successful}/4")
print(f"📊 Average quality: {avg_score:.1f}/100")
print(f"⚡ ER compliant: {er_compliant}/4")
# Show individual scores
print("\n📋 Individual Scores:")
for char, data in results.items():
if data['build']:
er_status = "✅" if data['er_info']['meets_requirement'] else "⚠️"
print(f" {char}: {data['average_score']:.1f}/100 {er_status}")
else:
print(f" {char}: No build found ❌")
else:
print("❌ No successful builds found!")
# Comparison analysis
print(f"\n{'='*80}")
print("📊 SCENARIO COMPARISON")
print(f"{'='*80}")
comparison_data = []
for scenario_name, results in all_results.items():
successful = sum(1 for data in results.values() if data['build'] is not None)
if successful > 0:
avg_score = sum(data['average_score'] for data in results.values() if data['build']) / successful
er_compliant = sum(1 for data in results.values()
if data['build'] and data['er_info']['meets_requirement'])
comparison_data.append({
'scenario': scenario_name,
'successful_builds': successful,
'average_score': avg_score,
'er_compliant': er_compliant,
'total_score': successful * avg_score # Weighted score
})
# Sort by total weighted score
comparison_data.sort(key=lambda x: x['total_score'], reverse=True)
print("🏆 Ranking (by total weighted score):")
for i, data in enumerate(comparison_data, 1):
print(f" {i}. {data['scenario']}")
print(f" Builds: {data['successful_builds']}/4 | "
f"Avg Score: {data['average_score']:.1f} | "
f"ER Compliant: {data['er_compliant']}/4 | "
f"Total: {data['total_score']:.1f}")
# Character-specific analysis
print(f"\n📈 CHARACTER PERFORMANCE ACROSS SCENARIOS")
print(f"{'='*60}")
characters = list(optimizer.character_data.keys())
for char in characters:
print(f"\n🔍 {char}:")
char_scores = []
for scenario_name, results in all_results.items():
if results[char]['build']:
score = results[char]['average_score']
er_ok = results[char]['er_info']['meets_requirement']
char_scores.append(score)
status = "✅" if er_ok else "⚠️"
print(f" {scenario_name}: {score:.1f}/100 {status}")
else:
print(f" {scenario_name}: No build ❌")
if char_scores:
print(f" 📊 Score range: {min(char_scores):.1f} - {max(char_scores):.1f}")
print(f" 📊 Average: {sum(char_scores)/len(char_scores):.1f}")
# Priority impact analysis
print(f"\n🎯 PRIORITY IMPACT ANALYSIS")
print(f"{'='*60}")
print("Key Findings:")
# Find which characters benefit most from high priority
char_variance = {}
for char in characters:
scores = []
for results in all_results.values():
if results[char]['build']:
scores.append(results[char]['average_score'])
if len(scores) > 1:
variance = max(scores) - min(scores)
char_variance[char] = variance
if char_variance:
most_affected = max(char_variance.items(), key=lambda x: x[1])
least_affected = min(char_variance.items(), key=lambda x: x[1])
print(f"• Most affected by priority: {most_affected[0]} (variance: {most_affected[1]:.1f})")
print(f"• Least affected by priority: {least_affected[0]} (variance: {least_affected[1]:.1f})")
# Save detailed results
with open('demo_results.json', 'w') as f:
json.dump(all_results, f, indent=2, default=str)
print(f"\n💾 Detailed results saved to 'demo_results.json'")
print(f"\n🎉 Demo completed! Try different scenarios to see how priority affects builds.")
if __name__ == "__main__":
run_demo()
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