""" Chart Generation for Policy Impact Simulator Creates visual charts and graphs for policy analysis results """ import matplotlib.pyplot as plt import numpy as np from typing import Dict, Any, List import os import base64 import io from datetime import datetime class PolicyChartGenerator: """Generates charts and graphs for policy impact analysis""" def __init__(self): # Set matplotlib to use non-interactive backend plt.switch_backend('Agg') def generate_scenario_comparison_chart(self, variants: Dict[str, Any], title: str = "Policy Scenario Analysis") -> str: """Generate a bar chart comparing scenario variants""" # Extract scenario data scenario_names = [] costs = [] colors = [] color_map = { 'best_case': '#28a745', # Green 'base_case': '#007bff', # Blue 'worst_case': '#dc3545' # Red } for scenario_type, data in variants.items(): if isinstance(data, dict) and 'total_cost' in data: scenario_names.append(scenario_type.replace('_', ' ').title()) costs.append(data['total_cost']) colors.append(color_map.get(scenario_type, '#6c757d')) # Create chart fig, ax = plt.subplots(figsize=(10, 6)) bars = ax.bar(scenario_names, costs, color=colors, alpha=0.8) # Customize chart ax.set_title(title, fontsize=16, fontweight='bold', pad=20) ax.set_ylabel('Total Cost (₹ Crores)', fontsize=12) ax.set_xlabel('Scenario', fontsize=12) # Add value labels on bars for bar, cost in zip(bars, costs): height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/2., height + max(costs) * 0.01, f'₹{cost:,.0f}', ha='center', va='bottom', fontweight='bold') # Format y-axis ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'₹{x:,.0f}')) # Add grid ax.grid(True, alpha=0.3, axis='y') ax.set_axisbelow(True) # Tight layout plt.tight_layout() # Convert to base64 string return self._fig_to_base64(fig) def generate_yearly_breakdown_chart(self, yearly_data: List[Dict], title: str = "Yearly Impact Breakdown") -> str: """Generate a line chart showing yearly breakdown""" years = [] impacts = [] beneficiaries = [] for year_data in yearly_data: years.append(f"Year {year_data.get('year', 0)}") impacts.append(year_data.get('impact', 0)) beneficiaries.append(year_data.get('affected_beneficiaries', 0)) # Create dual-axis chart fig, ax1 = plt.subplots(figsize=(12, 6)) # Plot impact on primary axis color1 = '#007bff' ax1.set_xlabel('Years', fontsize=12) ax1.set_ylabel('Impact (₹ Crores)', fontsize=12, color=color1) line1 = ax1.plot(years, impacts, color=color1, marker='o', linewidth=3, markersize=8, label='Financial Impact') ax1.tick_params(axis='y', labelcolor=color1) ax1.grid(True, alpha=0.3) # Create secondary axis for beneficiaries ax2 = ax1.twinx() color2 = '#28a745' ax2.set_ylabel('Beneficiaries', fontsize=12, color=color2) line2 = ax2.plot(years, beneficiaries, color=color2, marker='s', linewidth=3, markersize=8, linestyle='--', label='Affected Population') ax2.tick_params(axis='y', labelcolor=color2) # Add title ax1.set_title(title, fontsize=16, fontweight='bold', pad=20) # Add value labels for i, (year, impact, beneficiary) in enumerate(zip(years, impacts, beneficiaries)): ax1.annotate(f'₹{impact:.1f}', (i, impact), textcoords="offset points", xytext=(0,10), ha='center', fontweight='bold') ax2.annotate(f'{beneficiary:,}', (i, beneficiary), textcoords="offset points", xytext=(0,-15), ha='center', fontweight='bold', color=color2) # Add legend lines1, labels1 = ax1.get_legend_handles_labels() lines2, labels2 = ax2.get_legend_handles_labels() ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left') plt.tight_layout() return self._fig_to_base64(fig) def generate_ascii_chart(self, variants: Dict[str, Any], width: int = 50) -> str: """Generate ASCII chart for text-based display""" if not variants: return "No data available for chart generation" # Extract costs costs = [] names = [] for scenario_type, data in variants.items(): if isinstance(data, dict) and 'total_cost' in data: costs.append(data['total_cost']) names.append(scenario_type.replace('_', ' ').title()) if not costs: return "No cost data available" max_cost = max(costs) if costs else 1 chart = "📊 Policy Scenario Comparison (₹ Crores)\n" chart += "=" * (width + 20) + "\n" for name, cost in zip(names, costs): # Calculate bar length bar_length = int((cost / max_cost) * width) if max_cost > 0 else 0 bar = "█" * bar_length # Add emoji based on scenario type emoji = "🟢" if "Best" in name else "🔴" if "Worst" in name else "🔵" chart += f"{emoji} {name:12} │{bar:<{width}} ₹{cost:,.0f}\n" chart += "=" * (width + 20) + "\n" return chart def generate_implementation_timeline_chart(self, timeline_data: Dict[str, Any]) -> str: """Generate ASCII timeline chart for implementation phases""" phases = [ "📋 Planning Phase (Months 1-3)", "⚖️ Legal Review (Months 2-4)", "💰 Budget Approval (Months 4-6)", "🔄 System Updates (Months 6-8)", "📢 Communication (Months 8-10)", "🚀 Implementation (Months 10-12)" ] timeline = "🗓️ Implementation Timeline\n" timeline += "=" * 60 + "\n" for i, phase in enumerate(phases, 1): progress_bar = "▓" * (i * 2) + "░" * ((6 - i) * 2) timeline += f" {phase}\n" timeline += f" [{progress_bar}] {i}/6 phases\n\n" complexity = timeline_data.get('complexity', 'Medium') timeline += f"🔧 Implementation Complexity: {complexity}\n" timeline += f"⏱️ Estimated Duration: 12 months\n" timeline += f"💡 Key Success Factors: Budget approval, stakeholder buy-in, system readiness\n" return timeline def _fig_to_base64(self, fig) -> str: """Convert matplotlib figure to base64 string""" buffer = io.BytesIO() fig.savefig(buffer, format='png', dpi=300, bbox_inches='tight') buffer.seek(0) # Convert to base64 img_base64 = base64.b64encode(buffer.read()).decode('utf-8') # Close figure to free memory plt.close(fig) return f"data:image/png;base64,{img_base64}" def generate_comprehensive_report_charts(self, analysis_data: Dict[str, Any]) -> Dict[str, str]: """Generate all charts for a comprehensive policy analysis report""" charts = {} # Scenario comparison chart if 'variants' in analysis_data: charts['scenario_comparison'] = self.generate_scenario_comparison_chart( analysis_data['variants'], f"Policy Impact: {analysis_data.get('parameter_name', 'Analysis')}" ) # ASCII version for text display charts['scenario_ascii'] = self.generate_ascii_chart(analysis_data['variants']) # Yearly breakdown chart if 'scenario_projections' in analysis_data: charts['yearly_breakdown'] = self.generate_yearly_breakdown_chart( analysis_data['scenario_projections'], "Financial Impact Over Time" ) # Implementation timeline if 'implementation' in analysis_data: charts['implementation_timeline'] = self.generate_implementation_timeline_chart( analysis_data['implementation'] ) return charts # Standalone functions for easy integration def generate_policy_charts(analysis_data: Dict[str, Any]) -> Dict[str, str]: """Generate charts for policy analysis data""" generator = PolicyChartGenerator() return generator.generate_comprehensive_report_charts(analysis_data) def generate_ascii_scenario_chart(variants: Dict[str, Any]) -> str: """Generate ASCII chart for immediate text display""" generator = PolicyChartGenerator() return generator.generate_ascii_chart(variants)