Ux Researcher Designer

#!/usr/bin/env python3
"""
Data-Driven Persona Generator
Creates research-backed user personas from user data and interviews
"""

import json
from typing import Dict, List, Tuple
from collections import Counter, defaultdict
import random

class PersonaGenerator:
    """Generate data-driven personas from user research"""
    
    def __init__(self):
        self.persona_components = {
            'demographics': ['age', 'location', 'occupation', 'education', 'income'],
            'psychographics': ['goals', 'frustrations', 'motivations', 'values'],
            'behaviors': ['tech_savviness', 'usage_frequency', 'preferred_devices', 'key_activities'],
            'needs': ['functional', 'emotional', 'social']
        }
        
        self.archetype_templates = {
            'power_user': {
                'characteristics': ['tech-savvy', 'frequent user', 'early adopter', 'efficiency-focused'],
                'goals': ['maximize productivity', 'automate workflows', 'access advanced features'],
                'frustrations': ['slow performance', 'limited customization', 'lack of shortcuts'],
                'quote': "I need tools that can keep up with my workflow"
            },
            'casual_user': {
                'characteristics': ['occasional user', 'basic needs', 'prefers simplicity'],
                'goals': ['accomplish specific tasks', 'easy to use', 'minimal learning curve'],
                'frustrations': ['complexity', 'too many options', 'unclear navigation'],
                'quote': "I just want it to work without having to think about it"
            },
            'business_user': {
                'characteristics': ['professional context', 'ROI-focused', 'team collaboration'],
                'goals': ['improve team efficiency', 'track metrics', 'integrate with tools'],
                'frustrations': ['lack of reporting', 'poor collaboration features', 'no enterprise features'],
                'quote': "I need to show clear value to my stakeholders"
            },
            'mobile_first': {
                'characteristics': ['primarily mobile', 'on-the-go usage', 'quick interactions'],
                'goals': ['access anywhere', 'quick actions', 'offline capability'],
                'frustrations': ['poor mobile experience', 'desktop-only features', 'slow loading'],
                'quote': "My phone is my primary computing device"
            }
        }
    
    def generate_persona_from_data(self, user_data: List[Dict], 
                                  interview_insights: List[Dict] = None) -> Dict:
        """Generate persona from user data and optional interview insights"""
        
        # Analyze user data for patterns
        patterns = self._analyze_user_patterns(user_data)
        
        # Identify persona archetype
        archetype = self._identify_archetype(patterns)
        
        # Generate persona
        persona = {
            'name': self._generate_name(archetype),
            'archetype': archetype,
            'tagline': self._generate_tagline(patterns),
            'demographics': self._aggregate_demographics(user_data),
            'psychographics': self._extract_psychographics(patterns, interview_insights),
            'behaviors': self._analyze_behaviors(user_data),
            'needs_and_goals': self._identify_needs(patterns, interview_insights),
            'frustrations': self._extract_frustrations(patterns, interview_insights),
            'scenarios': self._generate_scenarios(archetype, patterns),
            'quote': self._select_quote(interview_insights, archetype),
            'data_points': self._calculate_data_points(user_data),
            'design_implications': self._derive_design_implications(patterns)
        }
        
        return persona
    
    def _analyze_user_patterns(self, user_data: List[Dict]) -> Dict:
        """Analyze patterns in user data"""
        
        patterns = {
            'usage_frequency': defaultdict(int),