path: root/prediction_controller.py

# prediction_controller.py
from dateutil.relativedelta import relativedelta
from datetime import datetime, timedelta
from typing import List, Tuple, Dict, Optional
import math

from calendar_manager import CalendarManager
from date_calculator import DateCalculator
from event_type_handler import EventTypeHandler
from config import EventConfig

class PredictionController:
    """Handles prediction calculation with proper business logic orchestration"""
    
    def __init__(self, calendar_manager: CalendarManager, date_calculator: DateCalculator):
        self.calendar_manager = calendar_manager
        self.date_calculator = date_calculator
        self.launch_date: Optional[datetime] = None
        self.duration: Optional[relativedelta] = None
        self.prediction: Optional[datetime] = None
        self.corrected_events: List[Tuple[datetime, datetime, str]] = []
        
    def set_parameters(self, launch_date: str, duration_years: int) -> bool:
        """Set launch date and duration with validation"""
        try:
            self.launch_date = datetime.fromisoformat(launch_date)
            self.duration = relativedelta(years=duration_years)
            
            # Validate parameters
            if duration_years <= 0:
                raise ValueError("Duration must be positive")
            if self.launch_date < datetime(1900, 1, 1):
                raise ValueError("Launch date too far in the past")
            if self.launch_date > datetime(2100, 1, 1):
                raise ValueError("Launch date too far in the future")
                
            return True
        except ValueError as e:
            print(f"Error setting parameters: {e}")
            return False
    
    def make_prediction(self, launch_date: str, duration_years: int) -> bool:
        """Calculate prediction with proper business logic orchestration"""
        if not self.set_parameters(launch_date, duration_years):
            return False
            
        try:
            # Calculate base prediction (launch + duration - 1 day)
            prediction_start = self.launch_date + self.duration - timedelta(days=1)
            
            # Categorize events by type
            categorized_events = EventTypeHandler.categorize_events(self.calendar_manager.entries)
            
            # Process events according to business rules
            processed_events = self._process_events_by_type(categorized_events)
            
            # Calculate final prediction
            self.prediction = self._calculate_final_prediction(prediction_start, processed_events)
            
            # Apply corrections to calendar entries
            self._apply_corrections_to_calendar()
            
            return True
            
        except Exception as e:
            print(f"Error calculating prediction: {e}")
            return False
    
    def _process_events_by_type(self, categorized_events: Dict[str, List[Tuple[datetime, datetime, str]]]) -> Dict[str, List[Tuple[datetime, datetime, str]]]:
        """Process events according to business rules"""
        processed = {}
        
        # Process full-time projects first (EZ 100% and EZ pauschal)
        full_projects = []
        for event_type in ["EZ 100%", "EZ pauschal"]:
            if event_type in categorized_events:
                full_projects.extend(categorized_events[event_type])
        
        if full_projects:
            processed["full_projects"] = self._process_full_projects(full_projects)
        
        # Process half-time projects (EZ 50%)
        if "EZ 50%" in categorized_events:
            processed["half_projects"] = self._process_half_projects(
                categorized_events["EZ 50%"], 
                processed.get("full_projects", [])
            )
        
        # Process other events (Sonstige)
        if "Sonstige" in categorized_events:
            processed["other_events"] = self._process_other_events(
                categorized_events["Sonstige"],
                processed.get("full_projects", []),
                processed.get("half_projects", [])
            )
        
        return processed
    
    def _process_full_projects(self, full_projects: List[Tuple[datetime, datetime, str]]) -> List[Tuple[datetime, datetime, str]]:
        """Process full-time projects"""
        # Sort and truncate periods
        sorted_projects = DateCalculator.sort_periods(full_projects)
        considered_projects = DateCalculator.truncate_periods(sorted_projects, self.launch_date)
        
        # Filter out any invalid periods
        valid_projects = DateCalculator.filter_valid_periods(considered_projects)
        
        # Round to month boundaries
        rounded_projects, total_months = DateCalculator.round_periods(valid_projects)
        
        return rounded_projects
    
    def _process_half_projects(self, half_projects: List[Tuple[datetime, datetime, str]], 
                              full_projects: List[Tuple[datetime, datetime, str]]) -> List[Tuple[datetime, datetime, str]]:
        """Process half-time projects"""
        # Sort and truncate periods
        sorted_projects = DateCalculator.sort_periods(half_projects)
        considered_projects = DateCalculator.truncate_periods(sorted_projects, self.launch_date)
        
        # Filter out any invalid periods
        valid_projects = DateCalculator.filter_valid_periods(considered_projects)
        
        # Find non-overlapping periods with full projects
        non_overlapping_projects = []
        for test_interval in valid_projects:
            non_overlapping_results = DateCalculator.find_non_overlapping_periods(full_projects, test_interval)
            non_overlapping_projects.extend(non_overlapping_results)
        
        # Filter valid periods again after overlap processing
        valid_non_overlapping = DateCalculator.filter_valid_periods(non_overlapping_projects)
        
        # Round to month boundaries
        rounded_projects, total_months = DateCalculator.round_periods(valid_non_overlapping)
        
        return rounded_projects
    
    def _process_other_events(self, other_events: List[Tuple[datetime, datetime, str]], 
                            full_projects: List[Tuple[datetime, datetime, str]], 
                            half_projects: List[Tuple[datetime, datetime, str]]) -> List[Tuple[datetime, datetime, str]]:
        """Process other events (Sonstige)"""
        # Sort and truncate periods
        sorted_events = DateCalculator.sort_periods(other_events)
        considered_events = DateCalculator.truncate_periods(sorted_events, self.launch_date)
        
        # Filter out any invalid periods
        valid_events = DateCalculator.filter_valid_periods(considered_events)
        
        # Find non-overlapping periods with all projects
        all_projects = DateCalculator.sort_periods(full_projects + half_projects)
        non_overlapping_events = []
        for test_interval in valid_events:
            non_overlapping_results = DateCalculator.find_non_overlapping_periods(all_projects, test_interval)
            non_overlapping_events.extend(non_overlapping_results)
        
        # Filter valid periods again after overlap processing
        valid_non_overlapping = DateCalculator.filter_valid_periods(non_overlapping_events)
        
        # Adjust overlapping periods
        adjusted_events = DateCalculator.adjust_periods(valid_non_overlapping)
        
        # Final validation to ensure all periods are valid
        final_valid_events = DateCalculator.filter_valid_periods(adjusted_events)

        return final_valid_events
    
    def _calculate_final_prediction(self, prediction_start: datetime, 
                                  processed_events: Dict[str, List[Tuple[datetime, datetime, str]]]) -> datetime:
        """Calculate the final prediction date"""
        # Calculate months from projects
        total_months = 0
        
        # Full projects count as full months
        if "full_projects" in processed_events:
            total_months += DateCalculator.calculate_total_months(processed_events["full_projects"])
        
        # Half projects count as half months
        if "half_projects" in processed_events:
            half_months = DateCalculator.calculate_total_months(processed_events["half_projects"])
            total_months += math.ceil(half_months / 2)
        
        # Other events count as days
        total_days = 0
        if "other_events" in processed_events:
            total_days = DateCalculator.calculate_total_days(processed_events["other_events"])
        
        # Calculate final prediction
        final_prediction = (prediction_start + 
                          relativedelta(months=total_months) + 
                          timedelta(days=total_days))
        
        # Apply maximum limit
        max_prediction = prediction_start + EventConfig.get_max_prediction_duration()
        final_prediction = DateCalculator.min_date(final_prediction, max_prediction)
        
        return final_prediction
    
    def _apply_corrections_to_calendar(self):
        """Apply corrected dates to calendar entries and calculate time_period"""
        # Collect all corrected events from processed results
        all_corrected_events = []
        
        # Get processed events from the prediction calculation
        categorized_events = EventTypeHandler.categorize_events(self.calendar_manager.entries)
        processed_events = self._process_events_by_type(categorized_events)
        
        # Collect corrected events from all categories
        for event_type, events in processed_events.items():
            all_corrected_events.extend(events)
        
        # Apply corrections to calendar entries
        self.calendar_manager.correct_dates(all_corrected_events)
    
    def get_launch_date(self) -> Optional[datetime]:
        """Get the launch date"""
        return self.launch_date
        
    def get_duration(self) -> Optional[relativedelta]:
        """Get the duration"""
        return self.duration
        
    def get_prediction(self) -> Optional[datetime]:
        """Get the prediction"""
        return self.prediction
    
    def validate_prediction_inputs(self, launch_date: str, duration_years: int) -> List[str]:
        """Validate prediction inputs and return list of errors"""
        errors = []
        
        try:
            launch_dt = datetime.fromisoformat(launch_date)
        except ValueError:
            errors.append("Invalid launch date format")
            return errors
        
        if duration_years <= 0:
            errors.append("Duration must be positive")
        
        if launch_dt < datetime(1900, 1, 1):
            errors.append("Launch date too far in the past")
        
        if launch_dt > datetime(2100, 1, 1):
            errors.append("Launch date too far in the future")
        
        return errors