job cards optional everywhere

DataService/enhanced_generator.py

@@ -256,7 +256,7 @@ class EnhancedMetroDataGenerator:
                     estimated_hours = random.randint(2, 24)
                 
                 job = {
-                    "job_card_id": f"JC-{random.randint(10000, 99999)}",
+                    "job_id": f"JC-{random.randint(10000, 99999)}",
                     "trainset_id": ts_id,
                     "work_order_number": f"WO-{random.randint(100000, 999999)}",
                     "job_type": random.choice(job_types),
@@ -391,8 +391,12 @@ class EnhancedMetroDataGenerator:
         
         return contracts
     
-    def generate_complete_enhanced_dataset(self) -> Dict:
-        """Generate complete enhanced dataset with all improvements."""
+    def generate_complete_enhanced_dataset(self, include_job_cards: bool = False) -> Dict:
+        """Generate complete enhanced dataset with all improvements.
+        
+        Args:
+            include_job_cards: Whether to include job cards in the dataset. Default False.
+        """
         print("Generating enhanced synthetic data...")
         
         dataset = {
@@ -413,7 +417,7 @@ class EnhancedMetroDataGenerator:
             "trainset_profiles": self.trainset_profiles,
             "trainset_status": self.generate_enhanced_trainset_status(),
             "fitness_certificates": self.generate_realistic_fitness_certificates(),
-            "job_cards": self.generate_correlated_job_cards(),
+            "job_cards": self.generate_correlated_job_cards() if include_job_cards else [],
             "component_health": self.generate_realistic_component_health(),
             "branding_contracts": self.generate_optimized_branding_contracts(),
             # Keep the existing generators for other data
@@ -444,6 +448,11 @@ class EnhancedMetroDataGenerator:
                     "bogie_2": round(random.uniform(0.5, 3.5) / reliability_factor, 2),
                     "unit": "mm/s"
                 },
+                "temperature": {
+                    "motor_1": round(random.uniform(45, 85) + (1 - reliability_factor) * 10, 1),
+                    "motor_2": round(random.uniform(45, 85) + (1 - reliability_factor) * 10, 1),
+                    "unit": "°C"
+                },
                 "overall_condition": "Good" if reliability_factor > 0.85 else "Fair" if reliability_factor > 0.75 else "Poor"
             }
             sensor_data.append(sensors)
@@ -555,7 +564,8 @@ class EnhancedMetroDataGenerator:
         return {
             "date": datetime.now().date().isoformat(),
             "weather": {
-                "condition": random.choice(["Clear", "Cloudy", "Rainy"])
+                "condition": random.choice(["Clear", "Cloudy", "Rainy"]),
+                "temperature": round(random.uniform(20, 35), 1)
             },
             "ridership_forecast": {
                 "expected_passengers": random.randint(80000, 150000),
@@ -563,9 +573,14 @@ class EnhancedMetroDataGenerator:
             }
         }
     
-    def save_to_json(self, filename: str = "metro_enhanced_data.json") -> Dict:
-        """Save enhanced data to JSON file."""
-        data = self.generate_complete_enhanced_dataset()
+    def save_to_json(self, filename: str = "metro_enhanced_data.json", include_job_cards: bool = False) -> Dict:
+        """Save enhanced data to JSON file.
+        
+        Args:
+            filename: Output filename.
+            include_job_cards: Whether to include job cards in the dataset. Default False.
+        """
+        data = self.generate_complete_enhanced_dataset(include_job_cards=include_job_cards)
         
         with open(filename, 'w') as f:
             json.dump(data, f, indent=2)

DataService/metro_models.py

@@ -103,7 +103,7 @@ class Trainset(BaseModel):
     
     # Compliance and health
     fitness_certificates: FitnessCertificates
-    job_cards: JobCards
+    job_cards: Optional[JobCards] = Field(default_factory=lambda: JobCards(open=0, blocking=[]))
     
     # Branding
     branding: Optional[Branding] = None

DataService/schedule_optimizer.py

@@ -29,7 +29,8 @@ class MetroScheduleOptimizer:
         num_trains: int,
         route: Route,
         train_health: List[TrainHealthStatus],
-        depot_name: str = "Muttom_Depot"
+        depot_name: str = "Muttom_Depot",
+        include_job_cards: bool = False
     ):
         self.date = date
         self.num_trains = num_trains
@@ -37,6 +38,7 @@ class MetroScheduleOptimizer:
         self.train_health = {t.trainset_id: t for t in train_health}
         self.depot_name = depot_name
         self.generator = MetroDataGenerator(num_trains)
+        self.include_job_cards = include_job_cards
         
         # Operating parameters
         self.op_hours = OperationalHours()
@@ -58,7 +60,7 @@ class MetroScheduleOptimizer:
         for i, train_id in enumerate(self.generator.trainset_ids):
             health = self.train_health[train_id]
             fitness_certs = self.generator.generate_fitness_certificates(train_id)
-            job_cards = self.generator.generate_job_cards(train_id)
+            job_cards = self.generator.generate_job_cards(train_id) if self.include_job_cards else JobCards(open=0, blocking=[])
             branding = self.generator.generate_branding()
             
             readiness = self.generator.calculate_readiness_score(

DataService/synthetic_base.py

@@ -281,8 +281,12 @@ class MetroSyntheticDataGenerator:
             }
         }
     
-    def generate_complete_dataset(self) -> Dict:
-        """Generate complete synthetic dataset for metro scheduling"""
+    def generate_complete_dataset(self, include_job_cards: bool = False) -> Dict:
+        """Generate complete synthetic dataset for metro scheduling.
+        
+        Args:
+            include_job_cards: Whether to include job cards in the dataset. Default False.
+        """
         dataset = {
             "metadata": {
                 "generated_at": datetime.now().isoformat(),
@@ -292,7 +296,7 @@ class MetroSyntheticDataGenerator:
             },
             "trainset_status": self.generate_trainset_status(),
             "fitness_certificates": self.generate_fitness_certificates(),
-            "job_cards": self.generate_job_cards(),
+            "job_cards": self.generate_job_cards() if include_job_cards else [],
             "component_health": self.generate_component_health(),
             "iot_sensors": self.generate_iot_sensors(),
             "branding_contracts": self.generate_branding_contracts(),
@@ -304,9 +308,14 @@ class MetroSyntheticDataGenerator:
         }
         return dataset
     
-    def save_to_json(self, filename: str = "metro_synthetic_data.json"):
-        """Save generated data to JSON file"""
-        data = self.generate_complete_dataset()
+    def save_to_json(self, filename: str = "metro_synthetic_data.json", include_job_cards: bool = False):
+        """Save generated data to JSON file.
+        
+        Args:
+            filename: Output filename.
+            include_job_cards: Whether to include job cards in the dataset. Default False.
+        """
+        data = self.generate_complete_dataset(include_job_cards=include_job_cards)
         with open(filename, 'w') as f:
             json.dump(data, f, indent=2)
         print(f"Synthetic data generated and saved to {filename}")

api/greedyoptim_api.py

@@ -104,7 +104,7 @@ class ScheduleOptimizationRequest(BaseModel):
     """Request for schedule optimization"""
     trainset_status: List[TrainsetStatusInput]
     fitness_certificates: List[FitnessCertificateInput]
-    job_cards: List[JobCardInput]
+    job_cards: Optional[List[JobCardInput]] = Field(default_factory=list, description="Job cards are optional, defaults to empty list")
     component_health: List[ComponentHealthInput]
     
     # Optional metadata
@@ -125,7 +125,7 @@ class CompareMethodsRequest(BaseModel):
     """Request to compare multiple optimization methods"""
     trainset_status: List[TrainsetStatusInput]
     fitness_certificates: List[FitnessCertificateInput]
-    job_cards: List[JobCardInput]
+    job_cards: Optional[List[JobCardInput]] = Field(default_factory=list, description="Job cards are optional, defaults to empty list")
     component_health: List[ComponentHealthInput]
     
     metadata: Optional[Dict[str, Any]] = None
@@ -181,7 +181,7 @@ def convert_pydantic_to_dict(request: ScheduleOptimizationRequest) -> Dict[str,
     data = {
         "trainset_status": [ts.dict() for ts in request.trainset_status],
         "fitness_certificates": [fc.dict() for fc in request.fitness_certificates],
-        "job_cards": [jc.dict() for jc in request.job_cards],
+        "job_cards": [jc.dict() for jc in request.job_cards] if request.job_cards else [],
         "component_health": [ch.dict() for ch in request.component_health],
         "metadata": request.metadata or {
             "generated_at": datetime.now().isoformat(),

greedyOptim/error_handling.py

@@ -54,8 +54,10 @@ class DataValidator:
         errors = []
         
         try:
-            # Check required top-level keys
-            required_keys = ['trainset_status', 'fitness_certificates', 'job_cards', 'component_health']
+            # Check required top-level keys (job_cards is now optional)
+            required_keys = ['trainset_status', 'fitness_certificates', 'component_health']
+            optional_keys = ['job_cards']
+            
             for key in required_keys:
                 if key not in data:
                     errors.append(f"Missing required data section: {key}")
@@ -69,6 +71,15 @@ class DataValidator:
                 section_errors = cls._validate_section(data[key], key)
                 errors.extend(section_errors)
             
+            # Validate optional keys if present
+            for key in optional_keys:
+                if key in data and data[key]:
+                    if not isinstance(data[key], list):
+                        errors.append(f"Data section {key} must be a list")
+                        continue
+                    section_errors = cls._validate_section(data[key], key)
+                    errors.extend(section_errors)
+            
             # Cross-validation
             if not errors:  # Only if basic structure is valid
                 cross_errors = cls._cross_validate(data)

greedyOptim/evaluator.py

@@ -33,9 +33,9 @@ class TrainsetSchedulingEvaluator:
                 self.fitness_map[ts_id] = {}
             self.fitness_map[ts_id][cert['department']] = cert
         
-        # Job cards by trainset
+        # Job cards by trainset (optional - may be empty)
         self.job_map = {}
-        for job in self.data['job_cards']:
+        for job in self.data.get('job_cards', []):
             ts_id = job['trainset_id']
             if ts_id not in self.job_map:
                 self.job_map[ts_id] = []