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Generated Models Reference

This library includes 280 auto-generated Pydantic models from the official BMRS OpenAPI specification. These models provide full type safety and IDE autocomplete for all API responses.

Usage

from elexon_bmrs import BMRSClient
from elexon_bmrs.generated_models import (
    DemandOutturnNational,
    ActualAggregatedGenerationPerTypeDatasetRow,
    WindGenerationForecast,
    # ... and 277 more!
)

client = BMRSClient(api_key="your-key")

# Get data from API
response = client.get_system_demand(
    from_date="2024-01-01",
    to_date="2024-01-02"
)

# Parse with Pydantic for full type safety
for item in response.data:
    demand = DemandOutturnNational(**item)
    # Full IDE autocomplete available!
    print(f"{demand.settlement_date}: {demand.demand} MW")

Available Models

All generated models are available in the elexon_bmrs.generated_models module:

generated_models

Auto-generated Pydantic models from BMRS OpenAPI specification.

This file is automatically generated. Do not edit manually.

Model Generation

These models are automatically generated from the OpenAPI specification using our code generation tools. This ensures:

  • ✅ 100% API coverage - All endpoints and models included
  • ✅ Always up-to-date - Can regenerate when API changes
  • ✅ Type safety - Full Pydantic validation
  • ✅ IDE support - Complete autocomplete

Regenerating Models

If you're developing the library, you can regenerate models:

# Download latest OpenAPI spec
make download-schema

# Generate models
make generate-models

# Or do both
make generate-all

See the Code Generation guide for more details.

Common Models

Demand Models

  • DemandOutturnNational - National demand outturn
  • DemandOutturnTransmission - Transmission demand
  • DemandForecast - Demand forecasts

Generation Models

  • ActualAggregatedGenerationPerTypeDatasetRow - Generation by fuel type
  • WindGenerationForecast - Wind generation forecasts
  • ActualGenerationOutputPerGenerationUnit - Generation by BMU

Pricing Models

  • SystemPrices - System buy/sell prices
  • ImbalancePrices - Imbalance pricing data
  • MarketIndexData - Market index prices

System Models

  • SystemFrequency - System frequency data
  • SystemWarnings - System warnings and alerts
  • BalancingServicesVolume - Balancing services data

Type Safety Benefits

Using generated models provides several advantages:

1. Validation

from elexon_bmrs.generated_models import DemandOutturnNational
from pydantic import ValidationError

try:
    demand = DemandOutturnNational(**data)
except ValidationError as e:
    print(f"Invalid data: {e}")

2. IDE Autocomplete

Your IDE will show all available fields and their types:

demand = DemandOutturnNational(**data)
# IDE shows: settlement_date, settlement_period, demand, etc.
print(demand.demand)  # ✓ Autocomplete works!

3. Type Checking

Static type checkers like mypy can verify your code:

def process_demand(demand: DemandOutturnNational) -> float:
    return demand.demand * 1.1  # Type-checked!

4. Documentation

Models include docstrings and field descriptions from the OpenAPI spec:

from elexon_bmrs.generated_models import DemandOutturnNational

# View model documentation
help(DemandOutturnNational)

Examples

See the Typed Usage guide and Examples for comprehensive examples using generated models.