Methods API
This section provides detailed API documentation for the methods modules of EnTiSe.
Accessing Methods
EnTiSe provides two ways to access methods:
Through TimeSeriesGenerator (Batch Processing):
from entise.core.generator import TimeSeriesGenerator # Initialize the generator gen = TimeSeriesGenerator() # Add objects gen.add_objects(objects) # Generate timeseries summary, df = gen.generate(data)
Direct Import (Individual Processing):
# Import a specific method from entise.methods.pv import PVLib # Create an instance pvlib = PVLib() # Generate timeseries result = pvlib.generate(obj, data) # Access results summary = result["summary"] timeseries = result["timeseries"]
Flexible Parameter Passing
When using direct method access, EnTiSe provides flexible ways to pass parameters:
Using Dictionaries:
# Pass parameters as dictionaries obj = {"latitude": 48.1, "longitude": 11.6, "power": 5000} data = {"weather": weather_df} result = pvlib.generate(obj=obj, data=data)
Using Named Parameters:
# Pass parameters directly by name result = pvlib.generate( latitude=48.1, longitude=11.6, power=5000, weather=weather_df )
Combining Both Approaches:
# Use dictionaries for most parameters obj = {"latitude": 48.1, "longitude": 11.6} data = {"weather": weather_df} # Override specific parameters with explicit values result = pvlib.generate( obj=obj, data=data, power=5000 # This overrides any "power" value in obj )
The method automatically determines whether each parameter belongs in the object dictionary or the data dictionary based on the method’s defined required_keys, optional_keys, required_timeseries, and optional_timeseries attributes.