Configuration Options

ADEPT needs a yaml file with the following datamodel to run the simulation. The datamodel is defined in the following class.

class adept._lpse2d.datamodel.ConfigModel(*, density: DensityModel, drivers: DriversModel, grid: GridModel, mlflow: MLFlowModel, save: SaveModel, solver: str, terms: TermsModel, units: UnitsModel)Source 

Each of the objects used to initialize this datamodel can be treated just like dictionaries. Each dictionary needs to be compiled into a megadictionary that is passed to the solver. The yaml configs accomplish this because a yaml is also a nested dictionary. The following documents those classes

High Level

These are the high level configuration options for the LPSE2D solver. Each of these either contains a fundamental type such as bool, int, float, or str or is another nested datamodel which can be treated just like a dictionary.

class adept._lpse2d.datamodel.UnitsModel(*, atomic_number: int, envelope_density: float, ionization_state: int, laser_intensity: str, laser_wavelength: str, reference_electron_temperature: str, reference_ion_temperature: str)Source 

class adept._lpse2d.datamodel.DensityModel(*, basis: str, gradient_scale_length: str, max: float, min: float, noise: NoiseModel)Source : Density profile for the simulation

class adept._lpse2d.datamodel.GridModel(*, boundary_abs_coeff: float, boundary_width: str, low_pass_filter: float, dt: str, dx: str, tmax: str, tmin: str, ymax: str, ymin: str)Source : Define the grid for the simulation

class adept._lpse2d.datamodel.SaveModel(*, t: TimeSaveModel, x: XSaveModel, y: YSaveModel)Source 

class adept._lpse2d.datamodel.MLFlowModel(*, experiment: str, run: str)Source 

class adept._lpse2d.datamodel.TermsModel(*, epw: EPWModel, zero_mask: bool)Source 

Low Level

The documentation for the nested datamodels is still TBD. To investigate them further, go to the source code.