chung_and_everhart module
Create the Chung and Everhart model, to compute SEs emission distribution.
You will need to provice emission energy distribution measurements.
Model parameters
Parameter |
Name |
Unit |
Initial |
Description |
|---|---|---|---|---|
\(W_f\) |
W_f |
\(\mathrm{eV}\) |
\(8.0\) |
Material work function. |
\(k\) |
norm |
\(\mathrm{1}\) |
\(1.0\) |
Distribution re-normalization constant. |
- class ChungEverhart(parameters_values: dict[str, Any] | None = None)
Bases:
ModelDefine the Chung and Everhart model, defined in [CE74].
- emission_data_types: list[Literal['Emission Yield', 'Emission Energy', 'Emission Angle']] = ['Emission Energy']
- model_config: ModelConfig = ModelConfig(emission_yield_files=(), emission_energy_files=('all',), emission_angle_files=())
- initial_parameters: dict[str, dict[str, str | float | bool]] = {'W_f': {'description': 'Material work function.', 'lower_bound': 0.0, 'markdown': 'W_f', 'unit': 'eV', 'value': 8.0}, 'norm': {'description': 'Distribution re-normalization constant.', 'lower_bound': 0.0, 'markdown': 'k', 'unit': '1', 'value': 1.0}}
- __init__(parameters_values: dict[str, Any] | None = None) None
Instantiate the object.
- Parameters:
parameters_values – Contains name of parameters and associated value. If provided, will override the default values set in
initial`_parameters.
- parameters: ChungEverhartParameters
- get_data(population: Literal['SE', 'EBE', 'IBE', 'all'], emission_data_type: Literal['Emission Yield', 'Emission Energy', 'Emission Angle'], energy: ndarray[tuple[Any, ...], dtype[float64]], theta: ndarray[tuple[Any, ...], dtype[float64]], *args, **kwargs) DataFrame | None
Return desired data according to current model.
Will return a dataframe only if the SEs energy distribution is asked.
- find_optimal_parameters(data_matrix: DataMatrix, **kwargs) None
Fit model parameters on measurements.
- _abc_impl = <_abc._abc_data object>