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piel.parametric

piel.parametric#

Module Contents#

Functions#

single_parameter_sweep(base_design_configuration, ...)

This function takes a base_design_configuration dictionary and sweeps a single parameter over a list of values. It returns a list of dictionaries that correspond to the parameter sweep.

multi_parameter_sweep(→ list)

This multiparameter sweep is pretty cool, as it will generate designer list of dictionaries that comprise of all the possible combinations of your parameter sweeps. For example, if you are sweeping parameter_1 = np.arange(0, 2) = array([0, 1]), and parameter_2 = np.arange(2, 4) = array([2, 3]), then this function will generate list of dictionaries based on the default_design dictionary, but that will comprise of all the potential parameter combinations within this list.
single_parameter_sweep(base_design_configuration: dict, parameter_name: str, parameter_sweep_values: list)[source]#

This function takes a base_design_configuration dictionary and sweeps a single parameter over a list of values. It returns a list of dictionaries that correspond to the parameter sweep.

Parameters:

  • base_design_configuration (dict) – Base design configuration dictionary.

  • parameter_name (str) – Name of parameter to sweep.

  • parameter_sweep_values (list) – List of values to sweep.

Returns:

List of dictionaries that correspond to the parameter sweep.

Return type:

parameter_sweep_design_dictionary_array(list)

multi_parameter_sweep(base_design_configuration: dict, parameter_sweep_dictionary: dict) → list[source]#

This multiparameter sweep is pretty cool, as it will generate designer list of dictionaries that comprise of all the possible combinations of your parameter sweeps. For example, if you are sweeping parameter_1 = np.arange(0, 2) = array([0, 1]), and parameter_2 = np.arange(2, 4) = array([2, 3]), then this function will generate list of dictionaries based on the default_design dictionary, but that will comprise of all the potential parameter combinations within this list.

For the example above, there arould be 4 combinations [(0, 2), (0, 3), (1, 2), (1, 3)].

If you were instead sweeping for parameter_1 = np.arange(0, 5) and parameter_2 = np.arange(0, 5), the dictionary generated would correspond to these parameter combinations of::

[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (1, 0), (1, 1), (1, 2), (1, 3), (1, 4), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (3, 0), (3, 1), (3, 2), (3, 3), (3, 4), (4, 0), (4, 1), (4, 2), (4, 3), (4, 4)].

Make sure to use the parameter_names from default_design when writing up the parameter_sweep dictionary key name.

Example project_structure formats:

example_parameter_sweep_dictionary = {
    "parameter_1": np.arange(1, -40, 1),
    "parameter_2": np.arange(1, -40, 1),
}

example_base_design_configuration = {
    "parameter_1": 10.0,
    "parameter_2": 40.0,
    "parameter_3": 0,
}
Parameters:

  • base_design_configuration (dict) – Dictionary of the default design configuration.

  • parameter_sweep_dictionary (dict) – Dictionary of the parameter sweep. The keys should be the same as the keys in the base_design_configuration dictionary.

Returns:

List of dictionaries that comprise of all the possible combinations of your parameter sweeps.

Return type:

parameter_sweep_design_dictionary_array(list)

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