optimpv.DDfits package

Subpackages

Submodules

optimpv.DDfits.CVAgent module

CVAgent class for steady-state CV simulations

class optimpv.DDfits.CVAgent.CVAgent(params, X, y, session_path, Vmin=0.5, Vmax=1.0, V_step=0.1, freq=10000.0, G_frac=1, del_V=0.01, simulation_setup=None, exp_format='CV', metric='mse', loss='linear', threshold=100, minimize=True, yerr=None, weight=None, tracking_metric=None, tracking_loss=None, tracking_exp_format=None, tracking_X=None, tracking_y=None, tracking_weight=None, name='CV', **kwargs)[source]

Bases: SIMsalabimAgent

CVAgent class for Capacitance-Voltage simulations with SIMsalabim

Parameters:

  • params (list of Fitparam() objects) – List of Fitparam() objects.

  • X (array-like) – 1-D or 2-D array containing the voltage values.

  • y (array-like) – 1-D array containing the current values.

  • session_path (str) – Path to the session directory.

  • Vmin (float, optional) – minimum voltage, by default 0.

  • Vmax (float, optional) – maximum voltage, by default 1.2.

  • V_step (float, optional) – Voltage difference, determines at which voltages the capacitance is determined, by default 0.1.

  • freq (float, optional) – Frequency of the CV measurement [Hz], by default 1e4.

  • G_frac (float, optional) – Fractional light intensity, by default 1.

  • del_V (float, optional) – Voltage step for the impedance simulation, by default 0.01.

  • simulation_setup (str, optional) – Path to the simulation setup file, if None then use the default file ‘simulation_setup.txt’in the session_path directory, by default None.

  • exp_format (str or list of str, optional) – Format of the CV data, possible values are: ‘CV’, ‘MottSchottky’, by default ‘CV’.

  • metric (str or list of str, optional) – Metric to evaluate the model, see optimpv.general.calc_metric for options, by default ‘mse’.

  • loss (str or list of str, optional) – Loss function to use, see optimpv.general.loss_function for options, by default ‘linear’.

  • threshold (int or list of int, optional) – Threshold value for the loss function used when doing multi-objective optimization, by default 100.

  • minimize (bool or list of bool, optional) – If True then minimize the loss function, if False then maximize the loss function (note that if running a fit minize should be True), by default True.

  • yerr (array-like or list of array-like, optional) – Errors in the current values, by default None.

  • weight (array-like or list of array-like, optional) – Weights used for fitting if weight is None and yerr is not None, then weight = 1/yerr**2, by default None.

  • tracking_metric (str or list of str, optional) – Additional metrics to track and report in run_Ax output, by default None.

  • tracking_loss (str or list of str, optional) – Loss functions to apply to tracking metrics, by default None.

  • tracking_exp_format (str or list of str, optional) – Experimental formats for tracking metrics, by default None.

  • tracking_X (array-like or list of array-like, optional) – X values for tracking metrics, by default None.

  • tracking_y (array-like or list of array-like, optional) – y values for tracking metrics, by default None.

  • tracking_weight (array-like or list of array-like, optional) – Weights for tracking metrics, by default None.

  • name (str, optional) – Name of the agent, by default ‘CV’.

  • **kwargs (dict) – Additional keyword arguments.

reformat_CV_data(df, X, exp_format='CV')[source]

Reformat the data depending on the exp_format and X values Also interpolates the data if the simulation did not return the same points as the experimental data (i.e. if some points did not converge)

Parameters:

  • df (dataframe) – Dataframe with the CV dara from run_CV_simulation function.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • exp_format (str, optional) – Format of the experimental data, by default ‘CV’.

Returns:

Tuple with the reformatted Xfit and yfit values.

Return type:

tuple

Raises:

ValueError – If the exp_format is not valid.

run(parameters, X=None, exp_format='CV')[source]

Run the simulation with the parameters and return an array with the simulated values in the format specified by exp_format (default is ‘Cf’)

Parameters:

  • parameters (dict) – Dictionary with the parameter names and values.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • exp_format (str, optional) – Format of the experimental data, by default ‘Cf’.

Returns:

1-D array with the simulated current values.

Return type:

array-like

run_Ax(parameters)[source]

Function to run the simulation with the parameters and return the target metric value for Ax optimization

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dictionary with the target metric value.

Return type:

dict

run_CV_simulation(parameters)[source]

Run the simulation with the parameters and return the simulated values

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dataframe with the simulated CV values.

Return type:

dataframe

target_metric(y, yfit, metric_name, X=None, Xfit=None, weight=None)[source]

Calculate the target metric depending on self.metric

Parameters:

  • y (array-like) – 1-D array containing the target values.

  • yfit (array-like) – 1-D array containing the fitted values.

  • metric_name (str) – Metric to evaluate the model, see optimpv.general.calc_metric for options.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • Xfit (array-like, optional) – 1-D array containing the x axis values, by default None.

  • weight (array-like, optional) – 1-D array containing the weights, by default None.

Returns:

Target metric value.

Return type:

float

optimpv.DDfits.HysteresisAgent module

HysteresisAgent class for transient hysteresis JV simulations

class optimpv.DDfits.HysteresisAgent.HysteresisAgent(params, X, y, session_path, Vmin=0, Vmax=1.2, scan_speed=0.1, steps=100, direction=1, G_frac=1, simulation_setup=None, exp_format='JV', metric='mse', loss='linear', threshold=100, minimize=True, yerr=None, weight=None, tracking_metric=None, tracking_loss=None, tracking_exp_format=None, tracking_X=None, tracking_y=None, tracking_weight=None, name='Hyst', **kwargs)[source]

Bases: SIMsalabimAgent

HysteresisAgent class for JV hysteresis simulations with SIMsalabim

Parameters:

  • params (list of Fitparam() objects) – List of Fitparam() objects.

  • X (array-like) – 1-D or 2-D array containing the voltage values.

  • y (array-like) – 1-D array containing the current values.

  • session_path (str) – Path to the session directory.

  • Vmin (float, optional) – minimum voltage, by default 0.

  • Vmax (float, optional) – maximum voltage, by default 1.2.

  • scan_speed (float, optional) – Voltage scan speed [V/s], by default 0.1.

  • steps (int, optional) – Number of time steps, by default 100.

  • direction (integer, optional) – Perform a forward-backward (1) or backward-forward scan (-1), by default 1.

  • G_frac (float, optional) – Fractional light intensity, by default 1.

  • simulation_setup (str, optional) – Path to the simulation setup file, if None then use the default file ‘simulation_setup.txt’in the session_path directory, by default None.

  • exp_format (str or list of str, optional) – Format of the hysteresis data, possible values are: ‘JV’, by default ‘JV’.

  • metric (str or list of str, optional) – Metric to evaluate the model, see optimpv.general.calc_metric for options, by default ‘mse’.

  • loss (str or list of str, optional) – Loss function to use, see optimpv.general.loss_function for options, by default ‘linear’.

  • threshold (int or list of int, optional) – Threshold value for the loss function used when doing multi-objective optimization, by default 100.

  • minimize (bool or list of bool, optional) – If True then minimize the loss function, if False then maximize the loss function (note that if running a fit minize should be True), by default True.

  • yerr (array-like or list of array-like, optional) – Errors in the current values, by default None.

  • weight (array-like or list of array-like, optional) – Weights used for fitting if weight is None and yerr is not None, then weight = 1/yerr**2, by default None.

  • tracking_metric (str or list of str, optional) – Additional metrics to track and report in run_Ax output, by default None.

  • tracking_loss (str or list of str, optional) – Loss functions to apply to tracking metrics, by default None.

  • tracking_exp_format (str or list of str, optional) – Experimental formats for tracking metrics, by default None.

  • tracking_X (array-like or list of array-like, optional) – X values for tracking metrics, by default None.

  • tracking_y (array-like or list of array-like, optional) – y values for tracking metrics, by default None.

  • tracking_weight (array-like or list of array-like, optional) – Weights for tracking metrics, by default None.

  • name (str, optional) – Name of the agent, by default ‘Hyst’.

  • **kwargs (dict) – Additional keyword arguments.

reformat_hysteresis_data(df, X, exp_format='JV')[source]

Reformat the data depending on the exp_format and X values Also interpolates the data if the simulation did not return the same points as the experimental data (i.e. if some points did not converge)

Parameters:

  • df (dataframe) – Dataframe with the hysteresis dara from run_hysteresis_simulation function.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • exp_format (str, optional) – Format of the experimental data, by default ‘JV’.

Returns:

Tuple with the reformatted Xfit and yfit values.

Return type:

tuple

Raises:

ValueError – If the exp_format is not valid.

run(parameters, X=None, exp_format='JV')[source]

Run the simulation with the parameters and return an array with the simulated values in the format specified by exp_format (default is ‘Cf’)

Parameters:

  • parameters (dict) – Dictionary with the parameter names and values.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • exp_format (str, optional) – Format of the experimental data, by default ‘Cf’.

Returns:

1-D array with the simulated current values.

Return type:

array-like

run_Ax(parameters)[source]

Function to run the simulation with the parameters and return the target metric value for Ax optimization

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dictionary with the target metric value.

Return type:

dict

run_hysteresis_simulation(parameters)[source]

Run the simulation with the parameters and return the simulated values

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dataframe with the simulated hysteresis values.

Return type:

dataframe

target_metric(y, yfit, metric_name, X=None, Xfit=None, weight=None)[source]

Calculate the target metric depending on self.metric

Parameters:

  • y (array-like) – 1-D array containing the target values.

  • yfit (array-like) – 1-D array containing the fitted values.

  • metric_name (str) – Metric to evaluate the model, see optimpv.general.calc_metric for options.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • Xfit (array-like, optional) – 1-D array containing the x axis values, by default None.

  • weight (array-like, optional) – 1-D array containing the weights, by default None.

Returns:

Target metric value.

Return type:

float

optimpv.DDfits.IMPSAgent module

IMPSAgent class for steady-state IMPS simulations

class optimpv.DDfits.IMPSAgent.IMPSAgent(params, X, y, session_path, f_min, f_max, f_steps=30, V=0, G_frac=1, GStep=0.05, simulation_setup=None, exp_format='ImY', metric='mse', loss='linear', threshold=100, minimize=True, yerr=None, weight=None, tracking_metric=None, tracking_loss=None, tracking_exp_format=None, tracking_X=None, tracking_y=None, tracking_weight=None, name='IMPS', **kwargs)[source]

Bases: SIMsalabimAgent

IMPSAgent class for IMPS simulations with SIMsalabim

Parameters:

  • params (list of Fitparam() objects) – List of Fitparam() objects.

  • X (array-like) – 1-D or 2-D array containing the voltage values.

  • y (array-like) – 1-D array containing the current values.

  • session_path (str) – Path to the session directory.

  • f_min (float) – Minimum frequency for the IMPS simulation in Hz.

  • f_max (float) – Maximum frequency for the IMPS simulation in Hz.

  • f_steps (float, optional) – Number of frequency steps for the IMPS simulation (log spaced), by default 30.

  • V (float, optional) – Voltage value for the simulation, by default 0.

  • G_frac (float, optional) – Fractional light intensity, by default 1.

  • GStep (float, optional) – Applied generation rate increase at t=0, by default 0.05.

  • simulation_setup (str, optional) – Path to the simulation setup file, if None then use the default file ‘simulation_setup.txt’in the session_path directory, by default None.

  • exp_format (str, optional) – Format of the IMPS data, possible values are: ‘ReY’, ‘ImY’, ‘ColeCole’, by default ‘ImY’.

  • metric (str or list of str, optional) – Metric to evaluate the model, see optimpv.general.calc_metric for options, by default ‘mse’.

  • loss (str or list of str, optional) – Loss function to use, see optimpv.general.loss_function for options, by default ‘linear’.

  • threshold (int or list of int, optional) – Threshold value for the loss function used when doing multi-objective optimization, by default 100.

  • minimize (bool or list of bool, optional) – If True then minimize the loss function, if False then maximize the loss function (note that if running a fit minize should be True), by default True.

  • yerr (array-like or list of array-like, optional) – Errors in the current values, by default None.

  • weight (array-like or list of array-like, optional) – Weights used for fitting if weight is None and yerr is not None, then weight = 1/yerr**2, by default None.

  • tracking_metric (str or list of str, optional) – Additional metrics to track and report in run_Ax output, by default None.

  • tracking_loss (str or list of str, optional) – Loss functions to apply to tracking metrics, by default None.

  • tracking_exp_format (str or list of str, optional) – Experimental formats for tracking metrics, by default None.

  • tracking_X (array-like or list of array-like, optional) – X values for tracking metrics, by default None.

  • tracking_y (array-like or list of array-like, optional) – y values for tracking metrics, by default None.

  • tracking_weight (array-like or list of array-like, optional) – Weights for tracking metrics, by default None.

  • name (str, optional) – Name of the agent, by default ‘IMPS’.

  • **kwargs (dict) – Additional keyword arguments.

reformat_IMPS_data(df, X, exp_format='IMPS')[source]

Reformat the data depending on the exp_format and X values Also interpolates the data if the simulation did not return the same points as the experimental data (i.e. if some points did not converge)

Parameters:

  • df (dataframe) – Dataframe with the IMPS dara from run_IMPS_simulation function.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • exp_format (str, optional) – Format of the experimental data, by default ‘IMPS’.

Returns:

Tuple with the reformatted Xfit and yfit values.

Return type:

tuple

Raises:

ValueError – If the exp_format is not valid.

run(parameters, X=None, exp_format='ImY')[source]

Run the simulation with the parameters and return an array with the simulated values in the format specified by exp_format (default is ‘Cf’)

Parameters:

  • parameters (dict) – Dictionary with the parameter names and values.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • exp_format (str, optional) – Format of the experimental data, by default ‘Cf’.

Returns:

1-D array with the simulated current values.

Return type:

array-like

run_Ax(parameters)[source]

Function to run the simulation with the parameters and return the target metric value for Ax optimization

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dictionary with the target metric value and any tracking metrics.

Return type:

dict

run_IMPS_simulation(parameters)[source]

Run the simulation with the parameters and return the simulated values

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dataframe with the simulated IMPS values.

Return type:

dataframe

target_metric(y, yfit, metric_name, X=None, Xfit=None, sample_weight=None)[source]

Calculate the target metric depending on self.metric

Parameters:

  • y (array-like) – 1-D array containing the target values.

  • yfit (array-like) – 1-D array containing the fitted values.

  • metric_name (str) – Metric to evaluate the model, see optimpv.general.calc_metric for options.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • Xfit (array-like, optional) – 1-D array containing the x axis values, by default None.

  • sample_weight (array-like, optional) – 1-D array containing the weights, by default None.

Returns:

Target metric value.

Return type:

float

optimpv.DDfits.ImpedanceAgent module

ImpedanceAgent class for impedance simulations

class optimpv.DDfits.ImpedanceAgent.ImpedanceAgent(params, X, y, session_path, f_min, f_max, f_steps, V_0, G_frac=1, del_V=0.01, simulation_setup=None, exp_format=['Cf'], metric=['mse'], loss=['linear'], threshold=[100], minimize=[True], yerr=None, weight=None, tracking_metric=None, tracking_loss=None, tracking_exp_format=None, tracking_X=None, tracking_y=None, tracking_weight=None, name='Imp', **kwargs)[source]

Bases: SIMsalabimAgent

ImpedanceAgent class for impedance simulations with SIMsalabim

Parameters:

  • params (list of Fitparam() objects) – List of Fitparam() objects.

  • X (array-like) – 1-D or 2-D array containing the voltage (1st column) and if specified the Gfrac (2nd column) values.

  • y (array-like) – 1-D array containing the current values.

  • session_path (str) – Path to the session directory.

  • f_min (float) – Minimum frequency for the impedance simulation.

  • f_max (float) – Maximum frequency for the impedance simulation.

  • f_steps (int) – Number of frequency steps for the impedance simulation.

  • V_0 (float) – Initial voltage for the impedance simulation.

  • G_frac (float, optional) – Fractional light intensity, by default 1.

  • del_V (float, optional) – Voltage step for the impedance simulation, by default 0.01.

  • simulation_setup (str, optional) – Path to the simulation setup file, if None then use the default file ‘simulation_setup.txt’in the session_path directory, by default None.

  • exp_format (str or list of str, optional) – Format of the impedance data, possible values are: ‘Cf’, ‘Gf’, ‘Nyquist’, ‘BodeImZ’, ‘BodeReZ’, ‘Bode’, by default ‘Cf’.

  • metric (str or list of str, optional) – Metric to evaluate the model, see optimpv.general.calc_metric for options, by default ‘mse’.

  • loss (str or list of str, optional) – Loss function to use, see optimpv.general.loss_function for options, by default ‘linear’.

  • threshold (int or list of int, optional) – Threshold value for the loss function used when doing multi-objective optimization, by default 100.

  • minimize (bool or list of bool, optional) – If True then minimize the loss function, if False then maximize the loss function (note that if running a fit minize should be True), by default True.

  • yerr (array-like or list of array-like, optional) – Errors in the current values, by default None.

  • weight (array-like or list of array-like, optional) – Weights used for fitting if weight is None and yerr is not None, then weight = 1/yerr**2, by default None.

  • tracking_metric (str or list of str, optional) – Additional metrics to track and report in run_Ax output, by default None.

  • tracking_loss (str or list of str, optional) – Loss functions to apply to tracking metrics, by default None.

  • tracking_exp_format (str or list of str, optional) – Experimental formats for tracking metrics, by default None.

  • tracking_X (array-like or list of array-like, optional) – X values for tracking metrics, by default None.

  • tracking_y (array-like or list of array-like, optional) – y values for tracking metrics, by default None.

  • tracking_weight (array-like or list of array-like, optional) – Weights for tracking metrics, by default None.

  • name (str, optional) – Name of the agent, by default ‘Imp’.

  • **kwargs (dict) – Additional keyword arguments.

reformat_impedance_data(df, X, exp_format='Cf')[source]

Reformat the data depending on the exp_format and X values Also interpolates the data if the simulation did not return the same points as the experimental data (i.e. if some points did not converge)

Parameters:

  • df (dataframe) – Dataframe with the impedance dara from run_impedance_simulation function.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • exp_format (str, optional) – Format of the experimental data, by default ‘Cf’.

Returns:

Tuple with the reformatted Xfit and yfit values.

Return type:

tuple

Raises:

ValueError – If the exp_format is not valid.

run(parameters, X=None, exp_format='Cf')[source]

Run the simulation with the parameters and return an array with the simulated values in the format specified by exp_format (default is ‘Cf’)

Parameters:

  • parameters (dict) – Dictionary with the parameter names and values.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • exp_format (str, optional) – Format of the experimental data, by default ‘Cf’.

Returns:

1-D array with the simulated current values.

Return type:

array-like

run_Ax(parameters)[source]

Function to run the simulation with the parameters and return the target metric value for Ax optimization

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dictionary with the target metric value and any tracking metrics.

Return type:

dict

run_impedance_simulation(parameters)[source]

Run the simulation with the parameters and return the simulated values

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dataframe with the simulated impedance values.

Return type:

dataframe

target_metric(y, yfit, metric_name, X=None, Xfit=None, sample_weight=None)[source]

Calculate the target metric depending on self.metric

Parameters:

  • y (array-like) – 1-D array containing the target values.

  • yfit (array-like) – 1-D array containing the fitted values.

  • metric_name (str) – Metric to evaluate the model, see optimpv.general.calc_metric for options.

  • X (array-like, optional) – 1-D array containing the x axis values, by default None.

  • Xfit (array-like, optional) – 1-D array containing the x axis values, by default None.

  • sample_weight (array-like, optional) – 1-D array containing the weights, by default None.

Returns:

Target metric value.

Return type:

float

optimpv.DDfits.JVAgent module

JVAgent class for steady-state JV simulations

class optimpv.DDfits.JVAgent.JVAgent(params, X, y, session_path, simulation_setup=None, exp_format=['JV'], metric=['mse'], loss=['linear'], threshold=[100], minimize=[True], yerr=None, weight=None, tracking_metric=None, tracking_loss=None, tracking_exp_format=None, tracking_X=None, tracking_y=None, tracking_weight=None, name='JV', **kwargs)[source]

Bases: SIMsalabimAgent

JVAgent class for steady-state JV simulations with SIMsalabim

Parameters:

  • params (list of Fitparam() objects) – List of Fitparam() objects.

  • X (array-like) – 1-D or 2-D array containing the voltage (1st column) and if specified the Gfrac (2nd column) values.

  • y (array-like) – 1-D array containing the current values.

  • session_path (str) – Path to the session directory.

  • simulation_setup (str, optional) – Path to the simulation setup file, if None then use the default file ‘simulation_setup.txt’in the session_path directory, by default None.

  • exp_format (str or list of str, optional) – Format of the experimental data, by default ‘JV’.

  • metric (str or list of str, optional) – Metric to evaluate the model, see optimpv.general.calc_metric for options, by default ‘mse’.

  • loss (str or list of str, optional) – Loss function to use, see optimpv.general.loss_function for options, by default ‘linear’.

  • threshold (int or list of int, optional) – Threshold value for the loss function used when doing multi-objective optimization, by default 100.

  • minimize (bool or list of bool, optional) – If True then minimize the loss function, if False then maximize the loss function (note that if running a fit minize should be True), by default True.

  • yerr (array-like or list of array-like, optional) – Errors in the current values, by default None.

  • weight (array-like or list of array-like, optional) – Weights used for fitting if weight is None and yerr is not None, then weight = 1/yerr**2, by default None.

  • tracking_metric (str or list of str, optional) – Additional metrics to track and report in run_Ax output, by default None.

  • tracking_loss (str or list of str, optional) – Loss functions to apply to tracking metrics, by default None.

  • tracking_exp_format (str or list of str, optional) – Experimental formats for tracking metrics, by default None.

  • tracking_X (array-like or list of array-like, optional) – X values for tracking metrics, by default None.

  • tracking_y (array-like or list of array-like, optional) – y values for tracking metrics, by default None.

  • tracking_weight (array-like or list of array-like, optional) – Weights for tracking metrics, by default None.

  • name (str, optional) – Name of the agent, by default ‘JV’.

  • **kwargs (dict) – Additional keyword arguments.

reformat_JV_data(df, X, exp_format='JV')[source]

Reformat the data depending on the exp_format and X values Also interpolates the data if the simulation did not return the same points as the experimental data (i.e. if some points did not converge)

Parameters:

  • df (dataframe) – Dataframe with the JV data from run_JV function.

  • X (array-like) – 1-D or 2-D array containing the voltage (1st column) and if specified the Gfrac (2nd column) values.

  • exp_format (str, optional) – Format of the experimental data, by default ‘JV’

Returns:

Tuple with the reformatted Xfit and yfit values.

Return type:

tuple

Raises:

ValueError – If the exp_format is not ‘JV’

run(parameters, X=None, exp_format='JV')[source]

Run the simulation with the parameters and return an array with the simulated values in the format specified by exp_format (default is ‘JV’)

Parameters:

  • parameters (dict) – Dictionary with the parameter names and values.

  • X (array-like, optional) – 1-D or 2-D array containing the voltage (1st column) and if specified the Gfrac (2nd column) values, it must match the X values used for the specified exp_format, by default None

  • exp_format (str, optional) – Format of the experimental data, by default ‘JV’

Returns:

1-D array with the simulated current values.

Return type:

array-like

run_Ax(parameters)[source]

Function to run the simulation with the parameters and return the target metric value for Ax optimization

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dictionary with the target metric value and any tracking metrics.

Return type:

dict

run_JV(parameters)[source]

Run the simulation with the parameters and return the simulated values

Parameters:

parameters (dict) – Dictionary with the parameter names and values.

Returns:

Dataframe with the simulated JV data.

Return type:

dataframe

target_metric(y, yfit, metric_name, X=None, Xfit=None, weight=None)[source]

Calculate the target metric depending on self.metric

Parameters:

  • y (array-like) – 1-D array containing the current values.

  • yfit (array-like) – 1-D array containing the fitted current values.

  • metric_name (str) – Metric to evaluate the model, see optimpv.general.calc_metric for options.

  • X (array-like, optional) – 1-D or 2-D array containing the voltage (1st column) and if specified the Gfrac (2nd column) values, by default None.

  • Xfit (array-like, optional) – 1-D or 2-D array containing the voltage (1st column) and if specified the Gfrac (2nd column) values, by default None.

  • weight (array-like, optional) – Weights used for fitting, by default None.

Returns:

Target metric value.

Return type:

float

optimpv.DDfits.SIMsalabimAgent module

Provides general functionality for Agent objects for SIMsalabim simulations

class optimpv.DDfits.SIMsalabimAgent.SIMsalabimAgent[source]

Bases: BaseAgent

Provides general functionality for Agent objects for SIMsalabim simulations

Gamma_pre_reset(cmd_pars, custom_pars)[source]

Prepare the Langevin pre-factor parameters for the SIMsalabim simulation but use it to calculate k_direct instead of passing preLangevin to SIMsalabim.

Parameters:

  • cmd_pars (list of dict) – list of dictionaries with the following form {‘par’: string, ‘val’: string}

  • custom_pars (list of dict) – list of dictionaries with the following form {‘par’: string, ‘val’: string}

Returns:

list of dictionaries with the following form {‘par’: string, ‘val’: string}

Return type:

list of dict

ambi_param_transform(param, value, cmd_pars, no_transform=False)[source]

Transform the ambipolar parameters to the SIMsalabim format example: mu_ions -> mu_anion and mu_cation N_ions -> N_anion and N_cation mu_np -> mu_n and mu_p C_np_bulk -> C_n_bulk and C_p_bulk C_np_int -> C_n_int and C_p_int

Parameters:

  • param (Fitparam() object) – Fitparam() object

  • value (float) – value of the parameter

  • cmd_pars (list of dict) – list of dictionaries with the following form {‘par’: string, ‘val’: string}

Returns:

list of dictionaries with the following form {‘par’: string, ‘val’: string}

Return type:

list of dict

check_duplicated_parameters(cmd_pars)[source]

Check if there are duplicated parameters in the cmd_pars

Parameters:

cmd_pars (list of dict) – list of dictionaries with the following form {‘par’: string, ‘val’: string}

Raises:

ValueError – There are duplicated parameters in the cmd_pars

convert_parameter_to_basename(name, value)[source]

Convert the parameter value to its basename if it is a file

Parameters:

  • name (str) – parameter name

  • value (str or float or int) – parameter value

Returns:

parameter value in its basename

Return type:

str

energy_level_offsets(custom_pars, clean_pars)[source]

Convert the energy level offsets to the SIMsalabim format energy levels

Parameters:

  • custom_pars (list of dict) – list of dictionaries these contain all the parameters that are not explicitely in the SIMsalabim format and not in the ambipolar format (see ambi_param_transform). The dictionaries are of the form {‘par’: string, ‘val’: string}

  • clean_pars (list of dict) – list of dictionaries these contain all the parameters that are explicitely in the SIMsalabim format. The dictionaries are of the form {‘par’: string, ‘val’: string}

Returns:

list of dictionaries these contain all the parameters converted to the SIMsalabim format. The dictionaries are of the form {‘par’: string, ‘val’: string}

Return type:

list of dict

Raises:

  • ValueError – Energy level offset between conduction bands must be defined from right to left

  • ValueError – Energy level offset between valence bands must be defined from left to right

  • ValueError – The offset of the work function of the left electrode with respect to the conduction band must be negative

  • ValueError – The offset of the work function of the left electrode with respect to the valence band must be positive

  • ValueError – The offset of the work function of the right electrode with respect to the conduction band must be negative

  • ValueError – The offset of the work function of the right electrode with respect to the valence band must be positive

get_SIMsalabim_clean_cmd(parameters, sim_type='simss')[source]

Get the command line arguments for the SIMsalabim simulation with properly formatted parameters

Parameters:

  • parameters (dict or list of Fitparam() objects) – dictionary of parameter names and values or list of Fitparam() objects

  • sim_type (str, optional) – type of simulation (‘simss’ or ‘zimt’), by default ‘simss’

Returns:

command line arguments for the SIMsalabim simulation

Return type:

str

get_SIMsalabim_clean_cmd_pars(parameters)[source]

Get the clean cmd_pars list for the SIMsalabim simulation with properly formatted parameters

Parameters:

parameters (dict or list of Fitparam() objects) – dictionary of parameter names and values or list of Fitparam() objects

Returns:

list of dictionaries containing the parameters that have a direct match in the SIMsalabim parameter files with the following form {‘par’: string, ‘val’: string}

Return type:

list of dict

Raises:

  • ValueError – Parameter name is not defined in the SIMsalabim parameter files. Please check the parameter names.

  • ValueError – Parameter name is defined in both the parameters and cmd_pars. Please remove one of them.

  • UserWarning – Parameter name is not defined in the SIMsalabim parameter files. Please check the parameter names. The optimization will proceed but the parameter will not be used by SIMsalabim.

is_inputFile(name, value)[source]

Check if the parameter is an input file

Parameters:

  • name (str) – parameter name

  • value (str or float or int) – parameter value

Returns:

True if the parameter is an input file, False otherwise

Return type:

bool

package_SIMsalabim_files(parameters, sim_type, save_path=None)[source]

Package the SIMsalabim files for the simulation

Parameters:

  • parameters (dict or list of Fitparam() objects) – dictionary of parameter names and values or list of Fitparam() objects

  • sim_type (str) – type of simulation (‘simss’ or ‘zimt’)

  • save_path (str, optional) – path to save the simulation files, if None, it will be saved in the session_path/tmp_results, by default None

Raises:

ValueError – if sim_type is not ‘simss’ or ‘zimt’

prepare_cmd_pars(parameters, custom_pars, clean_pars, VarNames)[source]

Prepare the cmd_pars for the SIMsalabim simulation

Parameters:

  • parameters (dict) – dictionary of parameter names and values

  • custom_pars (list of dict) – list of dictionaries containing the custom parameters that do not have a direct match in the SIMsalabim parameter files with the following form {‘par’: string, ‘val’: string}

  • clean_pars (list of dict) – list of dictionaries containing the parameters that have a direct match in the SIMsalabim parameter files with the following form {‘par’: string, ‘val’: string}

  • VarNames (list of str) – list of parameter names

Returns:

  • list of dict – list of dictionaries containing the custom parameters that do not have a direct match in the SIMsalabim parameter files with the following form {‘par’: string, ‘val’: string}

  • list of dict – list of dictionaries containing the parameters that have a direct match in the SIMsalabim parameter files with the following form {‘par’: string, ‘val’: string}

  • list of str – list of parameter names

Raises:

  • ValueError – If the parameter name is not in the self.params list

  • ValueError – If the parameter name is in both the parameters and cmd_pars

Module contents