data_fitting

PV_Circuit_Model.data_fitting

Fit_Parameter

Bases: Artifact

Single fit parameter with constraints and scaling.

Tracks nominal values, bounds, and linear/log scaling for optimization.

Parameters:

Name	Type	Description	Default
name	str	Parameter name.	'variable'
value	float	Current parameter value.	0.0
nominal_value	Optional[float]	Nominal value for regularization.	None
d_value	Optional[float]	Differential step for sensitivity.	None
abs_min	float	Absolute minimum bound.	-inf
abs_max	float	Absolute maximum bound.	inf
is_log	bool	If True, parameter is stored in log10 space.	False

Returns:

Name	Type	Description
Fit_Parameter		The constructed fit parameter.

Example

from PV_Circuit_Model.data_fitting import Fit_Parameter
param = Fit_Parameter(name="Rs", value=0.1)
param.get_parameter()

Fit_Parameters

Bases: Artifact

Collection of Fit_Parameter objects with helper utilities.

Supports enabling/disabling parameters, bounds, and differential mode.

Parameters:

Name	Type	Description	Default
fit_parameters	Optional[List[Fit_Parameter]]	Predefined parameters.	None
names	Optional[Sequence[str]]	Names to create default parameters.	None

Returns:

Name	Type	Description
Fit_Parameters		The constructed parameter collection.

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs", "Rsh"])
params.num_of_parameters()

initialize_from_sample(sample)

Set a reference sample for parameter application.

Parameters:

Name	Type	Description	Default
sample	Any	Reference object used during fitting.	required

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters()
params.initialize_from_sample(sample=None)
params.ref_sample is None

add_fit_parameter(fit_parameter)

Add a Fit_Parameter to the collection.

Parameters:

Name	Type	Description	Default
fit_parameter	Fit_Parameter	Parameter to add.	required

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters, Fit_Parameter
params = Fit_Parameters()
params.add_fit_parameter(Fit_Parameter(name="Rs"))
params.num_of_parameters()

enable_parameter(name=None)

Enable all or a named parameter.

Parameters:

Name	Type	Description	Default
name	Optional[str]	Parameter name to enable; None for all.	None

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.disable_parameter("Rs")
params.enable_parameter("Rs")

disable_parameter(name=None)

Disable all or a named parameter.

Parameters:

Name	Type	Description	Default
name	Optional[str]	Parameter name to disable; None for all.	None

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.disable_parameter("Rs")
params.fit_parameters[0].enabled

delete_fit_parameter(name)

Remove the first parameter with a matching name.

Parameters:

Name	Type	Description	Default
name	str	Parameter name to delete.	required

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.delete_fit_parameter("Rs")
params.num_of_parameters()

get(attribute, names=None, enabled_only=True)

Get an attribute list (or scalar if single) for parameters.

Parameters:

Name	Type	Description	Default
attribute	str	Attribute name or special key ("min", "max").	required
names	Optional[Union[str, List[str]]]	Filter by name(s).	None
enabled_only	bool	If True, include only enabled parameters.	True

Returns:

Type	Description
Union[Any, List[Any]]	Union[Any, List[Any]]: Attribute values.

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.get("name")

set(attribute, values, names=None, enabled_only=True)

Set an attribute for parameters.

Parameters:

Name	Type	Description	Default
attribute	str	Attribute name or auxiliary key.	required
values	Union[Any, List[Any], ndarray]	Values to set.	required
names	Optional[Union[str, List[str]]]	Filter by name(s).	None
enabled_only	bool	If True, apply only to enabled parameters.	True

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.set("value", 0.1)
params.get("value")

initialize(values, names=None, enabled_only=True)

Initialize current and nominal values together.

Parameters:

Name	Type	Description	Default
values	Union[Any, List[Any], ndarray]	Values to set.	required
names	Optional[Union[str, List[str]]]	Filter by name(s).	None
enabled_only	bool	If True, apply only to enabled parameters.	True

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.initialize(0.1)
params.get("nominal_value")

set_nominal()

Set all nominal values to their current values.

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.set("value", 0.2)
params.set_nominal()
params.get("nominal_value")

set_d_value()

Reset differential steps for all parameters.

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.set_d_value()
params.get("d_value")

set_differential(which=-1, enabled_only=True)

Enable differential mode for a single parameter index.

Parameters:

Name	Type	Description	Default
which	int	Index of parameter to mark; -1 disables.	-1
enabled_only	bool	If True, count only enabled parameters.	True

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs", "Rsh"])
params.set_differential(0)
params.is_differential

get_parameters()

Return a dictionary of effective parameter values.

Returns:

Type	Description
Dict[str, float]	Dict[str, float]: Name-to-value mapping.

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.get_parameters()

limit_order_of_mag(order_of_mag=1.0)

Limit parameter bounds by order of magnitude.

Parameters:

Name	Type	Description	Default
order_of_mag	float	Number of decades to allow.	1.0

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.limit_order_of_mag(1.0)
params.get("min")

num_of_parameters()

Return the total number of parameters.

Returns:

Name	Type	Description
int	int	Total parameter count.

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs", "Rsh"])
params.num_of_parameters()

num_of_enabled_parameters()

Return the number of enabled parameters.

Returns:

Name	Type	Description
int	int	Enabled parameter count.

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters(names=["Rs"])
params.num_of_enabled_parameters()

apply_to_ref(aux_info)

Apply parameters to the reference sample.

Warning

This is a stub meant to be overridden in subclasses.

Parameters:

Name	Type	Description	Default
aux_info	Any	Auxiliary data for applying parameters.	required

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters()
params.apply_to_ref(aux_info=None)

apply_to_device(device)

Apply parameters to a device instance.

Warning

This is a stub meant to be overridden in subclasses.

Parameters:

Name	Type	Description	Default
device	Any	Device object to modify.	required

Returns:

Type	Description
None	None

Example

from PV_Circuit_Model.data_fitting import Fit_Parameters
params = Fit_Parameters()
params.apply_to_device(device=None)

Fit_Dashboard

Dashboard for visualizing fit progress and comparisons.

Supports multiple plot types and notebook or GUI rendering.

Parameters:

Name	Type	Description	Default
nrows	int	Number of subplot rows.	required
ncols	int	Number of subplot columns.	required
save_file_name	Optional[str]	Base filename for saved images.	None
measurements	Optional[list]	Measurement objects to display.	None
RMS_errors	Optional[list]	Error values for plotting progress.	None

Returns:

Name	Type	Description
Fit_Dashboard		The constructed dashboard.

Example

from PV_Circuit_Model.data_fitting import Fit_Dashboard
dashboard = Fit_Dashboard(1, 1)
dashboard.close()

Interactive_Fit_Dashboard

Bases: Fit_Dashboard

Interactive Tk-based dashboard with parameter sliders.

Warning

Requires a Tkinter environment; will not work in headless mode.

Parameters:

Name	Type	Description	Default
measurement_samples	Any	Samples with measurements to simulate.	required
fit_parameters	Fit_Parameters	Parameter collection to adjust.	required
nrows	Optional[int]	Number of subplot rows.	None
ncols	Optional[int]	Number of subplot columns.	None
ref_fit_dashboard	Optional[Fit_Dashboard]	Reference dashboard settings.	None
**kwargs	Any	Optional configuration, including apply_function.	{}

Returns:

Name	Type	Description
Interactive_Fit_Dashboard		The constructed interactive dashboard.

Example

from PV_Circuit_Model.data_fitting import Interactive_Fit_Dashboard, Fit_Parameters
params = Fit_Parameters(names=["Rs", "Rsh"])
dashboard = Interactive_Fit_Dashboard([], params, nrows=1, ncols=1)
dashboard.close()

compare_experiments_to_simulations(fit_parameters, measurement_samples, aux)

Compare experimental measurements with simulated results.

Applies fit parameters to a reference sample and computes error vectors.

Parameters:

Name	Type	Description	Default
fit_parameters	Optional[Fit_Parameters]	Parameters to apply.	required
measurement_samples	Any	Measurement samples or collection.	required
aux	Dict[str, Any]	Auxiliary options for comparison.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Output dict with error and baseline vectors.

Example

from PV_Circuit_Model.data_fitting import compare_experiments_to_simulations, Fit_Parameters
output = compare_experiments_to_simulations(Fit_Parameters(), [], {})
"error_vector" in output or "differential_vector" in output

uncertainty_analysis(M, Y)

Estimate parameter resolution and error using SVD.

Parameters:

Name	Type	Description	Default
M	ndarray	Sensitivity matrix.	required
Y	ndarray	Error vector.	required

Returns:

Type	Description
Tuple[ndarray, ndarray]	Tuple[np.ndarray, np.ndarray]: (resolution, error) arrays.

Example

import numpy as np
from PV_Circuit_Model.data_fitting import uncertainty_analysis
M = np.eye(2)
Y = np.array([0.1, 0.2])
uncertainty_analysis(M, Y)

construct_M(iteration, measurement_samples, fit_parameters, comparison_function, aux)

Construct a differential measurement output for a fit iteration.

Parameters:

Name	Type	Description	Default
iteration	int	Iteration index for differential parameter selection.	required
measurement_samples	Any	Measurement sample collection.	required
fit_parameters	Fit_Parameters	Parameters to perturb.	required
comparison_function	Any	Function to compare measurements.	required
aux	Dict[str, Any]	Auxiliary configuration for progress.	required

Returns:

Type	Description
Dict[str, Any]	Dict[str, Any]: Output from the comparison function.

Example

from PV_Circuit_Model.data_fitting import construct_M, Fit_Parameters
params = Fit_Parameters(names=["Rs"])
def compare(fp, samples, aux):
    return {"differential_vector": [0.0]}
construct_M(0, [], params, compare, {})

fit_routine(measurement_samples, fit_parameters, routine_functions, fit_dashboard=None, aux=None, num_of_epochs=10, enable_pbar=True, parallel=False)

Run an iterative fitting routine over measurement samples.

Supports optional parallel evaluation and fit dashboards.

Parameters:

Name	Type	Description	Default
measurement_samples	Any	Measurement samples to fit.	required
fit_parameters	Fit_Parameters	Parameter collection to update.	required
routine_functions	Dict[str, Any]	Functions for comparison and update.	required
fit_dashboard	Optional[Fit_Dashboard]	Optional visualization dashboard.	None
aux	Optional[Dict[str, Any]]	Auxiliary configuration.	None
num_of_epochs	int	Number of fitting epochs.	10
enable_pbar	bool	If True, show progress bar updates.	True
parallel	bool	If True, use joblib parallel evaluation.	False

Returns:

Name	Type	Description
Any	Any	Output from the final comparison function or intermediate data.

Example

from PV_Circuit_Model.data_fitting import fit_routine, Fit_Parameters
params = Fit_Parameters(names=["Rs"])
routine_functions = {"comparison_function": lambda fp, s, a: {"error_vector": [0.0], "baseline_vector": [0.0], "measurement_samples": s},
                     "update_function": lambda M, Y, fp, a: None}
fit_routine([], params, routine_functions, num_of_epochs=1)