graphinglib.FitFromFOTF.__init__

FitFromFOTF.__init__(curve_to_be_fit: Curve | Scatter, label: str | None = None, guesses: TypeAliasForwardRef('ArrayLike') | None = None, color: str | Inherit = Inherit, line_width: int | Inherit = Inherit, line_style: str | Inherit = Inherit, alpha: float | Inherit = Inherit, max_iterations: int = 10000) → None

Create a curve fit (continuous Curve) from an existing Curve object using a first order transfer function (FOTF) fit.

Fits a first order transfer function of the form \(f(x) = K \left(1 - e^{-\frac{t}{\tau}}\right)\) to the given curve. All standard Curve attributes and methods are available.

Parameters:

curve_to_be_fitCurve or Scatter

The object to be fit.

labelstr, optional

Label to be displayed in the legend.

guessesArrayLike, optional

Initial guesses for the parameters of the fit. Order is K, tau.

colorstr

Color of the curve. Default depends on the figure_style configuration.

line_widthint

Line width of the curve. Typical range is 0.5 to 4. Default depends on the figure_style configuration.

line_stylestr

Line style of the curve. Values include "-", "--", "-.", ":", "solid", "dashed", "dashdot", and "dotted". Default depends on the figure_style configuration.

alphafloat

Opacity of the curve. Range is 0 (transparent) to 1 (opaque). Default depends on the figure_style configuration.

max_iterationsint

Maximum number of iterations for the fit. Default is 10000.

Attributes:

gainfloat

Gain of the first order transfer function.

time_constantfloat

Time constant of the first order transfer function.

cov_matrixnp.ndarray

Covariance matrix of the parameters of the fit.

standard_deviationnp.ndarray

Standard deviation of the parameters of the fit.

functionCallable

First order transfer function with the parameters of the fit.

Notes

Color parameters accept Matplotlib color formats: named colors ("blue"), short color strings ("b"), hex strings ("#0000ff"), grayscale strings ("0.5"), and RGB/RGBA tuples with values between 0 and 1 ((0, 0, 1) or (0, 0, 1, 0.5)).