Linear

class plasmapy.analysis.fit_functions.Linear(params: tuple[float, ...] | None = None, param_errors: tuple[float, ...] | None = None)[source]

Bases: AbstractFitFunction

A sub-class of AbstractFitFunction to represent a linear function.

\[ \begin{align}\begin{aligned}y &= f(x) = m \, x + b\\(\delta y)^2 &= (x \, \delta m)^2 + (m \, \delta x)^2 + (\delta b)^2\end{aligned}\end{align} \]

where \(m\) and \(b\) are real constants to be fitted and \(x\) is the independent variable. \(\delta m\), \(\delta b\), and \(\delta x\) are the respective uncertainties for \(m\), \(b\), and \(x\).

Parameters:

params (tuple[float, ...], optional) – Tuple of values for the function parameters. Equal in size to param_names.
param_errors (tuple[float, ...], optional) – Tuple of values for the errors associated with the function parameters. Equal in size to param_names.

Attributes Summary

`FitParamTuple`	A `namedtuple` used for attributes `params` and `param_errors`.
`curve_fit_results`	The results returned by the curve fitting routine used by `curve_fit`.
`latex_str`	LaTeX friendly representation of the fit function.
`param_errors`	The associated errors of the fitted `params`.
`param_names`	Names of the fitted parameters.
`params`	The fitted parameters for the fit function.
`rsq`	Coefficient of determination (r-squared) value of the fit.

Methods Summary

`__call__`(x[, x_err, reterr])	Direct call of the fit function \(f(x)\).
`curve_fit`(xdata, ydata, **kwargs)	Calculate a linear least-squares regression of (`xdata`, `ydata`) using `scipy.stats.linregress`.
`func`(x, m, b)	The fit function, a linear function.
`func_err`(x[, x_err, rety])	Calculate dependent variable uncertainties \(\delta y\) for dependent variables \(y=f(x)\).
`root_solve`(args, *kwargs)	The root \(f(x_r) = 0\) for the fit function.

Attributes Documentation

FitParamTuple: A namedtuple used for attributes params and param_errors. The attribute param_names defines the tuple field names.

curve_fit_results: The results returned by the curve fitting routine used by curve_fit. This is typically from scipy.stats.linregress or scipy.optimize.curve_fit.

latex_str: LaTeX friendly representation of the fit function.

param_errors: The associated errors of the fitted params.

param_names: Names of the fitted parameters.

params: The fitted parameters for the fit function.

rsq: Coefficient of determination (r-squared) value of the fit. Calculated by scipy.stats.linregress from the fit.

Methods Documentation

__call__(x, x_err=None, reterr=False)

Direct call of the fit function \(f(x)\).

Parameters:

x (array_like) – Dependent variables.
x_err (array_like, optional) – Errors associated with the independent variables x. Must be of size one or equal to the size of x.
reterr (bool, optional) – (Default: False) If True, return an array of uncertainties associated with the calculated independent variables

Returns:

y (numpy.ndarray) – Corresponding dependent variables \(y=f(x)\) of the independent variables x.
y_err (numpy.ndarray) – Uncertainties associated with the calculated dependent variables \(\delta y\)

curve_fit(xdata, ydata, **kwargs) → None[source]

Calculate a linear least-squares regression of (xdata, ydata) using scipy.stats.linregress. This will set the attributes params, param_errors, and rsq.

The results of scipy.stats.linregress can be obtained via curve_fit_results.

Parameters:

xdata (array_like) – The independent variable where data is measured. Should be 1D of length M.
ydata (array_like) – The dependent data associated with xdata.
**kwargs – Any keywords accepted by scipy.stats.linregress.

func(x, m, b)[source]

The fit function, a linear function.

\[f(x) = m \, x + b\]

where \(m\) and \(b\) are real constants representing the slope and intercept, respectively, and \(x\) is the independent variable.

Parameters:

x (array_like) – Independent variable.
m (float) – Value for slope \(m\)
b (float) – Value for intercept \(b\)

Returns:

y – Dependent variables corresponding to \(x\)

Return type:

array_like

func_err(x, x_err=None, rety=False)[source]

Calculate dependent variable uncertainties \(\delta y\) for dependent variables \(y=f(x)\).

\[(\delta y)^2 = (x \, \delta m)^2 + (m \, \delta x)^2 + (\delta b)^2\]

Parameters:

x (array_like) – Independent variables to be passed to the fit function.
x_err (array_like, optional) – Errors associated with the independent variables x. Must be of size one or equal to the size of x.
rety (bool) – Set to True to also return the associated dependent variables \(y = f(x)\).

Returns:

err (numpy.ndarray) – The calculated uncertainties \(\delta y\) of the dependent variables (\(y = f(x)\)) of the independent variables x.
y (numpy.ndarray, optional) – (if rety == True) The associated dependent variables \(y = f(x)\).

Notes

A good reference for formulating propagation of uncertainty expressions is:

J. R. Taylor. An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements. University Science Books, second edition, August 1996 (ISBN: 093570275X)

root_solve(*args, **kwargs)[source]

The root \(f(x_r) = 0\) for the fit function.

\[ \begin{align}\begin{aligned}x_r &= \frac{-b}{m}\\\delta x_r &= |x_r| \sqrt{ \left( \frac{\delta m}{m} \right)^2 + \left( \frac{\delta b}{b} \right)^2 }\end{aligned}\end{align} \]

Parameters:

*args – Not needed. This is to ensure signature comparability with AbstractFitFunction.
**kwargs – Not needed. This is to ensure signature comparability with AbstractFitFunction.

Returns:

root (float) – The root value for the given fit params.
err (float) – The uncertainty in the calculated root for the given fit params and param_errors.