ExponentialPlusLinear

class plasmapy.analysis.fit_functions.ExponentialPlusLinear(params: Optional[Tuple[float, ]] = None, param_errors: Optional[Tuple[float, ]] = None)

Bases: plasmapy.analysis.fit_functions.AbstractFitFunction

A sub-class of AbstractFitFunction to represent an exponential with an linear offset.

\[\begin{split}y =& f(x) = a \, e^{\alpha \, x} + m \, x + b\\ (\delta y)^2 =& \left( a e^{\alpha x}\right)^2 \left[ \left( \frac{\delta a}{a} \right)^2 + (x \, \delta \alpha)^2 + (\alpha \, \delta x)^2 \right]\\ & + \left(2 \, a \, \alpha \, m \, e^{\alpha x}\right) (\delta x)^2\\ & + \left[(x \, \delta m)^2 + (\delta b)^2 +(m \, \delta x)^2\right]\end{split}\]

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

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)

Use a non-linear least squares method to fit the fit function to (xdata, ydata), using scipy.optimize.curve_fit.

func(x, a, alpha, m, b)

The fit function, an exponential with a linear offset.

func_err(x[, x_err, rety])

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

root_solve(x0)

Solve for the root of the fit function (i.e.

Attributes Documentation

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
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.

\[ \begin{align}\begin{aligned}r^2 &= 1 - \frac{SS_{res}}{SS_{tot}}\\SS_{res} &= \sum\limits_{i} (y_i - f(x_i))^2\\SS_{tot} &= \sum\limits_{i} (y_i - \bar{y})^2\end{aligned}\end{align} \]

where \((x_i, y_i)\) are the sample data pairs, \(f(x_i)\) is the fitted dependent variable corresponding to \(x_i\), and \(\bar{y}\) is the average of the \(y_i\) values.

The \(r^2\) value is an indicator of how close the points \((x_i, y_i)\) lie to the model \(f(x)\). \(r^2\) values range between 0 and 1. Values close to 0 indicate that the points are uncorrelated and have little tendency to lie close to the model, whereas, values close to 1 indicate a high correlation to the model.

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

Use a non-linear least squares method to fit the fit function to (xdata, ydata), using scipy.optimize.curve_fit. This will set the attributes parameters, parameters_err, and rsq.

The results of scipy.optimize.curve_fit 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.optimize.curve_fit.

Raises
  • ValueError – if either ydata or xdata contain numpy.nan’s, or if incompatible options are used.

  • RuntimeError – if the least-squares minimization fails.

  • OptimizeWarning – if covariance of the parameters can not be estimated.

func(x, a, alpha, m, b)

The fit function, an exponential with a linear offset.

\[\begin{split}f(x) = a \, e^{\alpha \, x} + m \, x + b\\\end{split}\]

where \(a\), \(\alpha\), \(m\), and \(b\) are the real constants and \(x\) is the independent variable.

Parameters
  • x (array_like) – Independent variable.

  • a (float) – value for constant \(a\)

  • alpha (float) – value for constant \(\alpha\)

  • 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)

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

\[\begin{split}(\delta y)^2 =& \left( a e^{\alpha x}\right)^2 \left[ \left( \frac{\delta a}{a} \right)^2 + (x \, \delta \alpha)^2 + (\alpha \, \delta x)^2 \right]\\ & + \left(2 \, a \, \alpha \, m \, e^{\alpha x}\right) (\delta x)^2\\ & + \left[( x \, \delta m)^2 + (\delta b)^2 +(m \, \delta x)^2 \right]\end{split}\]
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 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(x0)

Solve for the root of the fit function (i.e. \(f(x_r) = 0\)). This mehtod used scipy.optimize.fsolve to find the function roots.

Parameters

x0 (ndarray) – The starting estimate for the roots of \(f(x_r) = 0\).

Returns

  • x (ndarray) – The solution (or the result of the last iteration for an unsuccessful call).

  • x_err (ndarray) – The uncertainty associated with the root calculation. Currently this returns an array of numpy.nan values equal in shape to x , since there is no determined way to calculate the uncertainties.

Notes

If the full output of scipy.optimize.fsolve is desired then one can do:

>>> func = Linear()
>>> func.params = (1.0, 5.0)
>>> func.param_errors = (0.0, 0.0)
>>> roots = fsolve(func, -4.0, full_output=True)
>>> roots
(array([-5.]),
 {'nfev': 4,
  'fjac': array([[-1.]]),
  'r': array([-1.]),
  'qtf': array([2.18...e-12]),
  'fvec': 0.0},
 1,
 'The solution converged.')