solarwindpy.fitfunctions.lines

Simple linear fit functions.

This module defines FitFunction subclasses for straight-line models. They are primarily used for quick trend estimation and serve as basic examples of the FitFunction interface.

Classes

Line(xobs, yobs, **kwargs)

Linear fit function for straight line relationships.

LineXintercept(xobs, yobs, **kwargs)

Linear fit with explicit x-intercept parameterization.
class Line(xobs, yobs, **kwargs)[source]

Bases: FitFunction

Linear fit function for straight line relationships.

Fits data to the form: y = m*x + b

__init__(xobs, yobs, **kwargs)[source]

Initialize fit function with observed data.

Parameters:

  • xobs (array-like) – Observed x values (independent variable). Shape must match yobs.

  • yobs (array-like) – Observed y values (dependent variable). Shape must match xobs.

  • **kwargs – The description is missing.

Notes

The fitting procedure uses scipy.optimize.least_squares with robust loss functions (Huber by default) to handle outliers. The initial parameter guess is provided by the p0 property, which must be implemented by subclasses.

All subclasses inherit this documentation automatically through the docstring-inheritance metaclass.

Examples

>>> import numpy as np
>>> from solarwindpy.fitfunctions import Gaussian
>>> x = np.linspace(-5, 5, 100)
>>> y = 3 * np.exp(-0.5 * x**2) + np.random.normal(0, 0.1, 100)
>>> fit = Gaussian(x, y, xmin=-3, xmax=3)
>>> fit.make_fit()
>>> print(f"Fitted mu: {fit.popt['mu']:.3f}")

See also

make_fit

Execute the fitting procedure

popt

Access optimized parameters

rsq

Calculate coefficient of determination

property function

Get the function that`curve_fit` fits.

The function is set at instantiation. It doesn’t make sense to change it unless you redefine the entire FitFunction, so there is no new kwarg.

property p0

Calculate the initial guess for the line parameters.

If this fails, return curve_fit()’s default value None.

Returns:

p0 – The initial guesses as [m, b].

Return type:

list

property TeX_function

Function written in LaTeX.

property x_intercept

Calculate the x-intercept of the fitted line.

Returns:

The x value where the line crosses y=0.

Return type:

float

property TeX_info
property argnames

The names of the actual function arguments pulled by getfullargspec.

build_TeX_info()
build_plotter()
property chisq_dof

Chisq per degree of freedom \(\chi^2_\nu\).

If None, not calculated by make_fit_old. If np.nan, fit failed.

property combined_popt_psigma

Return optimized parameters and uncertainties as a DataFrame.

Returns:

DataFrame with columns ‘popt’ and ‘psigma’, indexed by parameter names. Relative uncertainty can be computed as: df[‘psigma’] / df[‘popt’]

Return type:

pd.DataFrame

property dof

Degrees of freedom in the fit.

property fit_bounds

Bounds used when running the fit.

property fit_result
property initial_guess_info
property logger
make_fit(return_exception=False, **kwargs)

Fit the function with the independent xobs and dependent yobs.

Uses least_squares and returns the OptimizeResult object, but treats weights as in curve_fit.

Parameters:

  • return_exception (bool) – If True, return exceptions from fitting routine, instead of raising. This is useful when looping through many fits and wanting to identify failed fits after the fact.

  • **kwargs – The description is missing.

property nobs

The total number of observations used in the fit.

property observations
property pcov

Returns a copy so that the matrix isn’t accidentally edited.

property plotter
property popt

Optimized fit parameters.

property psigma
residuals(pct=False, use_all=False)

Calculate fit residuals.

Parameters:

  • pct (bool, default=False) – If True, return percentage residuals.

  • use_all (bool, default=False) – If True, calculate residuals for all input data including points excluded by constraints (xmin, xmax, etc.) passed during initialization. If False (default), calculate only for points used in fit.

Returns:

Residuals as observed - fitted.

Return type:

numpy.ndarray

Examples

>>> # Create FitFunction with constraints
>>> ff = Gaussian(x, y, xmin=3, xmax=7)
>>> ff.make_fit()
>>>
>>> # Residuals for fitted region only
>>> r_fit = ff.residuals()
>>>
>>> # Residuals for all original data
>>> r_all = ff.residuals(use_all=True)
>>>
>>> # Percentage residuals
>>> r_pct = ff.residuals(pct=True)

Notes

Addresses TODO: “calculate with all values…including those excluded by set_extrema” (though set_extrema doesn’t exist - constraints are passed in __init__).

property rsq

Coefficient of determination.

Source: <en.wikipedia.org/wiki/Coefficient_of_determination#Definitions>

set_fit_obs(xobs_raw, yobs_raw, weights_raw, xmin=None, xmax=None, xoutside=None, ymin=None, ymax=None, youtside=None, wmin=None, wmax=None, logx=False, logy=False)

Set the observed values we’ll actually use in the fit.

By applying limits to xobs_raw and yobs_raw and checking for finite values.

All boundaries are inclusive <= or >=.

If logy, then make selection of wmin and wmax based on \(w/(y \ln(10))\).

Parameters:

  • xobs_raw – The description is missing.

  • yobs_raw – The description is missing.

  • weights_raw – The description is missing.

  • xmin – The description is missing.

  • xmax – The description is missing.

  • xoutside – The description is missing.

  • ymin – The description is missing.

  • ymax – The description is missing.

  • youtside – The description is missing.

  • wmin – The description is missing.

  • wmax – The description is missing.

  • logx – The description is missing.

  • logy – The description is missing.

property sufficient_data

Ensure that we can fit the data before doing any computations.

class LineXintercept(xobs, yobs, **kwargs)[source]

Bases: FitFunction

Linear fit with explicit x-intercept parameterization.

Fits data to the form: y = m * (x - x0) where x0 is the x-intercept.

__init__(xobs, yobs, **kwargs)[source]

Initialize linear fit with x-intercept parameterization.

Parameters:

  • xobs (array-like) – Observed x values (independent variable). Shape must match yobs.

  • yobs (array-like) – Observed y values (dependent variable). Shape must match xobs.

  • **kwargs – The description is missing.

Notes

This parameterization is useful when fitting data where the x-intercept has physical meaning, such as threshold energies or cutoff velocities in solar wind measurements.

Examples

>>> import numpy as np
>>> from solarwindpy.fitfunctions import Gaussian
>>> x = np.linspace(-5, 5, 100)
>>> y = 3 * np.exp(-0.5 * x**2) + np.random.normal(0, 0.1, 100)
>>> fit = Gaussian(x, y, xmin=-3, xmax=3)
>>> fit.make_fit()
>>> print(f"Fitted mu: {fit.popt['mu']:.3f}")

See also

make_fit

Execute the fitting procedure

popt

Access optimized parameters

rsq

Calculate coefficient of determination

property function

Get the function that`curve_fit` fits.

The function is set at instantiation. It doesn’t make sense to change it unless you redefine the entire FitFunction, so there is no new kwarg.

property p0

Calculate the initial guess for the line parameters.

If this fails, return curve_fit()’s default value None.

Returns:

p0 – The initial guesses as [m, b].

Return type:

list

property TeX_function

Function written in LaTeX.

property y_intercept

Calculate the y-intercept of the fitted line.

Returns:

The y value where the line crosses x=0.

Return type:

float

property TeX_info
property argnames

The names of the actual function arguments pulled by getfullargspec.

build_TeX_info()
build_plotter()
property chisq_dof

Chisq per degree of freedom \(\chi^2_\nu\).

If None, not calculated by make_fit_old. If np.nan, fit failed.

property combined_popt_psigma

Return optimized parameters and uncertainties as a DataFrame.

Returns:

DataFrame with columns ‘popt’ and ‘psigma’, indexed by parameter names. Relative uncertainty can be computed as: df[‘psigma’] / df[‘popt’]

Return type:

pd.DataFrame

property dof

Degrees of freedom in the fit.

property fit_bounds

Bounds used when running the fit.

property fit_result
property initial_guess_info
property logger
make_fit(return_exception=False, **kwargs)

Fit the function with the independent xobs and dependent yobs.

Uses least_squares and returns the OptimizeResult object, but treats weights as in curve_fit.

Parameters:

  • return_exception (bool) – If True, return exceptions from fitting routine, instead of raising. This is useful when looping through many fits and wanting to identify failed fits after the fact.

  • **kwargs – The description is missing.

property nobs

The total number of observations used in the fit.

property observations
property pcov

Returns a copy so that the matrix isn’t accidentally edited.

property plotter
property popt

Optimized fit parameters.

property psigma
residuals(pct=False, use_all=False)

Calculate fit residuals.

Parameters:

  • pct (bool, default=False) – If True, return percentage residuals.

  • use_all (bool, default=False) – If True, calculate residuals for all input data including points excluded by constraints (xmin, xmax, etc.) passed during initialization. If False (default), calculate only for points used in fit.

Returns:

Residuals as observed - fitted.

Return type:

numpy.ndarray

Examples

>>> # Create FitFunction with constraints
>>> ff = Gaussian(x, y, xmin=3, xmax=7)
>>> ff.make_fit()
>>>
>>> # Residuals for fitted region only
>>> r_fit = ff.residuals()
>>>
>>> # Residuals for all original data
>>> r_all = ff.residuals(use_all=True)
>>>
>>> # Percentage residuals
>>> r_pct = ff.residuals(pct=True)

Notes

Addresses TODO: “calculate with all values…including those excluded by set_extrema” (though set_extrema doesn’t exist - constraints are passed in __init__).

property rsq

Coefficient of determination.

Source: <en.wikipedia.org/wiki/Coefficient_of_determination#Definitions>

set_fit_obs(xobs_raw, yobs_raw, weights_raw, xmin=None, xmax=None, xoutside=None, ymin=None, ymax=None, youtside=None, wmin=None, wmax=None, logx=False, logy=False)

Set the observed values we’ll actually use in the fit.

By applying limits to xobs_raw and yobs_raw and checking for finite values.

All boundaries are inclusive <= or >=.

If logy, then make selection of wmin and wmax based on \(w/(y \ln(10))\).

Parameters:

  • xobs_raw – The description is missing.

  • yobs_raw – The description is missing.

  • weights_raw – The description is missing.

  • xmin – The description is missing.

  • xmax – The description is missing.

  • xoutside – The description is missing.

  • ymin – The description is missing.

  • ymax – The description is missing.

  • youtside – The description is missing.

  • wmin – The description is missing.

  • wmax – The description is missing.

  • logx – The description is missing.

  • logy – The description is missing.

property sufficient_data

Ensure that we can fit the data before doing any computations.