snpl.fit module

Utilities for curve fitting analyses

Note

This module is not really useful anymore. It is encouraged to use directly the scipy.optimize package instead.

snpl.fit.AreaGaussian(p)

snpl.fit.AreaLorentzian(p)

snpl.fit.AreaPseudoVoigt(p)

I think it’s ok…

class snpl.fit.Baseline(xdata, ydata, x_range=[])

Bases: Function

A class for baseline.

class snpl.fit.BaselineStable(xdata, ydata, left, right, hwidth)

Bases: Function

snpl.fit.EMG(x, p)

class snpl.fit.Function(expr, params, share=[], lock=[], headers=[], **lineprops)

Bases: object

Bound(ind, bound=(-inf, inf))

Specify the boundary for the parameter. @param ind: index of the target parameter @param bound: two-tuple of the lower and upper boundary.

Decode(params)

Makes the “full” parameters containing locked parameters, from the “partial” parameters excluding locked ones.

DecodeErrors(errors)

Makes the “full” parameters containing locked parameters, from the “partial” parameters excluding locked ones.

Encode()

Makes the “partial” parameters & bounds excluding locked parameters, from the currently-set parameters & errors. @return: two-tuple of lists

Evaluate(x)

Get the value of the function at x with current parameters. @return: float number

EvaluateWithParam(x, params)

Get the value of the function at x with given parameters. NOTE: This method doesn’t overwrite the parameters held by this class. @return: float number

EvaluateWithPartialParam(x, *params)

Get the value of the function at x with given parameters. This method doesn’t overwrite the parameters held by this class. Unlike EvaluateWithParam(), this method accepts a “partial” parameter excluding locked values.

Fit(xs, ys, sigma=None, **kwargs)

Fit the model to given data set. @param xs: array of x @param ys: array of y @param sigma: array of std. dev., optional

GetLineProps()

GetParameterLength()

Get the number of parameters needed (including locked/shared parameters).

GetParamsHeadersString(fmt='{0:>15s}', sep='\t')

Returns a formatted string of parameter headers.

GetParamsString(fmt='{0:>15f}', sep='\t')

GetPlotData(start, stop, num=1000, xlog=False, baseline=None)

Make the arrays for plotting functions. @param start: starting value for x @param stop: stopping value for x @param num: number of points @param xlog: If True, x will be evenly spaced in log scale. @param baseline: another Function() instance @return: (array of x, array of y)

Integrate(xi, xf)

Lock(*args)

Lock the parameter(s). Locked parameters don’t change during fitting. @param *args: index (or indices) of the parameters to be locked. NOT A LIST OF INDICES.

Share(*args)

Specify the parameter(s) to be shared. @param *args: index (or indices) of the parameters to be shared. NOT A LIST OF INDICES.

Unbound(ind)

Unlock(*args)

Unshare(*args)

Update(params, errors=None)

Overwrite parameters.

UpdateParameter(params, errors)

Updates the currently-set parameter & error info with the given “partial” parameter & error excluding locked values.

snpl.fit.Gaussian(x, p)

p[0]: amp p[1]: position p[2]: fwhm

snpl.fit.Line(x, p)

snpl.fit.Lorentzian(x, p)

class snpl.fit.Model(funcs=[])

Bases: object

A class for fitting model function composed of multiple functions.

AddFunction(func)

add a function to the model @param func: Function object.

Decode(params)

Chop the given parameters into parameter lists for each function. @param params: a list of parameters @return: a list of lists of parameters (ret[i][j]: jth parameter of ith function)

Encode()

Return a list of current parameters (considering locked and shared parameters) @return: a list of parameters and a list of two-tuples of bounds

20160930 added bounds tuples

Evaluate(x)

Evalute the model using currently set parameters. @param x: x

EvaluateWithParams(x, *params)

Evaluate the model using x and given parameters. Note that the parameters given here do not affect the parameters of this instance. To update the parameters of the instance, call self.UpdateParameters. @param x: @param *params: parameters

Fit(xs, ys, sigma=None)

Fit the model to given data set. @param xs: array of x @param ys: array of y @param sigma: array of std. dev., optional

GetPlotData(start, stop, num=1000, xlog=False, ind=None, baseline=None)

Make the arrays for plotting functions. @param start: starting value for x @param stop: stopping value for x @param num: number of points @param xlog: use x evenly spaced in log space if True. @param ind: index of a function to be plotted. If None, the data for whole model will be evaluated. @param baseline: another Function() object @return: (array of x, array of y)

UpdateEncoderMap()

Make a map for encoding parameters.

UpdateParameters(params, errors=None)

Update the parameters with given parameter list. @param params: a list of parameters

snpl.fit.Parabolic(x, p)

p[0]: height (>0) p[1]: position p[2]: full width at half maximum (fwhm)

snpl.fit.PseudoVoigt(x, p)

Gaussian-Lorentzian Sum (Amplitude), from PeakFit v4.12 Manual 7-11

class snpl.fit.Residuals(residuals_function, p0)

Bases: object

A class for general least-square optimization. It calculates the sum of squared residuals and try to minimize it with respect to the parameters, starting from p0.

Bound(ind, bound=(-inf, inf))

Specify the boundary for the parameter. @param ind: index of the target parameter @param bound: two-tuple of the lower and upper boundary.

Fit()

snpl.fit.SplitGaussian(x, p)

snpl.fit.Voigt(x, p)