Home | Trees | Indices | Help |
|
---|
|
object --+ | fit_function
Abstract super-class for fitting explicit functions to 1D arrays of data using least squares. xs -- independent variable data ys -- dependent variable data pars_ic -- initial values defining the function Optional algorithmic parameters to minpack.leastsq can be passed in the algpars argument: e.g., ftol -- Relative error desired in the sum of squares (default 1e-6). xtol -- Relative error desired in the approximate solution (default 1e-6). gtol -- Orthogonality desired between the function vector and the columns of the Jacobian (default 1e-8). Other parameters may be used for concrete sub-classes. Pass these as a dict or args object in the opts argument. Returns an args object with attributes: ys_fit -- the fitted y values corresponding to the given x data, pars_fit -- the function parameters at the fit info -- diagnostic feedback from the leastsq algorithm results -- dictionary of other function specific information (such as peak position)
|
|||
|
|||
|
|||
|
|||
|
|||
Inherited from |
|
|||
Inherited from |
|
x.__init__(...) initializes x; see x.__class__.__doc__ for signature
|
Home | Trees | Indices | Help |
|
---|
Generated by Epydoc 3.0.1 on Sun Dec 2 23:44:24 2012 | http://epydoc.sourceforge.net |