Class Minimizer

x.__init__(...) initializes x; see help(type(x)) for signature

Overrides: object.__init__

(inherited documentation)

use one of the scaler minimization methods from scipy.
Available methods include:
  Nelder-Mead
  Powell
  CG  (conjugate gradient)
  BFGS
  Newton-CG
  Anneal
  L-BFGS-B
  TNC
  COBYLA
  SLSQP

If the objective function returns a numpy array instead
of the expected scalar, the sum of squares of the array
will be used.

Note that bounds and constraints can be set on Parameters
for any of these methods, so are not supported separately
for those designed to use bounds.

use Levenberg-Marquardt minimization to perform fit. This assumes that ModelParameters have been stored, and a function to minimize has been properly set up.

This wraps scipy.optimize.leastsq, and keyword arguments are passed directly as options to scipy.optimize.leastsq

When possible, this calculates the estimated uncertainties and variable correlations from the covariance matrix.

writes outputs to many internal attributes, and returns True if fit was successful, False if not.

err_nonparam

Value:

'params must be a minimizer.Parameters() instance or list of Parameter s()'

err_maxfev

Value:

'''Too many function calls (max set to %i)! Use: minimize(func, params, ...., maxfev=NNN) or set leastsq_kws[\'maxfev\'] to increase this maximum.'''