def curve_fit_unscaled(*args, **kwargs):
    """
    Use the reduced chi square to unscale :mod:`scipy`'s scaled :func:`scipy.optimize.curve_fit`. *\*args* and *\*\*kwargs* are passed through to :func:`scipy.optimize.curve_fit`. The tuple *popt, pcov, chisq_red* is returned, where *popt* is the optimal values for the parameters, *pcov* is the estimated covariance of *popt*, and *chisq_red* is the reduced chi square. See http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.optimize.curve_fit.html.
    """
    # Extract verbosity
    verbose = kwargs.pop('verbose', False)

    # Do initial fit
    popt, pcov = _spopt.curve_fit(*args, **kwargs)

    # Expand positional arguments
    func = args[0]
    x    = args[1]
    y    = args[2]

    ddof = len(popt)

    # Try to use sigma to unscale pcov
    try:
        sigma = kwargs['sigma']
        if sigma is None:
            sigma = _np.ones(len(y))
        # Get reduced chi-square
        y_expect = func(x, *popt)
        chisq_red = _chisquare(y, y_expect, sigma, ddof, verbose=verbose)

        # Correct scaled covariance matrix
        pcov = pcov / chisq_red
        return popt, pcov, chisq_red
    except ValueError:
        print('hello')
def curve_fit_unscaled(*args, **kwargs):
    """
    Use the reduced chi square to unscale :mod:`scipy`'s scaled :func:`scipy.optimize.curve_fit`. *\*args* and *\*\*kwargs* are passed through to :func:`scipy.optimize.curve_fit`. The tuple *popt, pcov, chisq_red* is returned, where *popt* is the optimal values for the parameters, *pcov* is the estimated covariance of *popt*, and *chisq_red* is the reduced chi square. See http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.optimize.curve_fit.html.
    """
    # Extract verbosity
    verbose = kwargs.pop('verbose', False)

    # Do initial fit
    popt, pcov = _spopt.curve_fit(*args, **kwargs)

    # Expand positional arguments
    func = args[0]
    x    = args[1]
    y    = args[2]

    ddof = len(popt)

    # Try to use sigma to unscale pcov
    try:
        sigma = kwargs['sigma']
        if sigma is None:
            sigma = _np.ones(len(y))
        # Get reduced chi-square
        y_expect = func(x, *popt)
        chisq_red = _chisquare(y, y_expect, sigma, ddof, verbose=verbose)

        # Correct scaled covariance matrix
        pcov = pcov / chisq_red
        return popt, pcov, chisq_red
    except ValueError:
        print('hello')
def curve_fit_unscaled ( * args , * * kwargs ) : # Extract verbosity verbose = kwargs . pop ( 'verbose' , False ) # Do initial fit popt , pcov = _spopt . curve_fit ( * args , * * kwargs ) # Expand positional arguments func = args [ 0 ] x = args [ 1 ] y = args [ 2 ] ddof = len ( popt ) # Try to use sigma to unscale pcov try : sigma = kwargs [ 'sigma' ] if sigma is None : sigma = _np . ones ( len ( y ) ) # Get reduced chi-square y_expect = func ( x , * popt ) chisq_red = _chisquare ( y , y_expect , sigma , ddof , verbose = verbose ) # Correct scaled covariance matrix pcov = pcov / chisq_red return popt , pcov , chisq_red except ValueError : print ( 'hello' )
Use the reduced chi square to unscale :mod:`scipy`'s scaled :func:`scipy.optimize.curve_fit`. *\*args* and *\*\*kwargs* are passed through to :func:`scipy.optimize.curve_fit`. The tuple *popt, pcov, chisq_red* is returned, where *popt* is the optimal values for the parameters, *pcov* is the estimated covariance of *popt*, and *chisq_red* is the reduced chi square. See http://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.optimize.curve_fit.html.
Use the reduced chi square to unscale : mod : scipy s scaled : func : scipy . optimize . curve_fit . * \ * args * and * \ * \ * kwargs * are passed through to : func : scipy . optimize . curve_fit . The tuple * popt pcov chisq_red * is returned where * popt * is the optimal values for the parameters * pcov * is the estimated covariance of * popt * and * chisq_red * is the reduced chi square . See http : // docs . scipy . org / doc / scipy - 0 . 15 . 1 / reference / generated / scipy . optimize . curve_fit . html .