5781ad6adae3c4e4f264eab4de9050095eab6a6d,scipy/optimize/minpack.py,,curve_fit,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,489

Before Change

        else:
            xdata = np.asarray(xdata)

    weights = 1.0 / asarray(sigma) if sigma is not None else None
    func = _wrap_func(f, xdata, ydata, weights)
    if callable(jac):
        jac = _wrap_jac(jac, xdata, weights)

After Change

            xdata = np.asarray(xdata)

    // Determine type of sigma
    if sigma is not None:
        sigma = np.asarray(sigma)

        // if 1-d, sigma are errors, define transform = 1/sigma
        if sigma.shape == (ydata.size, ):
            transform = 1.0 / sigma
        // if 2-d, sigma is the covariance matrix,
        // define transform = L such that L L^T = C
        elif sigma.shape == (ydata.size, ydata.size):
            try:
                // scipy.linalg.cholesky requires lower=True to return L L^T = A
                transform = cholesky(sigma, lower=True)
            except LinAlgError:
                raise ValueError("`sigma` must be positive definite.")
        else:
            raise ValueError("`sigma` has incorrect shape.")
    else:
        transform = None

    func = _wrap_func(f, xdata, ydata, transform)
    if callable(jac):
        jac = _wrap_jac(jac, xdata, transform)
    elif jac is None and method != "lm":

Instances