Frequently Asked Questions

A list of common questions.

How can I fit multi-dimensional data?

The fitting routines accept data arrays that are 1 dimensional and double precision. So you need to convert the data and model (or the value returned by the objective function) to be one dimensional. A simple way to do this is to use numpy’s numpy.ndarray.flatten(), for example:

def residual(params, x, data=None):
    ....
    resid = calculate_multidim_residual()
    return resid.flatten()

How can I fit complex data?

As with working with multidimensional data, you need to convert your data and model (or the value returned by the objective function) to be double precision floating point numbers. One way to do this would be to use a function like this:

def realimag(array):
    return np.array([(x.real, x.imag) for x in array]).flatten()

to convert the complex array into an array of alternating real and imaginary values. You can then use this function on the result returned by your objective function:

def residual(params, x, data=None):
    ....
    resid = calculate_complex_residual()
    return realimag(resid)

Can I constrain values to have integer values?

Basically, no. None of the minimizers in lmfit support integer programming. They all (I think) assume that they can make a very small change to a floating point value for a parameters value and see a change in the value to be minimized.