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| Non-Linear Least-Squares Minimization and Curve-Fitting for Python | FAQ | Develop |

# Frequently Asked Questions

A list of common questions.

## What’s the best way to ask for help or submit a bug report?

See Getting Help.

## Why did my script break when upgrading from lmfit 0.8.3 to 0.9.0?

See Version 0.9.0 Release Notes

## I get import errors from IPython

If you see something like:

        from IPython.html.widgets import Dropdown

    ImportError: No module named 'widgets'

then you need to install the ipywidgets package. Try ‘pip install ipywidgets’.

## 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 multiple data sets?

As above, the fitting routines accept data arrays that are 1 dimensional and double precision. So you need to convert the sets of data and models (or the value returned by the objective function) to be one dimensional. A simple way to do this is to use numpy’s `numpy.concatenate()`. As an example, here is a residual function to simultaneously fit two lines to two different arrays. As a bonus, the two lines share the ‘offset’ parameter:

> def fit_function(params, x=None, dat1=None, dat2=None):

>   model1 = params[‘offset’].value + x * params[‘slope1’].value model2 = params[‘offset’].value + x * params[‘slope2’].value

>   resid1 = dat1 - model1 resid2 = dat2 - model2 return numpy.concatenate((resid1, resid2))

## 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.

## How should I cite LMFIT?

See http://dx.doi.org/10.5281/zenodo.11813