%.. _faq_chapter:

# 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.

## 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 one 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.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 one 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.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:

import numpy as np
def fit_function(params, x=None, dat1=None, dat2=None):
model1 = params['offset'] + x * params['slope1']
model2 = params['offset'] + x * params['slope2']

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


## How can I fit complex data?¶

As with working with multi-dimensional data, you need to convert your data and model (or the value returned by the objective function) to be double precision floating point numbers. The simplest approach is to use numpy.ndarray.view, perhaps like:

import numpy as np
def residual(params, x, data=None):
....
resid = calculate_complex_residual()
return resid.view(np.float)


Alternately, you can use the lmfit.Model class to wrap a fit function that returns a complex vector. It will automatically apply the above prescription when calculating the residual. The benefit to this method is that you also get access to the plot routines from the ModelResult class, which are also complex-aware.

## 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.

## I get errors from NaN in my fit. What can I do?¶

The solvers used by lmfit use NaN (see https://en.wikipedia.org/wiki/NaN) values as signals that the calculation cannot continue. If any value in the residual array (typically (data-model)*weight) is NaN, then calculations of chi-square or comparisons with other residual arrays to try find a better fit will also give NaN and fail. There is no sensible way for lmfit or any of the optimization routines to know how to handle such NaN values. They indicate that numerical calculations are not sensible and must stop.

This means that if your objective function (if using minimize) or model function (if using Model) generates a NaN, the fit will stop immediately. If your objective or model function generates a NaN, you really must handle that.

### nan_policy¶

If you are using lmfit.Model and the NaN values come from your data array and are meant to indicate missing values, or if you using lmfit.minimize() with the same basic intention, then it might be possible to get a successful fit in spite of the NaN values. To do this, you can add a nan_policy=’omit’ argument to lmfit.minimize(), or when creating a lmfit.Model, or when running lmfit.Model.fit().

In order for this to be effective, the number of NaN values cannot ever change during the fit. If the NaN values come from the data and not the calculated model, that should be the case.

### Common sources of NaN¶

If you are seeing erros due to NaN values, you will need to figure out where they are coming from and eliminate them. It is sometimes difficult to tell what causes NaN values. Keep in mind that all values should be assumed to be either scalar values or numpy arrays of double precision real numbers when fitting. Some of the most likely causes of NaNs are:

• taking sqrt(x) or log(x) where x is negative.
• doing x**y where x is negative. Since y is real, there will be a fractional component, and a negative number to a fractional exponent is not a real number.
• doing x/y where both x and y are 0.

If you use these very common constructs in your objective or model function, you should take some caution for what values you are passing these functions and operators. Many special functions have similar limitations and should also be viewed with some suspicion if NaNs are being generated.

A related problem is the generation of Inf (Infinity in floating point), which generally comes from exp(x) where x has values greater than 700 or so, so that the resulting value is greater than 1.e308. Inf is only slightly better than NaN. It will completely ruin the ability to do the fit. However, unlike NaN, it is also usually clear how to handle Inf, as you probably won’t ever have values greater than 1.e308 and can therefore (usually) safely clip the argument passed to exp() to be smaller than about 700.