It is the responsibility of the user to define their own experiment structure. The usual definition will describe the sample of interest, the instrument configuration, and the measured data, and will provide a theory function which computes the expected data given the sample and instrument parameters. The theory function frequently has a physics component for computing the ideal data given the sample and an instrument effects component which computes the expected data from the ideal data. Together, sample, instrument, and theory function define the fitting model which needs to match the data.

The curve fitting problem can be expressed as:

\[P(\text{model}\ |\ \text{data}) = {P(\text{data}\ |\ \text{model})P(\text{model}) \over P(\text{data})}\]

That is, the probability of seeing a particular set of model parameter values given the observed data depends on the probability of seeing the measured data given a proposed set of parameter values scaled by the probability of those parameter values and the probability of that data being measured. The experiment definition must return the negative log likelihood as computed using the expression on the right. Bumps will explore the space of the sample and instrument parameters in the model, returning the maximum likelihood and confidence intervals on the parameters.

There is a strong relationship between the usual \(\chi^2\) optimization problem and the maximum likelihood problem. Given Gaussian uncertainty for data measurements, we find that data \(y_i\) measured with uncertainty \(\sigma_i\) will be observed for sample parameters \(p\) when the instrument is at position \(x_i\) with probability

\[P(y_i\ |\ f(x_i;p)) = \frac{1}{\sqrt{2\pi\sigma_i^2}} \exp\left(-\frac{(y_i-f(x_i;p))^2}{2\sigma_i^2}\right)\]

The negative log likelihood of observing all points in the data set for the given set of sample parameters is

\[-\log \prod_i{P(y_i\ |\ f(x_i;p))} = \tfrac12 \sum_i{\frac{(y_i-f(x_i;p))^2}{\sigma_i^2}} - \tfrac12 \sum_i{\log 2 \pi \sigma_i^2} = \tfrac12 \chi^2 + C\]

Note that this is the unnormalized \(\chi^2\), whose expected value is the number of degrees of freedom in the model, not the reduced \(\chi^2_R\) whose expected value is \(1\). The Bumps fitting process is not sensitive to the constant \(C\) and it can be safely ignored.

Casting the problem as a log likelihood problem rather than \(\chi^2\) provides several advantages. We can support a richer set of measurement techniques whose uncertainties do not follow a Gaussian distribution. For example, if we have a Poisson process with a low count rate, the likelihood function will be asymmetric, and a gaussian fit will tend to overestimate the rate. Furthermore, we can properly handle background rates since we can easily compute the probability of seeing the observed number of counts given the proposed signal plus background rate. Gaussian modeling can lead to negative rates for signal or background, which is fundamentally wrong. See Simple functions for a demonstration of this effect.

We can systematically incorporate prior information into our models, such as uncertainty in instrument configuration. For example, if our sample angle control motor position follows a Gaussian distribution with a target position of 3° and an uncertainty of 0.2°, we can set

\[-\log P(\text{model}) = -\frac{1}{2} \frac{(\theta-3)^2}{0.2^2}\]

ignoring the scaling constant as before, and add this to \(\tfrac12\chi^2\) to get log of the product of the uncertainties. Similarly, if we know that our sample should have a thickness of 100 ± 3.5 Å based on how we constructed the sample, we can incorporate this information into our model in the same way.

Simple experiments

The simplest experiment is defined by a python function which takes a list of instrument configuration and has arguments defining the parameters. For example, to fit a line you would need:

def line(x, m, b):
    return m*x + b

Assuming the data was in a 3 column ascii file with x, y and uncertainty, you would turn this into a bumps model file using:

# 3 column data file with x, y and uncertainty
x,y,dy = numpy.loadtxt('line.txt').T
M = Curve(line, x, y, dy)

Using the magic of python introspection, Curve is able to determine the names of the fittable parameters from the arguments to the function. These are converted to Parameter objects, the basis of the Bumps modeling system. For each parameter, we can set bounds or values:

M.m.range(0,1)  # limit slope between 0 and 45 degrees
M.b.value = 1   # the intercept is set to 1.

We could even set a parameter to a probability distribution, using Parameter.dev for Gaussian distributions or setting parameter.bounds to Distribution for other distributions.

Bumps includes code for polynomial interpolation including B-splines, monotonic splines, and chebyshev polynomials.

For counts data, PoissonCurve is also available.

Likelihood functions

If you are already have the negative log likelihood function and you don’t need to manage data, you can use it with PDF:

x,y,dy = numpy.loadtxt('line.txt').T
def nllf(m, b):
    return numpy.sum(((y - (m*x + b))/dy)**2)
M = PDF(nllf)

You can use M.m and M.b to the parameter ranges as usual, then return the model as a fitting problem:

problem = FitProblem(M)

Complex models

More sophisticated models, with routines for data handling and specialized plotting should define the Fitness interface. The Peak Fitting example sets up a problem for fitting multiple peaks plus a background against a 2-D data set.

Models are parameterized using Parameter objects, which identify the fitted parameters in the model, and the bounds over which they may vary. The fitness object must provide a set of fitting parameters to the fit problem using the parameters method. Usually this returns a dictionary, with the key corresponding to the attribute name for the parameter and the value corresponding to a parameter object. This allows the user of the model to guess that parameter “p1” for example can be referenced using model.p1. If the model consists of parts, the parameters for each part must be returned. The usual approach is to define a parameters method for each part and build up the dictionary when needed (the parameters function is only called at the start of the fit, so it does not need to be efficient). This allows the user to guess that parameter “p1” of part “a” can be referenced using model.a.p1. A set of related parameters, p1, p2, … can be placed in a list and referenced using, e.g., model.a.p[i].

The fitness constructor should accept keyword arguments for each parameter giving reasonable defaults for the initial value. The parameter attribute should be created using Parameter.default. This method allows the user to set an initial parameter value when the model is defined, or set the value to be another parameter in the fitting problem, or to a parameter expression. The name given to the default method should include the name of the model. That way when the same type of model is used for different data sets, the two sets of parameters can be distinguished. Ideally the model name would be based on the data set name so that you can more easily figure out which parameter goes with which data.

During an analysis, the optimizer will ask to evaluate a series of points in parameter space. Once the parameters have been set, the update method will be called, if there is one. This method should clear any cached results from the last fit point. Next the nllf method will be called to compute the negative log likelihood of observing the data given the current values of the parameters. This is usually just \(\sum{(y_i - f(x_i))^2 / (2 \sigma_i^2)}\) for data measured with Gaussian uncertainty, but any probability distribution can be used.

For the Levenberg-Marquardt optimizer, the residuals method will be called instead of nllf. If residuals are unavailable, then the L-M method cannot be used.

The numpoints method is used to report fitting progress. With Gaussian measurement uncertainty, the nllf return value is \(\chi^2/2\), which has an expected value of the number of degrees of freedom in the fit. Since this is an awkward number, the normalized chi-square, \(\chi^2_N = \chi^2/\text{DoF} = -2 \ln (P)/(n-p)\), is shown instead, where \(-\ln P\) is the nllf value, \(n\) is the of points and \(p\) is the number of fitted parameters. \(\chi^2_N\) has a value near 1 for a good fit. The same calculation is used for non-gaussian distributions even though nllf is not returning sum squared residuals.

The save and plot methods will be called at the end of the fit. The save method should save the model for the current point. This may include things such as the calculated scattering curve and the real space model for scattering inverse problems, or it may be a save of the model parameters in a format that can be loaded by other programs. The plot method should use the current matplotlib figure to draw the model, data, theory and residuals.

The resynth_data method is used for an alternative monte carlo error analysis where random data sets are generated from the measured value and the uncertainty then fitted. The resulting fitted parameters can be processed much like the MCMC datasets, yielding a different estimate on the uncertainties in the parameters. The restore_data method restores the data to the originally measured values. These methods are optional, and only used if the alternative error analysis is requested.

Linear models

Linear problems with normally distributed measurement error can be solved directly. Bumps provides bumps.wsolve.wsolve(), which weights values according to the uncertainty. The corresponding bumps.wsolve.wpolyfit() function fits polynomials with measurement uncertainty.

Foreign models

If your modeling environment already contains a sophisticated parameter handling system (e.g. sympy or PyMC) you may want to tie into the Bumps system at a higher level. In this case you will need to define a class which implements the FitProblem interface. This has been done already for PyMCProblem and interested parties are directed therein for a working example.

External constraints