# Parameters¶

Bumps fitting is centered on Parameter objects. Parameters define the search space, the uncertainty analysis and even the user interface. Constraints within and between models are implemented through parameters. Prior probabilities are defined by for parameters.

Model classes for Bumps should make it easy to define the initial value of fitting parameters and tie parameters together. When creating a model, you should be able specify parameter=value for each of the model parameters. Later, you should be able to reference the parameter within the model using M.parameter. Parameters can also be tied together by assigning the same Parameter object to two different parameters. For example, a hollow cylinder can be created using:

solvent = Parameter("solvent", value=1.2)
shell = Parameter("shell", value=4.5)
M = CoreShellCylinder(core=solvent, shell=shell, solvent=solvent,


The model parameter can also be a derived value that is the result of a parameter expression. For example, the following creates a cylinder whose length is twice the radius:

radius = Parameter("radius", value=3)


Any time you ask for M.length.value it will compute the result as 2*radius.value and return that.

You can also tie parameters together after the fact. For example, you can create the constrained cylinder using:

M = Cylinder(radius=3, length=6)


The advantage of this method is that you can easily comment out the constraint when exploring the model space, and fit length and radius freely.

Once you have defined your models and constraints you can set up you fitting parameters. There are several parameter methods which are helpful:

• range forces the parameter to lie within a fixed range. The parameter value can take on any value within the range with equal probability, and has zero probability outside the range.
• pm is a convenient way to set up a range based on the initial value of parameter. For example, M.thickness.pm(10) will allow the thickness parameter to vary by plus or minus 10. You can do asymmetric ranges by calling pm with plus and minus values, such as M.thickness.pm(-3,2). The actual range gets set to a nice_range that includes the bounds.
• pmp is like pm but the range is specified as a percent. For example, to let thickness vary by 10%, use M.thickness.pmp(10). Again, a nice_range is used.
• dev sets up a parameter whose prior probability is not equal across its range, but instead follows a normal distribution. If for example, you have measure the thickness to be $$32.1 \pm 0.6$$ by some other technique, you can use this information to constrain your model by initializing thickness to 32.1 and setting M.thickness.dev(0.6) as a fitting constraint. The dev method also accepts absolute limits, creating a truncated normal distribution. You can set the central value mu as well, but you probably want to do this in the model initialization so that you are free to turn fitting of the parameter on and off by commenting out the dev line.
• soft_range is a combination of range and dev in that the parameter has equal probability within [low,*high*] but Gaussian probability of width std as it strays outside of the range.
• pdf is like dev but works with any continuous scipy.stats distribution.

All these methods set the bounds attribute on the parameter in one way or another. See bumps.bounds for details. Technically, setting the parameter to dev, soft_range or pdf is equivalent to creating a probability distribution model with a single data point and Fitness.nllf equal to the negative log likelihood of seeing the parameter value in the distribution. This PDF model would be fit simultaneously with your target model with the parameter shared between them. The result is statistically sound (it is just more prior information), and conveniently, it does not affect the number of degrees of freedom in the fit.

When defining new model classes, use the static method Parameter.default() to initialize the parameter. This will accept the input argument passed in by the user and depending on its type, either create a new parameter slot and set its initial value, or link the slot to another parameter.

## Free Variables¶

When fitting multiple datasets, you will undoubtedly have models with many shared parameters, and some parameters that differ between the models. Common patterns include:

• different measurements may use the same material but different contrast agents,
• they may use the same contrast agent but different materials,
• the same material and contrast, but different sizes, or
• a cross product with several materials and several sizes.

Often with complex models the parameter of interest is buried within the model structure. One approach is to clone the models using a deep copy of the entire structure, then tie together parameters for the bits that are changing. This proves to be confusing and difficult for new python programmers, so instead FitProblem was extended to support FreeVariables. The FreeVariables class allows you to use the same model structure with different data sets, but have some parameters that vary between the models. Each varying parameter is a slot, and FreeVariables keeps an array of parameters (actually a ParameterSet) to fill that slot, one for each model.

To define the free variables, you need the names of the different models, a parameter slot to hold the values, and a list of the different parameter values for each model. You then define the free variables as follows:

free = FreeVariables(names=["model1", "model2", ...],
p1=model.p1, p2=model.p2, ...)
...
problem = FitProblem(experiments, freevars=free)


The slots can be referenced by name, with the underlying parameters referenced by variable number. In the above, free.p1[1] refers to the parameter p1 when fitting data2. You can also refer to the slots by name, such as free.p1[data2.name]. The parameters in the slots have the usual properties of parameters, such as values and fit ranges. Setting the fit range makes the parameter a fitted parameter, and the fit will give the uncertainty on each parameter independently. Parameters can be copied, so that a pair of models can share the same value.

The following examples shows a neutron scattering problems with two datasets, one measured with light water and the other measured with heavy water, you can share the same material object, but use the light water scattering factors in the first and the heavy water scattering factors in the second. The problem would be composed as follows:

material = SLD('silicon', rho=2.07)
solvent = SLD('solvent') # unspecified rho
M1 = ScatteringFitness(model, hydrogenated_data)
M2 = ScatteringFitness(model, deuterated_data)
free = FreeVariables(names=['hydrogenated', 'deuterated'],
solvent=solvent.sld)
free.solvent.values = [-0.561, 6.402]

fv.solvent.range(-1,7)

fv.solvent[0].range(-1,0)