Configs

Configuration files are used to control the behaviour model components in PyAutoFit, which perform the following tasks:

• Specify the default priors of model components, so that a user does not have to manually specify priors every time they create a model.

• Specify labels of every parameter, which are used for plotting and visualizing results.

This cookbook illustrates how to create configuration files for your own model components, so that they can be used with PyAutoFit.

Contents:

• No Config Behaviour: An example of what happens when a model component does not have a config file.

• Template: A template config file for specifying default model component priors.

• Modules: Writing prior config files based on the Python module the model component Python class is contained in.

• Labels: Config files which specify the labels of model component parameters for visualization.

No Config Behaviour

The examples seen so far have used Gaussian and Exponential model components, which have configuration files in the autofit_workspace/config/priors folder which define their priors and labels.

If a model component does not have a configuration file and we try to use it in a fit, PyAutoFit will raise an error.

Lets illustrate this by setting up the usual Gaussian object, but naming it GaussianNoConfig so that it does not have a config file.

class GaussianNoConfig:
    def __init__(
        self,
        centre=0.0,  # <- PyAutoFit recognises these constructor arguments
        normalization=0.1,  # <- are the Gaussian`s model parameters.
        sigma=0.01,
    ):
        """
        Represents a 1D `Gaussian` profile, which does not have a config file set up.
        """
        self.centre = centre
        self.normalization = normalization
        self.sigma = sigma

    def model_data_1d_via_xvalues_from(self, xvalues: np.ndarray) -> np.ndarray:
        """
        The usual method that returns the 1D data of the `Gaussian` profile.
        """
        transformed_xvalues = xvalues - self.centre

        return np.multiply(
            np.divide(self.normalization, self.sigma * np.sqrt(2.0 * np.pi)),
            np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))),
        )

When we try make this a Model and fit it, PyAutoFit raises an error, as it does not know where the priors of the GaussianNoConfig are located.

model = af.Model(GaussianNoConfig)

dataset_path = path.join("dataset", "example_1d", "gaussian_x1")
data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, "data.json"))
noise_map = af.util.numpy_array_from_json(
    file_path=path.join(dataset_path, "noise_map.json")
)

analysis = af.ex.Analysis(data=data, noise_map=noise_map)

search = af.DynestyStatic()

result = search.fit(model=model, analysis=analysis)

In all other examples, the fits runs because the priors have been defined in one of two ways:

• They were manually input in the example script.

• They were loaded via config files “behind the scenes”.

Checkout the folder autofit_workspace/config/priors, where .yaml files defining the priors of the Gaussian and Exponential model components are located. These are the config files that PyAutoFit loads in the background in order to setup the default priors of these model components.

If we do not manually override priors, these are the priors that will be used by default when a model-fit is performed.

Templates

For your model-fitting task, you therefore should set up a config file for every model component you defining its default priors.

Next, inspect the TemplateObject.yaml priors configuration file in autofit_workspace/config/priors.

You should see the following text:

parameter0:
  type: Uniform
  lower_limit: 0.0
  upper_limit: 1.0
parameter1:
  type: Gaussian
  mean: 0.0
  sigma: 0.1
  lower_limit: 0.0
  upper_limit: inf
parameter2:
  type: Uniform
  lower_limit: 0.0
  upper_limit: 10.0

This specifies the default priors on two parameters, named parameter0 and parameter1.

The type is the type of prior assumed by PyAutoFit by default for its corresponding parameter, where in this example:

• parameter0 is given a UniformPrior with limits between 0.0 and 1.0.

• parameter1 a GaussianPrior with mean 0.0 and sigma 1.0.

• parameter2 is given a UniformPrior with limits between 0.0 and 10.0.

The lower_limit and upper_limit of a GaussianPrior define the boundaries of what parameter values are physically allowed. If a model-component is given a value outside these limits during model-fitting the model is instantly resampled and discarded.

We can easily adapt this template for any model component, for example the GaussianNoConfig.

First, copy and paste the TemplateObject.yaml file to create a new file called GaussianNoConfig.yaml.

The name of the class is matched to the name of the configuration file, therefore it is a requirement that the configuration file is named GaussianNoConfig.yaml so that PyAutoFit can associate it with the GaussianNoConfig Python class.

Now perform the follow changes to the .yaml file:

• Rename parameter0 to centre and updates its uniform prior to be from a lower_limit of 0.0 and an upper_limit of 100.0.

• Rename parameter1 to normalization.

• Rename parameter2 to sigma.

The .yaml file should read as follows:

centre:
  type: Uniform
  lower_limit: 0.0
  upper_limit: 100.0
normalization:
  type: Gaussian
  mean: 0.0
  sigma: 0.1
  lower_limit: 0.0
  upper_limit: inf
sigma:
  type: Uniform
  lower_limit: 0.0
  upper_limit: 10.0

We should now be able to make a Model of the GaussianNoConfig class and fit it, without manually specifying the priors.

You may need to reset your Jupyter notebook’s kernel for the changes to the .yaml file to take effect.

model = af.Model(GaussianNoConfig)

dataset_path = path.join("dataset", "example_1d", "gaussian_x1")
data = af.util.numpy_array_from_json(file_path=path.join(dataset_path, "data.json"))
noise_map = af.util.numpy_array_from_json(
    file_path=path.join(dataset_path, "noise_map.json")
)

analysis = af.ex.Analysis(data=data, noise_map=noise_map)

search = af.DynestyStatic()

result = search.fit(model=model, analysis=analysis)

Modules

For larger projects, it may not be ideal to have to write a .yaml file for every Python class which acts as a model component.

We instead would prefer them to be in their own dedicated Python module.

Suppose the Gaussian and Exponential model components were contained in a module named profiles.py in your project’s source code.

You could then write a priors .yaml config file following the format given in the example config file autofit_workspace/config/priors/profiles.yaml, noting that there is a paring between the module name (profiles.py) and the name of the .yaml file (profiles.yaml).

The file autofit_workspace/config/priors/template_module.yaml provides the tempolate for module based prior configs and reads as follows:

ModelComponent0:
  parameter0:
    type: Uniform
    lower_limit: 0.0
    upper_limit: 1.0
  parameter1:
    type: LogUniform
    lower_limit: 1.0e-06
    upper_limit: 1000000.0
  parameter2:
    type: Uniform
    lower_limit: 0.0
    upper_limit: 25.0
ModelComponent1:
  parameter0:
    type: Uniform
    lower_limit: 0.0
    upper_limit: 1.0
  parameter1:
    type: LogUniform
    lower_limit: 1.0e-06
    upper_limit: 1000000.0
  parameter2:
    type: Uniform
    lower_limit: 0.0
    upper_limit: 1.0

This looks very similar to TemplateObject, the only differences are:

• It now contains the model-component class name in the configuration file, e.g. ModelComponent0, ModelComponent1.

• It includes multiple model-components, whereas TemplateObject.yaml corresponded to only one model component.

Labels

There is an optional configs which associate model parameters with labels:

autofit_workspace/config/notation.yaml

It includes a label section which pairs every parameter with a label, which is used when visualizing results (e.g. these labels are used when creating a corner plot).

label:
  label:
    sigma: \sigma
    centre: x
    normalization: norm
    parameter0: a
    parameter1: b
    parameter2: c
    rate: \lambda

It also contains a superscript section which pairs every model-component label with a superscript, so that models with the same parameter names (e.g. centre can be distinguished).

label:
  superscript:
    Exponential: e
    Gaussian: g
    ModelComponent0: M0
    ModelComponent1: M1

The label_format section sets Python formatting options for every parameter, controlling how they display in the model.results file.

label_format:
  format:
    sigma: '{:.2f}'
    centre: '{:.2f}'
    normalization: '{:.2f}'
    parameter0: '{:.2f}'
    parameter1: '{:.2f}'
    parameter2: '{:.2f}'
    rate: '{:.2f}'