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Installation Guide | readthedocs | Introduction on Binder | HowToFit
PyAutoFit is a Python based probabilistic programming language for the fully Bayesian analysis of extremely large datasets which:
PyAutoFit supports advanced statistical methods such as graphical and hierarchical models, model-fit chaining, sensitivity mapping and massively parallel model-fits .
The following links are useful for new starters:
PyAutoFit began as an Astronomy project for fitting large imaging datasets of galaxies after the developers found that existing PPLs (e.g., PyMC3, Pyro, STAN) were not suited to the model fitting problems many Astronomers faced. This includes:
If these challenges sound familiar, then PyAutoFit may be the right software for your model-fitting needs!
To illustrate the PyAutoFit API, we'll use an illustrative toy model of fitting a one-dimensional Gaussian to
noisy 1D data. Here's the
data (black) and the model (red) we'll fit:
We define our model, a 1D Gaussian by writing a Python class using the format below:
class Gaussian: def __init__( self, centre=0.0, # <- PyAutoFit recognises these normalization=0.1, # <- constructor arguments are sigma=0.01, # <- the Gaussian's parameters. ): self.centre = centre self.normalization = normalization self.sigma = sigma """ An instance of the Gaussian class will be available during model fitting. This method will be used to fit the model to data and compute a likelihood. """ def model_data_1d_via_xvalues_from(self, xvalues): transformed_xvalues = xvalues - self.centre return (self.normalization / (self.sigma * (2.0 * np.pi) ** 0.5)) * \ np.exp(-0.5 * (transformed_xvalues / self.sigma) ** 2.0)
PyAutoFit recognises that this Gaussian may be treated as a model component whose parameters can be fitted for via a non-linear search like emcee.
To fit this Gaussian to the
data we create an Analysis object, which gives PyAutoFit the
data and a
log_likelihood_function describing how to fit the
data with the model:
class Analysis(af.Analysis): def __init__(self, data, noise_map): self.data = data self.noise_map = noise_map def log_likelihood_function(self, instance): """ The 'instance' that comes into this method is an instance of the Gaussian class above, with the parameters set to values chosen by the non-linear search. """ print("Gaussian Instance:") print("Centre = ", instance.centre) print("normalization = ", instance.normalization) print("Sigma = ", instance.sigma) """ We fit the ``data`` with the Gaussian instance, using its "model_data_1d_via_xvalues_from" function to create the model data. """ xvalues = np.arange(self.data.shape) model_data = instance.model_data_1d_via_xvalues_from(xvalues=xvalues) residual_map = self.data - model_data chi_squared_map = (residual_map / self.noise_map) ** 2.0 log_likelihood = -0.5 * sum(chi_squared_map) return log_likelihood
We can now fit our model to the
data using a non-linear search:
model = af.Model(Gaussian) analysis = Analysis(data=data, noise_map=noise_map) emcee = af.Emcee(nwalkers=50, nsteps=2000) result = emcee.fit(model=model, analysis=analysis)
result contains information on the model-fit, for example the parameter samples, maximum log likelihood
model and marginalized probability density functions.
Support for installation issues, help with Fit modeling and using PyAutoFit is available by raising an issue on the GitHub issues page.
We also offer support on the PyAutoFit Slack channel, where we also provide the latest updates on PyAutoFit. Slack is invitation-only, so if you'd like to join send an email requesting an invite.