# Python API¶

## Top-Level¶

 default_backend NumPy backend for pyhf default_optimizer Optimizer that uses scipy.optimize.minimize(). tensorlib NumPy backend for pyhf optimizer Optimizer that uses scipy.optimize.minimize(). Get the current backend and the associated optimizer set_backend(*args, **kwargs)

## Probability Distribution Functions (PDFs)¶

 Normal The Normal distribution with mean loc and standard deviation scale. Poisson The Poisson distribution with rate parameter rate. Independent A probability density corresponding to the joint distribution of a batch of identically distributed random variables. Simultaneous A probability density corresponding to the joint distribution of multiple non-identical component distributions

## Making Models from PDFs¶

 Model The main pyhf model class. _ModelConfig Workspace A JSON-serializable object that is built from an object that follows the workspace.json schema. PatchSet A way to store a collection of patches (Patch). Patch A way to store a patch definition as part of a patchset (PatchSet). simplemodels.hepdata_like Construct a simple single channel Model with a shapesys modifier representing an uncorrelated background uncertainty.

## Backends¶

The computational backends that pyhf provides interfacing for the vector-based calculations.

 numpy_backend.numpy_backend NumPy backend for pyhf pytorch_backend.pytorch_backend PyTorch backend for pyhf tensorflow_backend.tensorflow_backend TensorFlow backend for pyhf jax_backend.jax_backend JAX backend for pyhf

## Optimizers¶

 mixins.OptimizerMixin Mixin Class to build optimizers. opt_scipy.scipy_optimizer Optimizer that uses scipy.optimize.minimize(). opt_minuit.minuit_optimizer Optimizer that uses iminuit.Minuit.migrad.

## Interpolators¶

 code0 The piecewise-linear interpolation strategy. code1 The piecewise-exponential interpolation strategy. code2 The quadratic interpolation and linear extrapolation strategy. code4 The polynomial interpolation and exponential extrapolation strategy. code4p The piecewise-linear interpolation strategy, with polynomial at $$\left|a\right| < 1$$.

## Inference¶

 hypotest(poi_test, data, pdf[, init_pars, …]) Compute $$p$$-values and test statistics for a single value of the parameter of interest. test_statistics.qmu(mu, data, pdf, …) The test statistic, $$q_{\mu}$$, for establishing an upper limit on the strength parameter, $$\mu$$, as defiend in Equation (14) in [1007.1727]. test_statistics.qmu_tilde(mu, data, pdf, …) The test statistic, $$\tilde{q}_{\mu}$$, for establishing an upper limit on the strength parameter, $$\mu$$, for models with bounded POI, as defiend in Equation (16) in [1007.1727]. test_statistics.tmu(mu, data, pdf, …) The test statistic, $$t_{\mu}$$, for establishing a two-sided interval on the strength parameter, $$\mu$$, as defiend in Equation (10) in [1007.1727]. test_statistics.tmu_tilde(mu, data, pdf, …) The test statistic, $$t_{\mu}$$, for establishing a two-sided interval on the strength parameter, $$\mu$$, for models with bounded POI, as defiend in Equation (11) in [1007.1727]. mle.twice_nll(pars, data, pdf) Twice the negative Log-Likelihood. mle.fit(data, pdf[, init_pars, par_bounds]) Run a unconstrained maximum likelihood fit. mle.fixed_poi_fit(poi_val, data, pdf[, …]) Run a maximum likelihood fit with the POI value fixed. calculators.generate_asimov_data(asimov_mu, …) Compute Asimov Dataset (expected yields at best-fit values) for a given POI value. The distribution the test statistic in the asymptotic case. calculators.AsymptoticCalculator(data, pdf) The Asymptotic Calculator.

## Exceptions¶

Various exceptions, apart from standard python exceptions, that are raised from using the pyhf API.

 InvalidMeasurement InvalidMeasurement is raised when a specified measurement is invalid given the specification. InvalidNameReuse InvalidSpecification InvalidSpecification is raised when a specification does not validate against the given schema. InvalidPatchSet InvalidPatchSet is raised when a given patchset object does not have the right configuration, even though it validates correctly against the schema. InvalidPatchLookup InvalidPatchLookup is raised when the patch lookup from a patchset object has failed PatchSetVerificationError PatchSetVerificationError is raised when the workspace digest does not match the patchset digests as part of the verification procedure InvalidWorkspaceOperation InvalidWorkspaceOperation is raised when an operation on a workspace fails. InvalidModel InvalidModel is raised when a given model does not have the right configuration, even though it validates correctly against the schema. InvalidModifier InvalidModifier is raised when an invalid modifier is requested. InvalidInterpCode InvalidInterpCode is raised when an invalid/unimplemented interpolation code is requested. ImportBackendError MissingLibraries is raised when something is imported by sustained an import error due to missing additional, non-default libraries. InvalidBackend InvalidBackend is raised when trying to set a backend that does not exist. InvalidOptimizer InvalidOptimizer is raised when trying to set an optimizer that does not exist. InvalidPdfParameters InvalidPdfParameters is raised when trying to evaluate a pdf with invalid parameters. InvalidPdfData InvalidPdfData is raised when trying to evaluate a pdf with invalid data.

## Utilities¶

 load_schema(schema_id[, version]) validate(spec, schema_name[, version]) digest(obj[, algorithm]) Get the digest for the provided object.