Python API

Top-Level

default_backend	NumPy backend for pyhf
default_optimizer	scipy.optimize-based Optimizer using finite differences.
tensorlib	NumPy backend for pyhf
optimizer	scipy.optimize-based Optimizer using finite differences.
get_backend()	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.

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

Optimizers

opt_pytorch.pytorch_optimizer	PyTorch Optimizer Backend.
opt_scipy.scipy_optimizer	scipy.optimize-based Optimizer using finite differences.
opt_tflow.tflow_optimizer	Tensorflow Optimizer Backend.
opt_minuit.minuit_optimizer	MINUIT Optimizer Backend.

Modifiers

histosys
normfactor
normsys
shapefactor
shapesys
staterror

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].
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 fixzed.
utils.generate_asimov_data(asimov_mu, data, …)	Compute Asimov Dataset (expected yields at best-fit values) for a given POI value.
utils.pvals_from_teststat(sqrtqmu_v, sqrtqmuA_v)	Compute p-values from test-statistic values.
utils.pvals_from_teststat_expected(sqrtqmuA_v)	Compute the expected \(p\)-values CLsb, CLb and CLs for data corresponding to a given percentile of the alternate hypothesis.

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.
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.
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])
options_from_eqdelimstring(opts)