API

Top-Level

default_backend

NumPy backend for pyhf

default_optimizer

tensorlib

NumPy backend for pyhf

optimizer

get_backend()

Get the current backend and the associated optimizer

set_backend(*args, **kwargs)

Making Probability Distribution Functions (PDFs)

Workspace

An object that is built from a JSON spec that follows workspace.json.

Model

_ModelConfig

Backends

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

mxnet_backend.mxnet_backend

MXNet backend for pyhf

numpy_backend.numpy_backend

NumPy backend for pyhf

pytorch_backend.pytorch_backend

PyTorch backend for pyhf

tensorflow_backend.tensorflow_backend

TensorFlow backend for pyhf

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 \(\left|a\right| < 1\)

Exceptions

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

InvalidInterpCode

InvalidInterpCode is raised when an invalid/unimplemented interpolation code is requested.

InvalidModifier

InvalidModifier is raised when an invalid modifier is requested.

Utilities

generate_asimov_data(asimov_mu, data, pdf, …)

loglambdav(pars, data, pdf)

pvals_from_teststat(sqrtqmu_v, sqrtqmuA_v[, …])

The \(p\)-values for signal strength \(\mu\) and Asimov strength \(\mu'\) as defined in Equations (59) and (57) of `arXiv:1007.1727`_

pvals_from_teststat_expected(sqrtqmuA_v[, …])

Computes the expected \(p\)-values CLsb, CLb and CLs for data corresponding to a given percentile of the alternate hypothesis.

qmu(mu, data, pdf, init_pars, par_bounds)

The test statistic, \(q_{\mu}\), for establishing an upper limit on the strength parameter, \(\mu\), as defiend in Equation (14) in `arXiv:1007.1727`_ .

hypotest(poi_test, data, pdf[, init_pars, …])

Computes \(p\)-values and test statistics for a single value of the parameter of interest