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)

`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

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

`numpy_backend.numpy_backend`	NumPy backend for pyhf
`pytorch_backend.pytorch_backend`
`tensorflow_backend.tensorflow_backend`

`opt_pytorch.pytorch_optimizer`
`opt_scipy.scipy_optimizer`
`opt_tflow.tflow_optimizer`
`opt_minuit.minuit_optimizer`

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

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.

`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