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_backend()

Get the current backend and the associated optimizer

set_backend(backend[, custom_optimizer, …])

Set the backend and the associated optimizer

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

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 “alternative” 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 (8) in [1007.1727]

test_statistics.tmu_tilde(mu, data, pdf, …)

The test statistic, \(\tilde{t}_{\mu}\), 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)

Two times the negative log-likelihood of the model parameters, \(\left(\mu, \boldsymbol{\theta}\right)\), given the observed data.

mle.fit(data, pdf[, init_pars, par_bounds, …])

Run a maximum likelihood fit.

mle.fixed_poi_fit(poi_val, data, pdf[, …])

Run a maximum likelihood fit with the POI value fixed.

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

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

intervals.upperlimit(data, model, scan[, …])

Calculate an upper limit interval (0, poi_up) for a single Parameter of Interest (POI) using a fixed scan through POI-space.

calculators.generate_asimov_data(asimov_mu, …)

Compute Asimov Dataset (expected yields at best-fit values) for a given POI value.

calculators.AsymptoticTestStatDistribution(shift)

The distribution the test statistic in the asymptotic case.

calculators.EmpiricalDistribution(samples)

The empirical distribution of the test statistic.

calculators.AsymptoticCalculator(data, pdf)

The Asymptotic Calculator.

calculators.ToyCalculator(data, pdf[, …])

The Toy-based Calculator.

utils.create_calculator(calctype, *args, …)

Creates a calculator object of the specified calctype.

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])

options_from_eqdelimstring(opts)

digest(obj[, algorithm])

Get the digest for the provided object.

Contrib

viz.brazil

Brazil Band Plots.

utils.download(archive_url, output_directory)

Download the patchset archive from the remote URL and extract it in a directory at the path given.