Python API#

Top-Level#

`tensorlib`	NumPy backend for pyhf
`optimizer`	Optimizer that uses `scipy.optimize.minimize()`.
`get_backend`	Get the current backend and the associated optimizer
`set_backend`	Set the backend and the associated optimizer
`readxml`
`writexml`
`compat`	Compatibility functions for translating between ROOT and pyhf
`schema`	See `Schema` for documentation.

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`	Configuration for the `Model`.
`_MainModel`	Factory class to create pdfs for the main measurement.
`_ConstraintModel`	Factory class to create pdfs for the constraint terms.
`_ChannelSummaryMixin`	A mixin that provides summary data of the provided channels.
`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.uncorrelated_background`	Construct a simple single channel `Model` with a `shapesys` modifier representing an uncorrelated background uncertainty.
`simplemodels.correlated_background`	Construct a simple single channel `Model` with a `histosys` modifier representing a background with a fully correlated bin-by-bin 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 minimizes via `iminuit.Minuit.migrad()`.

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#

Test Statistics#

`test_statistics.q0`	The test statistic, \(q_{0}\), for discovery of a positive signal as defined in Equation (12) in [1007.1727], for \(\mu=0\).
`test_statistics.qmu`	The test statistic, \(q_{\mu}\), for establishing an upper limit on the strength parameter, \(\mu\), as defined in Equation (14) in [1007.1727]
`test_statistics.qmu_tilde`	The "alternative" test statistic, \(\tilde{q}_{\mu}\), for establishing an upper limit on the strength parameter, \(\mu\), for models with bounded POI, as defined in Equation (16) in [1007.1727]
`test_statistics.tmu`	The test statistic, \(t_{\mu}\), for establishing a two-sided interval on the strength parameter, \(\mu\), as defined in Equation (8) in [1007.1727]
`test_statistics.tmu_tilde`	The test statistic, \(\tilde{t}_{\mu}\), for establishing a two-sided interval on the strength parameter, \(\mu\), for models with bounded POI, as defined in Equation (11) in [1007.1727]
`utils.get_test_stat`	Get the test statistic function by name.

Calculators#

`calculators.generate_asimov_data`	Compute Asimov Dataset (expected yields at best-fit values) for a given POI value.
`calculators.HypoTestFitResults`	Fitted model parameters of the fits in `AsymptoticCalculator.teststatistic`
`calculators.AsymptoticTestStatDistribution`	The distribution the test statistic in the asymptotic case.
`calculators.EmpiricalDistribution`	The empirical distribution of the test statistic.
`calculators.AsymptoticCalculator`	The Asymptotic Calculator.
`calculators.ToyCalculator`	The Toy-based Calculator.
`utils.create_calculator`	Creates a calculator object of the specified calctype.

Fits and Tests#

`mle.twice_nll`	Two times the negative log-likelihood of the model parameters, \(\left(\mu, \boldsymbol{\theta}\right)\), given the observed data.
`mle.fit`	Run a maximum likelihood fit.
`mle.fixed_poi_fit`	Run a maximum likelihood fit with the POI value fixed.
`hypotest`	Compute \(p\)-values and test statistics for a single value of the parameter of interest.
`utils.all_pois_floating`	Check whether all POI(s) are floating (i.e. not within the fixed set).

Confidence Intervals#

`intervals.upper_limits.upper_limit`	Calculate an upper limit interval `(0, poi_up)` for a single Parameter of Interest (POI) using root-finding or a linear scan through POI-space.
`intervals.upper_limits.toms748_scan`	Calculate an upper limit interval `(0, poi_up)` for a single Parameter of Interest (POI) using an automatic scan through POI-space, using the `toms748()` algorithm.
`intervals.upper_limits.linear_grid_scan`	Calculate an upper limit interval `(0, poi_up)` for a single Parameter of Interest (POI) using a linear scan through POI-space.
`intervals.upperlimit`

Schema#

`Schema`	A module-level wrapper around `pyhf.schema` which will provide additional functionality for interacting with schemas.
`load_schema`	Get a schema by relative path from cache, or load it into the cache and return.
`validate`	Validate the provided instance, `spec`, against the schema associated with `schema_name`.

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#

`options_from_eqdelimstring`
`digest`	Get the digest for the provided object.
`citation`	Get the bibtex citation for pyhf

Contrib#

`viz.brazil`	Brazil Band Plots.
`utils.download`	Download the patchset archive from the remote URL and extract it in a directory at the path given.