cabinetry

library0.6.0pypypi✓ verified 86d ago

cabinetry is a Python library designed for building and steering profile likelihood fits, particularly used in high-energy physics for statistical analysis. It currently stands at version 0.6.0 and maintains an active development cycle with regular patch and minor releases, often coinciding with updates to its core dependency, `pyhf`.

data ai-ml

pip install cabinetry

github ↗package ↗

INSTALL

IMPORT

SIG · CABINETRY

cabinetry

datapythonv0.6.0

Install

13.4s avg

Import

5315ms

Disk

342MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.6.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 5.407s · 342.4MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 13.4s · import 5.223s · 322MB

342MB installed

● package 342MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

cabinetry

✓ import cabinetry

templates

✓ from cabinetry import templates

fit

✓ from cabinetry import fit

model_utils

✓ from cabinetry import model_utils

workspace

✓ from cabinetry import workspace

visualize

✓ from cabinetry import visualize

This quickstart demonstrates the core workflow of cabinetry: defining a statistical model via a configuration, generating dummy data and sample histograms, building a `pyhf` workspace, performing a profile likelihood fit, and visualizing the results. The example generates in-memory histograms to ensure runnability without external files.

import cabinetry import numpy as np import pyhf import os # 1. Define configuration (simplified for quickstart) # In a real scenario, this would likely be loaded from a YAML/JSON file config = { "General": { "Measurement": "my_measurement", "POI": "Signal_norm", "Luminosity": 1.0 }, "Regions": [ { "Name": "SR", "HistogramFolders": ["data/", "signal/", "background/"], "Variable": "m_yy", "Binning": [40, 50, 60, 70, 80, 90, 100] } ], "Samples": [ {"Name": "Signal", "Observable": "m_yy", "Normalization": "Signal_norm"}, {"Name": "Background", "Observable": "m_yy"} ], "Systematics": [ { "Name": "lumi", "Type": "lumi", "Expression": "1.02" } ] } # 2. Create dummy data/histograms in memory bins = config["Regions"][0]["Binning"] data_hists = { "SR": np.array([5, 8, 12, 10, 7, 4]) } sample_hists = [ { "name": "Signal", "region": "SR", "nominal": np.array([2, 3, 4, 3, 2, 1]), "staterr": np.sqrt(np.array([2, 3, 4, 3, 2, 1])) # Placeholder for stat error }, { "name": "Background", "region": "SR", "nominal": np.array([3, 5, 8, 7, 5, 3]), "staterr": np.sqrt(np.array([3, 5, 8, 7, 5, 3])) } ] # Add systematic variation for "lumi" for h in sample_hists: h["lumi_up"] = h["nominal"] * 1.02 h["lumi_down"] = h["nominal"] * 0.98 h["bins"] = bins # Add binning for plotting # 3. Build workspace ws = cabinetry.workspace.build(config, sample_hists, data_hists) model, data = cabinetry.model_utils.model_and_data(ws) # 4. Perform fit fit_results = cabinetry.fit.fit(model, data) # 5. Visualize results (optional) plot_dir = "cabinetry_quickstart_plots" os.makedirs(plot_dir, exist_ok=True) cabinetry.visualize.data_mc( model, data, fit_results=fit_results, config=config, save_path=os.path.join(plot_dir, "data_mc_plot.pdf") ) cabinetry.visualize.ranking( fit_results, save_path=os.path.join(plot_dir, "ranking_plot.pdf") ) print("cabinetry quickstart complete. Plots saved to:", os.path.abspath(plot_dir))

Debug

Known issues

breakingAs of cabinetry v0.6.0, support for Python 3.7 has been dropped. Previous versions may still run on Python 3.7, but v0.6.0+ explicitly requires Python 3.8 or newer.

fix

Upgrade your Python environment to 3.8 or a later compatible version.

affects: >=0.6.0

breakingcabinetry v0.5.0 adopted the `pyhf` 0.7 API. Code written for older `pyhf` versions or cabinetry versions prior to 0.5.0 may experience compatibility issues if the `pyhf` model definitions or API calls are used directly.

fix

Ensure your `pyhf` installation is version 0.7.x or later (`pip install 'pyhf>=0.7.0'`). Review `pyhf`'s changelog for any direct API changes if you interact with `pyhf` objects outside of cabinetry's high-level API.

affects: >=0.5.0

breakingFrom cabinetry v0.4.0 onwards, all API arguments are keyword-only. This means you must explicitly use the keyword when calling functions, e.g., `func(arg=value)` instead of `func(value)`.

fix

Update all function calls to use keyword arguments. For instance, `cabinetry.fit.fit(model, data)` would become `cabinetry.fit.fit(model=model, data=data)`.

affects: >=0.4.0

gotchaSince cabinetry v0.2.2, cabinetry no longer overrides the `pyhf` backend. If you need a specific `pyhf` backend (e.g., 'jax', 'torch'), you must set it explicitly using `pyhf.set_backend()` before any `pyhf` operations are performed.

fix

Call `pyhf.set_backend('your_backend_name')` at the beginning of your script if you require a specific `pyhf` backend.

affects: >=0.2.2

gotchacabinetry v0.5.1 introduced a minimum required `matplotlib` version of 3.5.0 to address plotting issues and enable new features.

fix

Ensure your `matplotlib` installation is version 3.5.0 or newer (`pip install 'matplotlib>=3.5.0'`).

affects: >=0.5.1

Errors

Common errors & fixes

TypeError: <function_name> missing 1 required keyword-only argument: '<arg_name>'

Attempting to call a cabinetry function with positional arguments after v0.4.0, where all arguments became keyword-only.

fix

Update the function call to explicitly use keyword arguments. For example, change `cabinetry.fit.fit(model, data)` to `cabinetry.fit.fit(model=model, data=data)`.

RuntimeError: This version of cabinetry requires Python 3.8 or higher.

Running cabinetry v0.6.0 or newer on an unsupported Python version (e.g., Python 3.7).

fix

Upgrade your Python environment to version 3.8 or newer to meet the library's requirements.

pyhf.exceptions.InvalidSpecification: model specification not valid

Incompatibility between the `cabinetry` version and the installed `pyhf` version, particularly if `cabinetry` v0.5.0+ is used with an older `pyhf` (<0.7.0).

fix

Ensure `pyhf` is updated to a compatible version, specifically `pyhf>=0.7.0` for cabinetry v0.5.0 and later (`pip install 'pyhf>=0.7.0'`).

AttributeError: 'Figure' object has no attribute 'tight_layout' (or other plotting errors)

An outdated `matplotlib` version being used, which lacks features or has breaking API changes that cabinetry's visualization functions rely on.

fix

Upgrade `matplotlib` to at least version 3.5.0 (`pip install 'matplotlib>=3.5.0'`).

Upgrade

Version history

0.6.0latest on PyPI · released Sep 19, 2023

Audit

Dependencies

pyhfrequiredCore library for statistical models and inference. cabinetry builds on pyhf's functionality.

matplotlibrequiredFor visualizations and plotting of fit results and data/MC comparisons.

pythonrequiredRequires Python 3.8 or higher, as of v0.6.0.

Agent activity

14 hits · last 30 days

node

Amazon

OpenAI (training)

Resources

githubhttps://github.com/scikit-hep/cabinetry ↗

packagehttps://pypi.org/project/cabinetry/ ↗