{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/4"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/3"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/5"},{"label":"Utilities","url":"https://skillfed.io/packages/category/utilities/3"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"},{"label":"Physics","url":"https://skillfed.io/packages/category/scientific-engineering-physics"},{"label":"Education","url":"https://skillfed.io/packages/category/education"}],"enrichment":{"capability":"Performs arithmetic and mathematical operations on values with uncertainties, automatically propagating errors through calculations and tracking correlations between expressions.","skillfed_tags":["scientific-computing","error-analysis","measurement-uncertainty"],"use_cases":["Physics or chemistry experiments: track measurement uncertainties through multi-step calculations and report final results with error bars","Engineering design: propagate component tolerances through system models to predict overall system uncertainty","Data analysis: compute statistics on datasets where each point has an associated measurement error","Educational demonstrations: show students how errors accumulate through calculations without manual error propagation formulas","Calibration and metrology: combine multiple uncertain measurements and track how uncertainty changes through transformations"],"what_it_does":"The uncertainties package lets you perform calculations on numbers that have measurement errors or uncertainties attached to them, automatically computing how those errors propagate through your math. Instead of manually tracking error bars at each step, you work with uncertain values directly\u2014operations like addition, multiplication, and trigonometric functions all handle error propagation transparently. The package correctly accounts for correlations, so expressions like x - x evaluate to exactly zero rather than showing spurious uncertainty.\n\nIt supports most standard mathematical operations including functions from the math module, comparison operators, and array operations through a NumPy-like interface. You can also extract derivatives of any expression automatically, which the package uses internally for error propagation but exposes for your own use. The package is designed to require minimal changes to existing code\u2014you typically just replace float literals with uncertain values and the rest works as expected.","worth_installing":"Yes. The package is actively maintained, has no dependencies, installs easily, carries no security vulnerabilities, and solves a genuine problem in scientific and engineering workflows. It's production-stable (since 2010) and widely used. Install it if you work with measurements or experimental data where uncertainty quantification matters."},"id":"uncertainties","links":{"html":"https://skillfed.io/packages/uncertainties","md":"https://skillfed.io/packages/uncertainties.md","pypi":"https://pypi.org/project/uncertainties/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-04-21","license_spdx":null,"license_treatment":"permissive","name":"uncertainties","python_support":"supports_current","summary":"calculations with values with uncertainties, error propagation"},"popularity":{"monthly_downloads":2595858,"position":2976,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.2.3"}