Skip to main content

calculations with values with uncertainties, error propagation

Project description

https://readthedocs.org/projects/uncertainties/badge/?version=latest https://img.shields.io/pypi/v/uncertainties.svg https://pepy.tech/badge/uncertainties/week https://codecov.io/gh/lmfit/uncertainties/branch/master/graph/badge.svg https://img.shields.io/github/actions/workflow/status/lmfit/uncertainties/python-package.yml?logo=github%20actions

The uncertainties package allows calculations with values that have uncertaintes, such as (2 +/- 0.1)*2 = 4 +/- 0.2. uncertainties takes the pain and complexity out of error propagation and calculations of values with uncertainties. For more information, see https://uncertainties.readthedocs.io/

Basic examples

>>> from uncertainties import ufloat
>>> x = ufloat(2, 0.25)
>>> x
2.0+/-0.25

>>> square = x**2
>>> square
4.0+/-1.0
>>> square.nominal_value
4.0
>>> square.std_dev  # Standard deviation
1.0

>>> square - x*x
0.0  # Exactly 0: correlations taken into account

>>> from uncertainties.umath import sin, cos  # and many more.
>>> sin(1+x**2)
-0.95892427466313845+/-0.2836621854632263

>>> print (2*x+1000).derivatives[x]  # Automatic calculation of derivatives
2.0

>>> from uncertainties import unumpy  # Array manipulation
>>> varr = unumpy.uarray([1, 2], [0.1, 0.2])
>>> print(varr)
[1.0+/-0.1 2.0+/-0.2]
>>> print(varr.mean())
1.50+/-0.11
>>> print(unumpy.cos(varr))
[0.540302305868+/-0.0841470984808 -0.416146836547+/-0.181859485365]

Main features

  • Transparent calculations with uncertainties: Little or no modification of existing code is needed to convert calculations of floats to calculations of values with uncertainties.

  • Correlations between expressions are correctly taken into account. Thus, x-x is exactly zero.

  • Most mathematical operations are supported, including most functions from the standard math module (sin,…). Comparison operators (>, ==, etc.) are supported too.

  • Many fast operations on arrays and matrices of numbers with uncertainties are supported.

  • Extensive support for printing numbers with uncertainties (including LaTeX support and pretty-printing).

  • Most uncertainty calculations are performed analytically.

  • This module also gives access to the derivatives of any mathematical expression (they are used by error propagation theory, and are thus automatically calculated by this module).

Installation or upgrade

To install uncertainties, use:

pip install uncertainties

To upgrade from an older version, use:

pip install --upgrade uncertainties

Further details are in the on-line documentation.

Git branches

The GitHub master branch is the latest development version, and is intended to be a stable pre-release version. It will be experimental, but should pass all tests.. Tagged releases will be available on GitHub, and correspond to the releases to PyPI. The GitHub gh-pages branch will contain a stable test version of the documentation that can be viewed at https://lmfit.github.io/uncertainties/. Other Github branches should be treated as unstable and in-progress development branches.

License

This package and its documentation are released under the Revised BSD License.

History

This package was created back around 2009 by Eric O. LEBIGOT.

Ownership of the package was taken over by the lmfit GitHub organization in 2024.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

uncertainties-3.2.3.tar.gz (160.5 kB view details)

Uploaded Source

Built Distribution

uncertainties-3.2.3-py3-none-any.whl (60.1 kB view details)

Uploaded Python 3

File details

Details for the file uncertainties-3.2.3.tar.gz.

File metadata

  • Download URL: uncertainties-3.2.3.tar.gz
  • Upload date:
  • Size: 160.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for uncertainties-3.2.3.tar.gz
Algorithm Hash digest
SHA256 76a5653e686f617a42922d546a239e9efce72e6b35411b7750a1d12dcba03031
MD5 72e1787b1c7c87ba472a47b084550e62
BLAKE2b-256 fe0ccb09f33b26955399c675ab378e4063ed7e48422d3d49f96219ab0be5eba9

See more details on using hashes here.

File details

Details for the file uncertainties-3.2.3-py3-none-any.whl.

File metadata

File hashes

Hashes for uncertainties-3.2.3-py3-none-any.whl
Algorithm Hash digest
SHA256 313353900d8f88b283c9bad81e7d2b2d3d4bcc330cbace35403faaed7e78890a
MD5 f175e31183ec8a35be82e2f754298517
BLAKE2b-256 8f5ef1e1dd319e35e962a4e00b33150a8868b6329cc1d19fd533436ba5488f09

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page