Uncertainties¶
The uncertainties package is an open source Python library for doing
calculations on numbers that have uncertainties (like 3.14±0.01) that are
common in many scientific fields. The calculations done with this package will
propagate the uncertainties to the result of mathematical calculations.
The uncertainties package takes the pain and
complexity out of uncertainty calculations and error propagation. Here is a
quick taste of how to use uncertainties:
>>> from uncertainties import ufloat
>>> x = ufloat(2, 0.1) # x = 2+/-0.1
>>> y = ufloat(3, 0.2) # y = 3+/-0.2
>>> print(2*x)
4.00+/-0.20
>>> print(x+y)
5.00+/-0.22
>>> print(x*y)
6.0+/-0.5
The uncertainties library calculates uncertainties using linear error
propagation theory by automatically calculating derivatives and analytically propagating these to the results. Correlations
between variables are automatically handled. This library can also yield the
derivatives of any expression with respect to the variables that have uncertain
values. For other approaches, see soerp (using higher-order terms) and mcerp
(using a Monte-Carlo approach).
The source code for the uncertainties package is licensed under the Revised BSD License. This documentation is licensed under the CC-SA-3 License.
Version 4.0 Coming Soon¶
The uncertainties team is working on developing a new major release.
This release modernizes the uncertainties codebase and simplifies the underlying
architecture used for uncertainty propagation. These changes greatly improve the
maintainability of this code base which helps guarantee its functionality and support
into the future. At the heart of the architecture change is the idea that
UFloat objects should closely resemble mathematical random variables. Here are
some specific benefits from the architecture update.
UFloatobjects will now be immutable and hashing them will follow the pythondata model rule that two objects which are equal should have the same hash.
- Previously,
UFloatinstances did not preserve correlations upon copying, pickling, or any other form of serialization. Now
UFloatinstances will preserve correlations upon copying and pickling.
- Previously,
- Previously when users generated a number with uncertainties, the resulting object may
have been a
VariableorAffineScalarFuncinstance depending on how the object was created. Now all numbers with uncertainty are uniformly represented byUFloatinstances.AffineScalarFuncandVariableremain as legacy names forUFloat.
In addition to reformating the underlying architecture, this update makes a number of changes and simplifications to the API to improve consistency. Some of these API changes break backwards compatibility. Notable changes are
Elimination of the
AffineScalarFunc.derivativesproperty.- Replacing the
AffineScalarFunc.error_components()method with the UFloat.error_componentsproperty.
- Replacing the
- Elimination of a number of
UFloatinstance methods andumathmodule functions which do not make sense for random variables such as comparison methods and modulo methods and functions. These changes are recorded in the 3.2 series changelog. These deprecated methods and functions emit warnings in the most recent
uncertaintiesversions.
- Elimination of a number of
Note that basic usage of the uncertainties package including creating numbers
with uncertainty and manipulating them using basic arithmetic or math methods and
functions (other than those which have been removed) will still work as it did
previously.
There will continue to be support for the latest version 3 release as the community transitions to the new version.
Table of Contents¶
- Installation and Credits
- User Guide
- Basic usage
- Creating Variables: numbers with uncertainties
- Basic math with uncertain Variables
- Mathematical operations with uncertain Variables
- Comparison operators
- Handling NaNs and infinities
- Power Function Behavior
- Automatic correlations
- Access to the individual sources of uncertainty
- Covariance and correlation matrices
- Correlated variables
- Making custom functions accept numbers with uncertainties
- Miscellaneous utilities
- Derivatives
- Additional information
- Uncertainties and numpy arrays
- Formatting Variables with uncertainties
- Advanced Topics