AutoUncertainties

AutoUncertainties is a package that makes handling linear uncertainty propagation for scientific applications straightforward and automatic using auto-differentiation.

• View the full documentation here.

Statement of Need

AutoUncertainties is a Python package for uncertainty propagation of independent random variables. It provides a drop-in mechanism to add uncertainty information to Python scalar and NumPy array objects. It implements manual propagation rules for the Python dunder math methods, and uses automatic differentiation via JAX to propagate uncertainties for most NumPy methods applied to both scalar and NumPy array variables. In doing so, it eliminates the need for carrying around additional uncertainty variables or for implementing custom propagation rules for any NumPy operator with a gradient rule implemented by JAX. In most cases, it requires minimal modification to existing code—typically only when uncertainties are attached to central values.

One of the most important aspects of AutoUncertainties is its seamless support for NumPy:

python import numpy as np from auto_uncertainties import Uncertainty vals = np.array([0.5, 0.75]) errs = np.array([0.05, 0.3]) u = Uncertainty(vals, errs) print(np.cos(u)) # [0.877583 +/- 0.0239713, 0.731689 +/- 0.204492]

This is in contrast to the uncertainties package, which would have required the use of unumpy, a module containing several hand-implemented analogs of the true NumPy functions.

Supported Features

• [x] Scalars
• [x] Arrays, with support for most NumPy ufuncs and functions

Prerequisites

For array support:

• jax
• jaxlib
• numpy

Installation

To install, simply run:

pip install auto-uncertainties

Build Documentation

To build the documentation locally, clone the repository, create a virtual Python environment (if desired), and run the following commands within the repository directory:

bash pip install -e .[docs] sphinx-build docs/source docs/build

Once built, the docs can be found under the docs/build subdirectory.

CI and Unit Testing

Development of AutoUncertainties relies on a series of unit tests located in the tests directory. These are automatically run using GitHub actions when commits are pushed to the repository. To run the tests manually, first install the package with testing capabilities:

bash pip install -e .[CI] coverage run -m pytest --cov --cov-report=term

Basic Usage

• Creating a scalar Uncertainty variable is relatively simple:

python from auto_uncertainties import Uncertainty value = 1.0 error = 0.1 u = Uncertainty(value, error) print(u) # 1 +/- 0.1

As is creating a NumPy array of Uncertainties:

python from auto_uncertainties import Uncertainty import numpy as np value = np.linspace(start=0, stop=10, num=5) error = np.ones_like(value)*0.1 u = Uncertainty(value, error) print(u) # [0 +/- 0.1, 2.5 +/- 0.1, 5 +/- 0.1, 7.5 +/- 0.1, 10 +/- 0.1]

The Uncertainty class automatically determines which methods should be implemented based on whether it represents a vector uncertainty, or a scalar uncertainty. When instantiated with sequences or NumPy arrays, vector-based operations are enabled; when instantiated with scalars, only scalar operations are permitted.

• Scalar uncertainties implement all mathematical and logical dunder methods explicitly using linear uncertainty propagation.

python from auto_uncertainties import Uncertainty u = Uncertainty(10.0, 3.0) v = Uncertainty(20.0, 4.0) print(u + v) # 30 +/- 5

• Array uncertainties implement a large subset of the NumPy ufuncs and methods using jax.grad or jax.jacfwd, depending on the output shape.

```python from autouncertainties import Uncertainty import numpy as np value = np.linspace(start=0, stop=10, num=5) error = np.oneslike(value)*0.1 u = Uncertainty(value, error) print(np.exp(u)) # [1 +/- 0.1, 12.1825 +/- 1.21825, 148.413 +/- 14.8413, 1808.04 +/- 180.804, 22026.5 +/- 2202.65]

print(np.sum(u)) # 25 +/- 0.223607 print(u.sum()) # 25 +/- 0.223607 print(np.sqrt(np.sum(error**2))) # 0.223606797749979 ```

• The central value, uncertainty, and relative error are available as attributes:

python from auto_uncertainties import Uncertainty u = Uncertainty(10.0, 3.0) print(u.value) # 10.0 print(u.error) # 3.0 print(u.relative) # 0.3

• To strip central values and uncertainty from arbitrary variables, accessor functions nominal_values and std_devs are provided:

```python from autouncertainties import nominalvalues, stddevs u = Uncertainty(10.0, 3.0) v = 5.0 print(nominalvalues(u)) # 10.0 print(std_devs(u)) # 3.0

print(nominalvalues(v)) # 5.0 print(stddevs(v)) # 0.0 ```

• Displayed values are automatically rounded according to the g format specifier. To enable rounding consistent with the Particle Data Group (PDG) standard, the set_display_rounding function can be called as follows:

python from auto_uncertainties import Uncertainty, set_display_rounding import numpy as np value = np.linspace(start=0, stop=10, num=5) error = np.ones_like(value)*0.1 u = Uncertainty(value, error) set_display_rounding(True) # enable PDG rules print(np.sum(u)) # 25.0 +/- 0.22 set_display_rounding(False) # default behavior print(np.sum(u)) # 25 +/- 0.223607

If enabled, the PDG rounding rules will, in general, cause Uncertainty objects to be displayed with: - Error to 2 significant digits. - Central value to first signficant digit of error, or two significant figures (whichever is more significant digits).

• If numpy.array is called on an Uncertainty object, it will automatically get cast down to a numpy array (losing all uncertainty information!), and emit a warning. To force an exception to be raised instead, use set_downcast_error:

python from auto_uncertainties import Uncertainty, set_downcast_error import numpy as np set_downcast_error(True) value = np.linspace(start=0, stop=10, num=5) error = np.ones_like(value)*0.1 u = Uncertainty(value, error) print(np.array(u)) # Traceback (most recent call last): # ... # auto_uncertainties.exceptions.DowncastError: The uncertainty is stripped when downcasting to ndarray.

Current Limitations and Future Work

Dependent Random Variables

To simplify operations on Uncertainty objects, AutoUncertainties assumes all variables are independent. This means that, in the case where the programmer assumes dependence between two or more Uncertainty objects, unexpected and counter-intuitive behavior may arise during uncertainty propagation. This is a common pitfall when working with Uncertainty objects, especially since the package will not prevent you from manipulating variables in a manner that implies dependence.

• Subtracting Equivalent Uncertainties

Subtracting an Uncertainty from itself will not result in a standard deviation of zero:

python x = Uncertainty(5.0, 0.5) print(x - x) # 0 +/- 0.707107

• Mean Error Propagation

When multiplying a vector by a scalar Uncertainty object, each component of the resulting vector is assumed to be a multivariate normal distribution with no covariance, which may not be the desired behavior. For instance, taking the mean of such a vector will return an Uncertainty object with an unexpectedly small standard deviation.

python u = Uncertainty(5.0, 0.5) arr = np.ones(10) * 10 print(np.mean(u * arr)) # 50 +/- 1.58114, rather than 50 +/- 5 as expected

To obtain the uncertainty corresponding to the case where each element of the array is fully correlated, two workaround techniques can be used:

1. Separate the central value from the relative error, multiply the vector by the central value, take the mean of the resulting vector, and then multiply by the previously stored relative error.

   ```python u = Uncertainty(5.0, 0.5) scaleerror = Uncertainty(1, u.relative) # collect relative error scalevalue = u.value # collect central value

   arr = np.ones(10) * 10 print(np.mean(scalevalue * arr) * scaleerror) # 50 +/- 5 ```

2. Take the mean of the vector, and then multiply by the Uncertainty:

   python u = Uncertainty(5.0, 0.5) arr = np.ones(10) * 10 print(u * np.mean(arr)) # 50 +/- 5

These workarounds are nevertheless cumbersome, and cause AutoUncertainties to fall somewhat short of the original goals of automated error propagation. In principle, this could be addressed by storing a full computational graph of the result of chained operations, similar to what is done in uncertainties. However, the complexity of such a system places it out of scope for AutoUncertainties at this time.

It should be noted that, in cases where random variables have covariance that lies somewhere between fully correlated and fully independent, calculations like those described above would be more complex. To accurately propagate uncertainty, one would need to specify individual correlations between each variable, and adjust the computation as necessary. This is also currently out of scope for AutoUncertainties.

Inspirations

The class structure of Uncertainty and the NumPy ufunc implementation is heavily inspired by the excellent package Pint.