CUQIpy 0.1.1

Computational Uncertainty Quantification for Inverse problems in Python

CUQIpy: Computational Uncertainty Quantification for Inverse Problems in python

Computational Uncertainty Quantification for Inverse Problems in python (CUQIpy) is a python package for modeling and solving inverse problems in a Bayesian inference framework. CUQIpy provides a simple high-level interface to perform UQ analysis of inverse problems, while still allowing full control of the models and methods. The package comes equipped with a number of predefined distributions, samplers, models and test problems and is built to be easily further extended when needed.

You can find the full CUQIpy documentation here.

This software package is part of the CUQI project funded by the Villum Foundation.

Quick Example - UQ in 5 steps

Image deconvolution with uncertainty quantification

# Imports
import numpy as np
import matplotlib.pyplot as plt
from cuqi.testproblem import Deconvolution2D
from cuqi.data import grains
from cuqi.distribution import Laplace_diff, GaussianCov 
from cuqi.problem import BayesianProblem

# Step 1: Model and data, y = Ax
A, y_data, info = Deconvolution2D.get_components(dim=128, phantom=grains())

# Step 2: Prior, x ~ Laplace_diff(0, 0.01)
x = Laplace_diff(location=np.zeros(A.domain_dim),
                 scale=0.01,
                 bc_type='neumann',
                 physical_dim=2)

# Step 3: Likelihood, y ~ N(Ax, 0.0036^2)
y = GaussianCov(mean=A@x, cov=0.0036**2)

# Step 4: Set up Bayesian problem and sample posterior
BP = BayesianProblem(y, x).set_data(y=y_data)
samples = BP.sample_posterior(200)

# Step 5: Analysis
info.exactSolution.plot(); plt.title("Exact solution")
y_data.plot(); plt.title("Data")
samples.plot_mean(); plt.title("Posterior mean")
samples.plot_std(); plt.title("Posterior standard deviation")

Getting Started

To run cuqipy on your local machine, clone the cuqipy repository:

git clone https://github.com/CUQI-DTU/CUQIpy.git

Then go to the project directory:

cd cuqipy

You can run some demos, for example:

cd demos
python demo00_MinimalExample.py 

Required Dependencies

Requirements of cuqipy are listed in cuqipy/requirements.txt and can be installed via conda by (while in cuqipy directory)

conda install --file requirements.txt

or using pip by

pip install -r requirements.txt 

Optional Dependencies

CUQIpy can be extended with additional functionality by installing optional plugins. These can be found at CUQI-DTU.

Running the Tests

To make sure that cuqipy runs as expected on your machine and that all requirements are met, you can run the tests. While in the project directory cuqipy, run:

python -m pytest 

Building Documentation

To generate sphinx html documentation in your local machine, make sure you have working installation of sphinx and sphinx-rtd-theme. Then run the following commands in cuqipy directory:

cd docs
sphinx-build -b html . _build

Then open docs/_build/index.html using your preferred web browser to browse cuqipy documentation.

Contributors

See the list of contributors who participated in this project.