Skip to main content

CUQIpy plugin for FEniCS

Project description

CUQIpy-FEniCS

CUQIpy-FEniCS is a plugin for CUQIpy software package. It provides an interface between FEniCS PDE models and CUQIpy modules. The documentation for CUQIpy-FEniCS can be found here.

Installation

First install FEniCS, we recommend using Anaconda from the available installation options. Then install CUQIpy-FEniCS with pip:

pip install cuqipy-fenics

If CUQIpy is not installed, it will be installed automatically. We provide additional information about FEniCS installation, below, in case the above approach was problematic.

Quickstart

import numpy as np
import matplotlib.pyplot as plt
import cuqi
import cuqipy_fenics

# Load a fenics forward model and data from testproblem library
model, y_data, info = cuqipy_fenics.testproblem.FEniCSDiffusion1D(
    dim=20,
    endpoint=1,
    exactSolution='smooth_step',
    mapping='exponential',
    SNR=10000,
    left_bc=0,
    right_bc=8
).get_components()

# Set up Bayesian model
x = cuqi.distribution.GMRF(np.zeros(model.domain_dim),
                           25, 1, 'zero', geometry=model.domain_geometry)
# y ~ N(model(x), 0.01^2)
y = cuqi.distribution.Gaussian(mean=model(x), cov=0.05**2)

# Set up Bayesian Problem object
BP = cuqi.problem.BayesianProblem(y, x).set_data(y=y_data)

# Sample from the posterior
samples = BP.sample_posterior(5000)

# Analyze the samples
samples.burnthin(1000).plot_ci(95, plot_par=True,
                               exact=info.exactSolution, linestyle='-', marker='.')

For more examples, see the demos folder.

More on using and installing FEniCS

FEniCS on Google Colaboratory

If you do not wish to install FEniCS on your machine or you have difficulty doing so, one option is to install FEniCS on Google Colaboratory (Colab) Jupyter Notebook service. We show below how to achieve this using FEM on Colab packages. First open a notebook in Google Colab (an introduction to Colab can be found here) and write in a cell:

try:
    import dolfin
except ImportError:
    !wget "https://fem-on-colab.github.io/releases/fenics-install-real.sh" -O "/tmp/fenics-install.sh" && bash "/tmp/fenics-install.sh"
    import dolfin

Then in a following cell use pip to install cuqipy-fenics as follows:

!pip install cuqipy-fenics

Test that you can import cuqi and cuqipy_fenics

import cuqi
import cuqipy_fenics

Note that with this set up on Google Colab, you need to use the FEniCS package ufl_legacy instead of ufl. To enable this, add the following line to your code right after importing cuqipy_fenics:

cuqipy_fenics.config.UFL_LEGACY = True

Create a Docker image for FEniCS

Here is another approach of installing FEniCS that could be useful in some cases (e.g. MAC M1 machines). We create a Docker image that contains a conda environment for FEniCS.

Run the following command lines to create a directory named, for example, my_dir.

mkdir my_dir
cd my_dir

In my_dir, run the following command lines to create the files needed for the Docker image and to pull a base image continuumio/miniconda3.

touch Dockerfile
touch environment.yml
docker pull continuumio/miniconda3

Edit the file Dockerfile you just created to have the following lines:

# Define base image
FROM continuumio/miniconda3
 
# Set working directory for the project
WORKDIR /app
 
# Create Conda environment from the YAML file
COPY environment.yml .
RUN conda env create -f environment.yml

Edit the file environment.yml you just created to have the following lines

name: env
channels:
   - conda-forge
dependencies:
   - python=3.10
   - fenics
   - pip
   - pip:
   # works for regular pip packages
     - cuqipy-fenics

(consider also python=3.8 if python=3.10 turn out to be problematic)

Build the docker image, named my_image for example, by running the following command inside my_dir (this might take a little while):

docker build -t my_image .

Run the docker image:

docker run --entrypoint=/bin/bash -it -p 127.0.0.1:8090:8000 -v $(pwd):/app -w /app my_image

Inside the Docker container, activate the conda environment and run python:

conda activate env
python

Run the following python commands to make sure FEniCS and cuqipy-fenics are installed

import fenics
import cuqi
import cuqipy_fenics

Project details


Download files

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

Source Distribution

cuqipy_fenics-0.8.0.tar.gz (62.3 kB view details)

Uploaded Source

Built Distribution

CUQIpy_FEniCS-0.8.0-py3-none-any.whl (45.4 kB view details)

Uploaded Python 3

File details

Details for the file cuqipy_fenics-0.8.0.tar.gz.

File metadata

  • Download URL: cuqipy_fenics-0.8.0.tar.gz
  • Upload date:
  • Size: 62.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for cuqipy_fenics-0.8.0.tar.gz
Algorithm Hash digest
SHA256 05e64a6ce3fa18d8e8de7707086a0a7240170e7d30e9570623bb6ed19dbf8189
MD5 6ec9fb534434ba4abcf0ed65e7c51200
BLAKE2b-256 42360bdb424db104240b53b93bc5ddb9c92fa3fe87660800daa728e295bbfe96

See more details on using hashes here.

File details

Details for the file CUQIpy_FEniCS-0.8.0-py3-none-any.whl.

File metadata

File hashes

Hashes for CUQIpy_FEniCS-0.8.0-py3-none-any.whl
Algorithm Hash digest
SHA256 68ccbc1985c7b91dc9f88d84f8dd201ea3f05b25ef00f867f31a244e90f3051d
MD5 0d4a1b7fa52fcb37f4b0592a636dac8a
BLAKE2b-256 c7668059d21ed33ae62a0ef2df6d38094e7ebfc7cf999d879537e0348e13bde4

See more details on using hashes here.

Supported by

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