Skip to main content

A package for LISA Data Analysis

Project description

LISA Analysis Tools

Doc badge DOI

LISA Analysis Tools is a package for performing LISA Data Analysis tasks, including building the LISA Global Fit.

To install the latest version of lisaanalysistools using pip, simply run:

# For CPU-only version
pip install lisaanalysistools

# For GPU-enabled versions with CUDA 11.Y.Z
pip install lisaanalysistools-cuda11x

# For GPU-enabled versions with CUDA 12.Y.Z
pip install lisaanalysistools-cuda12x

To know your CUDA version, run the tool nvidia-smi in a terminal a check the CUDA version reported in the table header:

$ nvidia-smi
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
...

Now, in a python file or notebook:

import lisatools

You may check the currently available backends:

>>> for backend in ["cpu", "cuda11x", "cuda12x", "cuda", "gpu"]:
...     print(f" - Backend '{backend}': {"available" if lisatools.has_backend(backend) else "unavailable"}")
 - Backend 'cpu': available
 - Backend 'cuda11x': unavailable
 - Backend 'cuda12x': unavailable
 - Backend 'cuda': unavailable
 - Backend 'gpu': unavailable

Note that the cuda backend is an alias for either cuda11x or cuda12x. If any is available, then the cuda backend is available. Similarly, the gpu backend is (for now) an alias for cuda.

If you expected a backend to be available but it is not, run the following command to obtain an error message which can guide you to fix this issue:

>>> import lisatools
>>> lisatools.get_backend("cuda12x")
ModuleNotFoundError: No module named 'lisatools_backend_cuda12x'

The above exception was the direct cause of the following exception:
...

lisatools.cutils.BackendNotInstalled: The 'cuda12x' backend is not installed.

The above exception was the direct cause of the following exception:
...

lisatools.cutils.MissingDependencies: LISAanalysistools CUDA plugin is missing.
    If you are using lisatools in an environment managed using pip, run:
        $ pip install lisaanalysistools-cuda12x

The above exception was the direct cause of the following exception:
...

lisatools.cutils.BackendAccessException: Backend 'cuda12x' is unavailable. See previous error messages.

Once LISA Analysis Tools is working and the expected backends are selected, check out the examples notebooks on how to start with this software.

Installing from sources

Prerequisites

To install this software from source, you will need:

  • A C++ compiler (g++, clang++, ...)
  • A Python version supported by scikit-build-core (>=3.7 as of Jan. 2025)

If you want to enable GPU support in LISA Analysis Tools, you will also need the NVIDIA CUDA Compiler nvcc in your path as well as the CUDA toolkit (with, in particular, the libraries CUDA Runtime Library, cuBLAS and cuSPARSE).

Installation instructions using conda

We recommend to install LISA Analysis Tools using conda in order to have the compilers all within an environment. First clone the repo

git clone https://github.com/mikekatz04/LISAanalysistools.git
cd LISAanalysistools

Now create an environment (these instructions work for all platforms but some adjustements can be needed, refer to the detailed installation documentation for more information):

conda create -n lisatools_env -y -c conda-forge --override-channels |
    cxx-compiler pkgconfig conda-forge/label/lapack_rc::liblapacke

activate the environment

conda activate lisatools_env

Then we can install locally for development:

pip install -e '.[dev, testing]'

Installation instructions using conda on GPUs and linux

Below is a quick set of instructions to install the LISA Analysis Tools package on GPUs and linux.

conda create -n lisatools_env -c conda-forge lisaanalysistools-cuda12x python=3.12
conda activate lisatools_env

Test the installation device by running python

import lisatools
lisatools.get_backend("cuda12x")

Running the installation

To start the from-source installation, ensure the pre-requisite are met, clone the repository, and then simply run a pip install command:

# Clone the repository
git clone https://github.com/mikekatz04/LISAanalysistools.git
cd LISAanalysistools

# Run the install
pip install .

If the installation does not work, first check the detailed installation documentation. If it still does not work, please open an issue on the GitHub repository or contact the developers through other means.

Running the Tests

The tests require a few dependencies which are not installed by default. To install them, add the [testing] label to LISA Analysis Tools package name when installing it. E.g:

# For CPU-only version with testing enabled
pip install lisaanalysistools[testing]

# For GPU version with CUDA 12.Y and testing enabled
pip install lisaanalysistools-cuda12x[testing]

# For from-source install with testing enabled
git clone https://github.com/mikekatz04/LISAanalysistools.git
cd LISAanalysistools
pip install '.[testing]'

To run the tests, open a terminal in a directory containing the sources of LISA Analysis Tools and then run the unittest module in discover mode:

$ git clone https://github.com/mikekatz04/LISAanalysistools.git
$ cd LISAanalysistools
$ python -m lisatools.tests  # or "python -m unittest discover"
...
----------------------------------------------------------------------
Ran 20 tests in 71.514s
OK

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

If you want to develop LISA Analysis Tools and produce documentation, install lisatools from source with the [dev] label and in editable mode:

$ git clone https://github.com/mikekatz04/LISAanalysistools.git
$ cd LISAanalysistools
pip install -e '.[dev, testing]'

This will install necessary packages for building the documentation (sphinx, pypandoc, sphinx_rtd_theme, nbsphinx) and to run the tests.

The documentation source files are in docs/source. To compile the documentation locally, change to the docs directory and run make html.

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Contributors

A (non-exhaustive) list of contributors to the LISA Analysis Tools code can be found in CONTRIBUTORS.md.

License

This project is licensed under the Apache License - see the LICENSE file for details.

Citation

Please make sure to cite LISA Analysis Tools papers and the LISA Analysis Tools software on Zenodo. We provide a set of prepared references in PAPERS.bib. There are other papers that require citation based on the classes used. For most classes this applies to, you can find these by checking the citation attribute for that class. All references are detailed in the CITATION.cff file.

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

lisaanalysistools_cuda11x-1.1.9-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

lisaanalysistools_cuda11x-1.1.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

lisaanalysistools_cuda11x-1.1.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

lisaanalysistools_cuda11x-1.1.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

lisaanalysistools_cuda11x-1.1.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.8 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

File details

Details for the file lisaanalysistools_cuda11x-1.1.9-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda11x-1.1.9-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 be96be28e310889de298e02e1d4e800f9edd62db588c07d48de9658087e5c59e
MD5 f31f91270a94bb90ddb16888d36a9012
BLAKE2b-256 2043c3b8fb64220aed79781b75472572e0bd973da8772c3f541bc982c4b93e6f

See more details on using hashes here.

File details

Details for the file lisaanalysistools_cuda11x-1.1.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda11x-1.1.9-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c594b1da5af4b91ce4f8f7f629fb8d29b854994eaeeb2efa7d536b4d45eba6b3
MD5 55df163397cb5fb8b045b517cd662b25
BLAKE2b-256 a58bbeb3b21c6c870fb86a962c49f39c5060e70a8d10403d062bdbc34154fce3

See more details on using hashes here.

File details

Details for the file lisaanalysistools_cuda11x-1.1.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda11x-1.1.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 74d6592e731c07df31d8b732f3bda3829c09fbbf229a167d5eed1a88c86d4163
MD5 e140879d0b5b57f4accd492aa07dc484
BLAKE2b-256 5b26fbfc44da3585506e66c1b16467714b58e833fab280d4799ca0da392d63b2

See more details on using hashes here.

File details

Details for the file lisaanalysistools_cuda11x-1.1.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda11x-1.1.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 edbe802eae6f646731a0bd5046b97ecbacb5206c17d982fee5762136d39dac7f
MD5 d25cf64ac0dbb82f77cc20d9534b2d79
BLAKE2b-256 9a4715ea0f3c447e364c283bac931968bb75ee5f942f9d2efc294cd2646bf5d9

See more details on using hashes here.

File details

Details for the file lisaanalysistools_cuda11x-1.1.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda11x-1.1.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9b3dc2f394d647cf2e7333fdb7daddf81e5ba1c713aaab899c16e2ad1a2e2709
MD5 c2ac2a1ebdbe9150ff1a7e4bfa10b356
BLAKE2b-256 d97d9228fb5a08518c54378e74cdd0fc4a1f2b382b6988593d87cadc32d4075b

See more details on using hashes here.

Supported by

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