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_cuda12x-1.2.3b0-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_cuda12x-1.2.3b0-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_cuda12x-1.2.3b0-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_cuda12x-1.2.3b0-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

File details

Details for the file lisaanalysistools_cuda12x-1.2.3b0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda12x-1.2.3b0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8fd26ceb12b9d74e403d66671c8a8b660d21d6638a4eab0f19347d730db9c524
MD5 745a345d2d02279df62b4a2e5e73555a
BLAKE2b-256 a2b85ef2a7fdc2ea8b56db883a108aba435a89c88a188fc7b1aca3e02deb0d00

See more details on using hashes here.

File details

Details for the file lisaanalysistools_cuda12x-1.2.3b0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda12x-1.2.3b0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ef568e007d7f680968e82776182e86ae033e0ec2a62128f4098f9dce91a9fdba
MD5 06c0302b854777dcdd409c1e9e56eee9
BLAKE2b-256 726b17b878832e06ad44011ed579552a996490d215afb2d6360295dc682a208f

See more details on using hashes here.

File details

Details for the file lisaanalysistools_cuda12x-1.2.3b0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda12x-1.2.3b0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 87a1de752afda60349ab1c88c3c9f24e75f74abd44f2253e6fe48626c4cd7011
MD5 07e86b31fed43fe2427611432722386d
BLAKE2b-256 9ee7597bbcb2602e755c932ed5a64a17cd331325e2f1682564c108599669d7e1

See more details on using hashes here.

File details

Details for the file lisaanalysistools_cuda12x-1.2.3b0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for lisaanalysistools_cuda12x-1.2.3b0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7bd25cf91a891a0652c4a6d8b48536d7b4fe664219f59ee2d29fe816675cb307
MD5 e5c43ceed287faa7b39afc8dee018d1d
BLAKE2b-256 f7a510d5fd7e23f48f8b7010b9ecbcf30513bbe67bfd0f9246676a9fc811a6d2

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