Skip to main content

Python

License

Version


SensaLisa is a lightweight, user-friendly Python toolkit for generating and visualizing sensitivity curves for the Laser Interferometer Space Antenna (LISA).

Designed for both gravitational-wave researchers and students, SensaLisa provides an intuitive interface for computing detector sensitivity without requiring extensive setup or familiarity with the underlying implementation.


Features

  • Generate LISA sensitivity curves with minimal code.
  • Fast and lightweight implementation.
  • Simple, intuitive API designed for research workflows.
  • Publication-quality plots with customizable visualization.
  • Modular design for easy integration into existing analysis pipelines.
  • Suitable for education, rapid prototyping, and scientific research.

Why SensaLisa?

Many sensitivity curve implementations are embedded inside larger software packages or require unnecessary setup for simple analyses.

SensaLisa focuses on one objective:

Making LISA sensitivity calculations simple, transparent, and accessible.

Whether you are exploring detector performance, testing waveform models, or preparing figures for a publication, SensaLisa allows you to generate sensitivity curves in just a few lines of code.


Installation

git clone https://github.com/BHUVANAKASHI/SensaLisa.git

cd SensaLisa

pip install -r requirements.txt

or install directly from source

pip install .

Quick Start

from sensalisa import ...

# Example code here

Generate a LISA sensitivity curve with only a few commands.


Applications

SensaLisa can be used for

  • LISA sensitivity studies
  • Signal-to-noise ratio calculations
  • Gravitational-wave data analysis
  • Detector performance visualization
  • Research and teaching

Citation

If SensaLisa contributes to your research, please cite the repository and any accompanying publication.

@software{SensaLisa,
  author = {Bhuvaneshwari Kashi},
  title  = {SensaLisa: A User-Friendly Toolkit for LISA Sensitivity Curves},
  year   = {2025},
  url    = {https://github.com/BHUVANAKASHI/SensaLisa}
}

Contributing

Contributions, feature requests, and bug reports are welcome.

Please open an Issue or submit a Pull Request.


Acknowledgements

SensaLisa is inspired by and partially adapts components from the LISA_Sensitivity toolkit developed by the eXtreme Gravity Institute. We gratefully acknowledge the original authors for their implementation of the LISA sensitivity model and their contribution to the gravitational-wave community.

SensaLisa extends this foundation by providing a streamlined, user-friendly interface, simplified workflows, and enhanced visualisation tools for generating and exploring LISA sensitivity curves.

License

This project is released under the MIT License.

References

This project builds upon the following work:

Download files

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

Source Distribution

sensalisa-0.1.0.tar.gz (9.9 kB view details)

Uploaded Source

Built Distribution

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

sensalisa-0.1.0-py3-none-any.whl (8.1 kB view details)

Uploaded Python 3

File details

Details for the file sensalisa-0.1.0.tar.gz.

File metadata

  • Download URL: sensalisa-0.1.0.tar.gz
  • Upload date:
  • Size: 9.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.12.3

File hashes

Hashes for sensalisa-0.1.0.tar.gz
Algorithm Hash digest
SHA256 e68508f290fac1bd26e05def47b93951b75eb5f874bf0b3436d74bd51a4fb1c8
MD5 b011108fd3a18ca866c6931d5df602cc
BLAKE2b-256 0a444429424c9749a462d257f45a58289b30cbb36b7e803b9fccd71873bed39e

See more details on using hashes here.

File details

Details for the file sensalisa-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: sensalisa-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 8.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/7.0.0 CPython/3.12.3

File hashes

Hashes for sensalisa-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 47410fe116b01b0f1cc3fa5ac707d3e9c47bfa4333e3472f41cf78cb351515f7
MD5 7dcd71dcf06c711b02254e0abdc492cf
BLAKE2b-256 70225a982fa8abdcaa595e6a1c3753837f487615f96c578d8dd8fe5b42f956a9

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