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:

Metadata

Release files for SensaLisa 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for SensaLisa 0.1.0
File Size Uploaded
sensalisa-0.1.0.tar.gz 9.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for SensaLisa 0.1.0
File Interpreter ABI Platform
sensalisa-0.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 18.0 kB

Release files / sensalisa-0.1.0.tar.gz

Download URL sensalisa-0.1.0.tar.gz
Size 9.9 kB
Tags Source
SHA-256 checksum
How to use checksums
e68508f290fac1bd26e05def47b93951b75eb5f874bf0b3436d74bd51a4fb1c8
BLAKE2b-256 checksum
How to use checksums
0a444429424c9749a462d257f45a58289b30cbb36b7e803b9fccd71873bed39e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.12.3

Release files / sensalisa-0.1.0-py3-none-any.whl

Download URL sensalisa-0.1.0-py3-none-any.whl
Size 8.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
47410fe116b01b0f1cc3fa5ac707d3e9c47bfa4333e3472f41cf78cb351515f7
BLAKE2b-256 checksum
How to use checksums
70225a982fa8abdcaa595e6a1c3753837f487615f96c578d8dd8fe5b42f956a9
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.12.3

Release history Release notifications | RSS feed

This release

0.1.0 This release

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page