GWFIT
Gravitational WaveForm Integration Tools
GWFIT is a Python package for gravitational-wave modelling and analysis, with utilities for waveform generation, inspiral modelling, numerical-relativity data analysis, horizon fluxes, and neutron-star calculations.
Features
- Gravitational-waveform and inspiral modelling
- Numerical-relativity data handling
- Horizon flux and tidal-heating calculations
- Neutron-star utilities
- Physical constants and useful relations
Installation
Create and activate a conda environment
conda create -n gwfit-env python=3.11 -y
conda activate gwfit
There are several ways to install GWFIT depending on whether you want the latest stable release or the development version.
1. Install the latest stable release from PyPI
For most users, the recommended method is to install GWFIT directly from PyPI:
python -m pip install gwfit
2. Install the latest stable release from git repository
python -m pip install git+https://gitlab.com/samanwaya-mukherjee/gwfit.git
2. Install by cloning the source
Clone the repository:
git clone https://gitlab.com/samanwaya-mukherjee/gwfit.git
cd gwfit
python -m pip install .
[ For development, the editable installation is recommended so that changes to the source code are immediately available.
Install GWFIT in editable mode:
python -m pip install -e .
]
Legacy functionality
Some older implementations are included under gwfit.legacy. Their additional dependencies can be installed with:
python -m pip install -e ".[legacy]"
Basic usage
After installation, GWFIT can be imported as:
import gwfit
The main components are organized into subpackages:
from gwfit import physics
from gwfit import waveforms
from gwfit import nr
from gwfit import neutron_star
For example, specific modules can be imported with:
from gwfit.waveforms import inspiral_model
from gwfit.nr import nr_data_utils
from gwfit.physics import constants
Project structure
gwfit/
├── LICENSE
├── pyproject.toml
├── README.md
└── src/
└── gwfit/
├── __init__.py
├── legacy/
│ ├── nr_data_utils_legacy.py
│ ├── nr_heat_legacy.py
│ └── inspiral_th_model_legacy.py
├── neutron_star/
│ ├── __init__.py
│ └── neutron_star_functions.py
├── nr/
│ ├── __init__.py
│ ├── nr_data_utils.py
│ └── nr_heat.py
├── physics/
│ ├── __init__.py
│ ├── constants.py
│ └── useful_relations.py
└── waveforms/
├── __init__.py
├── inspiral_model.py
└── waveform_functions.py
Dependencies
The core package currently depends on:
- NumPy
- SciPy
- SXS
- Matplotlib
Additional dependencies for legacy functionality are:
- h5py
- PyCBC
Development
GWFIT is under active development. Contributions, suggestions, and bug reports are welcome.
License
GWFIT is distributed under the MIT License.
Author
Samanwaya Mukherjee
Metadata
Release files for GWFIT 1.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gwfit-1.0.1.tar.gz | 168.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| gwfit-1.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 200.3 kB
Release files / gwfit-1.0.1.tar.gz
| Download URL | gwfit-1.0.1.tar.gz |
|---|---|
| Size | 168.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
af0b899cace2eff2091dc45d6ccb33089919e5ed8697c5b624a35795edb79893
|
|
BLAKE2b-256 checksum How to use checksums |
d7170dfd1ebcedcde6a401c26bbba00e636dd47857f4f33502bf2c9cb867c28a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.16
|
Release files / gwfit-1.0.1-py3-none-any.whl
| Download URL | gwfit-1.0.1-py3-none-any.whl |
|---|---|
| Size | 32.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3bd9323c052b82cc2424825ff5723b4b67c864565b5bc9db365541b689fb0aa1
|
|
BLAKE2b-256 checksum How to use checksums |
96802dce6c086db616875f2b2d5673d79177e9887a4083fe9c68bf5e9d047e9e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.16
|