Metadata-Version: 2.4
Name: qtam
Version: 0.1.0
Summary: PyTorch implementation of invertible Q-Transform with Ampltude Modulation
Author-email: Lorenzo Asprea <lorenzo.asprea@gmail.com>, Francesco Sarandrea <fsarandrea94@gmail.com>
License: MIT
Project-URL: Homepage, https://github.com/dottormale/Qtransform_torch/tree/main/QTAM
Project-URL: Repository, https://github.com/dottormale/Qtransform_torch/tree/main/QTAM
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=1.9.0
Requires-Dist: numpy>=1.20.0
Provides-Extra: dev
Requires-Dist: pytest>=6.0; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: flake8; extra == "dev"
Provides-Extra: full
Requires-Dist: torch_spline_interpolation>=1.0.0; extra == "full"
Dynamic: license-file

# QTAM - Q-Transform with Amplitude Modulation

A full PyTorch implementation of the invertible Q-transform. QTAM exploits Amplitude Modulation and de-Modulation to increase and decrease the size of the produced spectrograms, making it possible to encode all the physical information of a signal in 2D images of modest dimensions. The analytical invertibility of QTAM ensures that no physically relevant features are lost when going from time to time-frequency representation and vice versa. The package include classes for multi-configuration Q-scanning for time-frequency analysis; the user can perform a scan over the parameter space to choose the frequency window and Q value which best suite their analysis.  

More information can be found at: "https://github.com/dottormale/Qtransform_torch/tree/main/QTAM".

## Installation

```bash
pip install qtam
