https://github.com/maxbalrog/laser-jitter
Helper data and model classes for time-series forecasting on temporal and STFT features
https://github.com/maxbalrog/laser-jitter
jitter laser-jitter python pytorch rnn time-series
Last synced: 5 months ago
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Helper data and model classes for time-series forecasting on temporal and STFT features
- Host: GitHub
- URL: https://github.com/maxbalrog/laser-jitter
- Owner: maxbalrog
- License: gpl-3.0
- Created: 2023-08-29T08:58:52.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2025-03-30T10:23:08.000Z (over 1 year ago)
- Last Synced: 2025-03-30T11:25:29.038Z (over 1 year ago)
- Topics: jitter, laser-jitter, python, pytorch, rnn, time-series
- Language: Jupyter Notebook
- Homepage:
- Size: 14.9 MB
- Stars: 1
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Predict spatial laser jitter with NNs
The package provides with a number of helper classes for time-series data pre-processing and model training/inference. Currently supported data classes and models (find details in [usage notebook](usage.ipynb)):
1. Temporal
2. STFT
## Temporal models
Given time-series (past time steps), predict a certain amount of future time steps. Preprocessing includes smoothing (optional) and scaling.
## STFT models
Transform given time-series with Short Time Fourier Transform to spectrogram, filter non-dominant frequencies and scale them. Use NNs to predict future variation of real/imag part of each frequency band. Possible options: 1) one model to predict real/imag part for all frequency bands, 2) one model to predict real part for all frequency bands, another to predict imaginary part, 3) separate model for each frequency band.
## Installation
Clone the repository
```bash
git clone https://github.com/maxbalrog/laser-jitter.git
```
Create micromamba environment and activate it, e.g.,
```bash
micromamba create -n laser-jitter python=3.12
micromamba activate laser-jitter
```
Install the package in editable mode (specify path to the cloned repo)
```bash
pip install -e laser-jitter
```
Alternatively, install laser-jitter package from github repository (but the package would be unmodifiable)
```bash
pip install git+https://github.com/maxbalrog/laser-jitter.git
```
(Optional)
Run tests to check everything runs as expected
```bash
python -m pytest laser-jitter/tests/
```
## TODO
- Add attention architecture
- Add hyperparameter optimization module
## Acknowledgements
Paper: in progress...