semvec/pypi-packages huggingface dataset:
hamill 2.0.5
A lightweight markup language
License: MIT
Requires Python: >=3.5
Keywords: hamill, lml, lightweight markup language, markup, text, html

# Hamill

A simple lightweight markup language outputting HTML.

Implementations are available in [JavaScript](https://www.npmjs.com/package/hamill) and [Python 3](https://pypi.org/project/hamill/).

<ins>Rationale</ins>: More complete than [the original Markdown](https://daringfireball.net/projects/markdown/syntax)<sup>2004</sup>, easier to parse and write.

First version released in 2020. Version 2 released in 2022.

Complete documentation in [English](https://xitog.github.io/dgx/hamill/hamill_en.html) and [French](https://xitog.github.io/dgx/hamill/hamill.html).

[You can try it live here](https://xitog.github.io/dgx/hamill/index.html).

<p style="text-align: right">
Damien Gouteux 2020-2024
</p>
slicetca 1.0.4
Package to perform Slice Tensor Component Analysis
License: MIT
Requires Python: >=3.8

# SliceTCA

This library provides tools to perform [sliceTCA](https://www.biorxiv.org/content/10.1101/2023.03.01.530616v1).

___

<p align="center">
  <img width="700" src="https://raw.githubusercontent.com/arthur-pe/slicetca/fb51e536afad9bfab60b5fc1def764ad6af1983c/img/decomposition.svg">
</p>

## Installation 

```commandline
pip install slicetca
```

## Full documentation

The full documentation can be found [here](https://github.com/arthur-pe/slicetca/blob/master/documentation.md).

## Examples

### Quick example 

```python
import slicetca
import torch
from matplotlib import pyplot as plt

device = ('cuda' if torch.cuda.is_available() else 'cpu')

# your_data is a numpy array of shape (trials, neurons, time).
data = torch.tensor(your_data, dtype=torch.float, device=device)

# The tensor is decomposed into 2 trial-, 0 neuron- and 3 time-slicing components.
components, model = slicetca.decompose(data, (2,0,3))

# For a not positive decomposition, we apply uniqueness constraints
model = slicetca.invariance(model)

slicetca.plot(model)

plt.show()
```

### Notebook

See the [example notebook](https://github.com/arthur-pe/slicetca/blob/master/sliceTCA_notebook_1.ipynb) for an application of sliceTCA to publicly available neural data.

<a target="_blank" href="https://colab.research.google.com/github/arthur-pe/slicetca/blob/master/sliceTCA_notebook_1.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>

## Reference

A. Pellegrino<sub>@</sub><sup>†</sup>, H. Stein<sup>†</sup>, N. A. Cayco-Gaijc<sub>@</sub>. (2024). Dimensionality reduction beyond neural subspaces with slice tensor component analysis. *Nature Neuroscience* [https://www.nature.com/articles/s41593-024-01626-2](https://www.nature.com/articles/s41593-024-01626-2).