semvec/pypi-packages huggingface dataset:
fliser 0.1.0
Helper library for doing tiled inference with PyTorch.
Requires Python: >=3.10

fliser
======

Helper library for doing tiled inference with PyTorch.

## Installation

```
pip install fliser
```

## Examples

Use `Fliser.from_sizes` to choose the best tile size from a set of candidates.
The tile size that requires the least amount of tiles to cover the full image will be chosen.

```python
from fliser import Fliser

device = torch.device("cuda:0")

# Create model
model = MyModel().to(device)

# Read image
image = load_image(...)
image = image.unsqueeze(0).to(device)
_, c, h, w = x.shape

# Create Fliser helper class
fliser = Fliser.from_sizes(
    image_size=(h, w),
    num_channels=c,
    tile_sizes=[(416, 632), (512, 512), (632, 416)]
    min_overlap=32,
)

for tile in fliser.tiles():
    # Do inference for single tile
    with torch.inference_mode():
        pred = model(image[tile.slice()])
    
    # Update Fliser state
    fliser.update(tile, pred)

# Compute combined output
output = fliser.compute()
```

Use `Fliser.from_aspect_ratios` to instead choose tile size from a set of aspect ratios.

```python
fliser = Fliser.from_aspect_ratios(
    image_size=(h, w),
    num_channels=c,
    tile_num_pixels=512 ** 2,
    aspect_ratios=(0.5, 1.0, 2.0),
    min_overlap=32,
)
```
ionix 0.0
Add your description here
Requires Python: >=3.12

# Ionix

Ionix is a library for training JAX models. It should be familiar anyone who has used Pytorch Lightning.

Ionix defines a set of interfaces for training and provides some reference implementations for common tasks while remaining as minimal and expandable as possible

## Installation

```bash
uv add ionix
```

or using pip:

```bash
pip install ionix
```
textual-golden 0.1.0
Animated golden wave text widget for Textual
Requires Python: >=3.10

<img src="demo.gif" width="400">

# textual-golden

Animated golden text widget for [Textual](https://textual.textualize.io/).

## Installation

```bash
pip install textual-golden
```

## Usage

```python
from textual.app import App, ComposeResult
from textual_golden import Golden

class MyApp(App):
    def compose(self) -> ComposeResult:
        yield Golden("Loading...")

MyApp().run()
```

## Custom Colors

```python
from textual_golden import Golden, BLUE, FIRE

# Use preset color schemes
yield Golden("Processing...", colors=BLUE)
yield Golden("Warning!", colors=FIRE)

# Or define your own
from textual.color import Color
MY_COLORS = [Color(255, 0, 0), Color(255, 255, 255), Color(255, 0, 0)]
yield Golden("Custom!", colors=MY_COLORS)
```

## Presets

- `BLUE` - Cool blue shimmer
- `GREEN` - Nature green
- `PURPLE` - Royal purple
- `FIRE` - Red/orange fire effect
lussac 2.0.0rc5
Python package for automatic post-processing and merging of multiple spike-sorting analyses.
Requires Python: >=3.10

[![Python version](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue.svg)](https://img.shields.io/badge/python-3.10-blue.svg)
[![Documentation Status](https://readthedocs.org/projects/lussac/badge/?version=latest)](http://lussac.readthedocs.io/)
[![Build status](https://github.com/BarbourLab/lussac/actions/workflows/unit-tests-linux.yml/badge.svg)](https://github.com/BarbourLab/lussac/actions/workflows/unit-tests.yml)
[![Coverage report](https://codecov.io/gh/barbourlab/lussac/graphs/badge.svg)](https://app.codecov.io/github/barbourlab/lussac)

# Lussac 2.0

You can use the version 1 with `git checkout v1`

Lussac is an **automated** and **configurable** analysis pipeline for post-processing and/or merging multiple spike-sorting analyses.
The goal is to improve the **yield** and **quality** of data from multielectrode extracellular recordings by comparing the outputs of different spike-sorting algorithms and/or multiple runs with different parameters.
For more information, check out our [preprint](https://www.biorxiv.org/content/10.1101/2022.02.08.479192v2).


## Installation

You can install the latest release version of Lussac in under a minute:

```bash
# OPTIONAL: Use a conda environment.
conda create -n lussac python=3.12  # Must be >= 3.10
conda activate lussac

pip install lussac
# pip install --upgrade lussac  # To upgrade in case a new version is released.
```

Or if you prefer downloading the latest developmental version:

```bash
# Download Lussac in any directory you want.
git clone https://github.com/BarbourLab/lussac.git --branch dev
cd lussac

# OPTIONAL: Use a conda environment.
conda create -n lussac python=3.12  # Must be >= 3.10
conda activate lussac

# Install Lussac.
pip install -e ".[dev]"

# To upgrade Lussac.
git pull

# If you want to check whether the installation was successful (optional, will take about 10 minutes)
pytest
```


## Documentation

You can find the documentation [here](https://lussac.readthedocs.io/).


## Migration from Lussac1

Lussac2 is not backwards-compatible with Lussac1.  We advise you to make a new conda environment, and to remake your `params.json` file (which is also not backwards-compatible).