semvec/pypi-packages huggingface dataset:
bliss 2.3.0
BeamLine Instrumentation Support Software
Requires Python: >=3.10

[![build status](https://gitlab.esrf.fr/bliss/bliss/badges/master/pipeline.svg)](https://gitlab.esrf.fr/bliss/bliss/pipelines/master/latest)
<!-- [![coverage report](https://gitlab.esrf.fr/bliss/bliss/badges/master/coverage.svg)](https://bliss.gitlab-pages.esrf.fr/bliss/master/htmlcov) -->


# Documentation

- [latest](https://bliss.gitlab-pages.esrf.fr/bliss/master)
- [2.2.x](https://bliss.gitlab-pages.esrf.fr/bliss/2.2.x)
- [2.1.x](https://bliss.gitlab-pages.esrf.fr/bliss/2.1.x)
- [2.0.x](https://bliss.gitlab-pages.esrf.fr/bliss/2.0.x)
- [1.11.x](https://bliss.gitlab-pages.esrf.fr/bliss/1.11.x)
- [1.10.x](https://bliss.gitlab-pages.esrf.fr/bliss/1.10.x)

# How to install

Installation at an ESRF beamline: **https://bliss.gitlab-pages.esrf.fr/bliss/master/installation_esrf.html**

Installation anywhere else: **https://bliss.gitlab-pages.esrf.fr/bliss/master/installation.html**
snkmt 0.2.4
Realtime monitoring of Snakemake workflows
Requires Python: >=3.11

<div align="center">
    <picture>
    <source media="(prefers-color-scheme: dark)" srcset="docs/logo/snkmt_logo_dark.svg">
    <source media="(prefers-color-scheme: light)" srcset="docs/logo/snkmt_logo_light.svg">
    <img alt="snkmt Logo" src="docs/logo/snkmt_logo_light.svg" width="350" height="auto">
    </picture>
</div>

## Overview

snkmt (Snakemate) works with the `snakemake-logger-plugin-snkmt` plugin to capture and store Snakemake workflow execution data in a SQLite database. This allows you to monitor workflow progress, view job statuses, and troubleshoot errors through an interactive terminal interface.

> **Note**: This project is still under active development. Please report bugs, weird UI behavior, and feature requests - they are greatly appreciated!

## How it Works

1. The `snakemake-logger-plugin-snkmt` plugin captures workflow events during Snakemake execution
2. Events are written to a local SQLite database 
3. snkmt provides tools to view and monitor this data through a terminal UI and CLI commands

## Installation

Install via the logger plugin (recommended):

```bash
pip install snakemake-logger-plugin-snkmt
```

This will automatically install snkmt as a dependency.

## Usage

### Execute a Snakemake workflow

```bash
snakemake --logger snkmt ...
```

### Interactive Console

Launch the real-time monitoring interface:

- `Tab` / `Shift+Tab`: Navigate between interface elements
- `Enter`: Select workflow rows or log files
- `Escape`: Close modals/dialogs
- `q` / `Ctrl+C`: Quit application

```bash
snkmt console
```

Options:
- `--db-path, -d`: Specify custom database path

### Database Commands

View database information:
```bash
snkmt db info [DB_PATH]
```

Migrate database to latest version:
```bash
snkmt db migrate [DB_PATH]
```

## Configuration

By default, snkmt stores data in the [XDG Base Directory](https://specifications.freedesktop.org/basedir-spec/latest/) specified user data directory. You can customize this location using the `--db-path` option or by configuring the logger plugin.


## Screenshots

### Main Dashboard
<img src="docs/screenshots/dashboard.png" alt="snkmt dashboard showing workflow list and details" width="800">

### Error Inspection
<img src="docs/screenshots/failedjob_dropdown.png" alt="Error view showing failed jobs and log files" width="800">

<img src="docs/screenshots/logfile_modal.png" alt="Error view showing failed jobs and log files" width="800">

## License

This project is licensed under the MIT License. See the `LICENSE` file for details.
imagedataspliter 0.1.9
This utility splits an image dataset into train, validation, and test subsets while preserving the class folder structure.
Requires Python: >=3.8

## Dataset Splitter

This utility splits an image dataset into train, validation, and test subsets while preserving the class folder structure.

## Description

The split_dataset function takes a list of source folders, where each folder represents a class containing image files. The function randomly splits the images into training, validation, and testing sets and copies them into a new output directory.

The resulting directory structure looks like this:

output_folder/
  train/
    class_name/
  val/
    class_name/
  test/
    class_name/

The split is random but reproducible due to a fixed random seed.

## Supported Image Formats

.jpg
.jpeg
.png
.bmp
.gif
.tiff
.webp

## Usage

from dataset_splitter import split_dataset

source_folders = [
    "data/class_1",
    "data/class_2"
]

output_folder = "dataset_split"

split_dataset(
    source_folders=source_folders,
    output_folder=output_folder,
    train_ratio=0.7,
    val_ratio=0.15,
    test_ratio=0.15,
    seed=42
)

## Function Signature

split_dataset(
    source_folders: list[str],
    output_folder: str,
    train_ratio: float = 0.7,
    val_ratio: float = 0.15,
    test_ratio: float = 0.15,
    seed: int = 42
)

## Parameters

source_folders
List of paths to class directories containing images.

output_folder
Path to the directory where the split dataset will be saved.

train_ratio
Fraction of images used for training.

val_ratio
Fraction of images used for validation.

test_ratio
Fraction of images used for testing.

seed
Random seed to ensure reproducible splits.

The sum of train_ratio, val_ratio, and test_ratio must equal 1.0.

## Output

Image files are copied, not moved.
Original data remains unchanged.
Class folder names are preserved.
Progress information is printed to the console.

## Requirements

Python 3.8 or newer.
Uses only Python standard library modules.
simple-chetec 0.4.3
Stellar Interpretation for Meteoritic data and PLotting (for Everyone)
Requires Python: >=3.9

# Stellar Interpretation for Meteoritic data and PLotting (for Everyone) - SIMPLE
This is a development version of the SIMPLE package.

This project is licensed under the GNU Lesser General Public License v3.0 or later (LGPLv3+).

## Stable
[![Tests](https://github.com/mattias-ek/SIMPLE/actions/workflows/tests.yml/badge.svg?branch=master)](https://github.com/mattias-ek/SIMPLE/actions/workflows/tests.yml)
[![Build & Release](https://github.com/mattias-ek/SIMPLE/actions/workflows/release.yml/badge.svg?branch=master)](https://github.com/mattias-ek/SIMPLE/actions/workflows/release.yml)
[![PyPI](https://img.shields.io/pypi/v/simple-chetec.svg)](https://pypi.org/project/simple-chetec/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/simple-chetec.svg)
![PyPI - License](https://img.shields.io/pypi/l/simple-chetec.svg)


This latest release of the stable version of SIMPLE. Documentation for this version can be 
found [here](https://mattias-ek.github.io/SIMPLE/).

To install this version use
```
pip install simple-chetec
```

or, to upgrade use 

```
pip install --upgrade simple-chetec
```

## Pre-release
[![Tests](https://github.com/mattias-ek/SIMPLE/actions/workflows/tests.yml/badge.svg?branch=staging)](https://github.com/mattias-ek/SIMPLE/actions/workflows/tests.yml)
[![Build & Release](https://github.com/mattias-ek/SIMPLE/actions/workflows/release.yml/badge.svg?branch=staging)](https://github.com/mattias-ek/SIMPLE/actions/workflows/release.yml)
[![codecov](https://codecov.io/gh/mattias-ek/SIMPLE/branch/staging/graph/badge.svg?token=tOp92fxmeF)](https://codecov.io/gh/mattias-ek/SIMPLE)
[![PyPI](https://img.shields.io/pypi/v/simple-chetec.svg?include_prereleases)](https://pypi.org/project/simple-chetec/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/simple-chetec.svg?include_prereleases)
![PyPI - License](https://img.shields.io/pypi/l/simple-chetec.svg?include_prereleases)


The pre-release version of SIMPLE includes features and fixes ahead of stable, but may 
also be less reliable. Documentation for the pre-release version can be found [here](https://mattias-ek.github.io/SIMPLE/pre-release/)


To install this version use
```
pip install --pre simple-chetec
```

or to upgrade use, 

```
pip install --upgrade --pre simple-chetec
```