semvec/pypi-packages huggingface dataset:
solace-ai-connector-rest 0.0.3
rest plugin for the Solace AI Connector - this provides an input and output component to talk to rest interface
Requires Python: >=3.10.16

# Solace AI Connector REST

EST API interface to view and configure a solace-ai-connector app

[![PyPI - Version](https://img.shields.io/pypi/v/solace-ai-connector-rest.svg)](https://pypi.org/project/solace-ai-connector-rest)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/solace-ai-connector-rest.svg)](https://pypi.org/project/solace-ai-connector-rest)

## Prerequisites

- Python 3.11 and above
- Required Python dependencies
solace-ai-connector-slack 0.0.2
Slack plugin for the Solace AI Connector - this provides an input and output component to talk to Slack
Requires Python: >=3.10.16

# Solace AI Connector Slack

Plugin for the solace-ai-connector to communicate with slack

[![PyPI - Version](https://img.shields.io/pypi/v/solace-ai-connector-slack.svg)](https://pypi.org/project/solace-ai-connector-slack)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/solace-ai-connector-slack.svg)](https://pypi.org/project/solace-ai-connector-slack)

## Prerequisites

- Python 3.11 and above
- Required Python dependencies
vlbi-cat-creation 0.1.1
Create master catalogues from radio images
Requires Python: >=3.12

# vlbi-cat-creation
Repository for developing tools for VLBI master catalogue creation


## Package Goals

This package aims to solve the following cataloguing problems in LOFAR-VLBI imaging:

- Producing and then combining catalogues at different resolutions and depth (eg. 0.3'', 0.6'', 1.2'' and 6'')
- Source deblending when multiple sources are detected at high-resolution
- Optical/IR identification if such a catalogue is provided
- Creating a master catalogue with relevant detection flags, component flags and fluxes given all information available for a source
- Minimising visual inspection
- Handling facet boundaries

## Running the Package

The package provides a cli interface which users can access after installation. The tool can also be run without a full download using a tool such as `uv`.

### Full Installation 

```bash
pip install vlbi-cat-creation
vlbi-cat-creation -h # To view command description and available options
vlbi-cat-creation --basic --images="['image1.fits', 'image2.fits']"
```


Or alternatively using uv:
```bash
uvx vlbi-cat-creation 
```
GermGenie 0.1.3
Python wrapper for multi-sample 16S classification using EMU
Requires Python: >=3.7

# GermGenie

GermGenie was specifically designed to analyse 16S data from clinical FFPE specimens, however it can be used to analyse any bacterial sample. GermGenie outputs stacked barplot showing the abundance of every species in your sample. By setting an abundance threshold, any species below the threshold will be added to an 'other' category (>1% by default).  
This tool was designed with Oxford Nanopore sequencing reads (ONT), and was not tested with any other sequencing data. The input should be a folder containing one or more samples in a fastq.gz format.

## Dependencies
The pipeline is based on [EMU](https://github.com/treangenlab/emu). Data is visualized using the [Plotly library](https://plotly.com).


# Installation

Follow EMU's installation instructions from the [repo](https://github.com/treangenlab/emu).  
After installing EMU, install GermGenie in the same conda environment.
```bash
python -m pip install GermGenie
```



# Usage
```bash
usage: GermGenie [-h] [--threads THREADS] [--threshold THRESHOLD] [--tsv]
                 [--nreads] [--subsample SUBSAMPLE]
                 fastq output db

EMU wrapper for analyzing and plotting relative abundance from 16S data

positional arguments:
  fastq                 Path to folder containing gzipped fastq files
  output                Path to directory to place results (created if not
                        exists.)
  db                    Path to EMU database

optional arguments:
  -h, --help            show this help message and exit
  --threads THREADS, -t THREADS
                        Number of threads to use for EMU classification
                        (defaults to 2)
  --threshold THRESHOLD, -T THRESHOLD
                        Percent abundance threshold. Abundances below
                        threshold will be shown as 'other' (defaults to 1
                        percent)
  --tsv                 Write abundances to tsv file (abundances.tsv)
  --nreads, -nr         Visualize number of reads per sample in barplot
  --subsample SUBSAMPLE, -s SUBSAMPLE
                        WARNING: DO NOT USE !!!

Developed by Daan Brackel & Sander Boden @ ATLS-Avans
```
onetouchlite 0.0.8
一个简单的轻量，集成了大多数OneTouch用法的工具包
Requires Python: >=3.6

### 1 
#### printc 带有颜色的print
#### prints 带有样式的print
### 2
#### timer 函数计时装饰器
### 3
#### pace 类似于tqdm的进度条