semvec/pypi-packages huggingface dataset:
scmiac 0.1.0
Single-Cell Multi-modality Integration via cell type filtered Anchors using Contrastive learning
Requires Python: >=3.8
Keywords: single-cell, multi-modality, integration, contrastive-learning, scRNA-seq, scATAC-seq, bioinformatics

# scMIAC: Single-Cell Multi-modality Integration via cell type filtered Anchors using Contrastive learning

## Overview

scMIAC is a comprehensive framework for single-cell multi-modality data integration, designed to tackle the most challenging problem in single-cell integration: **diagonal integration** (integrating unpaired cells from different feature spaces across modalities).

The methodological innovations of scMIAC include:
- scMIAC utilizes cell type information to select high-quality anchor cells for contrastive learning, improving integration of challenging cells such as imbalanced, rare, or isolated cell types.
- scMIAC innovatively introduces contrastive learning to diagonal integration task, where previous methods could only be applied to horizontal or vertical integration scenarios.
- As a diagonal integration approach, scMIAC preserves each modality's original biological characteristics through modality-specific VAEs, which serves as a regularizer preventing over-emphasis on modality alignment.

![scMIAC framework overview](https://raw.githubusercontent.com/TianLab-Bioinfo/scMIAC/main/figs/fig1.webp)
## Installation

1. Create and activate a Conda environment:
   ```bash
   conda create -n scmiac python=3.11
   conda activate scmiac
   ```

2. Clone the repository and install:
   ```bash
   git clone https://github.com/TianLab-Bioinfo/scMIAC.git
   cd scMIAC
   pip install .
   ```

## Usage

scMIAC provides two usage modes:

### 1. CLI Mode - Quick start for non-interactive, command-line workflow:

```bash
scmiac train \
  --rna-h5ad data/10x/input/adata_rna_10x.h5ad \
  --atac-h5ad data/10x/input/adata_atac_10x.h5ad \
  --output-dir data/10x/output/scmiac_results/ \
  --rna-latent-key X_pca \
  --atac-latent-key lsi49 \
  --rna-celltype-key cell_type \
  --atac-celltype-key pred

scmiac train -h  # For viewing all available parameters
```

**MUST Required parameters:**
- `--rna-h5ad`: Path to RNA AnnData file
- `--atac-h5ad`: Path to ATAC AnnData file
- `--output-dir`: Output directory

**Output files:**
- `anchors.csv`: Anchor pairs
- `rna_vae.pth`: RNA VAE model weights
- `atac_vae.pth`: ATAC VAE model weights
- `rna_embeddings.csv`: RNA cell embeddings
- `atac_embeddings.csv`: ATAC cell embeddings
- `scmiac_latent_umap.png`: UMAP visualization



### 2. API Mode - For flexible research and experimentation:

Refer to the [Full API Tutorial & Examples](https://github.com/TianLab-Bioinfo/scMIAC/blob/main/docs/tutorial.ipynb) for detailed usage and examples.
load-case-data 0.2.0
指令改為指定使用uvx
Requires Python: >=3.12

# MCP-1 Server

這是一個 MCP Server 範例，提供案件查詢功能。此專案展示了如何建立一個支援雙模式（Demo SQLite / 真實 PostgreSQL）的 MCP Server。

## 功能

- **案件查詢**：提供 `get_case` 工具，依據案件編號查詢資訊。
- **雙模式資料庫**：
    - **預設模式**：若未設定環境變數，自動使用內建的 SQLite Demo 資料庫，開箱即用。
    - **生產模式**：支援 `DATABASE_URL` 環境變數，可無縫切換至 PostgreSQL 或其他 SQLAlchemy 支援的資料庫。

## 專案結構

本專案採用標準 Python 套件結構，專為 MCP Server 設計。

```text
mcp_1/
├── pyproject.toml       # [核心設定] 專案依賴、打包設定、指令入口
├── README.md            # [文件] 專案說明文件
├── .gitignore           # [Git] 設定 Git 忽略的檔案
├── scripts/             # [工具腳本] 開發與維護用的輔助程式
│   ├── build_demo_db.py #    └── 重建 Demo 資料庫的腳本
│   └── inspect_db.py    #    └── 查看資料庫內容的腳本
└── src/                 # [原始碼] 核心程式碼放置處
    └── mcp_1/           #    └── Python 套件主目錄
        ├── __init__.py  #        └── 套件入口，匯出 main 函式
        ├── config.py    #        └── 設定管理 (處理資料庫連線字串與 Fallback 邏輯)
        ├── server.py    #        └── MCP Server 定義 (工具註冊與 API 介面)
        ├── store.py     #        └── 資料存取層 (負責 SQL 執行與資料格式化)
        └── data/        #        └── 資料檔目錄
            └── demo_cases.db #       └── 內建的 SQLite Demo 資料庫
```

### 檔案詳細說明

*   **`src/mcp_1/server.py` (核心)**
    *   定義 MCP Server 實體與對外介面。
    *   使用 `@mcp.tool()` 註冊 `get_case` 工具，供 MCP Client (如 Claude) 呼叫。
*   **`src/mcp_1/store.py` (資料層)**
    *   負責與資料庫互動，封裝 SQLAlchemy 操作。
    *   統一處理不同資料庫的查詢邏輯，回傳標準化的字典資料。
*   **`src/mcp_1/config.py` (設定)**
    *   決定連線目標。檢查 `DATABASE_URL`，若無則回傳內建 Demo DB 路徑。
*   **`scripts/build_demo_db.py`**
    *   開發用腳本，用於生成或重置 `src/mcp_1/data/demo_cases.db`。
*   **`scripts/inspect_db.py`**
    *   開發用腳本，用於在終端機快速查看 SQLite 資料庫內容。

## 安裝與執行

### 1. 安裝專案

在專案根目錄下執行以下指令，以「編輯模式 (Editable Mode)」安裝專案及其依賴：

```bash
pip install -e .
```

此步驟會：
- 安裝 `mcp`, `sqlalchemy`, `psycopg` 等必要套件。
- 註冊 `load-case-data` 指令到您的系統中。

### 2. 執行 Server

#### 方式 A：使用內建 Demo 資料庫 (預設)
直接執行指令，無需任何設定：

```bash
load-case-data
```
*注意：此指令啟動後會監聽 stdio，終端機不會有明顯反應，這是正常的。請在 MCP Client 設定中使用此指令。*

#### 方式 B：連接真實資料庫 (PostgreSQL)
設定 `DATABASE_URL` 環境變數後執行：

**PowerShell:**
```powershell
$env:DATABASE_URL="postgresql+psycopg://user:pass@host:5432/dbname"
load-case-data
```

**Bash:**
```bash
export DATABASE_URL="postgresql+psycopg://user:pass@host:5432/dbname"
load-case-data
```

### 3. 開發工具使用

**查看目前資料庫內容：**
```bash
python scripts/inspect_db.py
```

**重建 Demo 資料庫 (重置資料)：**
```bash
python scripts/build_demo_db.py
```

## MCP Client 設定範例 (Claude Desktop)

若要讓 Claude Desktop 使用此 Server，請編輯設定檔 (通常位於 `%APPDATA%\Claude\claude_desktop_config.json`)：

```json
{
  "mcpServers": {
    "case-search": {
      "command": "uvx",
      "args": ["load-case-data"],
      "env": {
        "DATABASE_URL": "您的連線字串 (若要用 Demo DB 則免填此行)"
      }
    }
  }
}
```
beadsflow 0.1.0
Standalone automation loop for repo-local Beads workflows
Requires Python: >=3.11

# beadsflow

[![Documentation Status](https://readthedocs.org/projects/beadsflow/badge/?version=latest)](https://beadsflow.readthedocs.io/en/latest/?badge=latest)

Standalone tool/package for automating Beads-driven work (epic → child beads) in a repo-local `.beads` database.

## Dev quickstart

```bash
uv sync
just test
just typecheck
just lint
```

## Docs

Hosted docs: https://beadsflow.readthedocs.io/en/latest/

```bash
just docs
```
geotribu 0.35.1
Une ligne de commande pour Geotribu qui offre des outils pour rechercher et consulter les contenus et images, et faciliter les tâches récurrentes des contributeur/ices.
Requires Python: >=3.10
Keywords: cli, Geotribu, GIS, images, lunr, Mastodon, markdown, Pillow, search, tinify

# Geotribu CLI

Outil en ligne de commande pour les tâches récurrentes du projet Geotribu.

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![flake8](https://img.shields.io/badge/linter-flake8-green)](https://flake8.pycqa.org/)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/geotribu/cli/main.svg)](https://results.pre-commit.ci/latest/github/geotribu/cli/main)
[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=geotribu_cli&metric=alert_status)](https://sonarcloud.io/summary/new_code?id=geotribu_cli)

[![🎳 Tester](https://github.com/geotribu/cli/actions/workflows/tests.yml/badge.svg)](https://github.com/geotribu/cli/actions/workflows/tests.yml)
[![📦 Build & 🚀 Release](https://github.com/geotribu/cli/actions/workflows/build_release.yml/badge.svg)](https://github.com/geotribu/cli/actions/workflows/build_release.yml)
[![📚 Documentation](https://github.com/geotribu/cli/actions/workflows/documentation.yml/badge.svg)](https://github.com/geotribu/cli/actions/workflows/documentation.yml)
[![codecov](https://codecov.io/gh/geotribu/cli/branch/main/graph/badge.svg?token=YRLQ6OPFRL)](https://codecov.io/gh/geotribu/cli)

[![PyPi version badge](https://badgen.net/pypi/v/geotribu)](https://pypi.org/project/geotribu/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/geotribu)](https://pypi.org/project/geotribu/)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/geotribu)](https://pypi.org/project/geotribu/)

## Installer

Avec [pipx](https://pipx.pypa.io/) (recommandé) :

```sh
pipx install geotribu[all]
```

Via _pip_ :

```sh
pip install geotribu
```

Via Docker :

```sh
docker pull ghcr.io/geotribu/cli
```

Via un exécutable pré-compilé : [télécharger pour son système d'exploitation](https://github.com/geotribu/cli/releases/latest).

## Utiliser

Avec une installation avec un gestionnaire de paquets Python (pipx, pip, uv...) :

```sh
geotribu --help
```

Ou avec l'image Docker :

```sh
docker run --rm -it ghcr.io/geotribu/cli:latest --help
```

Pour plus d'informations, [consulter la documentation](https://cli.geotribu.fr/).
datatrust 0.1.0
Detect untrustworthy, fabricated, or suspicious data in datasets
Requires Python: >=3.13

# datatrust 🧠

A Python library to **measure data trustworthiness and detect silent data corruption**.

## Why datatrust?
Most libraries clean data.
**datatrust asks: Can you trust this data at all?**
## License
MIT License


## Installation
```bash
pip install datatrust
stackit-scf 0.4.0
STACKIT Cloud Foundry API
Requires Python: <4.0,>=3.9

# stackit.scf
API endpoints for managing STACKIT Cloud Foundry

For more information, please visit [https://www.stackit.de/en/product/cloud-foundry/](https://www.stackit.de/en/product/cloud-foundry/)

This package is part of the STACKIT Python SDK. For additional information, please visit the [GitHub repository](https://github.com/stackitcloud/stackit-sdk-python) of the SDK.


## Installation & Usage
### pip install

```sh
pip install stackit-scf
```

Then import the package:
```python
import stackit.scf
```

## Getting Started

[Examples](https://github.com/stackitcloud/stackit-sdk-python/tree/main/examples) for the usage of the package can be found in the [GitHub repository](https://github.com/stackitcloud/stackit-sdk-python) of the SDK.