semvec/pypi-packages huggingface dataset:
sevendt-tractor 0.1.0
7DT image-specific Tractor photometry pipeline
Requires Python: >=3.10

# sevendt-tractor

7DT image-specific Tractor photometry pipeline packaged from the reference workflow.
This distribution vendors the Tractor source code and is licensed under GPL-2.0.

## Quick start

1) Install `astrometry.net` (required by Tractor):

```bash
pip install git+https://github.com/dstndstn/astrometry.net.git
```

2) Create a YAML config (see `sevendt_tractor/data/sample_config.yaml`).

3) Run the full pipeline:

```bash
sevendt-tractor run --config /path/to/config.yaml
```

## Commands

- `run`: full pipeline (load → ePSF → patches → patch inputs → patch runs → merge)
- `run-epsf`: build ePSF only
- `build-patches`: build patch definitions only
- `build-patch-inputs`: build patch inputs only
- `run-patches`: run Tractor on patch inputs
- `merge`: merge patch results into final catalog

## Notes

- The package preserves the reference behavior; changes are limited to packaging and path handling.
- `astrometry.net` must be installed separately (PyPI does not allow direct URL dependencies).
- Tractor prerequisites include `numpy` and `scipy` (included in dependencies). Optional extras from Tractor (eg `fitsio`, `emcee`) are included for safety.
- Input images are provided via a text file (one FITS path per line).
- Parameter mapping reference: `docs/CONFIG_MAPPING.md`.
iflow-mcp_gutjahrai-sap-odata-mcp-py 1.0.0
Connect any AI assistant to your SAP system via OData services
Requires Python: >=3.8

# sap-odata-mcp-py# SAP OData MCP Server

Connect any AI assistant to your SAP system via OData services.

## Quick Start

1. **Install:**
```bash
python3 mcp_server.py
```

2. **Configure `.env` file:**
```
SAP_URL=https://your-sap-server.com/sap/opu/odata/sap
SAP_USERNAME=your_username  
SAP_PASSWORD=your_password
```

3. **Test:**
```bash
npx @modelcontextprotocol/inspector python3 mcp_server.py
```

## Main Features

- **Auto-discovers** all SAP OData services
- **Query, create, update, delete** SAP data
- **Works with Claude Desktop** and other AI assistants
- **No coding required** - just configure and use

## Key Tools

| Tool | What it does |
|------|-------------|
| `sap_smart_query` | Find and query any entity automatically |
| `sap_discover_services` | See all available SAP services |
| `sap_test_connection` | Check if everything works |


## Claude Desktop Setup

Add to `claude_desktop_config.json`:
```json
{
  "mcpServers": {
    "sap": {
      "command": "python3",
      "args": ["/path/to/mcp_server.py"]
    }
  }
}
```

## Troubleshooting

**Connection failed?**
- Check SAP_URL, username, password
- Run `sap_test_connection` to diagnose

**Can't find entity?**
- Run `sap_discover_services` to see what's available
- Use `sap_smart_query` instead of `sap_query`

## What Makes This Special

- Works with **any SAP OData service** 
- **Automatically finds** the right service for your data
- **AI-friendly** - no technical knowledge needed
- **Secure** - credentials in .env file only

Ready to chat with your SAP data! 🚀
gllm-core-binary 0.4.16
A library containing core components for Gen AI applications.
Requires Python: <3.14,>=3.11

# GLLM Core

## Description
A core library providing foundational components and utilities for Generative AI applications.

---

## Installation

### Prerequisites

Mandatory:
1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)

Extras (required only for Artifact Registry installations):
1. gcloud CLI (for authentication) — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:
   ```bash
   gcloud auth login
   ```

---

### Option 1: Install from Artifact Registry

This option requires authentication via the `gcloud` CLI.

```bash
uv pip install \
  --extra-index-url "https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/" \
  gllm-core
```

---

### Option 2: Install from PyPI

This option requires no authentication.
However, it installs the **binary wheel** version of the package, which is fully usable but **does not include source code**.

```bash
uv pip install gllm-core-binary
```

---

## Local Development Setup

### Prerequisites

1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)
4. gcloud CLI — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:

   ```bash
   gcloud auth login
   ```
5. Git — [Install here](https://git-scm.com/downloads)
6. Access to the [GDP Labs SDK GitHub repository](https://github.com/GDP-ADMIN/gl-sdk)

---

### 1. Clone Repository

```bash
git clone git@github.com:GDP-ADMIN/gl-sdk.git
cd gl-sdk/libs/gllm-core
```

---

### 2. Setup Authentication

Set the following environment variables to authenticate with internal package indexes:

```bash
export UV_INDEX_GEN_AI_INTERNAL_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_INTERNAL_PASSWORD="$(gcloud auth print-access-token)"
export UV_INDEX_GEN_AI_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_PASSWORD="$(gcloud auth print-access-token)"
```

---

### 3. Quick Setup

Run:

```bash
make setup
```

---

### 4. Activate Virtual Environment

```bash
source .venv/bin/activate
```

---

## Local Development Utilities

The following Makefile commands are available for quick operations:

### Install uv

```bash
make install-uv
```

### Install Pre-Commit

```bash
make install-pre-commit
```

### Install Dependencies

```bash
make install
```

### Update Dependencies

```bash
make update
```

### Run Tests

```bash
make test
```

---

## Contributing

Please refer to the [Python Style Guide](https://docs.google.com/document/d/1uRggCrHnVfDPBnG641FyQBwUwLoFw0kTzNqRm92vUwM/edit?usp=sharing)
for information about code style, documentation standards, and SCA requirements.