semvec/pypi-packages huggingface dataset:
traffyapi 0.1.0
Async wrapper for Traffy API
Requires Python: >=3.8

# TraffyAPI

Asynchronous Python wrapper for the [Traffy API](https://api.traffy.site).  
This library is designed to simplify integration with Traffy task-based systems, especially for Telegram bots and automation platforms.

## Features

- Check if a task is completed  
- Fully asynchronous using `aiohttp`  
- Easy to use, with `resource_id` set once in the client

## Installation

Install directly from GitHub:

```bash
pip install git+https://github.com/laymi0/traffy.git
```

## Usage

```bash
import asyncio
from traffyapi import TraffyAPI

async def main():
    api = TraffyAPI("your_resource_id")

    # Get available tasks for a user
    tasks = await api.get_tasks("123456789")
    print("Tasks:", tasks)

    # Check if a task was completed
    result = await api.check_task("123456789", task_id="task_id_here")
    print("Check result:", result)

    await api.close()

asyncio.run(main())
```
sferriol-zarr 0.4.0
Read only Zarr storage
Requires Python: >=3.5

# sferriol-zarr

sferriol-zarr is a module with new zarr storage class TarStore (only read-only).

It also has the create_store shortcut that returns the store corresponding to the specified url


## Installation

	pip install git+https://gitlab.in2p3.fr/sferriol-ip2i/sferriol-zarr.git


## TarStore Usage

    import zarr
    import sferriol.zarr

    # original zarr storage

    dir_store =  zarr.DirectoryStore('data/example.zarr')
    z = zarr.create(store=dir_store, overwrite=True, shape=1000000, dtype='i4')


    # convert it to tar file ('data/example.zarr.tar')

    tar_fpath = sferriol.zarr.create_tar('data/example.zarr')


    # now use TarStore

    tar_store =  sferriol.zarr.TarStore('data/example.zarr.tar')
    tz = zarr.open(store=tar_store, mode='r')
    tar_store.close()  # don't forget to call this when you're done


    # or in a with statement

    with sferriol.zarr.TarStore('data/example.zarr.tar') as tar_store:
        tz = zarr.open(store=tar_store, mode='r')


## create_store Usage

    from sferriol.zarr import create_store

    # local storage

    store = create_store('/.../my_data.zarr')
    store = create_store('/.../my_data.zarr.tar')
    store = create_store('/.../my_data.zarr.zip')

    # xrootd storage

    store = create_store('root://host:port///.../my_data.zarr')
    store = create_store('root://host:port///.../my_data.zarr.tar')
    store = create_store('root://host:port///.../my_data.zarr.zip')
liner-tables 0.1.1
Print table
Requires Python: >=3.6

# liner_tables

**`liner_tables`** is a lightweight Python module for creating and manipulating clean, printable text tables in the terminal. Ideal for CLI tools, debugging, or simple tabular reports.

---

## 🔧 Features

- Easily create and add rows to a table
- Remove rows by index or column value
- Print a clean, aligned text-based table to the terminal
- Doesn't require a module

---

## 📦 Installation

If using locally, just save `liner_tables.py` in your project directory.

```bash
pip install liner_tables
```

---

## Code example:
```python
import liner_tables as table

headers = ['Name', 'From']
data = ['John', 'US']

# Create first table
t = table.create(headers, data)

# To add another data
t.add(['Mike', 'CAD'])

# Delete from column value
t = table.remove(t, {'From': 'CAD'})

# Print the results
print(t.render())
```

## Output:
``` bash
 # │ Name │ From │
───┼──────┼──────┼
1. │ John │ US   │
```
qcog-python-client 0.4.5
qcog python client

# Qognitive Python Client Library

[![Documentation Status](https://readthedocs.com/projects/qognitive-qcog-python-client/badge/?version=latest)](https://qognitive-qcog-python-client.readthedocs-hosted.com/en/latest/?badge=latest)

[![.github/workflows/pypi.yml](https://github.com/qognitive/qcog-python-client/actions/workflows/pypi.yml/badge.svg)](https://github.com/qognitive/qcog-python-client/actions/workflows/pypi.yml)

The Qognitive Python Client library is a programmatic way of interacting with Qognitive's cloud computation API for running Quantum Cognition Machine Learning (QCML) models.

More information on QCML can be found at our website <https://qognitive.io> and from our initial [white paper](https://www.qognitive.io/QCML%20-%20Qognitive,%20Inc.pdf)

## Documentation

The documentation for the API can be found at our [Read the Docs site here](https://qognitive-qcog-python-client.readthedocs-hosted.com/en/latest/).

## Getting API access

Access is currently in closed Beta. Please reach out through [this link](https://www.qognitive.io/api-request) in order to request access to our system.

## Support

For any bugs or issues with the client please email <support@qognitive.io> or submit a github issue.

For any security vulnerabilities please contact <support@qognitive.io>.

Please do not message the maintainers directly, you are more likely to get your problem resolved quicker through the above steps.
figshare-get 0.1.0
Download from figshare
Requires Python: >=3.7

# figshare_get

Download a figshare article or collection using its DOI

## Installation

Install this Python package:

```bash
pip install figshare-get
```

## Usage

Find the DOI of the figshare article or collection - for example, click on the
"Cite" button on the figshare page and then "Copy DOI".

```bash
figshare_get 10.6084/m9.figshare.c.4081802.v1
```
pointr-cloud-mcp-server 0.1.0
Indoor location assistant using Pointr MCP server to provide QR code navigation for venues.
Requires Python: >=3.13

# Pointr Indoor Location Assistant

This project provides an indoor location assistant powered by the Pointr MCP server. Users can interact with the assistant to get QR code links for directions to specific places or categories (e.g., toilets, restaurants, cafes, entertainment centers) within a venue.

## Features
- Find directions to specific points of interest (POIs) or general categories.
- Generate QR codes for navigation.
- Uses Pointr MCP server for location logic.
- Logs client identifiers and actions to `pointr_agent.log`.

## Usage
1. **Setup Environment**
   - Ensure Python 3.13+ is installed.
   - Install dependencies from `requirements.txt`:
     ```bash
     pip install -r requirements.txt
     ```
   - Configure environment variables in `.env` or via VS Code settings (see `settings.json`).

2. **Run the MCP Server**
   - Start the server:
     ```bash
     python PointrAgent.py
     ```
   - Or use the VS Code MCP integration as configured in `settings.json`.

3. **Interact with the Assistant**
   - Ask for directions (e.g., "Where is the nearest cafe?", "How can I go to the meeting room?").
   - The assistant will respond with a QR code link for navigation.

## File Structure
- `PointrAgent.py`: Main agent logic and MCP tool definitions.
- `app.py`: Example runner for the agent.
- `requirements.txt`: Python dependencies.
- `pointr_agent.log`: Log file for client identifiers and actions.
- `settings.json`: VS Code and MCP server configuration.

## MCP Tools
- `find_exact_poi_from_user_input`: Extracts POI ID from user input.
- `find_exact_category_from_user_input`: Extracts category from user input.
- `generate_qr_code_for_specific_poi`: Generates QR code for a specific POI.
- `generate_qr_code_for_category`: Generates QR code for a category.

## Environment Variables
- `PC_API_URL`: Pointr API endpoint.
- `PC_CLIENT_ID`: Client identifier.
- `PC_CLIENT_SECRET`: Client secret.

## Logging
All client identifiers and tool actions are logged to `pointr_agent.log` for debugging and audit purposes.

## License
MIT