semvec/pypi-packages huggingface dataset:
rekonify-python-sdk 2.1.4
The Rekonify Python SDK is the official client for seamless, async-ready integration with the Rekonify API, streamlining transaction ingestion
Requires Python: >=3.10.8
Keywords: rekonify, reconciliation, finance, transactions, payment, automation, sdk, api-client, async, event-tracking, financial-data, accounting, python-sdk, transaction-matching, integration, pay-in, payout, erp, banking, data-ingestion

# 🔁 Rekonify Python SDK

[![PyPI Version](https://img.shields.io/pypi/v/rekonify?color=blue)](https://pypi.org/project/rekonify-python-sdk/)
[![License](https://img.shields.io/pypi/l/rekonify?color=green)](LICENSE)
[![Python Versions](https://img.shields.io/pypi/pyversions/rekonify)](https://pypi.org/project/rekonify-python-sdk/)
[![Build Status](https://img.shields.io/github/actions/workflow/status/rekonify/rekonify-python-sdk/tests.yml?branch=main)](https://github.com/Frndz-org/rekonify-python-sdk/actions)
[![Downloads](https://static.pepy.tech/personalized-badge/rekonify?period=month&units=international_system&left_color=grey&right_color=blue&left_text=Downloads)](https://pepy.tech/project/rekonify)

**Rekonify Python SDK** is the official Python client for interacting with the [Rekonify API](https://rekonify.com) — a
platform that automates financial data ingestion, reconciliation, and rule-based matching across diverse systems.

This SDK provides both **synchronous** and **asynchronous** interfaces for ingesting transactions, identifying
customers, and managing reconciliation workflows.

---

## 🚀 Features

- 🔐 API key authentication
- 🔁 Transaction ingestion (pay-in / payout)
- ⚖️ Reconciliation rule management
- ⚡ Sync and async support
- 🧩 Easy to integrate into any Python app or workflow

---

## 📦 Installation

```bash
pip install rekonify-python-sdk9
```

---

## Usage

```python
from rekonify import RekonifyClient

from rekonify.models import Payer, Transaction

client = RekonifyClient(api_key="your_api_key", app_key="your_app_key")

payer: Payer = Payer(
    first_name='John',
    last_name='Doe',
    email='johndoe@example.com',
    phone_number='0231112312',
    identity={
        'external_id': 'LT-123123'  # Identifier
    }
)

payload: Transaction = Transaction(
    type="PAY-IN",  # PAY-IN or PAYOUT
    reference="REF-123131",
    amount='2.00',
    description='Payments for order #041312',
    transaction_date='2025-05-26T15:00:00',
    payer=payer
)

res = client.post_transaction(payload)

print(res)
```
mcp-server-hacker-news 0.1.4
Hacker News integration server for Model Context Protocol (MCP)
Requires Python: >=3.10
Keywords: automation, hacker-news, llm, mcp, model-context-protocol

# MCP Hacker News Server

This project is a Hacker News information retrieval server implementation for the Model Context Protocol (MCP). It uses the FastMCP framework to provide HN API functionality.

## Available Tools

### get_top_stories

A function that retrieves the current top stories from Hacker News.

- `limit` (int, optional): Number of articles to retrieve. Defaults to 10.
  - **Returns**: A list of story object

### get_story

A function that retrieves details of a specific story.

- `id` (string, required): The story ID to retrieve
  - **Returns**: A story object containing title, url, score, etc.

## Installation

### Using `uv` (Recommended)

No special installation is required when using `uv`. You can run `mcp-server-hacker-news` directly using `uvx`.

### Using PIP

Alternatively, you can install `mcp-server-hacker-news` using pip:

```bash
pip install mcp-server-hacker-news
```

After installation, you can run the server as follows:

```bash
mcp-server-hacker-news
```

## Configuration

### Configure for Claude.app

Add to your Claude settings:

<details>
<summary>Using uvx</summary>

```json
{
  "mcpServers": {
    "hacker-news": {
      "command": "uvx",
      "args": ["mcp-server-hacker-news"]
    }
  }
}
```

</details>

### Configure for VS Code

For quick installation, use one of the one-click install buttons below...

[![Install with UV in VS Code](https://img.shields.io/badge/VS_Code-UV-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://insiders.vscode.dev/redirect/mcp/install?name=hacker-news&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22mcp-server-hacker-news%22%5D%7D)

### Command Line Options

You can specify the following options when running the server:

- `--sse`: Enable SSE transport

  - **Choices**: `on`, `off`
  - **Default**: `off`
  - **Description**: Enables SSE transport when set to "on"

- `--host`: Host to bind the server

  - **Default**: `localhost`
  - **Description**: Specifies the host address the server should bind to

- `--port`: Port to bind the server

  - **Type**: Integer
  - **Default**: `8000`
  - **Description**: Specifies the port number the server should listen on
moni-auth 1.0.7
Django app to integrate with the centralized Moni authentication system
Requires Python: >=3.7

Quick start
-----------

1. Add ``moni_auth`` to your ``INSTALLED_APPS`` setting like this::

    INSTALLED_APPS = [
        ...,
        "moni_auth",
    ]

2. Include the moni-auth URLconf in your ``urls.py`` like this::

    path("moni-auth/", include("moni_auth.urls")),
    path("admin/login/", CustomAdminLoginView.as_view()), # Import this view from moni_auth.views

3. Include the exception handler into the ``REST_FRAMEWORK`` config::

    REST_FRAMEWORK = {
        ...,
        'EXCEPTION_HANDLER': 'moni_auth.exceptions.custom_exception_handler',
    }

4. Add the following variables to the settings::

    MONI_AUTH = {
        "JWT_SECRET_KEY": env.str("JWT_SECRET_KEY", "DJjZ1sFPZAf1cfA0yqd3ufvLbc7E1r_JYMuu3Rqx5Pk"),
        "JWT_DEBUG": env.bool("JWT_DEBUG", False),
        "JWT_KEY": env.str("JWT_KEY", "jwt_wt"), # -> Main JWT for the service
        "JWT_KEYS": env.list("JWT_KEYS", default=["jwt_wt", "jwt_cs"]), # -> Array of JWTs that will be checked on the BaseJWTPermission to check for pages
        "BO_AUTH_LOGIN_URL": env.str("BO_AUTH_LOGIN_URL", "http://localhost:3000/login"),
        "AUTHENTICATION_SERVICE_URL": env.str("AUTHENTICATION_SERVICE_URL", "http://auth-django-1:8000"),
        "SHARED_SECRET_TOKEN": env.str("SHARED_SECRET_TOKEN", "3bqLVv3TlI_-2x2vEwN5N0Q_6IuX7n5kCnpJhZ8UAGo4xImfD"),
        "PRODUCTS": {}, # -> Import the product constants for the project
    }
swiss-qr-bill 0.0.31
A python library to generate swiss QR bills
Requires Python: >=3.9

# Swiss QR bill

Generate various swiss QR bills.

This is a Python port of **Manuel Bleichenbacher**'s great Java/C# code.

Manuel's projects can be found on Github :

- Java : [https://github.com/manuelbl/SwissQRBill](https://github.com/manuelbl/SwissQRBill)
- C# : [https://github.com/manuelbl/SwissQRBill.NET](https://github.com/manuelbl/SwissQRBill.NET)

All Manuel's features are not yet implemented. At this time, only SVG generation is supported.

# Installation

```python
pip install swiss-qr-bill
```
# Notes

* The user manual is currently being drafted...
* We are currently updating and simplifying our code (the project was started end 2021 !)
streamlit-cropper 0.3.1
A simple image cropper for Streamlit
Requires Python: >=3.10

# Streamlit - Cropper

A streamlit custom component for easy image cropping

![](./demo.gif)

## Installation

```shell script
pip install streamlit-cropper
```

## Example Usage

```python
import streamlit as st
from streamlit_cropper import st_cropper
from PIL import Image
st.set_option('deprecation.showfileUploaderEncoding', False)

# Upload an image and set some options for demo purposes
st.header("Cropper Demo")
img_file = st.sidebar.file_uploader(label='Upload a file', type=['png', 'jpg'])
realtime_update = st.sidebar.checkbox(label="Update in Real Time", value=True)
box_color = st.sidebar.color_picker(label="Box Color", value='#0000FF')
aspect_choice = st.sidebar.radio(label="Aspect Ratio", options=["1:1", "16:9", "4:3", "2:3", "Free"])
aspect_dict = {
    "1:1": (1, 1),
    "16:9": (16, 9),
    "4:3": (4, 3),
    "2:3": (2, 3),
    "Free": None
}
aspect_ratio = aspect_dict[aspect_choice]

if img_file:
    img = Image.open(img_file)
    if not realtime_update:
        st.write("Double click to save crop")
    # Get a cropped image from the frontend
    cropped_img = st_cropper(img, realtime_update=realtime_update, box_color=box_color,
                                aspect_ratio=aspect_ratio)
    
    # Manipulate cropped image at will
    st.write("Preview")
    _ = cropped_img.thumbnail((150,150))
    st.image(cropped_img)
```

## References

- [streamlit-drawable-canvas](https://github.com/andfanilo/streamlit-drawable-canvas)

## Acknowledgments

Big thanks to zoncrd and yanirs for their contributions
taot 0.2.4
Tool Ahead of Time (TAoT)
Requires Python: >=3.8

# Tool-Ahead-of-Time (TAoT): Because Why Wait? 🕒
Ever found yourself staring at a shiny new LLM through LangChain's window, but can't use tool calling because it's "not supported yet"? 

*Sad agent noises* 😢

Well, hold my JSON parser, because this repo says "NOT TODAY!" 🦾

## What is this sorcery? 🧙‍♂️

This is a Python package that enables tool calling for any model available through Langchain's ChatOpenAI library (and by extension, any model available through OpenAI's library) and Langchain's AzureAIChatCompletionsModel library, even before LangChain and LangGraph officially supports it! 

Yes, you read that right. We're living in the age of AI and things move fast 🏎️💨

It essentially works by reformatting the output response of the model into a JSON parser and passing this on to the relevant tools.

This repo showcases an example with DeepSeek-R1 671B, which isn't currently supported with tool calling by LangChain and LangGraph (as of 16th Feb 2025).

## Features 🌟

- Tool calling support for OpenAI and non-OpenAI models available on:
  - Langchain's ChatOpenAI library (and by extension, OpenAI and non-OpenAI models available on the base OpenAI's library).
  - Langchain's AzureAIChatCompletionsModel library.
- This package follows a similar method to LangChain's and LangGraph's `create_react_agent` method for tool calling, so makes it easy for you to read the syntax. 😊
- Zero waiting for official support required.
- More robust than a caffeinated developer at 3 AM. ☕

## Quick Start 🚀

Follow the notebook tutorials in the "tutorial" folder in this repo for a fast and practical guide:
- "taot_tutorial_ChatOpenAI.ipynb" file for Langchain's ChatOpenAI library.
- "taot_tutorial_AzureAIChatCompletionsModel.ipynb" file for Langchain's AzureAIChatCompletionsModel library.

## Change Log 📖

20th Feb 2025:
- Package now available on PyPI! Just "pip install taot" and you're ready to go.
- Completely redesigned to follow LangChain's and LangGraph's intuitive `create_react_agent` tool calling methods.
- Produces natural language responses when tool calling is performed.

1st Mar 2025:
- Package now available in TypeScript on npm! Just "npm install taot-ts" and you're ready to go. (https://github.com/leockl/tool-ahead-of-time-ts)

8th Mar 2025:
- Updated package to include implementation support for Microsoft Azure via Langchain's AzureAIChatCompletionsModel library.

## Contributions 🤝

Feel free to contribute! Whether it's adding features, fixing bugs, adding comments in the code or any suggestions to improve this repo, all are welcomed 😄

## Disclaimer ⚠️

This package is like that friend who shows up to the party early - technically not invited yet, but hopes to bring such good vibes that everyone's glad they came.

## License 📜

MIT License - Because sharing is caring, and we care about you having tool calling RIGHT NOW.

---

Made with ❤️ and a healthy dose of impatience.
threads-arg 0.2.1
Highly scalable inference of ancestral recombination graphs (ARGs)
Requires Python: >=3.9

# Threads

This software implements the Threads algorithm, described in

`Á. F. Gunnarsson, J. Zhu, B. C. Zhang, Z. Tsangalidou, A. Allmont, P. Palamara. A scalable approach for genome-wide inference of ancestral recombination graphs. bioRxiv, 2024.`

The user manual for threads can be found [here](https://palamaralab.github.io/software/threads/).

## Installation

```sh
pip install threads_arg
```
# threads Release Notes

## [0.2.1] - 2025-06-03

### Added

- VCF conversion backwards compatibility (#76)
- Add Linux ARM platform to Python wheels (#82)

## [0.2.0] - 2025-04-29

### Added

- Optimised imputation (#28)
- Variant mapping (#38)
- Upgrade to numpy 2.0 (#39)
- Consistent SHAPEIT format for regions (#42)
- Ability to write impute output directly to stdout (#42)
- Allele age estimation and data consistency (#51)

### Fixed

- Fix os.sched_getaffinity macOS error (#49)

## [0.1.0] - 2024-07-04

### Added

- Initial version of threads