semvec/pypi-packages huggingface dataset:
panditor 0.2.0
Utilities for comparing missing value imputations in pandas DataFrames, Release 0.1.1 → Now you can install and use panditor via uv package manager, Release 0.2.0 → Now you can do basic data checks to your dataframe by using basic_checker module
Requires Python: >=3.8

# Imputation Utils

A small utility package to compare missing value imputation results in pandas.

## Installation

```bash
pip install panditor
ShamsiCalendar 1.0.1
Persian (Shamsi) Calendar and Date Entry for Python Tkinter
Requires Python: >=3.6

# ShamsiCalendar

**ShamsiCalendar** یک پکیج پایتون برای نمایش و انتخاب تاریخ شمسی (Persian / Jalali) در رابط کاربری Tkinter است. این پکیج شامل یک تقویم شمسی و یک ویجت ورودی تاریخ است که استفاده از آن برای برنامه‌های GUI ساده و سریع است.

---

## ویژگی‌ها

- تقویم شمسی با قابلیت انتخاب روز
- دکمه "امروز" برای انتخاب سریع تاریخ فعلی
- تغییر ماه و سال به راحتی
- رنگ‌بندی مخصوص روز جاری و جمعه‌ها
- ورودی تاریخ شمسی با Popup تقویم
- فارسی‌سازی کامل نام ماه‌ها و روزهای هفته

---

## نصب

```bash
pip install ShamsiCalendar
```

## استفاده از ShamsiCalendar

```python
import tkinter as tk
from shamsicalendar import ShamsiCalendar
import jdatetime

def on_date_selected(date):
    print("Selected date:", date)

root = tk.Tk()
root.title("Persian Shamsi Calendar")

cal = ShamsiCalendar(root, year=1404, month=6, select_callback=on_date_selected)
cal.pack(padx=10, pady=10)

root.mainloop()
```

---

## استفاده از ShamsiDateEntry

```python
import tkinter as tk
from shamsicalendar import ShamsiDateEntry

root = tk.Tk()
root.title("Persian Date Entry")

date_entry = ShamsiDateEntry(root)
date_entry.pack(padx=10, pady=10)

def show_date():
    print("Selected date:", date_entry.get())

btn = tk.Button(root, text="Show Date", command=show_date)
btn.pack(pady=5)

root.mainloop()
```

---

## عکس<img width="1366" height="768" alt="Screenshot (254)" src="https://github.com/user-attachments/assets/03bc53d5-070c-4674-b5fc-d65104730ecd" />


---

## لینک‌ها

- GitHub Repository: [https://github.com/p7deli/ShamsiCalendar](https://github.com/p7deli/ShamsiCalendar)
- PyPI: [https://pypi.org/project/ShamsiCalendar/](https://pypi.org/project/ShamsiCalendar/)

---

## Keywords

Persian calendar, Shamsi calendar, Jalali date, Tkinter date picker, Python GUI, Persian date entry, Python ShamsiCalendar, تاریخ شمسی
edwh-passbolt-plugin 0.1.1
Passbolt tasks plugin for edwh
Requires Python: >=3.11
Keywords: edwh, passbolt

# edwh-passbolt-plugin

[![PyPI - Version](https://img.shields.io/pypi/v/edwh-passbolt-plugin.svg)](https://pypi.org/project/edwh-passbolt-plugin)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/edwh-passbolt-plugin.svg)](https://pypi.org/project/edwh-passbolt-plugin)

-----

**Table of Contents**

- [Installation](#installation)
- [Usage](#usage)
- [License](#license)

## Installation

```console
edwh plugin.add passbolt
```

If you don't have `edwh` yet:

```console
uvenv install edwh[passbolt]
# or pipx, pip, ...
```

## Usage

Typical flow starts with a login:

```console
edwh passbolt.login
```

You will be prompted for:
- Passbolt host URL (e.g. `https://passbolt.edwh.nl`).
- Your Passbolt user UUID (find it in the Passbolt UI under users; it is in the URL).
- How to import a private key: provide a path to a recovery kit, paste a PGP private key block, or skip.
- Your GPG key passphrase.

After login, your OS may occasionally show a passphrase prompt popup (via `gpg-agent`/`pinentry`). 
This depends on your system and the GPG Agent cache/expiry time.

Main commands:

```console
edwh passbolt.list [--folder ...]
edwh passbolt.get <name-or-id> [--field password|user|uri]
edwh passbolt.set [--name ...] [--password ...] [--username ...] [--uri ...] [--folder ...]
edwh passbolt.search <term> [--limit 10] [--threshold 70]
edwh passbolt.list-folders
edwh passbolt.delete <name-or-id>
edwh passbolt.share <name-or-id> [--user "Alice, Bob"] [--group "Admins"] [--permission read|update|owner]
edwh passbolt.unshare <name-or-id> [--user "Alice, Bob"] [--group "Admins"]
```

For full command listings and help text:

```console
edwh help passbolt
edwh help passbolt.<command>
```

## License

`edwh-passbolt-plugin` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
yaml-oop 0.0.19
A Python library that enables features such as inheritance, variables, and instantiation in YAML files.
Requires Python: >=3.12

# yaml_oop

A Python library that enables features such as inheritance, variables, and instantiation in YAML files.

## Requirements

Python 3.8 or later 
pyyaml 6.0.2

## Features

YAML in-line keywords that enable inheritance and variables from other files. Ultimately, this allows YAML files to be fully modular through in-line modifications of the YAML file.

### Inheritance:
Files can be nested within elements of YAML files and apply composition and inheritance logic to concatenante elements.
Inheritance logic applies to matching keys within the tree node and to multiple (nested) layers of YAML file inheritance.
Multiple inheritance, overriding, and keywords such as sealed, abstract, private is supported.

### Variables:
Variables can be injected into YAML files during inheritance or Python code to modify scalar values.
Variables allow scalars to represent other scalars, lists, dicts, or dynamic inheritance paths.
Variables can also operate on in-line keywords for additional customization.

### Supported Keywords:
Multiple keywords can be assigned a single key or sequence element.

**(base_config)**: Specifies file path(s) to inherit elements from base YAML file(s).
**(variables)**: Specifies scalar values to replace. Can be injected into root YAML file or (base_config) YAML files or declared in-line and apply to child elements.
**(override)**: Overrides values of base YAML files for matching keys.
**(append) (prepend)**: Concatenates sequences with a matching parent key.
**(merge)**: Applies inheritance rules to sequence items of the same indicies.
**(abstract) (sealed) (private)**: To enforce override, prevent override, and hide keys, sequence elements, and variables from inheritance respecitvely.
**(optional)**: Deletes keys or sequence elements if variables do not replace values.
**(carryover)**: Allows variables to be pass through muliple layers of inheritance.

## Installation

```cmd
pip install yaml_oop
```

## Usage

```python
import yaml
from yaml_oop import oopify

output_yaml = oopify(file_path=input_yaml_path, directory=input_yaml_directory, Loader=yaml.SafeLoader, variables=variables)
```

```yaml

```

## Project Structure

```
yaml_oop/
├── yaml_oop/
│   ├── __init__.py
│   └── yaml_object.py
├── tests/
│   └── test_yaml_object.py
├── example.yaml
├── README.md
└── requirements.txt
```

## License

MIT License
yymake 0.9.5
A cross build dsl make tool
Requires Python: >=2.6

# ymake
ymake is a make dsl build tools


## 安装

```bash
pip install yymake
```

## 使用

新建ya.py

```python
project("yiyiya",
    version='12.0',
    desc='yiyiya is an os',
)

target('hello')
add_kind("binary")
add_files("./main.c")

```

编译运行

```bash
ya
```
transpiler-mate 0.26.0
Schema.org 2 metadata formats transpiler
Requires Python: >=3.8

# Transpiler Mate

A small and light yet powerful Python API + CLI to extract [Schema.org/SoftwareApplication](https://schema.org/SoftwareApplication) Metadata from an annotated [CWL](https://www.commonwl.org/) document and:

* transpile it to the [CodeMeta](https://codemeta.github.io/index.html) format;
* transpile it to the [DataCite Metadata](https://inveniordm.docs.cern.ch/reference/metadata/#metadata) properties;
* transpile it to [OGC API - Records](https://ogcapi.ogc.org/records/);
* transpile & publish a Record on [Invenio RDM](https://inveniosoftware.org/products/rdm/).

Read the documentation on: https://terradue.github.io/transpiler-mate/
pandeia.engine 2026.2
Pandeia 3D Exposure Time Calculator compute engine

The Pandeia engine is a 'simulation-hybrid' exposure time calculation engine that performs calculations on two-dimensional astronomical scenes created by the user. It uses a pixel-based 3D approach, modeling both the spatial and wavelength dimensions using realistic PSFs for each instrument mode, and handles saturation, correlated read noise, and inter-pixel capacitance. It supports both the James Webb Space Telescope and the Wide-field Imager mode of the Nancy Grace Roman Space Telescope.

Installation and setup instructions (including required data packages) for the Pandeia engine can be found at this Space Telescope Science Institute page: https://outerspace.stsci.edu/display/PEN/Pandeia+Engine+News

A JWST-focused usage guide can be found here: https://jwst-docs.stsci.edu/jwst-exposure-time-calculator-overview/jwst-etc-pandeia-engine-tutorial

A Roman-focused usage guide can be found here: https://roman-docs.stsci.edu/simulation-tools-handbook-home/pandeia-for-roman

Any questions about the Pandeia engine should be directed to the JWST Help Desk at https://jwsthelp.stsci.edu or the Roman Help Desk at https://romanhelp.stsci.edu
pyxislm 0.1.0
High-performance, vendor-agnostic LLM inference library
Requires Python: >=3.10

# Pyxis

High-performance, vendor-agnostic LLM inference library.

## Status snapshot (2026-02-13)

- Sprints 1-5 are completed (`docs/SPRINT_CHECKLIST.md`).

## Quick start (local, 3 processes)

1. Install deps (in a venv):

   - `pip install -e .`

   Model downloads follow your local HuggingFace/Transformers cache settings.

2. Start the core worker:

   - `python scripts/run_core.py --model TinyLlama/TinyLlama-1.1B-Chat-v1.0`

3. Start the tokenizer + detokenizer worker:

   - `python scripts/run_tokenizer.py --model TinyLlama/TinyLlama-1.1B-Chat-v1.0`

4. Start the HTTP API:

   - `python scripts/run_api.py --host 127.0.0.1 --port 8000`

5. Verify streaming:

   - `python scripts/verify_api_real.py`

## Useful environment variables

- `PYXIS_MODEL_PATH`: model name/path for `scripts/run_core.py` (defaults to `TinyLlama/TinyLlama-1.1B-Chat-v1.0`)
- `PYXIS_TOKENIZER_PATH`: tokenizer name/path for `scripts/run_tokenizer.py` (defaults to `TinyLlama/TinyLlama-1.1B-Chat-v1.0`)
- `PYXIS_TOKENIZER_INGRESS`: API → tokenizer IPC address override
- `PYXIS_DETOK_TO_API`: detok → API IPC address override
- `PYXIS_CORE_REQUEST_QUEUE_SIZE`: max queued generation requests inside core worker (default `1024`)
- `PYXIS_MAX_INFLIGHT_REQUESTS`: max concurrent API streaming requests before `429 overloaded` (default `128`)
- `PYXIS_PER_REQUEST_QUEUE_MAXSIZE`: per-request detok queue bound in API (default `128`)
- `PYXIS_STREAM_IDLE_TIMEOUT_S`: stream idle timeout before API emits an error chunk (default `30`)
- `PYXIS_TOKENIZER_READY_WAIT_S`: wait for tokenizer ingress readiness per enqueue (default `1.0`)

## Smoke / integration scripts

- `python scripts/verify_ingestion.py`: API-less ingestion test (tokenizer → core request)
- `python scripts/verify_api_ingress.py`: API streaming test with a mocked core
- `python scripts/verify_api_real.py`: API streaming test with real core+tokenizer

## Realtime usage

- Interactive chat REPL:
  - `python scripts/chat_repl.py`
- End-to-end realtime harness (starts services, checks streaming/cancel/backpressure):
  - `powershell -ExecutionPolicy Bypass -File scripts/test_realtime.ps1 -SkipInstall`

## Architecture (high level)

`HTTP API` → `TokenizerWorker` → `CoreWorker` → `TokenizerWorker (detok)` → `HTTP streaming response`

`POST /v1/chat/completions` streams SSE (`text/event-stream`) with OpenAI-style `chat.completion.chunk` payloads and a final `[DONE]`.

`GET /health` includes readiness and API stage latency snapshots (`stage_latency_ms`).

See `docs/ARCHITECTURE.md` for details.

Session notes and recent implementation memory are tracked in `docs/MEMORY.md`.
iflow-mcp_zxs731-qwen3-music-player 0.1.0
MCP server for music player using FastMCP
Requires Python: >=3.11

## qwen3-online-mcp-speech-agent

## Qwen3-235B-A22b+MCP语音助手

### 进入client目录修改env环境变量，运行 python voice-qwen3-235b.py
【Qwen3 235B 语音助手 接入MCP】 https://www.bilibili.com/video/BV12YGhz5EGf/?share_source=copy_web&vd_source=245c190fe77b507d57968a57b3d6f9cf


### 支持了SSE MCP的配置，增加了isActive节点，默认配置了一个高德地图的MCP托管，请到百炼生成key替换下面的xxx。（该高德地图可以用来查询天气！）
{
  "mcpServers": {
    "music_player": {
      "isActive":true,
      "command": "python",
      "args": [
        "../server/mplayer/main.py"
      ]
    },
    "amap-maps": {
      "isActive":true,
      "type": "sse",
      "url": "https://mcp.api-inference.modelscope.cn/sse/xxx"
      }
  }
}
iflow-mcp_delynchoong-azure-openai-agent-multi-mcp-starter 0.1.0
A starter template for building Model Context Protocol (MCP) agents using Azure OpenAI
Requires Python: >=3.8

# azure-multi-mcp-starter

This project is a starter template for building Model Context Protocol (MCP) agents using Azure OpenAI, not OpenAI. It demonstrates how to use the MCP protocol with Azure's hosted models, providing a foundation for developing advanced AI agents that can interact with external tools and services.

## What is MCP?

Model Context Protocol (MCP) is an open protocol that standardizes how language models interact with external tools, APIs, and environments. MCP enables models to call functions, access data, and perform actions beyond simple text generation, making them more useful and interactive in real-world applications.

## LangChain MCP Adapter

The LangChain MCP adapter is a library that bridges LangChain agents with MCP servers. It allows you to use LangChain's agent framework to communicate with any MCP-compliant server, enabling seamless integration of tool use, function calling, and advanced agent behaviors.

## MCP Options

There are several MCP server implementations and options available:

- **Python MCP Server**: Run your own MCP server in Python, customizing tool support and logic.
- **LangChain MCP Adapter**: Use LangChain's adapter to connect agents to MCP servers.

## About This Template

This repository is designed as a quick-start template for developers building MCP agents with specifically Azure OpenAI, as other guides only provided template for OpenAI endpoints. It includes sample code for connecting to an MCP server, using the LangChain MCP adapter, and running an agent that can interact with external tools. You can create your own sample MCP Server with MCP tools for your AI to perform agentic actions. It is intended for Azure OpenAI users and requires adaptation to work with OpenAI's public API.

## Quickstart

To run this template follow the steps:

1. Clone repository
2. ```` pip install -r requirements.txt ```
3. Populate your .env file with the following: AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT,AZURE_OPENAI_VERSION
4. In terminal, run ``` python frontend_api.py ```
5. This will start all the MCP servers and the LangChain MCP adapter.

## Sample application
![alt text](screenshot1.png)
## Upcoming features

Include more baseline MCP server integration with Azure AI Foundry, Azure MCP, Fabric MCP Server

---

For more information, see the official documentation for MCP, LangChain, and Azure OpenAI.