semvec/pypi-packages huggingface dataset:
mcp-hn 0.1.0
MCP Server for Hacker News
Requires Python: >=3.12

# Hacker News MCP Server

A Model Context Protocol (MCP) server that provides tools for fetching information from Hacker News.

## Tools

- `get_stories` Fetching (top, new, ask_hn, show_hn) stories
- `get_story_info` Fetching comments associated with a story
- `search_stories` Searching for stories by query
- `get_user_info` Fetching user info

## Example Usage

Use prompts like the following:

```
- Get the top stories of today (will use `get_stories` tool`)
- What does the details of the story today that talks about the future of AI (will use `get_story_info` tool` based on the results of the previous tool)
- What has the user `pg` been up to? (will use `get_user_info` tool)
- What does hackernews say about careers in AI? (will use `search_stories` tool)
```

## Quickstart

### Claude Desktop:

Update the following:

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

With the following for development:

```json
{
  "mcpServers": {
    "mcp-hn": {
      "command": "uv",
      "args": [
        "--directory",
        "<dir_to>/mcp-hn",
        "run",
        "mcp-hn"
      ]
    }
  }
}
```

Or with the following for production:

```json
{
  "mcpServers": {
    "mcp-hn": {
      "command": "uvx",
      "args": ["mcp-hn"]
    }
  }
}
```
imess 0.1.2
Analyze your iMessage data
Requires Python: >=3.10

# iMessage Analysis

Python scripts to analyze your iMessage data. Completely private and locally hosted. Please see [TODO](__) for more details and examples.

![Example Plot](assets/example.png)

Table of Contents:
1. [💽 Installation](#installation)
2. [⚡️ Quickstart](#quickstart)
3. [🧑‍💻 Advanced](#advanced)


<a name="installation" />

## 💽 Installation
```bash
pip install imess
```

<a name="quickstart" />

## ⚡️ Quickstart

First, load your messages and contacts data.
```bash
# Copy data into /tmp/imess/ so that this script can access it
imess_init

# Preprocess data
imess_load
```

Second, generate plots:
```bash
# General plots (top-K) of all-time
imess_run --mode top_K_chats_with_sender_counts --K 20

# General plots (top-K) of chats over time
imess_run --mode top_K_chats_over_time --K 20

# Plots about a specific named group chat
imess_run --mode specific_chat_over_time --chat <chat-name>

# Plots about a specific set of senders
imess_run --mode specific_chat_over_time --senders <comma-separated-list-of-senders>

# Plots about a specific chat's word cloud
imess_run --mode specific_chat_word_cloud --senders <comma-separated-list-of-senders>

# Outputs all chat histories, one CSV and TXT per chat
imess_run --mode output_chat_history
```

All plots and outputs will be saved to `./plots/`.

<a name="advanced" />

## 🧑‍💻 Advanced

### Install

For local development:
```bash
conda create -n imess python=3.10 -y
conda activate imess
pip install -e .
```

### Merging Contacts

**TODO**

If you have contacts that you want to merge but don't think that's captured in the `contacts.vcf` file, you can provide a `data/merges.txt` file to `imless_load` to specify how you want to merge contacts.

First, create a `data/merges.txt` file. Each row is a separate contact that will be merged into a single contact. The row should contain a comma-separated list of contact info that will be merged into a single contact.

An example is below:

```
test@gmail.com,test@comcast.com,599-559-4499
1121122222,999-999-9999,mom@icloud.com
```

Anytime we see `test@gmail.com`, `test@comcast.com`, or `599-559-4499`, we will merge them into a single contact.

The script `imess_load` will automatically try to load `data/merges.txt`. If you don't want to use this file, you can set the `--path_to_merges_txt` flag to `None` or delete the file.

```bash
imess_load --path_to_merges_txt ./data/merges.txt
```
npDoseResponse 0.0.10
Nonparametric Inference on Dose-Response Curve and its Derivative: With and Without Positivity
Requires Python: >=3.8

[![PyPI pyversions](https://img.shields.io/pypi/pyversions/npDoseResponse.svg)](https://pypi.python.org/pypi/npDoseResponse/)
[![PyPI version](https://badge.fury.io/py/npDoseResponse.svg)](https://badge.fury.io/py/NPDoseResponse)
[![Downloads](https://static.pepy.tech/badge/npDoseResponse)](https://pepy.tech/project/npDoseResponse)
[![Documentation Status](https://readthedocs.org/projects/npdoseresponse/badge/?version=latest)](http://npdoseresponse.readthedocs.io/?badge=latest)

# Nonparametric Inference on Dose-Response Curve and its Derivative

This package implements the proposed integral estimator and a localized derivative estimator for estimating the covariate-adjusted regression function (or the dose-response curve in causal inference) and its derivative. We also document all the code for simulations and real-world case study in our paper [here](https://github.com/zhangyk8/NPDoseResponse/tree/main/Paper_Code).

* Free software: MIT license
* Python Package Documentation: [https://npdoseresponse.readthedocs.io](https://npdoseresponse.readthedocs.io).
* We also provide an R package [npDoseResponse](https://cran.r-project.org/package=npDoseResponse), though the Python package will be numerically stabler.

Installation guide
--------

```npDoseResponse``` requires Python 3.8+ (earlier version might be applicable) and [NumPy](http://www.numpy.org/). To install the latest version of ```npDoseResponse``` from this repository, run:

```
python setup.py install
```

To pip install a stable release, run:
```
pip install npDoseResponse
```

References
--------

<a name="npdoseresponse">[1]</a> Y. Zhang, Y.-C. Chen, and A. Giessing (2024+) Nonparametric Inference on Dose-Response Curves Without the Positivity Condition [arXiv:2405.09003](https://arxiv.org/abs/2405.09003).
Argparse-Interface 0.1.2
An automatic, terminal based interactive interface for any Python 3 `argparse` command line with keyboard and mouse support.
Requires Python: >=3.10

# ArgUI

![ArgUI's Logo](https://github.com/Sorcerio/Argparse-Interface/blob/master/assets/ArgUILogo_transparent.png?raw=true)

An automatic, terminal based interactive interface for any Python 3 `argparse` command line with keyboard and mouse support.

- [ArgUI](#argui)
  - [See It in Action](#see-it-in-action)
  - [Usage](#usage)
    - [Install as a Dependency](#install-as-a-dependency)
    - [Setup Your Argparse](#setup-your-argparse)
    - [Run Your Program](#run-your-program)
    - [Navigation](#navigation)
  - [Development Setup](#development-setup)

---

## See It in Action

![Demo of the features in ArgUI](https://github.com/Sorcerio/Argparse-Interface/blob/master/assets/ArgUIDemo_small.gif?raw=true)

Get a feel for the features of ArgUI using the [Demo.py](./argui/Demo.py) code included in this project.

## Usage

### Install as a Dependency

The ArgUI package is [available on PyPi](https://pypi.org/project/Argparse-Interface/).

It can be installed by calling: `pip install Argparse-Interface`

### Setup Your Argparse

ArgUI supports wrapping any implementation of the Python 3 `argparse` native library.
This will all you to use both standard terminal and interface modes to interact with your program.

```python
# Import
import argparse
import argui

# Setup your ArgumentParser normally
parser = argparse.ArgumentParser(prog="Demo")

# `add_argument`, `add_argument_group`, etc...

# Wrap your parser
interface = argui.Wrapper(parser)

# Get arguments
args: argparse.Namespace = interface.parseArgs()

# `args` is the same as if you had called `parser.parse_args()`
```

See [Demo.py](./argui/Demo.py) for more information.

### Run Your Program

Your program can now be run in both CLI and GUI modes.

To run in CLI mode, simply use your script as normal like `python foo.py -h`.

To run in GUI mode, provide only the `--gui` (by default) argument like `python foo.py --gui`.

### Navigation

Mouse navigation of the GUI is possible in _most_ terminals.

There are known issues with the VSCode terminal on Windows 10 and some others.
However, Mouse navigation does work in Powershell when opened on its own.

Keyboard navigation is always available using `Tab`, `Arrow Keys`, and `Enter`.
But make note that if you are using a terminal within another program (like VSCode), that some more advanced keyboard commands (like `CTRL+S`) may be captured by the container program and not sent to the GUI.

## Development Setup

1. Clone this repo and enter the directory with a terminal.
1. Setup a Python Env: `python -m venv .venv --prompt "argUI"`
1. Enter the Python Env.
1. Install requirements: `pip install -r requirements.txt`
mathalgo2 0.4.0
MathAlgo2 是一個全面的 Python 數學演算法工具包，專注於提供多樣化的數學計算與演算法實現。本工具包適合研究人員、學生以及需要進行數學計算的開發者使用。
Requires Python: >=3.7

# MathAlgo2 數學演算法工具包

## 專案簡介
MathAlgo2 是一個全面的 Python 數學演算法與資料處理工具包，提供多樣化的演算法實現、視覺化功能、檔案處理工具以及數據分析功能。本工具包適合研究人員、數據分析師以及需要進行演算法分析和資料處理的開發者使用。

## 版本資訊
- 當前版本：0.4.0
- 發布日期：2024-12-28
- 更新日誌：
  - 0.4.0 (2024-12-28)
    - 基礎數學運算功能（矩陣運算、向量計算、複數運算）
    - 進階演算法功能（最佳化演算法、數值分析、統計分析工具）
    - 實用工具（資料視覺化、效能分析、錯誤處理機制）

## 核心功能

### 數學運算
- 微積分計算（定積分、極限、泰勒級數）
- 矩陣運算（加減乘、轉置、行列式）
- 向量空間運算
- 數學函數視覺化

### 資料結構
- 二元樹（插入、搜尋、序列化）
- 堆疊（LIFO）
- 佇列（FIFO）
- 鏈結串列
- 圖結構（含視覺化）

### 搜尋演算法
- 二分搜尋
- 線性搜尋
- 視覺化搜尋過程
- 效能分析工具

### 圖論演算法
- 深度優先搜尋 (DFS)
- 廣度優先搜尋 (BFS)
- Dijkstra 最短路徑
- 視覺化圖形演算法

### 加密與編碼
- 基礎編碼（ASCII、Base64、摩斯密碼）
- 古典密碼（凱薩密碼、柵欄密碼）
- 現代加密（RSA、AES、DES）
- 檔案加密功能

### 檔案處理工具
- 多格式檔案讀寫
- 檔案壓縮與解壓縮
- 檔案加密與解密
- 圖片處理功能
- 檔案備份管理

### 日誌系統
- 多級別日誌記錄
- 日誌檔案輪轉
- 自定義日誌格式
- 性能監控

## 使用範例

### 數學運算
```python
from mathalgo2.BaseMath import Calculus

# 創建計算器實例
calc = Calculus("x**2 + 2*x + 1")

# 計算定積分
result = calc.definite_integral(0, 1)
print(f"定積分結果: {result}")

# 繪製函數圖形
calc.plot(-5, 5)
```

### 資料結構
```python
from mathalgo2.Structure import Tree, Stack

# 創建二元樹
tree = Tree()
tree.insert(5)
tree.insert(3)
tree.insert(7)

# 使用堆疊
stack = Stack(max_size=10)
stack.push(1)
stack.push(2)
print(stack.pop())  # 輸出: 2
```

### 加密解密
```python
from mathalgo2.Code import ModernCipher

# AES 加密
cipher = ModernCipher()
encrypted, key = cipher.aes_encrypt("Hello, World!")
decrypted = cipher.aes_decrypt(encrypted, key)
```

## 環境需求
- Python 3.7+
- NumPy >= 1.19.0
- SciPy >= 1.6.0
- Pandas >= 1.2.0
- Matplotlib >= 3.3.0
- Seaborn >= 0.11.0
- SymPy >= 1.8
- Cryptography >= 3.4.0
- Pillow >= 8.0.0
- OpenCV >= 4.5.0

## 安裝

### 基本安裝
```bash
pip install mathalgo2
```

### 開發者安裝
```bash
pip install mathalgo2[dev]
```

### 文件開發安裝
```bash
pip install mathalgo2[docs]
```

## 文件
完整文件請參考 [專案文件](docs/)

## 參與貢獻
我們歡迎各種形式的貢獻：

1. Fork 本專案
2. 建立特性分支 (`git checkout -b feature/新功能`)
3. 提交變更 (`git commit -m '新增某功能'`)
4. 推送分支 (`git push origin feature/新功能`)
5. 提交 Pull Request

## 作者
- Donseking - [GitHub](https://github.com/Donseking)
- Email: 0717albert@gmail.com

## 授權條款
本專案採用 MIT 授權條款 - 詳見 [LICENSE](LICENSE) 檔案

## 問題回報
如發現任何問題，請至 [Issues](https://github.com/Donseking/MathAlgo2/issues) 頁面回報
snow2dbt 0.2.2
Command Line to reverse engineering a Snowflake Table to DBT Model file
Requires Python: >=3.7

<p align="center" style="vertical-align:center;">
    <img src="img/dbt.png" alt="dbtIcon" width="75" height="35" margin="auto" display="block"><br>
</p>

<h1 style="text-align:center;">Snow2dbt</h1>

<h2 style="text-align:center; font-style: italic;">Command Line to reverse engineering a Snowflake Table to DBT model and SQL</h2>


Be careful this tool is under development. There's no stable release yet.

### 1. Installing 

• From pip

```bash
pip install snow2dbt

```

• From Github repository

```bash
pip install https://github.com/tensor365/snow2dbt

```
### 2. Authentication


**Dbt SSO**

If you have already a Snowflake Connection in your profile.yml , snow2dbt will use this identity to connect to your Snowflake tenant. If you've multiple identity in your profile.yml, you can specify the profile you want by using the argument: --profile . 

**Standard Authentication**

If you want to use a standard authentification to Snowflake, you can specify following arguments: 

    • Password Authentication

    ```bash 

    --account <snowflake_accountname>
    --user <snowflake_user>
    --password <snowflake_password> 

    ```

    • Keypair Authentication

    Not available yet. Incoming soon

    ```bash

    --account <snowflake_accountname>
    --key <snowflake_private_key>

    ```

### 3. Reversing a Snowflake Table into model/contract


```bash

snow2dbt --target <databse>.<schema>.<table>

```

**CLI arguments**

| Option                | Description                                                                             |
|-----------------------|-----------------------------------------------------------------------------------------|
| `-l`, `--lower`       | Output type names as lowercase in YAML file                                             |
| `--snake`             | Convert field names to snake_case                                                       |
| `--prefix`            | Prefix to add to columns names (default: None)                                          |
| `--suffix`            | Suffix to add to column names (default: None)                                           |
| `--output`            | Destination folder for scripts. (default: target/bq2dbt)                                |
| `--empty_description` | Add empty description property to YAML file if field description is empty (placeholder) |
| `--tabs`              | Use tabs instead of 4 spaces in SQL file indentation                                    |
| `--leading_comma`     | Put comma at the start of line in SQL file column list instead of end of line           |
cleanpycache 0.1.2
Multiple ways to clean __pycache__ folders recursively from a specified root path
Requires Python: >=3.9
Keywords: util, utility

# CleanPycache

**CleanPycache** is intended to be used as a CLI tool for clearing \_\_pycache\_\_ folders from a specified root path.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install **cleanpycache**.

```bash
pip install cleanpycache
```

## CLI Utility

The following CLI is provided in this package.

```bash
# clean_pycache -h
usage: clean_pycache [-h] [<path>] [<runtype>]

-.-.-. Cleaning __pycache__ folders!

positional arguments:
  <path>      absolute root path to start clearing
  <runtype>   run type for clearing __pycache__ folders (shell cmds or native python)

options:
  -h, --help  show this help message and exit

-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.
```


## License

[MIT](https://choosealicense.com/licenses/mit/)
trx-viewer 0.1.1
A helper program for reading .NET test files (TRX)
Requires Python: >=3.8

# TRX Viewer

<img src="trx_viewer/ViewerGUI/logo.svg" alt="TRX VIewer logo" style="width:200px;"/>

TRX Viewer is a multi-platform software for viewing .NET test (TRX) files.
It is highly performant, and can handle TRX files with hundreds of thousands of tests.

Users can see and filter individual tests based on test parameters such as outcome and duration, and can see specific test information such as the stdout and stderr values of selected tests.

## Installation

TRX viewer is available on pip:

```bash
pipx install trx-viewer
```

Currently no standalone application launcher is provided, so simply start it from the terminal.

## Program Showcase

![App screenshot regular](Screenshots/regular.png)
![App screenshot filter success](Screenshots/filter_success.png)
![App screenshot empty app](Screenshots/empty_app.png)
![App screenshot about open](Screenshots/about_open.png)