semvec/pypi-packages huggingface dataset:
chronosx 1.0.1
Minimal macOS menubar chronometer with lap history
Requires Python: >=3.9
Keywords: macos, menubar, chronometer, stopwatch, timer, productivity

# ChronosX

Minimal macOS menubar chronometer with lap history.

![macOS](https://img.shields.io/badge/platform-macOS-lightgrey) ![Python](https://img.shields.io/badge/python-3.9+-blue) ![License](https://img.shields.io/badge/license-MIT-green)

## Install

### Via pip

```bash
pip install chronosx
chronosx
```

### As macOS App (clickable icon)

```bash
git clone https://github.com/lpalbou/chronosx.git
cd chronosx
./scripts/build_app.sh
cp -r dist/ChronosX.app /Applications/
open /Applications/ChronosX.app
```

## Usage

| Action | Result |
|--------|--------|
| **Click** | Start/stop timer |
| **Right-click** | Open laps popup |
| **Double-click title** | Edit lap title |
| **Clear** | Delete all laps |
| **Exit** | Quit app |

### Display

- `◉` — Idle
- `MM:SS.m` — Running (minutes:seconds.tenths)

## Data

Laps stored in `~/.chronosx_laps.json`

## Requirements

- macOS 10.14+
- Python 3.9+

## License

MIT
pip-toolbox 0.4.5
A toolbox for managing Python packages

# pip-toolbox

### **Python Pip 软件包管理器（GUI 版，基于 Tkinter）**
本脚本使用 **Tkinter** 创建了一个 **Pip 软件包管理器**，允许用户 **搜索、安装、卸载和管理 Python 软件包**，并提供直观的交互界面。

## **主要功能：**
1. **显示已安装的 Python 软件包及其版本。**  
2. **按名称搜索和筛选软件包。**  
3. **安装指定版本的软件包。**  
4. **卸载已安装的软件包。**  
5. **查询并显示可用的软件包版本。**  
6. **切换 PyPI 源。**  
7. **一键全部更新**

## 安装
pip install pip-toolbox

## 运行
pip-toolbox

---
---
### **Python Pip Package Manager (GUI) using Tkinter**
This script creates a GUI-based **Pip Package Manager** using **Tkinter**. It allows users to **search**, **install**, **uninstall**, and **manage Python packages** through an interactive interface.

## **Main Features:**
1. **Display installed Python packages and their versions.**
2. **Search and filter packages by name.**
3. **Install a specific version of a package.**
4. **Uninstall a specified package.**
5. **Query and display available versions of packages.**
6. **Switch PyPI source.**
7. **One-click update all**

## Installation
pip install pip-toolbox

## Usage
pip-toolbox

## Links
pip 软件包管理器 Python pip Package Manager (GUI) using Tkinter https://pypi.org/project/pip-toolbox/
filmsmik 0.0.2
SDK Python pour accéder à l’API MovieLens (Data Analysts/Data Scientists friendly)
Requires Python: >=3.14

# MovieLens `filmsmik`

Un SDK Python simple pour interagir avec l'API REST MovieLens. Il est conçu pour les **Data Analysts** et **Data Scientists**, avec une prise en charge native de **Pydantic**, **Dictionnaires** et **DataFrames Pandas**.

[![PyPI version](https://badge.fury.io/py/filmsmik.svg)](https://badge.fury.io/py/filmsmik)
[![Licence : MIT](https://pypi.org/search/?c=License+%3A%3A+OSI+Approved+%3A%3A+MIT+License)](https://pypi.org/search/?c=License+%3A%3A+OSI+Approved+%3A%3A+MIT+License)

---

## Installation
```bash 
pip install filmsmik
```
## Configuration

```python
from filmsmik import MovieClient MovieConfig
# configuration avec l'URL de votre API (Render ou local)
config = MovieConfig(movie_base_url="https://movie-backend-4sx2.onrender.com")
client = MovieClient(config=config)
```
---
## Tester le SDK

### 1. Health check

```python
Client.health_check()
# Retourne : {"status" : "ok"}
```

### 2. Récupérer un film
```python
movie = client.get_movie(1)
print(movie.title)
```
### 3. Liste de films au format DataFrame
```python
df = client.list_movies(limit=5, output_format="pandas")
print(df.head())
```
---
## Modes de sortie disponibles
Toutes les méthodes des listes (`list_movies`, `list_ratings`, etc.) peuvent retourner

- des objets **Pydantic** par défaut
- des **Dictionnaires**
- des **DataFrames pandas**

Exemples : 
```python
client.list_movie(limit=10, output_format="dict")
client.list_ratings(limit=10, output_format="pandas")
```
## Tester en local
Vous pouvez également utiliser en local l'API
```python
config = MovieConfig(movie_base_url="http:localhost:8000")
client = MivieClient(config=config)
```

----

## Public cible

- Data analysts
- Data scientists
- Etudiants et curieux en data
- Développeurs Python

---

## Licence
MIT License

---

## Liens

- API Render : [https://movie-backend-4sx2.onrender.com](https://movie-backend-4sx2.onrender.com)
- PyPI : [(https://badge.fury.io/py/filmsmik.svg)](https://badge.fury.io/py/filmsmik)
msffs 0.1.0
mSFFS feature selection with a functional API and sklearn-compatible transformer
Requires Python: >=3.10
Keywords: feature selection, msffs, scikit-learn

# mSFFS
mSFFS (Modified Sequential Floating Forward Selection) is a Python library for feature selection, offering a simple functional API and a scikit-learn–compatible transformer.

## Install

```bash
pip install msffs
```

## Minimal usage (defaults)

```python
import numpy as np
from msffs import msffs_select

X = np.array(
    [
        [0.1, 0.2, 0.3, 0.4],
        [0.2, 0.1, 0.4, 0.3],
        [0.15, 0.25, 0.35, 0.45],
        [0.9, 0.8, 0.7, 0.6],
        [0.8, 0.9, 0.6, 0.7],
        [0.85, 0.75, 0.65, 0.55],
    ]
)
y = np.array([0, 0, 0, 1, 1, 1])

result = msffs_select(X, k=2, y=y)
print(result["selected_indices"])
```

## Scikit-learn pipeline usage

```python
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from msffs import MSFFSSelector

pipe = Pipeline(
    steps=[
        ("scale", StandardScaler()),
        (
            "select",
            MSFFSSelector(
                k=2,
                estimator=SVC(),
                grid_params={"param_grid": {"C": [1.0], "kernel": ["linear"]}, "cv": 2},
            ),
        ),
    ]
)

X_selected = pipe.fit_transform(X, y)
```

## API overview

- `msffs_select(X, k, y, estimator=None, grid_params=None)` returns a dict with:
  - `selected_indices`: indices of the selected features
  - `selected_X`: `X` subset to selected features (when available)
  - `best_scores`: cross-validated scores by subset size
  - `winner_list`: selected feature indices by subset size
  - `best_params_list`: best estimator params by subset size
- `MSFFSSelector`: scikit-learn compatible transformer for pipelines.

Defaults:
- Estimator: `sklearn.linear_model.LogisticRegression(max_iter=1000, random_state=0)`
- Grid params:
  - `C`: [0.1, 1.0, 10.0]
  - `cv`: 3

## Notes

- mSFFS is supervised, so `y` is required.
- For very small datasets, reduce CV folds via `grid_params={"cv": 2, ...}`.
- Defaults stay lightweight and deterministic; pass any sklearn-compatible estimator
  when you need something heavier (e.g., XGBoost).
  
## Development

```bash
pip install -e ".[dev,test]"
ruff check .
black --check .
pytest
python -m build
```

## Citation

If you use this library in academic work, please cite:

- J. Li, D. De Ridder, D. Adhia, M. Hall, R. Mani and J. D. Deng, "Modified Feature
  Selection for Improved Classification of Resting-State Raw EEG Signals in Chronic
  Knee Pain," in IEEE Transactions on Biomedical Engineering, vol. 72, no. 5,
  pp. 1688-1696, May 2025, doi: 10.1109/TBME.2024.3517659.
numgear 0.0.1b2.post1.dev0
This Python package extends the functionality of numpy by introducing additional methods for data manipulation, and a specialized format for reading and writing numpy arrays.
Requires Python: >=3.7

# Numpy Extension

This Python package extends the functionality of numpy by introducing additional methods for data manipulation, and a specialized format for reading and writing numpy arrays.

## Features

- **Read `NDArray` in Need with Keys**: Using a key to load the required array without load all.
- **Array Mapping and Transformation Functions**: Functions to apply operations across arrays, adjust their ranges, and produce linearly spaced values with specific properties.

## Installation
```bash
pip install numgear
```

## Usage

### Transformation Functions

- **apply_from_axis**: Apply a function to slices of an array along a specified axis.
- **map_range**: Transform elements in an array to a specified range.
- **map_ranges**: Transform elements in an array to multiple specified ranges based on conditions.
- **linspace_at**: Generate linearly spaced numbers for given indices.
- **linspace_cumprod_at**: Generate a cumulative product series for linearly spaced values at specific indices.

### Save and Load Utilities

- **savek**: Save multiple numpy arrays to a custom binary format `.npk`.
- **loadk**: Load arrays from a `.npk` file and read in need for easy access.
mahoraga 0.5.0
Reverse proxy for Python mirrors
Requires Python: >=3.14
Keywords: conda, pypi, mirror

# Mahoraga

[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/hingebase/mahoraga/publish-pypi.yml?label=ci&logo=github)](https://github.com/hingebase/mahoraga/actions)
[![GitHub commit activity](https://img.shields.io/github/commit-activity/y/hingebase/mahoraga?logo=github)](https://github.com/hingebase/mahoraga/commits)
[![PyPI - Version](https://img.shields.io/pypi/v/mahoraga)](https://pypi.org/project/mahoraga)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mahoraga)
![Apache-2.0 License](https://img.shields.io/pypi/l/mahoraga)  
![basedpyright](https://img.shields.io/badge/basedpyright-checked-42b983)
![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)
![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)

A reverse proxy for Python mirrors. [Docs](https://hingebase.github.io/mahoraga/)