semvec/pypi-packages huggingface dataset:
zns-logging 1.1.5
A simple and flexible logging library for Python
Requires Python: >=3.10

<h1 align="center">zns-logging</h1>

<h3 align="center">A simple and flexible logging library for Python</h3>

# Installation

```bash
pip install zns-logging
```

# Usage

```python
from zns_logging.ZnsLogger import ZnsLogger

logger = ZnsLogger(__name__, "DEBUG")

logger.debug("This is a debug message")
logger.info("This is an info message")
logger.warning("This is a warning message")
logger.error("This is an error message")
logger.critical("This is a critical message")
```

# Output

```
[2025-01-26 23:22:06] [DEBUG   ] [__main__]: This is a debug message
[2025-01-26 23:22:06] [INFO    ] [__main__]: This is an info message
[2025-01-26 23:22:06] [WARNING ] [__main__]: This is a warning message
[2025-01-26 23:22:06] [ERROR   ] [__main__]: This is an error message
[2025-01-26 23:22:06] [CRITICAL] [__main__]: This is a critical message
```

# Change Log

```markdown
1.0.0

- Status: Yanked
- Reason: The `colorama` dependency was missing from `install_requires`, causing errors during logger usage.

1.0.1

- Status: Released
- Changes: Added `colorama` to `install_requires`.

1.0.2

- Status: Released
- Changes: Added description and other metadata to `Setup.py`.

1.0.3

- Status: Released
- Changes: Removed unnecessary variables from `Setup.py`.

1.0.4

- Status: Released
- Changes: Update `zns_logging.py` logic.

1.0.5 & 1.0.6 & 1.0.7

- Status: Yanked
- Reason: Unnecessary releases.

1.0.8

- Status: Released
- Changes: Rename `zns_logging.py` to `ZnsLogging.py` and update logic.

1.0.9

- Status: Released
- Changes: Update automatically create log directory.

1.1.0

- Status: Released
- Changes: Change function `get_logger` to class `ZnsLogger`.

1.1.1

- Status: Released
- Changes: Remove `ZnsLogging.py`.

1.1.2

- Status: Released
- Changes:
    - Update auto create log directory.
    - `log_and_raise` now does not need to pass the caller's name.

1.1.3

- Status: Released
- Changes:
    - Swap the order of the StreamHandler and FileHandler in the logger.
    - Integrate the `log_and_raise` function into the `ZnsLogger` class.
    - Fix logic in `LogConsoleFormatter`.

1.1.4

- Status: Released
- Changes: Create `LogHandlerFactory` class to create log handlers dynamically.

1.1.5

- Status: Released
- Changes: Automatically create log directory if it does not exist.
```
erkc63 0.2.40
Клиент личного кабинета ЕРКЦ.
Requires Python: <3.14,>=3.12
Keywords: async, client, erkc63

# erkc63

Клиент [личного кабинета ЕРКЦ](https://lk.erkc63.ru/).

Писался как отдельная [PyPI](https://pypi.org/project/erkc63/) библиотека для интеграции данных в [Home Assistant](https://www.home-assistant.io/).
deelink 2.0.0
Конвертер коротких Deezer-ссылок в чистые URL
Requires Python: >=3.6

# Deelink - Конвертер Deezer-ссылок

Утилита для преобразования коротких ссылок Deezer (dzr.page.link) в чистые URL треков.

## Установка

```bash
pip install deelink


Использование
После установки запустите утилиту командой:

deelink


Затем вставьте короткую ссылку Deezer и нажмите Enter. Программа покажет:

1. Оригинальную ссылку

2. URL после редиректа

3. Чистую ссылку на трек

Для выхода введите exit или нажмите Ctrl+C.

Контакты автора

Email: shizofrin@gmail.com

Twitter (X): @Lanaev0li

elegram: @akrim



Лицензия
MIT License © 2025 Avinion
adalasso 0.1.1
Adaptive Lasso implementation following scikit-learn API
Requires Python: >=3.6

# Adaptive Lasso


[![PyPI version](https://img.shields.io/pypi/v/adalasso.svg)](https://pypi.org/project/adalasso/)
[![Tests](https://github.com/ErikHartman/adalasso/actions/workflows/tests.yml/badge.svg)](https://github.com/ErikHartman/adalasso/actions/workflows/tests.yml)

A scikit-learn compatible implementation of the Adaptive Lasso algorithm for feature selection and regression.

## Overview

Adaptive Lasso is an extension of the standard Lasso method that provides improved feature selection properties through weighted L1 penalties. It assigns different weights to different coefficients in the L1 penalty, usually based on preliminary estimates of the coefficients.

This implementation follows the scikit-learn API design and can be used as a drop-in replacement for other scikit-learn linear models.

## Installation

```bash
pip install adalasso
```

## Usage

```python
from adalasso import AdaptiveLasso
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split

# Generate sample data
X, y = make_regression(n_samples=100, n_features=20, n_informative=5, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize and fit the model
model = AdaptiveLasso(alpha=0.1, gamma=1.0)
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Access model coefficients
print("Coefficients:", model.coef_)
print("Intercept:", model.intercept_)
```

## Parameters

- `alpha`: Regularization strength
- `gamma`: Power parameter for the adaptive weights
- `fit_intercept`: Whether to calculate the intercept
- `max_iter`: Maximum number of iterations
- `tol`: Tolerance for optimization

See the class documentation for a full list of parameters.

## Development

### Running Tests

To run the tests, you'll need to install the development dependencies:

```bash
pip install pytest pytest-cov
```

Then run the tests with:

```bash
pytest --cov=adalasso adalasso/tests/
```

## Visualization Examples

### Regularization Path Comparison

The following figure shows how coefficients evolve with different regularization strengths for standard Lasso versus Adaptive Lasso:

![Adaptive Lasso Regularization Path](adaptive_lasso_regularization_path.png)

### Feature Selection Performance

This figure demonstrates how Adaptive Lasso performs better at selecting the correct number of non-zero features across different regularization strengths:

![Feature Selection vs Regularization](adaptive_lasso_feature_count.png)

## References

- Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476), 1418-1429.

## License

MIT License
closeformik 1.0.0
closeformik Package with dynamic libraries for both Linux and Windows
Requires Python: >=3.9

closeformik is an analytical inverse kinematics calculation library for a 6-DOF robotic arm. When solving for inverse kinematics, please call the CIK function. The input to the function is eeTform (end-effector flange position), MdhParams (DH parameter table: d, a, alpha, theta), jointlimits (joint angle limits), joint_ref (reference joint angles): A typical application example is: 

from CloseFormIK import CIK

MdhParams = np.array([
    [a1,    alpha1,     d1,     theta1],
    [a2,    alpha2,     d2,     theta2],
    [a3,    alpha3,     d3,     theta3],
    [a4,    alpha4,     d4,     theta4],
    [a5,    alpha5,     d5,     theta5],
    [a6,    alpha6,     d6,     theta6]
])

Jointlimits = [
    [limit_n_j1, limit_p_j1],  # Joint 1
    [limit_n_j2, limit_p_j2],  # Joint 2
    [limit_n_j3, limit_p_j3],   # Joint 3
    [limit_n_j4, limit_p_j4],  # Joint 4
    [limit_n_j5, limit_p_j5],   # Joint 5
    [limit_n_j6, limit_p_j6]   # Joint 6
]

sol = CIK(eeTform, MdhParams, Jointlimits, joint_ref=joint_ref)
qmaze-cipher 1.0.0
Quantum-resistant chaos-based encryption algorithm
Requires Python: >=3.7

# QMazeCipher

QMazeCipher, kuantum bilgisayarlara dayanıklı, kaotik sistem tabanlı bir şifreleme algoritmasıdır. Logistic map ve dinamik hash tabanlı blok permütasyonu ile geleneksel frekans analizlerini boşar. Python ile yazılmıştır ve her türlü metni güvenli bir şekilde şifrelemek için uygundur.

## Özellikler
- Logistic Map ile kaotik karışıklık
- SHA3-256 tabanlı blok şifreleme
- Nonce (tuzlama) ile non-deterministik çıktı
- PKCS7 benzeri padding sistemi
- Base64 destekli çıktı

## Kurulum
```
pip install qmaze_cipher
```

## Kullanım
```python
from qmaze_cipher import QMazeCipher

cipher = QMazeCipher("gizli_anahtar")

sifreli = cipher.encrypt("Bu bir testtir.")
cozulen = cipher.decrypt(sifreli)

print(cozulen)  # Bu bir testtir.
```

## Testler
```bash
pytest tests/
```

## Lisans
MIT License
J4useragent 0.1.2
Generator User-Agent untuk berbagai perangkat
Requires Python: >=3.9

# J4useragent

J4useragent adalah pustaka Python untuk menghasilkan User-Agent yang realistis untuk berbagai merek dan model perangkat Android.

## Instalasi

Gunakan pip untuk menginstal paket ini:
```sh
pip install J4useragent
```

## Penggunaan

```python
from J4useragent import GenerateUseragent

# Inisialisasi objek GenerateUseragent
os = GenerateUseragent()

# Menghasilkan User-Agent chrome dari sistem
ua = os.chromeuseragent(system=True)
print(ua)

# Menghasilkan User-Agent chrome dari sistem
ua = os.chromeuseragent()
print(ua)

# Menghasilkan User-Agent Facebook dari sistem
ua = os.facebookuseragent(system=True)
print(ua)

# Menghasilkan User-Agent Facebook secara acak
ua = os.facebookuseragent()
print(ua)

# Menghasilkan User-Agent instagram app dari sistem
ua = os.instagramuseragent(system=True)
print(ua)

# Menghasilkan User-Agent Instagram app secara acak
ua = os.instagramuseragent()
print(ua)
```

## Fitur
- Menghasilkan User-Agent Chrome
- Menghasilkan User-Agent Facebook
- Menghasilkan User-Agent Instagram 
- Mendukung berbagai merek dan model perangkat
- Dapat mengambil informasi perangkat dari sistem

## Lisensi

Proyek ini dilisensikan di bawah [MIT License](LICENSE).