semvec/pypi-packages huggingface dataset:
xa-izipy 0.0.5
Упрощает какие-то функции
Requires Python: >=3.6

# xa_izipy Lib

**🎯 Target:** Make Python easier (izi) 😎🔥

This library provides a few simple functions to simplify Python scripting — from detecting paths to importing built-in modules faster.

---

**❓ Changes:**

- `typing_text()` - Changed the logic of the command
- `check_available()` - New function

---

## 🛠️ Function List (default module)

- `help(choice="en")` – Shows info about the library in selected language  
  - `"en"` by default  
  - `"ru"` for Russian  
  - Example: `help("ru")` or `help(choice="ru")`

- `getdirectory()` – Returns the full path to the script location 📂

- `fast_import(show_loading=True)` – Automatically imports common Python libraries (`os`, `time`, `random`, `logging`, `json`)  
  - Set `show_loading=False` to disable import messages

- `get_platform_info()` – Returns detailed system information as a dictionary:  
  - `os` — Operating system name (e.g., Windows, Linux, macOS)  
  - `release` — OS version or release number (e.g., 10, 11, kernel version)  
  - `arch` — CPU architecture (e.g., x86_64, AMD64, ARM)  
  - `hostname` — Network name of the computer (PC name)  
  - `ram_total_GB` — Total RAM in gigabytes (rounded to 2 decimals)  
  - `ram_available_GB` — Available/free RAM in gigabytes (rounded to 2 decimals)  

- `typing_text("text", 0.1)` — Outputs text with typing animation  
  - `"text"` – Output text  
  - `"0.1"` – Delay in seconds  
  - Example: `typing_text("text", 0.1)` or `typing_text("text", delay=0.1)`

---

## 🧰 Function List (`xa_izipy.tools`)

- `create_logger(name="xa_izipy", level=logging.INFO)` – Creates a logger instance  
  - `name` – Logger name (default: `"xa_izipy"`)  
  - `level` – Logging level (default: `logging.INFO`)  
  - Example: `create_logger()` or `create_logger("mylog", logging.DEBUG)`

- `check_available(site, port=80, timeout=5)` – Checks if a site/server is reachable on a given port within the timeout.  
  - `site` – Hostname or IP address to check  
  - `port` – Port number to connect to (default: 80)  
  - `timeout` – Connection timeout in seconds (default: 5)  
  - Returns `True` if the connection is successful, otherwise `False`  

---

## 💻 Example usage

```python
from xa_izipy import getdirectory, help, fast_import, get_platform_info, typing_text

print(f"This directory: {getdirectory()}")  # Output full path of script

help()         # Show info in English
help("ru")     # Show info in Russian

fast_import()  # Import default modules with logs

info = get_platform_info()
print("System info:")
for key, value in info.items():
    print(f" - {key}: {value}")

typing_text("Hello World!", 0.15)
```

## 💻 Example usage (`xa_izipy.tools`)

```python
from xa_izipy.tools import create_logger, check_available

logger = create_logger()

logger.info("I like cookie ;>")
logger.warning("I not love cookie :<")
logger.error("I hate cookie :/")

if check_available("google.com", port=80, timeout=5) == True:
    print("Working!")
else:
    print("Not working!")
```
modelradar 0.2.1
Aspect-based Forecasting Accuracy
Requires Python: >=3.9
Keywords: Data Science, Evaluation, Forecasting, Machine Learning, Time Series

# Model Radar 🎯

A framework for aspect-based evaluation of time series forecasting models based on Nixtla's ecosystem.

[![PyPi Version](https://img.shields.io/pypi/v/modelradar)](https://pypi.org/project/modelradar/)
[![GitHub](https://img.shields.io/github/stars/vcerqueira/modelradar?style=social)](https://github.com/vcerqueira/modelradar)
[![Downloads](https://static.pepy.tech/badge/modelradar)](https://pepy.tech/project/modelradar)

## Overview

Model Radar introduces a novel aspect-based forecasting evaluation approach that goes beyond traditional aggregate metrics. Our framework enables:
- Fine-grained performance analysis across different forecasting aspects
- Better understanding of model behavior in varying conditions
- More informed model selection based on specific use case requirements

## 🚀 Getting Started

Check the `notebooks` folder for usage examples and tutorials.

Check **[ModelRadar-Experiments](https://github.com/vcerqueira/experiments-modelradar)** repository for a thorough
application of ModelRadar.

## Installation

You can install modelradar using pip:

```bash
pip install modelradar
```

### [Optional] Installation from source

To install modelradar from source, clone the repository and run the following command:

```bash
git clone https://github.com/vcerqueira/modelradar
pip install -e modelradar
```

### Prerequisites

Required dependencies:
```
utilsforecast==0.2.11
numpy==1.26.4
plotnine==0.14.5
```

⚠️ I've noticed some issues when running with more recent versions of numpy and utilsforecast. 
Try to use the versions above.

### Examples

Besides the examples in the `notebooks` folder, here's some outputs you can get from *modelradar*:

- Spider chart with overview on several dimensions:

![radar](assets/examples/radar.png)

- Parallel coordinates chart with overview on several dimensions:

![radar2](assets/examples/parcoords.png)

- Barplot chart controlling for a given variable (in this case, anomaly status):

![radar2](assets/examples/anomaly_status.png)

- Grouped bar plot showing win/draw/loss ratios wrt different models:

<img src="assets/examples/win_ratios.png" width="70%" alt="radar2">


## 📑 References

> Cerqueira, V., Roque, L., & Soares, C. "Forecasting with Deep Learning: Beyond Average of Average of Average Performance." Discovery Science: 27th International Conference, DS 2024, Pisa, Italy, 2024, Proceedings 27. Springer International Publishing, 2024.

Check DS24 folder to reproduce the experiments published on this paper.
The main repository and package contains an updated framework.

### **⚠️ WARNING**

> modelradar is in the early stages of development. 
> The codebase may undergo significant changes. 
> If you encounter any issues, please report
> them in [GitHub Issues](https://github.com/vcerqueira/modelradar/issues)
chronotui 0.4.0
Python TUI with Textual for tracking time 
Requires Python: >=3.10

# ChronoTUI

ChronoTUI is a modern terminal-based stopwatch and timer manager built with [Textual](https://textual.textualize.io/). It lets you track multiple timers with a beautiful, keyboard-driven interface, and automatically saves your session state.

## Features

- Multiple stopwatches, each with custom names
- Start, stop, reset, and rename timers
- Keyboard navigation and control (Vim-like bindings)
- Autosave and autoload: your timers persist between sessions
- Dark/light mode toggle
- Fast, responsive UI in your terminal

## Installation

You can install ChronoTUI using either `pip` or [`uv`](https://github.com/astral-sh/uv):

```sh
pip install chronotui
```

Alternatively, if you have `uv` installed, you can install it in a special virtual environment:

```sh
uv tool run chronotui
# identical to the shortcut alias
uvx chronotui
```

Note that when installed via `uv`, you have to run it with the `uvx` command on every start.

## Usage

After installation, launch ChronoTUI from your terminal:

```sh
chronotui
```

If this doesn't work (possibly due to incorrect symlink creation during installation), you can also run it as a module:

```sh
python -m chronotui
```

## Keyboard Shortcuts

- `q` — Save and quit
- `space` — Start/stop selected stopwatch
- `r` — Reset selected stopwatch
- `a` — Add a new stopwatch
- `d` — Delete selected stopwatch
- `n` — reName timer (alternatively `c` for "change name")
- `t` — Theme selection
- `s` — Settings
- `up`/`down`/`j`/`k` — Select stopwatch (hidden)
- `S` — Save stopwatches manually (hidden)
- `L` — Load stopwatches manually (hidden)

## State Persistence

ChronoTUI automatically saves your timers and their states to `session.json` in your user data directory when you quit, and reloads them when you start the app. If a stopwatch was running when you quit, its elapsed time will be updated when you restart.

User data directory is typically located at `~/.local/share/chronotui/` on Linux, or `%APPDATA%\Local\chronotui\` on Windows.

Similarly, the configuration file is saved to `config.json` in the user config directory, allowing you to customize settings like the theme and key bindings.

User config directory is typically located at `~/.config/chronotui/` on Linux, or `%APPDATA%\Local\chronotui\` on Windows (same as user data).
ponika 0.4.0
Teltonika Device API Client
Requires Python: >=3.13

# Ponika

Ponika is a Python library for interacting with the Teltonika devices.

## Installation

You can install Ponika using pip:

```bash
pip install ponika
```

## Usage

To use Ponika, you need to create an instance of `PonikaClient` with the appropriate parameters. Here's a basic example:

```python
from ponika import PonikaClient

client = PonikaClient(
    host="192.168.1.1",
    username="your_username",
    password="your_password",
    # port=80,       # Optional, default is 443 if tls=True else 80
    # tls=False,     # Optional, default is True
    # verify_tls=False,  # Optional, default is True
)
```

The library follows the structure of the Teltonika API endpoints. For example, to get the internet status from the endpoint `/api/v1/internet_connection/status`, you can do the following:

```python
response = client.internet_connection.get_status()

if response.success and response.data:
    print("Internet Status:")
    print("IPv4:", response.data.ipv4_status)
    print("IPv6:", response.data.ipv6_status)
    print("DNS: ", response.data.dns_status)
else:
    print("Error:", response.errors)
```

> [!NOTE]
> Not all endpoints are implemented. If you need a specific endpoint that's missing, the existing endpoints should be a good reference for how to implement new ones.

## Examples

#### Get Internet Status

```python
response = client.internet_connection.get_status()

if response.success and response.data:
    print("Internet Status:")
    print("IPv4:", response.data.ipv4_status)
    print("IPv6:", response.data.ipv6_status)
    print("DNS: ", response.data.dns_status)
else:
    print("Error:", response.errors)
```

#### Get GPS Position

```python
response = client.gps.position.get_status()

if response.success and response.data:
    print("GPS Position:")
    print("Latitude:", response.data.latitude)
    print("Longitude:", response.data.longitude)
    print("Altitude:", response.data.altitude)
else:
    print("Error:", response.errors)
```

## Contributing

If you want to contribute to Ponika, feel free to open a pull request on the GitHub repository. Contributions are welcome!

The project is setup to use [`uv`](https://docs.astral.sh/uv/) for development and requires prior installation. Once you've cloned the repository, you can set up the development environment by running:

```bash
uv sync
```

To run the tests, you can use the following command, updating the environment variables as needed:

```bash
# Required variables
# ---------------------
export TELTONIKA_HOST=192.168.1.1
export TELTONIKA_USERNAME=admin
export TELTONIKA_PASSWORD=password

# Optional variables
# ---------------------
# Enables SMS sending tests
export MOBILE_NUMBER=441234567890

# Run the tests
uv run pytest
```
open-logistics 1.0.1
AI-Driven Air Defense Supply Chain Optimization Platform
Requires Python: >=3.9
Keywords: ai, defense, logistics, machine-learning, mlx, optimization, sap-btp, supply-chain

# Open Logistics

AI-Driven Air Defense Supply Chain Optimization Platform.

This project is a mission-critical, enterprise-grade Python platform that revolutionizes 
air defense logistics through advanced AI optimization, real-time supply 
chain management, and predictive analytics.

## Getting Started

Please refer to the main `README.md` in the root of the repository for full details.
To install this package for development:

```bash
uv pip install -e .
```