semvec/pypi-packages huggingface dataset:
gli99 1.1.10
Web scraper for gifcities.org
Requires Python: >3.10.0

<div align="center">
    <img src="https://i.imgur.com/kGH6PC0.gif">
</div>

**gli99 (GIF Like Its '99) was created as an alternative to the official [GifCities API](https://gifcities.archive.org/api/v1/gifsearch?q=hamster) and was mostly just a personal practice project for Selenium WebDriver. The GifCities server is simply slow to serve image data, so either way will yield about the same result. But hey, it's Python :D**

---

<div>
    <img src="https://i.imgur.com/ys9tSyW.gif"><img src="https://i.imgur.com/jJbTPjg.gif">
</div>

Run the following to install:

```python
pip install gli99
```

<div>
    <img src="https://i.imgur.com/Bdtlrz0.gif"><img src="https://i.imgur.com/wjqbAoA.gif">
</div>

```python
from gli99.tools import GifScraper

gs = GifScraper(browser="firefox")
gs.load(query="brazil",amount=5)
gs.download()
```

currently supported browsers:

* Edge
* Chrome
* Firefox

<div align="center">
    <img src="https://i.imgur.com/GCJ3VNQ.gif">
</div>
ogorodlibrary 1.0.1
Это библиотека с датами посадки с/х культур для моей бабушки
Requires Python: >=3.12

README.md
Это библиотека с датами посадки сельско-хозяйственных культур для моей бабушки.
По мере обновления в библиотеке будут появлятся и новые овощи, и она будет становится более универсальной.
Пока что все файлы кроме year_2024.py и __init__.py и months.py не используются.
Используется MIT License.
rf-quick-dashboard 0.1.5
A package to generate a simple html dashboard report for Robot Framework using junit xml output
Requires Python: >=3.6

# RF Quick Dashboard

## Overview
RF Quick Report is a tool designed to generate an HTML dashboard from JUnit test results. This dashboard provides a comprehensive view of test execution, helping you quickly identify test outcomes and trends.

## Features
- Converts JUnit XML results into an interactive HTML dashboard.
- Summarizes test results with visual charts and graphs.
- Easy to integrate with CI/CD pipelines.

## Installation
pip install rf-quick-dashboard


## Usage
To generate the HTML dashboard, run the following command:
```bash
python -m generate_dashboard <junit file path>```
pyverbplt 24.9
Python library designed to load `.plt` files, created by the Versatile Electron Radiation Belts (VERB) code
Requires Python: >=3.9

# pyverbplt

**pyverbplt** is a Python library designed to load `.plt` files, created by the Versatile Electron Radiation Belts (VERB) code into structured numpy arrays. 

## Features

- Load data from `.plt` files into structured numpy arrays.
- Supports loading multiple variables from the `.plt` file.
- Options to permute, squeeze, and reshape the loaded data.
- Ability to specify zones for selective data loading.

## Installation

To install pyverbplt, clone the repository:
```bash
git clone https://github.com/radiation-belts/pyverbplt.git
```

To install using PyPI:
```bash
pip install pyverbplt
```


## Usage
The main functionality is provided by the `load_plt` function in `pyverbplt.py`. Below are some examples of how to use this function:

```python
import pyverbplt

# Example 1: Load multiple variables from a diffusion coefficients .plt file
L, E, A, Daa = pyverbplt.load_plt('path/to/dxx_file.plt')

# Example 2: Load data from a .plt file but only unpack one variable
_, _, _, Daa = pyverbplt.load_plt('path/to/dxx_file.plt')

# Example 3: Load data from a PSD .plt file into a single variable. Load all zones (times).
psd = pyverbplt.load_plt('path/to/psd_file.plt', first_zone=0, skip_zones=0, n_zones=None)
```

## Documentation
Please see the file header. The readthedoc version is comming soon.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.
image-text-recognition 0.1.1
A package for recognizing and evaluating text in images
Requires Python: >=3.6

A Python package for recognizing and evaluating text in images.

## Installation

```
pip install image-text-recognition
```

## Usage

```python
from image_text_recognition import ImageTextRecognizer, evaluate_model, generate_test_images

# Recognize text in an image
recognizer = ImageTextRecognizer()
image_path = "path/to/your/image.jpg"
results = recognizer.recognize_text(image_path)

for bbox, text, probability in results:
    print(f"Text: {text}, Probability: {probability}")

# Generate test images
test_texts = ["Hello World", "Python Code", "OpenCV"]
generate_test_images("test_images", test_texts)

# Evaluate the model
ground_truth = {
    "test1.jpg": "Hello World",
    "test2.jpg": "Python Code",
    "test3.jpg": "OpenCV"
}
evaluation_results = evaluate_model("test_images", ground_truth)
print(evaluation_results)
```

## License

This project is licensed under the MIT License.
ogorod-library 1.0.0
Это библиотека с датами посадки с/х культур для моей бабушки
Requires Python: >=3.12

README.md
Это библиотека с датами посадки сельско-хозяйственных культур для моей бабушки.
По мере обновления в библиотеке будут появлятся и новые овощи, и она будет становится более универсальной.
Пока что все файлы кроме year_2024.py и __init__.py и months.py не используются.
Используется MIT License.