semvec/pypi-packages huggingface dataset:
screenbolt 1.1.0
ScreenBolt - A PySide6-based GUI application for screen recording and editing.
License: MIT
Requires Python: <4.0,>=3.8

# ScreenBolt
ScreenBolt is an open-source desktop application designed for screen recording. With ScreenBolt, you can easily capture your screen and apply various effects such as mouse tracking and zooming. It’s compatible with Windows, Linux, and macOS.

## Features
- Screen Recording: Capture your entire screen or a selected region.
- Effects Editing: Apply effects such as tracking mouse movements and zooming to enhance your recordings.
- Cross-Platform: Available for Windows, Linux, and macOS.

## Installation
### For End Users
- Download: Go to the Releases page and download the appropriate version for your operating system.
- Install and Run: Follow the installation instructions provided with the release.
### For Developers
1. Set Up Environment:

- Install Anaconda.
- Create a new Conda environment if needed.
2. Prepare the Application:
- Clone the repository or download the source code.
- Navigate to the project directory.
3. Compile Resources:

```bash
# Suppose you're in screenbolt/screenbolt
python compile_resources.py
```
4. Run the Application:
```bash
# Suppose you're in the root directory
python -m screenbolt.main
```
## Contributing
We welcome contributions to ScreenBolt! If you’d like to contribute, please fork the repository and submit a pull request.

## License
ScreenBolt is released under the MIT License.

## Contact
For any questions or issues, please contact us at tamnvhustcc@gmail.com.
vlidt 0.2.2
dataclass & validation simple as well as hell.
License: MIT
Requires Python: <4.0,>=3.8

# Validata

Dataclass and validation made simple as well as powerful.

## Installation

You can install **Validata** using pip:

```bash
pip install validata
```
To install from GitHub:

```bash
git clone https://github.com/MirS0bhan/validata && cd validata
pip install .
```
## Usage
### Basic Example

Here’s how to define a dataclass and use the validation features:
```python
from vlidt import BaseModel

class A(BaseModel):
    x: int
    y: int

class B(A):
    z: list[str]

c = B(5, 6, ["abc", "xyz"])
```
### Validation

You can validate the fields of your dataclass instances easily:
```python
from vlidt import validate

# Validate the instance
is_valid = validate(c)
print(is_valid)  # Returns True if valid, otherwise False
```
### Dumping and Loading Data

You can convert your dataclass instances to and from dictionaries:
```python
# Dumping the instance to a dictionary
data_dict = dump(c)
print(data_dict)  # Output: {'x': 5, 'y': 6, 'z': ['abc', 'xyz']}

# Loading from a dictionary
new_instance = load(B, data_dict)
print(new_instance)  # Output: B(x=5, y=6, z=['abc', 'xyz'])
```
### Nested Dataclasses

Validata supports nested dataclasses, allowing you to create more complex data structures:
```python
class C(BaseModel):
    a: A
    additional_info: str

instance_c = C(a=c, additional_info="Some info")
print(instance_c)
```
#### Features

- Automatic Validation: Validate dataclass fields with minimal effort.
- Dump and Load: Easily convert dataclass instances to and from dictionaries.
- Nested Support: Handle nested dataclass instances seamlessly.
- Type Annotations: Leverage Python’s type hints for better code clarity and error checking.

#### Contributing

Contributions are welcome! If you would like to contribute to Validata, please follow these steps:

- [ ] Fork the repository.
- [ ] Create a new branch for your feature or bugfix.
- [ ] Make your changes and commit them.
- [ ] Push to your branch and submit a pull request.

#### License

This project is licensed under the MIT License. See the LICENSE file for more details.
Contact

For any inquiries or suggestions, feel free to reach out via GitHub Issues.