semvec/pypi-packages huggingface dataset:
inline_test 0.1.1
rust-style test macro
Requires Python: >=3.9

# inline-tests

Rust-like inline tests for Python. Write and run tests right next to your code!

Haven't tested this on large projects, but it provides the amazing feature of
rust which is a single file with its tests

Surprised this didn't exist prior

No async, no fixtures, no fancy stuff

## Installation

```bash
pip install inline-tests
```

## Usage

In your Python files:

```python
from inline_tests.decorators import test, before_each, after_each

def add(a: int, b: int) -> int:
    return a + b

@test
def test_add():
    assert add(2, 2) == 4

@test(tag="math")
def test_add_negative():
    assert add(-2, -2) == -4
```

Run tests:

```bash
# Run all tests in project
run-tests

# Run specific files
run-tests file1.py file2.py

# Run tests with specific tag
run-tests --tag math

# Run tests in specific directory
run-tests --path /path/to/project
```
Mantis-SDK 1.0.0
SDK for interacting with the Mantis API
Requires Python: >=3.7

# SDK for Your Django API

## Overview
This SDK provides a Python interface for interacting with your Django-based API. It simplifies the process of making requests to various endpoints, handling authentication, and parsing responses.

## Features
- Fetch user emails by ID.
- Rename spaces.
- Update shared users for a space.
- Retrieve shared spaces.
- Create, update, and delete spaces.
- Upload images to spaces.
- Manage metadata and visibility for spaces.
- Monitor space creation progress.

## Installation

Install the SDK using pip:

```bash
pip install your-sdk-name
```

## Usage

### Initialization

```python
from your_sdk_name import YourAPIClient

# Initialize the client
client = YourAPIClient(base_url="https://api.example.com", token="your-auth-token")
```

### Available Methods

#### Fetch Email by User ID

```python
email_data = client.get_email_by_user_id(user_id="12345")
print(email_data)
```

#### Rename Space

```python
response = client.rename_space(space_id="space-id", new_name="New Space Name")
print(response)
```

#### Update Shared Users

```python
response = client.update_shared_users(space_id="space-id", user_ids=["user1", "user2"])
print(response)
```

#### Retrieve Shared Spaces

```python
shared_spaces = client.get_shared_spaces()
print(shared_spaces)
```

#### Create Space

```python
space_data = {
    "space_name": "My Space",
    "is_public": True,
    "data_types": [{"name": "field1", "semantic": True}],
    "file": open("data.csv", "rb")
}
response = client.create_space(data=space_data)
print(response)
```

#### Update Space Visibility

```python
response = client.update_space_visibility(space_id="space-id")
print(response)
```

#### Delete Space

```python
response = client.delete_space(space_id="space-id")
print(response)
```

#### Update Space Metadata

```python
metadata = {"key": "value"}
response = client.update_space_metadata(space_id="space-id", metadata=metadata)
print(response)
```

#### Get Space Metadata

```python
metadata = client.get_space_metadata(space_id="space-id")
print(metadata)
```

#### Get Space Progress

```python
progress = client.get_space_progress(space_id="space-id")
print(progress)
```

#### Upload Images

```python
files = [open("image1.jpg", "rb"), open("image2.jpg", "rb")]
response = client.upload_images(files=files, space_name="My Space", is_public=True)
print(response)
```

## Contributing

Contributions are welcome! Please submit issues and pull requests to improve the SDK.

## License

This SDK is licensed under the MIT License. See the LICENSE file for details.
lfepy-gpu 1.0.0
lfepy is a Python package for local feature extraction.
Requires Python: >=3.0

# lfepy-gpu

**Local Feature Extractors for Python** is a Python package for local descriptors. It provides feature extraction from grayscale images, facilitating AI tasks like object detection, facial recognition, and classification.

## Table of Contents

1. [Overview](#overview)
2. [Features](#features)
3. [Requirements](#Requirements)
4. [Installation](#installation)
5. [Documentation](#documentation)
6. [License](#license)
7. [Contact](#contact)

## Overview

**lfepy-gpu** is a GPU-based Python package for local feature extraction. It contains 27 different descriptors that can be used for various tasks in computer vision and image processing.

## Features

- **Feature 1**: 27 different local descriptors.
- **Feature 2**: Contains essential methods for feature extraction.
- **Feature 3**: Can be used for computer vision and image processing tasks.

## Requirements

- **python>=3.0**
- **cupy-cuda11x>=13.3.0**
- **numpy>=1.26.4**
- **scipy>=1.13.0**
- **scikit-image>=0.23.2**

## Installation

To install lfepy, use the following command:

```bash
pip install lfepy-gpu
```
or
```bash
pip install git+https://github.com/lfepy/lfepy-gpu.git
```
## Usage
Basic Example
```python
# Import the required packages
import matplotlib.pyplot as plt
from matplotlib.image import imread
from lfepy.Descriptor import LBP

# Read the image
image = imread("PATH")
# Extract feature using a specified descriptor
histogram, imgDesc = LBP(image, mode='nh', radius=1, mappingType='riu2')
# Show the descriptor
plt.imshow(imgDesc.get(), cmap='gray')
plt.axis('off')
plt.show()
```
![Alt text](example/Example.png)
## Documentation
Comprehensive documentation for lfepy is available at [lfepydocs](documentation). It includes:

- **Getting Started**
- **API Reference**
- **Tutorials**

## License
lfepy is licensed under the MIT License. See the [LICENSE](LICENSE.md) file for more details.

## Contact
For any questions or support, please contact us at lfepython@gmail.com or visit our [GitHub](https://github.com/lfepy/lfepy) Issues page.