semvec/pypi-packages huggingface dataset:
img-masker 1.0.2
A simple image masking tool.
Requires Python: >=3.8
Keywords: image processing, masking, Python

# img-masker

A simple and lightweight Python tool for masking specific colors in images. Supports both local files and remote image URLs. Built on top of [Pillow](https://python-pillow.org/) for image processing.

# Sample Output

| Original Image                                                                               | Masking Sample 1                                                                 | Masking Sample 2                                                                 |
| -------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| ![Original](https://raw.githubusercontent.com/j-ncel/img-masker/main/samples/test_image.png) | ![1.png](https://raw.githubusercontent.com/j-ncel/img-masker/main/samples/1.png) | ![2.png](https://raw.githubusercontent.com/j-ncel/img-masker/main/samples/2.png) |

_Sample Image: Meoscarada image credit to [PokeAPI](https://pokeapi.co/)._

## Features

- Mask (replace) pixels of a specific color or transparency in an image.
- Supports both local image files and remote image URLs.
- Customizable mask and background colors.

## Installation

You can install `img-masker` using pip:

```sh
pip install img-masker
```

## Requirements

- [Pillow](https://pypi.org/project/Pillow/)
- [requests](https://pypi.org/project/requests/)

## Usage

### Basic Example

```python
from img_masker import mask

# Mask all transparent pixels in an image with black, set background to white
masked_img = mask("https://example.com/image.png",)
masked_img.show()
```

### Parameters

- `image_url` (str): Path to a local image file or a remote image URL.
- `mask_color` (str): Color to use for masked pixels (default: `"black"`). Accepts any Pillow-compatible color string, e.g., `"red"`, `"#FF0000"`, `"transparent"`.
- `bg_color` (str): Color for non-masked pixels (default: `"white"`).
- `filter_color` (str): Target color to mask (default: `"transparent"`). Pixels matching this color will be replaced by `mask_color`.

### Masking a Specific Color

```python
# Mask all pure red pixels with blue, set background to white
masked_img = mask(
    "input.jpg",
    mask_color="blue",
    bg_color="white",
    filter_color="red"
)
masked_img.show()
```

### Masking Transparent Pixels

```python
# Mask all transparent pixels with black, keep others white
masked_img = mask(
    "input.png",
    mask_color="black",
    bg_color="white",
    filter_color="transparent"
)
masked_img.save("masked.png")
```

## License

This project is licensed under the MIT License. See [LICENSE](LICENSE) for details.
geo-country-service 0.5.0
A service that provides an alpha-3 country code from given coordinates
Requires Python: >=3.11

# 🌎 Geo country service API 🌎
A service to get alpha3 country code from provided coordinate point

---

# Usage

Pass latitude and longitude coordinates to **get_country_code** function:
```
{"latitude": (float), "longitude": (float)}
```
Result contains ```message``` and ```result```. 
If coordinate payload is valid Alpha-3 country will be stored in ```result```.
---
*Example:*

```python
from geo_country_service import get_country_code


result = get_country_code(latitude=45.123, longitude=-25.123)
print(result)
```
*Result*
```
{"message":"success","result":"LTU"}
```
hunyuan-tc-travel 0.0.1
mcp server to invoke hunyuan tc travel
Requires Python: >=3.10
Keywords: hunyuan, image, mcp

## 快速开始使用 MCP 客户端

1. 从[元器智能体平台](https://yuanqi.tencent.com/)获取你需要插件的 API 密钥。
2. 安装`uv`（Python包管理器），使用`curl -LsSf https://astral.sh/uv/install.sh | sh`安装或查看`uv` [仓库](https://github.com/astral-sh/uv)获取其他安装方法。
3. **重要提示: 每个元器插件的ID和密钥都不相同**，两者需要匹配，否则会有 `token验证失败` 的错误

### Claude Desktop

前往`Claude > Settings > Developer > Edit Config > claude_desktop_config.json`包含以下内容：

```
{
  "mcpServers": {
    "hunyuanTCTravel": {
      "command": "uvx",
      "args": [
        "hunyuan-tc-travel"
      ],
      "env": {
        "API_KEY": "填写你调用的插件Token"
      }
    }
  }
}
```

⚠️ 注意：API_KEY需要与插件匹配。如果出现“token验证失败”错误，请检查您的API_KEY和插件。
如果你使用Windows，你需要在Claude Desktop中启用"开发者模式"才能使用MCP服务器。点击左上角汉堡菜单中的"Help"，然后选择"Enable Developer Mode"。

### Cursor

前往`Cursor -> Preferences -> Cursor Settings -> MCP -> Add new global MCP Server`添加上述配置。


## Transport

我们仅支持 stdio 传输方式
sparkpipelineframework.testing 4.0.5
Testing Framework for SparkPipelineFramework
Requires Python: >=3.10

[![Build and Test](https://github.com/imranq2/SparkPipelineFramework.Testing/actions/workflows/build_and_Test.yml/badge.svg)](https://github.com/imranq2/SparkPipelineFramework.Testing/actions/workflows/build_and_Test.yml)

[![Upload Python Package](https://github.com/imranq2/SparkPipelineFramework.Testing/actions/workflows/python-publish.yml/badge.svg)](https://github.com/imranq2/SparkPipelineFramework.Testing/actions/workflows/python-publish.yml)

[![Known Vulnerabilities](https://snyk.io/test/github/imranq2/SparkPipelineFramework.Testing/badge.svg?targetFile=requirements.txt)](https://snyk.io/test/github/imranq2/SparkPipelineFramework.Testing?targetFile=requirements.txt)

# SparkPipelineFramework.Tests
Testing framework that can tests SparkPipelineFramework library by just providing input files to setup before running the transformer and output files to use for verifying the output

## Usage
1. Create a folder structure similar to the folder structure of your library in SparkPipelineFramework (This is how the Testing Framework finds the Transformer to run)
2. Create an input folder and put in files that represent the input views.  These files can be csv, json or parquet
3. (Optionally) Create an input_schema folder and put in any schemas you want applied to the above views.  This follows the Spark Json Schema format.
4. (Optional) Create an output folder and put in files that represent the output views you expect.  These files can be csv, json or parquet
5. (Optional) Create an output_schema folder and put in any schemas you want applied to the output views
6. Copy the following test code and put it in a test file in this folder

```python
from pathlib import Path

from pyspark.sql import SparkSession

from spark_pipeline_framework_testing.test_runner import SparkPipelineFrameworkTestRunner


def test_folder(spark_session: SparkSession) -> None:
    data_dir: Path = Path(__file__).parent.joinpath('./')

    SparkPipelineFrameworkTestRunner.run_tests(spark_session=spark_session, folder_path=data_dir)
```
7. Now just run this test.

Note: the test finds files in sub-folders too.

## Example
For the transformer defined here: https://github.com/imranq2/SparkPipelineFramework.Testing/tree/main/library/features/people/my_people_feature
You can find the test here: https://github.com/imranq2/SparkPipelineFramework.Testing/tree/main/tests/library/features/people/my_people_feature

## Publishing a new package
1. Create a new release
2. The GitHub Action should automatically kick in and publish the package
3. You can see the status in the Actions tab
python-churn 1.1.2
CHAin sluRM is a simple batch job runner.
Requires Python: >=3.10

# Churn

Churn(corruption of Charm, **Cha**in SLU**RM**) is a simple Python batch job runner. It automates the process of creating parametrized batch jobs and submitting multiple dependent jobs.

## Installation

### PyPI

Via pip:
```bash
pip install python-churn
```
### From source

Via uv:
```bash
uv sync
uv build
```

## Usage

Churn can either be used as a command line tool or as a Python library. The command line tool runs a set of jobs using a YAML configuration file, whereas the library makes it easier to dispatch related jobs with varying parameters from within Python code.

You may find an exemplary configuration in the ```examples/basic directroy```. This may either be run using 
```bash
python -m churn examples/basic
```
or by executing the equivalent python file that does the required library calls:
```bash
python examples/basic.py
```

### Schema

To make writing `run.yaml` files easier, Churn provides a schema that can be used to validate the configuration. The schema is located in `schema/schema.json`. You can use it with a YAML validator or editor that supports JSON Schema validation. For example in VSCode, you can add the following to your settings:
```json
"yaml.schemas": {
    "https://git.rwth-aachen.de/cats-gitolite/public/tools/churn/-/raw/main/schema/schema.json?ref_type=heads": "run.yaml"
}
```