semvec/pypi-packages huggingface dataset:
HARD-lib 1.0.3
Harmony Analysis Ready Dataset

## Pre-requisites
1. Create an ECMWF account to access the ERA5 CDS API (for setup go to [cds.climate.copernicus.eu](https://cds.climate.copernicus.eu/how-to-api)). This is needed for wind-field information.

2. Create an Alaskan Satellite Fascility (ASF) account so that you may querry their API using `asf_search`. Store your ASK credentials in a `.netrc` file. This will be needed to download data from ASF.

An example `.netrc`:
```bash
machine asf
login YOURUSERNAME
password YOURPASSWORD
```

## Installation 
Create a new environment and activate
```bash
conda create -n ENV_NAME python==3.12

conda activate ENV_NAME
```
Conda install GDAL (not enabled from pip)
```bash
conda install GDAL
```
Clone HARD repository

```bash
git clone https://gitlab.tudelft.nl/yyuan6/hard.git
```

Navigate to cloned folder and install an editibale version
```bash
cd HARD

pip install -e .
```

## Usage
### Python
For Python tutorials refer to the notebook tutorials in `hard/Tutorial/`

### Command Line Interface (CLI)
To run an automated download, processing and saving exercise using a bash script go to either:

- `hard/HARD/CLI/download_process_save_local_example.sh`
- `hard/HARD/CLI/download_process_save_DelftBlue_example.sh`

Please copy the files before editing and do not track them in this git repository.

#### NOTE 
When submitting the bash script on Delft Blue, please make sure a `logs` subdirectory exists in the same location as the `.sh` file, e.g.:

```bash
mkdir -p logs
sbatch /home/.../hard/HARD/CLI/download_process_save_DelftBlue_example.sh
```


## To Do
### ERA5
- check if ERA5 file exists before downloading
- give downlaoded era5 files descriptive names relating to aoi (so that they may be reused)
- enable using pre-downlaoded era5 files

### Processing
- return the xsar object instead of arbitrary roughness field
- land masking on radar data 
- add attributes to file for all processing steps and 
- get land mask from xsar variable
- streamline output types so that `.values` and `.item()` can be kept to a minimum
- remove unnecessary variables after preprocessing
smart-image-cleaner 0.1.0
A smart tool to clean duplicate and blurry images
Requires Python: >=3.6

# Smart Image Cleaner

智能图片清理工具 - 快速扫描并清理相似或模糊的图片。

## 功能特点

- **Web 界面**: 简单易用的浏览器界面
- **相似图片查找**: 使用感知哈希算法 (pHash) 查找相似图片
- **模糊图片检测**: 基于拉普拉斯方差检测模糊图片
- **安全删除**: 文件被移动到回收站，而不是直接永久删除
- **RAW 文件清理**: 自动清理同名 JPG 对应的 RAW 文件

## 安装

```bash
pip install smart-image-cleaner
```

## 使用方法

安装完成后，在终端运行：

```bash
smart-image-cleaner
```

浏览器将自动打开 `http://127.0.0.1:5000`。

## 开发

1. 克隆仓库
2. 安装依赖: `pip install -r requirements.txt`
3. 运行: `python -m smart_image_cleaner.main`

## License

MIT