rdetoolkit

Name	rdetoolkit JSON
Version	1.3.1 JSON
	download
home_page	None
Summary	A module that supports the workflow of the RDE dataset construction program
upload_time	2025-07-14 06:06:54
maintainer	None
docs_url	None
author	None
requires_python	>=3.9
license	None
keywords	rdetoolkit rde toolkit structure dataset
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
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![coverage](docs/img/coverage.svg)

> [日本語ドキュメント](docs/README_ja.md)

# RDEToolKit



RDEToolKit is a fundamental Python package for creating workflows of RDE-structured programs.
By utilizing various modules provided by RDEToolKit, you can easily build processes for registering research and experimental data into RDE.
Additionally, by combining RDEToolKit with Python modules used in your research or experiments, you can achieve a wide range of tasks, from data registration to processing and visualization.

## Documents

See the [documentation](https://nims-mdpf.github.io/rdetoolkit/) for more details.

## Contributing

If you wish to make changes, please read the following document first:

- [CONTRIBUTING.md](CONTRIBUTING.md)

## Install

To install, run the following command:

```shell
pip install rdetoolkit
```

## Usage

Below is an example of building an RDE-structured program.

### Create a Project

First, prepare the necessary files for the RDE-structured program. Run the following command in your terminal or shell:

```python
python3 -m rdetoolkit init
```

If the command runs successfully, the following files and directories will be generated.

In this example, development proceeds within a directory named `container`.

- **requirements.txt**
  - Add any Python packages you wish to use for building the structured program. Run `pip install` as needed.
- **modules**
  - Store programs you want to use for structured processing here. Details are explained in a later section.
- **main.py**
  - Defines the entry point for the structured program.
- **data/inputdata**
  - Place data files to be processed here.
- **data/invoice**
  - Required even as an empty file for local execution.
- **data/tasksupport**
  - Place supporting files for structured processing here.

```shell
container
├── data
│   ├── inputdata
│   ├── invoice
│   │   └── invoice.json
│   └── tasksupport
│       ├── invoice.schema.json
│       └── metadata-def.json
├── main.py
├── modules
└── requirements.txt
```

### Implementing Structured Processing

You can process input data (e.g., data transformation, visualization, creation of CSV files for machine learning) and register the results into RDE. By following the format below, you can incorporate your own processing into the RDE structured workflow.

The `dataset()` function should accept the following two arguments:

- srcpaths (RdeInputDirPaths): Paths to input resources for processing
- resource_paths (RdeOutputResourcePath): Paths to output resources for saving results

```python
def dataset(srcpaths: RdeInputDirPaths, resource_paths: RdeOutputResourcePath):
    ...
```

In this example, we define a dummy function `display_messsage()` under `modules` to demonstrate how to implement custom structured processing. Create a file named `modules/modules.py` as follows:

```python
# modules/modules.py
def display_messsage(path_list):
    print(f"Test Message!: {path_list}")

def dataset(srcpaths, resource_paths):
    display_messsage(srcpaths)
    display_messsage(resource_paths)
```

### About the Entry Point

Next, use `rdetoolkit.workflow.run()` to define the entry point. The main tasks performed in the entry point are:

- Checking input files
- Obtaining various directory paths as specified by RDE structure
- Executing user-defined structured processing

```python
import rdetoolkit
from modules.modules import dataset  # User-defined structured processing function

# Pass the user-defined structured processing function as an argument
rdetoolkit.workflows.run(custom_dataset_function=dataset)
```

If you do not wish to pass a custom structured processing function, define as follows:

```python
import rdetoolkit

rdetoolkit.workflows.run()
```

### Running in a Local Environment

To debug or test the RDE structured process in your local environment, simply add the necessary input data to the `data` directory. As long as the `data` directory is placed at the same level as `main.py`, it will work as shown below:

```shell
container/
├── main.py
├── requirements.txt
├── modules/
│   └── modules.py
└── data/
    ├── inputdata/
    │   └── <experimental data to process>
    ├── invoice/
    │   └── invoice.json
    └── tasksupport/
        ├── metadata-def.json
        └── invoice.schema.json
```


            

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Run the following command in your terminal or shell:\n\n```python\npython3 -m rdetoolkit init\n```\n\nIf the command runs successfully, the following files and directories will be generated.\n\nIn this example, development proceeds within a directory named `container`.\n\n- **requirements.txt**\n  - Add any Python packages you wish to use for building the structured program. Run `pip install` as needed.\n- **modules**\n  - Store programs you want to use for structured processing here. Details are explained in a later section.\n- **main.py**\n  - Defines the entry point for the structured program.\n- **data/inputdata**\n  - Place data files to be processed here.\n- **data/invoice**\n  - Required even as an empty file for local execution.\n- **data/tasksupport**\n  - Place supporting files for structured processing here.\n\n```shell\ncontainer\n\u251c\u2500\u2500 data\n\u2502   \u251c\u2500\u2500 inputdata\n\u2502   \u251c\u2500\u2500 invoice\n\u2502   \u2502   \u2514\u2500\u2500 invoice.json\n\u2502   \u2514\u2500\u2500 tasksupport\n\u2502       \u251c\u2500\u2500 invoice.schema.json\n\u2502       \u2514\u2500\u2500 metadata-def.json\n\u251c\u2500\u2500 main.py\n\u251c\u2500\u2500 modules\n\u2514\u2500\u2500 requirements.txt\n```\n\n### Implementing Structured Processing\n\nYou can process input data (e.g., data transformation, visualization, creation of CSV files for machine learning) and register the results into RDE. By following the format below, you can incorporate your own processing into the RDE structured workflow.\n\nThe `dataset()` function should accept the following two arguments:\n\n- srcpaths (RdeInputDirPaths): Paths to input resources for processing\n- resource_paths (RdeOutputResourcePath): Paths to output resources for saving results\n\n```python\ndef dataset(srcpaths: RdeInputDirPaths, resource_paths: RdeOutputResourcePath):\n    ...\n```\n\nIn this example, we define a dummy function `display_messsage()` under `modules` to demonstrate how to implement custom structured processing. Create a file named `modules/modules.py` as follows:\n\n```python\n# modules/modules.py\ndef display_messsage(path_list):\n    print(f\"Test Message!: {path_list}\")\n\ndef dataset(srcpaths, resource_paths):\n    display_messsage(srcpaths)\n    display_messsage(resource_paths)\n```\n\n### About the Entry Point\n\nNext, use `rdetoolkit.workflow.run()` to define the entry point. 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