https://github.com/australianbiocommons/gen3schemadev

Gen3 Schema Development tools

https://github.com/australianbiocommons/gen3schemadev

Science Score: 26.0%

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    Low similarity (13.5%) to scientific vocabulary

Keywords

gen3
Last synced: 10 months ago · JSON representation

Repository

Gen3 Schema Development tools

Basic Info
  • Host: GitHub
  • Owner: AustralianBioCommons
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 101 MB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 1
  • Open Issues: 1
  • Releases: 1
Topics
gen3
Created over 4 years ago · Last pushed 10 months ago
Metadata Files
Readme License

Readme.md

Tools for Gen3 Data Dictionary Development

This repository facilitates Gen3 data modeling using Google Sheets. It includes tools to convert Google Sheets into YAML files and then into a bundled JSON format. Additionally, it offers tools for schema validation and local data model visualization.

Setup

1. Set up environment

bash git clone --recurse-submodules "https://github.com/AustralianBioCommons/gen3schemadev.git" python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt pip install -e . cd acdc-tools && pip install -e .

2. Install Docker

To install Docker Desktop, download it from the Docker website and follow the installation instructions for your operating system. After installation, verify by running docker --version in the terminal.

3. Spin up containers

bash cd umccr-dictionary make down make pull make up make ps cd ..

Usage

You can run using the Schema Development Framework Notebook or by following the usage below.

Alternatively you can run the script: bash bash scripts/generate_schema.sh --help bash scripts/generate_schema.sh

1. Pull Data Schema from Google Sheets

This step involves pulling the schema design from a Google Sheet template. The template can be accessed here. Feel free to duplicate this spreadsheet and input your own google sheet id along with the tab ids for objects, links, properties, and enums.

bash [ -d "schema_out" ] && rm -rf "schema_out" python3 sheet2yaml-CLI.py --google-id '1zjDBDvXgb0ydswFBwy47r2c8V1TFnpUj1jcG0xsY7ZI' --objects-gid 0 --links-gid 270346573 --properties-gid 613332252 --enums-gid 1807456496

2. Move Schema Output

Move the generated schema files to the umccr-dictionary directory:

bash mkdir -p umccr-dictionary/dictionary/schema_dev/gdcdictionary/schemas cp schema_out/* umccr-dictionary/dictionary/schema_dev/gdcdictionary/schemas/ ls -lsha umccr-dictionary/dictionary/schema_dev/gdcdictionary/schemas/

3. Compile and Bundle into JSON

Compile and bundle the schema into a JSON format:

bash cd umccr-dictionary && make compile program=schema_dev

4. Run Validation

Validate the compiled schema:

bash cd umccr-dictionary && make validate program=schema_dev

5. Visualize Data Dictionary

Open the data dictionary visualization in your web browser:

bash open http://localhost:8080/#schema/schema_dev.json

6. View Outputs

After running the script, you can view the generated outputs in the output folder. This folder contains the schema files pulled from the Google Sheets. You can access this folder directly in your file system or use the following command to open it:

Owner

  • Name: AustralianBioCommons
  • Login: AustralianBioCommons
  • Kind: organization
  • Email: systems@biocommons.org.au

Documentation for the development, deployment and/or optimisation of key community-endorsed bioinformatics tools and workflows

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Last synced: 10 months ago

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  • JoshuaHarris391 (7)
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Dependencies

requirements.txt pypi
  • PyYAML *
  • argparse *
  • boto3 *
  • dictionaryutils *
  • ete3 *
  • gen3 *
  • jsonschema *
  • ldap3 *
  • matplotlib *
  • networkx *
  • numpy *
  • openpyxl *
  • oyaml *
  • pandas *
  • requests *
  • setuptools *
setup.py pypi
  • gen3 *