Metadata-Version: 2.3
Name: reshipe
Version: 0.1.0
Summary: re(sh)ipe is a versatile Python module designed for efficient data transformation and validation.
Project-URL: Homepage, https://dvm-shlee.github.io
Author-email: SungHo Lee <shlee@unc.edu>
Maintainer-email: SungHo Lee <shlee@unc.edu>
License: MIT
License-File: LICENSE
Keywords: metadata,parser,recipe,reshape,yalm
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Requires-Dist: pyyaml>=6.0.1
Provides-Extra: dev
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: types-pyyaml; extra == 'dev'
Description-Content-Type: text/markdown

# re(sh)ipe

### **Introducing re(sh)ipe: Streamlined Data Transformation and Validation**

re(sh)ipe is a sophisticated Python module designed to integrate data reshaping with recipe-driven processing. Developed primarily for parsing metadata from MRI raw data to ensure BIDS compliance, re(sh)ipe leverages a YAML-based configuration that simplifies handling complex data structures, making it highly suitable for researchers and developers in neuroimaging.

**Core Features:**

- **Detailed Data Specifications:** (WIP) Utilize YAML files to define data types and constraints rigorously. This feature includes regex-aware type restrictions that are essential for adhering to BIDS standards.
- **Flexible Parsing Mechanisms:** The module's recipe-based approach allows for the dynamic parsing of metadata from Python objects. It includes options for conditional parsing paths and the integration of pre-processing scripts to enhance data workflow efficiency.

Explore the capabilities of re(sh)ipe, where sophisticated data processing is seamlessly combined with precision and adaptability for neuroimaging data management.