EZInput: A Cross-Environment Python Library for Easy UI Generation in Scientific Computing

Jul 16, 2026

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EZInput addresses the accessibility gap in scientific computing by enabling researchers to generate graphical user interfaces without requiring end-users to program. The EZInput Python library uses a declarative specification system so developers define input requirements and validation constraints once. It automatically detects runtime environments, renders interfaces, validates parameters, and persists user sessions across Jupyter notebooks, Google Colab, and terminal environments. A YAML-based parameter persistence mechanism supports reproducible, shareable configurations for iterative and batch execution across platforms.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Problem: parameter configuration and fragmented interfaces

Solution: EZInput declarative specifications and cross-environment GUIs

Parameter persistence and reproducibility

Related work and remaining gap

Limitations of existing approaches (bespoke GUIs vs notebooks)

EZInput: A Cross-Environment Python Library for Easy UI Generation in Scientific Computing Bruno M. Saraiva 1,􀀀, Iván Hidalgo-Cenalmor2 , António D. Brito 1 , Damián Martínez 1 , Tayla Shakespeare 1 , Guillaume Jacquemet2,3,4,5,􀀀, and Ricardo Henriques 1,6,􀀀 1 Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2 Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland 3 InFLAMES Research Flagship Center, University of Turku, Turku, Finland 4Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland 5 Foundation for the Finnish Cancer Institute, Helsinki, Finland 6 UCL Laboratory for Molecular Cell Biology, University College London, London, United Kingdom arXiv :2601 .08859v2 [ cs . SE] 10 Jul 2026 Researchers face a persistent barrier when applying computational algorithms with parameter configuration typically demanding programming skills, interfaces differing across environments, and settings rarely persisting between sessions. This fragmentation forces repetitive input, slows iterative exploration, and undermines reproducibility because parameter choices are difficult to record, share, and reuse. We present EZInput, across-runtime environment Python library enabling algorithm developers to automatically generate graphical user interfaces that make their computational tools accessible to end-users without programming expertise. EZInput employs a declarative specification system where developers define input requirements and validation constraints once; the library then handles environment detection, interface rendering, parameter validation, and session persistence across Jupyter notebooks, Google Colab, and terminal environments. This "write once, run anywhere"architecture enables researchers to prototype in notebooks and deploy identical parameter configurations for batch execution on remote systems without code changes or manual transcription. Parameter persistence, inspired by ImageJ/FIJI and adapted to Python workflows, saves and restores user configurations via lightweight YAML files, eliminating redundant input and producing shareable records that enhance reproducibility. EZInput supports the input types common in scientific computing, with built-in validation and clear feedback. User Interface | Python | Jupyter Notebook | Terminal User Interface | Scientific Computing Correspondence: (B. M. Saraiva) [bsaraiva@itqb.unl.pt](bsaraiva@itqb.unl.pt); (G. Jacquemet) guil[laume.jacquemet@abo.fi](laume.jacquemet@abo.fi); (R. Henriques) [r.henriques@itqb.unl.pt](r.henriques@itqb.unl.pt) Main Computational algorithms represent essential tools for extracting quantitative insights from scientific data, yet their widespread adoption remains constrained by the challenge of creating accessible interfaces. Algorithm developers often lack the time to build user-friendly graphical interfaces, while end-users may lack the programming skills to apply these methods directly. This gap keeps powerful algorithms from reaching the people who need them. While powerful algorithms are being created, their adoption by non-programming users has been hampered by interfaces demanding programmatic expertise, creating a dichotomy between algorithm development and practical application. Recent community efforts have begun addressing this challenge, with notable successes in specific domains such as ZeroCostDL4Mic and DL4MicEverywhere for deep learning Fig. 1. EZInput framework architecture and workflow integration. The EZInput library implements a declarative parameter specification system that automatically generates graphical user interfaces across multiple computational environments, both Jupyter notebooks and terminal environments, without additional interface development. Parameter persistence mechanism inspired by ImageJ/FIJI (Schneider et al. , 2012; Schindelin et al. , 2012), where user configurat

FAQs

What problem does EZInput solve in scientific computing workflows?

EZInput targets the difficulty of making computational tools accessible when parameter configuration usually requires programming and environments differ. It also improves reproducibility by ensuring parameter choices can be recorded, shared, and reused across sessions.

How does EZInput let developers create interfaces without manual GUI work?

Developers provide a declarative specification of input requirements and validation constraints once. EZInput then handles environment detection, interface rendering, validation, and session persistence automatically.

How does EZInput persist parameters to support reproducibility?

EZInput saves and restores user configurations using lightweight YAML files. This produces shareable records that enable consistent parameter reuse across Jupyter notebooks, Google Colab, and terminal environments.

Jul 16, 2026

Upload by Anda