Published March 14, 2026 | Version v1.0.4
Software Restricted

PICA: Advanced High-Precision Transport Measurement Automation with Python

Authors/Creators

  • 1. UGC-DAE Consortium for Scientific Research, Mumbai Centre, Bhabha Atomic Research Centre, Mumbai, 400 085, Maharashtra, India
  • 2. Savitribai Phule Pune University, Ganeshkhind Road, Pune, 411 007, Maharashtra, India

Description

PICA addresses the critical need for a turnkey, high-precision automation platform in experimental research environments. By abstracting the underlying control logic into a unified dashboard, it allows experimentalists to focus on data acquisition without the overhead of developing custom codebases.

Key Technical Features:

  • Hardware Abstraction: Utilises PyVISA to manage GPIB, USB, and Ethernet communications across diverse instrumentation.

  • Fault Tolerance: Isolated process execution ensures that hardware communication errors do not compromise the main application or data collection.

  • Operational Transparency: Replaces "black box" automation with real-time console logs, showing every SCPI command sent to the instruments for instant troubleshooting and verification.

  • Versatility: Capable of orchestrating measurements under varying magnetic fields and temperatures (5-380 K) without physical reconfiguration of the setup.

  • Modular CLI: Includes command-line interface counterparts for headless automation and integration into existing computational workflows.

PICA serves as a robust software foundation for the characterisation of spintronic devices, superconductors, and multiferroic systems, fostering a community-driven approach to instrument control through its open-source, extensible architecture.

 

Full Changelog: https://github.com/prathameshnium/PICA-Python-Instrument-Control-and-Automation/compare/v1.0.3...v1.0.4

Technical info (English)

PICA addresses the critical need for a turnkey, high-precision automation platform in experimental research environments. By abstracting the underlying control logic into a unified dashboard, it allows experimentalists to focus on data acquisition without the overhead of developing custom codebases.

Key Technical Features:

  • Hardware Abstraction: Utilises PyVISA to manage GPIB, USB, and Ethernet communications across diverse instrumentation.

  • Fault Tolerance: Isolated process execution ensures that hardware communication errors do not compromise the main application or data collection.

  • Operational Transparency: Replaces "black box" automation with real-time console logs, showing every SCPI command sent to the instruments for instant troubleshooting and verification.

  • Versatility: Capable of orchestrating measurements under varying magnetic fields and temperatures (5-380 K) without physical reconfiguration of the setup.

  • Modular CLI: Includes command-line interface counterparts for headless automation and integration into existing computational workflows.

PICA serves as a robust software foundation for the characterisation of spintronic devices, superconductors, and multiferroic systems, fostering a community-driven approach to instrument control through its open-source, extensible architecture.

Notes (English)

If you use this software in your work, please cite it as below.

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Additional details

Identifiers

DOI
10.17605/OSF.IO/7QK2S

Related works

Is described by
Preprint: 10.17605/OSF.IO/7QK2S (DOI)
Software documentation: https://pica-python-instrument-control-and-automation.readthedocs.io/en/latest/ (URL)
Is supplement to
Software: https://github.com/prathameshnium/PICA-Python-Instrument-Control-and-Automation/tree/v1.0.4 (URL)

Funding

Department of Science and Technology
Anusandhan National Research Foundation (ANRF) SERB-CRG project grant No. CRG/2022/005676

Dates

Available
2026-03-14
v1.0.4

Software

Repository URL
https://github.com/prathameshnium/PICA-Python-Instrument-Control-and-Automation
Programming language
Python
Development Status
Active

References