PICA: Advanced High-Precision Transport Measurement Automation with Python

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Overview

High-precision, low-noise transport measurements are essential for advancing research in spintronics and materials characterisation. PICA (Python-based Instrument Control and Automation) is a modular, open-source software suite designed to automate advanced transport measurements for electronic devices and material samples. It operates as a versatile framework capable of running on any standard laboratory workstation.

PICA provides an extensible, unified graphical user interface (GUI) for orchestrating high-precision instruments, specifically current source (DC/AC) units, nanovoltmeters, high resistance electrometers, impedance analyser, and temperature controllers. Built on the robust Python scientific ecosystem, PICA ensures that the entire hardware ecosystem functions seamlessly as a cohesive unit.

The suite performs automated protocols including:

• Temperature-dependent wide-range resistance measurement ($10^{-8}$ - $10^{16}$ Ω).
• Current-voltage (I-V) characterisation.
• Capacitance characterisation and magnetocapacitance studies (20 Hz - 2 MHz).
• Pyroelectric current measurement (resolution $10^{-15}$ A).

PICA Launcher Interface for accessing all measurement modules.

More details here: For more information, go through the User Manual.

Table of Contents

• Overview
• Motivation
• Key Features
• Design and Implementation
• Supported Hardware Modules
• Demonstration of PICA (Screencast)
• Getting Started
• Running the Software
• Resources & Documentation
• Citation
• Authors & Acknowledgments
• License

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Motivation

Advancements in experimental physics and device manufacturing depend on the precise characterisation of material properties under extreme physical conditions. Researchers often face a binary choice: purchase expensive proprietary software or develop custom measurement scripts from scratch.

While libraries such as PyVISA and PyMeasure provide foundational drivers, requiring users to write and maintain low-level code, PICA builds upon these powerful libraries to offer a turnkey application. It provides a ready-to-run graphical interface that abstracts the underlying control logic, allowing experimentalists to focus on data acquisition without extensive software development overhead.

PICA enables continuous operation across a full range of measurements, from ultra low resistance measurements (removing constant offsets) to high-impedance, electrometric, pyroelectric, and capacitance measurements, all within a single, unified framework.

Key Features

• Accessibility: A professional GUI dashboard allows researchers without coding experience to configure and run complex measurement protocols immediately using pre-packaged measurement modules.
• Operational Validation: Validated via cryogenic transport measurements using a custom-designed probe in conjunction with a Physical Property Measurement System (PPMS) (5-380 K, up to 14 Tesla) at the UGC DAE Consortium for Scientific Research, Mumbai Centre.
• Fault Tolerance: Control logic is isolated from the user interface. Hardware timeouts or driver crashes are prevented from freezing the main dashboard.
• Modular CLI Architecture: Measurement modules contain CLI counterparts, allowing researchers to utilize PICA's protocol logic for headless automation or integration into other workflows without GUI overhead.
• Operational Transparency: PICA rejects the "black box" paradigm by exposing real-time, time-stamped command logs (e.g., [10:05:25] Keithley 6221: Ramping current to 10 mA). This aids debugging, ensures scientific reproducibility, and allows verification of measurement protocols.
• Open Source Extensibility: Researchers can integrate new instrument drivers or experimental protocols by subclassing existing templates, fostering a community-driven ecosystem.

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Design and Implementation

PICA is built on a modular architecture characterized by self-contained modules, ensuring future extensibility without impacting core system stability.

Process Isolation and Concurrency

Unlike simple script-based automation, PICA decouples the User Interface (UI) from the instrumentation control logic using Python's standard multiprocessing libraries.

• Stability: If an instrument hangs, the isolated process can be terminated safely without freezing the main GUI or losing previous data.
• Responsiveness: The tkinter-based frontend remains responsive for live data plotting (using matplotlib with blitting) even while the backend waits for hardware triggers.
• Data Integrity: A "write on acquisition" strategy using pandas saves data to CSV immediately after every acquisition point, preventing data loss during power failures.

Hardware Abstraction Layer

PICA utilizes PyVISA to abstract low-level communication protocols (GPIB, USB, Ethernet). The software implements a strict initialization routine:

1. Connection Verification: A built-in "VISA Instrument Scanner" queries the bus (*IDN?) to map instrument addresses.
2. Instrument Reset Protocol: Explicitly resets all stored data and buffers to provide a clean initial state.
3. Graceful Shutdown: Ensures sources are ramped down and heaters disabled safely, even if the software is interrupted.

Testing and Simulation

PICA includes a testing suite using pytest and unittest.mock to simulate VISA resources, allowing verification of backend logic streams and command sequences without constant access to physical instruments.

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Supported Hardware Modules

The system is currently validated with industry-standard hardware, covering a resistance range spanning 24 orders of magnitude.

Module	Configuration / Instrument	Use Case	Range
Ultra Low Resistance Measurements	Keithley 6221 + K2182 + Lakeshore 350/340	Superconductors & metallic films; cancels thermal EMFs via AC Delta method.	10 nΩ - 100 MΩ
Mid-Resistance (Standard)	Keithley 2400 SourceMeter + Lakeshore 350/340	Semiconductors, oxides, general transport.	100 µΩ - 200 MΩ
Mid-Resistance (High-Precision)	Keithley 2400 + K2182 + Lakeshore 350/340	Detecting subtle phase transitions.	1 µΩ - 100 MΩ
High-Resistance	Keithley 6517B Electrometer + Lakeshore 350/340	High bandgap materials, polymers, & ceramics.	1 Ω - 10 PΩ
Capacitance Analysis	Keysight E4980A + Lakeshore 350/340	C-V Analysis and Magnetocapacitance characterisation.	20 Hz - 2 MHz
Pyroelectric	K6517B + Lakeshore 350/340	Current vs Temp (detecting Curie temperature).	$10^{-15}$ A Resolution

While the current implementation drives specific instruments, the underlying framework is highly customizable. Researchers need only replace specific SCPI commands to utilize the suite with different models.

Note

Understanding "Delta Mode": The term "Delta Mode" refers specifically to a technique used by Keithley Models 6220 and 6221 Current Sources in conjunction with the Model 2182/2182A Nanovoltmeter for very low resistance measurements. This method is described in detail in the Keithley Low Level Measurements Handbook. In this documentation, "Ultra Low Resistance Measurements" is used as the general scientific term, while "Delta Mode" may appear when specifically referencing the Keithley-specific method or program files.

Module Previews

R-T measurement interface using the K6221/2182 for low-resistance samples, employing the Ultra Low Resistance Measurement technique to cancel thermal EMFs.

Interface for current-voltage (I-V) characterisation of high-impedance materials using the Keithley 6517B Electrometer.

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Demonstration of PICA (Screencast)

A screencast demonstrating the high resistance IV module is available at this link.

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Pre-requisites: The VISA Driver

Warning

A VISA Backend is Required: PyVISA is a Python wrapper, not a driver. For PICA to communicate with hardware, you must install a VISA backend on your system first. If you attempt to run the software on a clean machine without a VISA implementation, it will fail to find the instruments. This is the most common failure point for new instrument control setups.

Choose one of the following:

• NI-VISA: The industry standard from National Instruments. Download and install it from the NI website.
• PyVISA-py: A backend written in pure Python that is installed automatically with PICA. It can be used as a fallback but may have limitations compared to vendor-specific drivers like NI-VISA. PyVISA-py GitHub

Before proceeding, verify your VISA installation.

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Getting Started

PICA is structured as a standard Python package. The following instructions are for the supported Windows platform.

1. Clone the Repository

   git clone https://github.com/prathameshnium/PICA-Python-Instrument-Control-and-Automation.git
   cd PICA-Python-Instrument-Control-and-Automation

2. Create Virtual Environment & Install

   # Create and activate a virtual environment
   python -m venv venv
   venv\Scripts\activate
   
   # Install the package and its dependencies
   pip install .

   To update PICA to the latest version, run the following command in the project directory:

   pip install --upgrade .

   If you are already in your virtual environment and want to force Python to reinstall the package (overwriting the old one), run this in your root project folder:

   pip install --force-reinstall .

   Note: Ensure you have the NI-VISA drivers installed on your host machine to allow PyVISA to communicate with the hardware.

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Running the Software

1. Graphical Launcher (Recommended) The central dashboard for accessing all modules, the plotter, and the scanner.

   pica-gui

   You can also run any of the individual GUI measurement modules independently. This is useful for quickly accessing a specific measurement without opening the main launcher. To do this, simply run the Python script for the desired module. For example:

   python pica/keithley/k6517b/High_Resistance/IV_K6517B_GUI.py

2. Command Line Interface (CLI) For headless operation (e.g., Raspberry Pi).

   pica-cli

   [!IMPORTANT] Template Scripts: The instrument control modules (CLI's) provided are designed as template scripts. Users are expected to modify these scripts programmatically to adapt them to their specific experimental requirements and custom workflows. They are also excellent for developing custom measurement protocols, sequences, and for learning instrument automation. These scripts are typically named with an 'Instrument_Control' suffix to denote their programmatic nature. This approach ensures maximum flexibility and customization for advanced research applications.

   [!NOTE] Legacy CLI Notice: The PICA CLI (pica-cli) is retained to support legacy headless workflows. While fully functional for specific protocols, this interface is less frequently maintained and may not support recent features available in the GUI.

   We strongly recommend new users utilize the PICA GUI for the most complete and supported experience.

System Requirements & Compatibility

Supported Platform: Windows 10 / 11 Architecture: x86_64

Important

Windows Only PICA is currently designed and validated exclusively for Windows environments. Linux and macOS are not currently supported due to dependencies on Windows-specific GUI libraries and font rendering. Attempting to run this software on non-Windows platforms may result in crashes or UI failures. Linux support is experimental for now.

Example Usage: Running a Measurement

Upon launching PICA (pica-gui), select your desired measurement module from the main dashboard. Each module opens as a unified interface divided into two primary sections:

1. Configuration (Control Window - Left):

   • Setup: Enlarge this window first to view all experimental parameters.
   • Parameters: Specify the Sample Name, select the File Storage Location, and choose the Instrument Address (GPIB/VISA) via the dropdown selection box.
   • Protocol: Define measurement specific settings such as voltage/current limits, temperature step sizes, and delay times.
   • Console: A scrollable log at the bottom provides a continuous, time-stamped stream of all operations and SCPI commands sent to the instruments.

2. Visualization (Plotter Window - Right):

   • Real-Time Monitoring: Displays up to three simultaneous plots (e.g., Resistance vs. Temperature, Voltage vs. Current) updated in real-time.
   • Data Analysis: Includes options to switch axes to logarithmic scales for better visualization of wide-range data.
   • Utilities: Two buttons above the plot area provide instant access to:
     • VISA Instrument Scanner: To verify instrument connections and addresses.
     • PICA Plotter Utility: For post or during measurements comparison and detailed analysis.

The interface is designed to be minimalistic to reduce unnecessary user interaction during active high-precision, low-noise transport measurements.

Running Tests Locally

To run the test suite locally, first install the development dependencies:

pip install -r requirements-dev.txt

Then, you can run the tests:

python -B -m pytest -p no:cacheprovider

How to Check Coverage Locally

To see the coverage percentage on your local machine, run this command instead:

python -B -m pytest --cov=pica --cov-report=term-missing -p no:cacheprovider

Experimental Linux Instructions

Warning

Experimental Support: The following instructions are for experimental purposes only. PICA is not officially supported on Linux (for now), and you will likely encounter functional or UI-related issues.

For users who wish to experiment with PICA on Linux, please be aware of the following:

1. Prerequisites:

   • Tkinter Dependency: On Linux, you must ensure tkinter is installed, as it is often not included by default.
     • On Debian/Ubuntu: sudo apt-get install python3-tk
     • For other distributions, use your package manager to install python3-tk.
   • Virtual Environment Activation: To activate the virtual environment, use:

     source venv/bin/activate

2. Installation: Follow the standard installation steps outlined in the Getting Started section. While the commands should run, be aware that the application GUI may not function correctly.

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Project History

For downloadable release builds, please visit the releases page.

PICA evolved from simple offline scripts in 2022 to a full-stack automated suite.

• v1.0.0 (Initial Public Release): Version numbering has been reset from legacy development builds (v17.0) to v1.0.0 to standardize the package for public distribution and citation.
• v17.0: Professional directory restructuring, Semantic Versioning, and documentation overhaul.
• v15.0: JOSS submission preparation, CI/CD integration.
• v13.0: Transition to Multiprocessing and standardized GUI themes.

The project's earlier history and foundational development are outlined below:

2022 - 2023: Inception & Prototyping

• 2023 (Migration): Moved from offline lab systems to GitHub; organized scripts into instrument modules (Keithley/Lake Shore).
• 2022 (Origins): Started in an air-gapped lab with pyvisa scripts replacing manual logging.
  • Concept: Proposed by Dr. Sudip Mukherjee to automate characterisation workflows.
  • Prototypes: Built alongside hardware upgrades and cryogenic probe work at UGC-DAE CSR.

Background: For a detailed chronological log, see CHANGELOG.md.

Resources & Documentation

• User Manual: Details on the project and usage guides are available in the User Manual.
• Instrument Manuals: A list of instrument manuals is available in docs/Instruments_Manuals_Lists.md.
• GitLab Backup: This project is manually backed up weekly to a GitLab repository.
• Project Web Page: https://prathameshdeshmukh.site/pages/project-pica.html.

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Citation

If you use this software in your research, please cite it:

@software{Deshmukh_PICA_2025,
  author       = {Deshmukh, Prathamesh Keshao and Mukherjee, Sudip},
  title        = {{PICA: Advanced High-Precision Transport Measurement Automation with Python}},
  month        = dec,
  year         = 2025,
  publisher    = {GitHub},
  version      = {1.0.0},
  url          = {https://github.com/prathameshnium/PICA-Python-Instrument-Control-and-Automation}
}

Authors & Funding

This project is led by Prathamesh Deshmukh under the supervision of Dr. Sudip Mukherjee at the UGC-DAE Consortium for Scientific Research, Mumbai Centre, Bhabha Atomic Research Centre, Mumbai, 400 085, Maharashtra, India.

We acknowledge the financial support provided under the SERB-CRG project grant No. CRG/2022/005676 from the Anusandhan National Research Foundation (ANRF), a statutory body of the Department of Science and Technology (DST), Government of India.

License

This project is licensed under the MIT License - see the LICENSE file for details.