update to the paper

Author: prathameshnium

paper/paper.md

@@ -30,17 +30,17 @@ bibliography: paper.bib
 # Summary
 
 PICA (Python-based Instrument Control and Automation) is a modular, open-source software suite designed to automate complex characterisation experiments in condensed matter physics. While initially developed to operate a custom laboratory-built measurement system,PICA is designed as a
-hardware agnostic framework.It provides an extensible unified graphical user interface (GUI) for orchestrating high-precision instruments, specifically Keithley SourceMeters/Nanovoltmeters, Lakeshore Temperature Controllers, and Keysight LCR metres. The suite controls temperature setpoints to perform automated protocols, including temperature-dependent resistivity, current-voltage (I-V) characteristics, and pyroelectric current measurement.
+hardware agnostic framework. It provides an extensible unified graphical user interface (GUI) for orchestrating high-precision instruments, specifically Keithley SourceMeters/Nanovoltmeters, Lakeshore Temperature Controllers, and Keysight LCR metres. The suite controls temperature setpoints to perform automated protocols, including temperature-dependent resistivity, current-voltage (I-V) characteristics, and pyroelectric current measurement.
 
 # Statement of need
 
-Advancements in experimental physics depend on the precise characterisation of material properties under extreme physical conditions. Researchers have to choose between expensive proprietary software like LabVIEW or developing a custom measurement script from scratch. While Python libraries such as PyVISA [@grecco2023pyvisa] and PyMeasure [pymeasure_2025] provide excellent low-level driver support, they primarily function as developer toolkits. They often require significant boilerplate code to handle data visualisation and error handling, effectively forcing physicists to become software engineers.
-PICA fills this niche by providing a **"lab-ready" application layer** built on top of these libraries. It differentiates itself through the following unique features:
+Advancements in experimental physics depend on the precise characterisation of material properties under extreme physical conditions. Researchers have to choose between expensive proprietary software like LabVIEW or developing a custom measurement script from scratch. While powerful ecosystem libraries such as PyVISA [@grecco2023pyvisa] and PyMeasure [pymeasure_2025] provide the foundational drivers for instrumental communication, they are fundamentally software libraries that require the user to write and maintain code.This creates a technical barrier for researchers who lack programming expertise. PICA addresses this gap by functioning as a turnkey application rather than a library. It offers a ready to run graphical interface that abstracts the underlying control logic, allowing the experimentalist to focus on data acquisition without the need to develop custom software scripts.
+It differentiates itself through the following unique features:
 * **Accessibility:** A professional dashboard that allows researchers without coding experience to configure and run a complex measurement protocol immediately.
 * **Physical Validation:** Unlike general-purpose drivers, PICA's protocols are actively used for cryogenic transport measurements (80K - 320K) at the UGC DAE Consortium for Scientific Research, Mumbai Centre, validating the software's core architecture in a real world research environment and providing a stable, tested foundation for the university and researchers to build upon.
 * **Process Isolation:** PICA deploys a `multiprocessing` architecture that runs instrumentation control logic in an isolated process. This ensures that hardware timeouts or driver crashes do not freeze the main dashboard, which is a common problem in single-threaded Python scripts.
-*  **Modular CLI Architecture:** As demonstrated in the repository, measurement modules also contain a CLI measurement module that allows researchers to utilise PICA's measurement protocol and logic for headless automation or integration into other workflows without GUI overhead.
-*  **Operational Transparency:** Unlike a Black box solution, PICA exposes the real-time command logs, aiding in debugging and ensuring scientific reproducibility.
+*  **Modular CLI Architecture:** As demonstrated in the repository, measurement modules also contain CLI measurement module counterparts that allow researchers to utilise PICA's measurement protocol and logic for headless automation or integration into other workflows without GUI overhead.
+*  **Operational Transparency:** Unlike a black box solution, PICA exposes real-time command logs, aiding in debugging and ensuring scientific reproducibility.
 *  **Open Source Extensibility:** PICA's modular design allows researchers to easily integrate new instrument drivers or experimental protocols by subclassing existing templates, fostering a community-driven ecosystem for instrument control.
 
 
@@ -55,14 +55,14 @@ PICA is built on a modular architecture characterised by self-contained modules,
 
 Unlike simple script-based automation, PICA decouples the User Interface (UI) from the instrumentation control logic. It utilises Python's standard 'multiprocessing' libraries to spawn isolated processes for measurement tasks.
 * **Stability:** If an instrument hangs or a communication bus times out, 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` [@hunter2007matplotlib] with blitting) even while the backend waits for hardware triggers. Numpy [@harris2020numpy] is utilised throughout this pipeline for efficient array manipulation and data validation during the real-time updates.
+* **Responsiveness:** The `tkinter`-based frontend remains responsive for live data plotting (using `matplotlib` [@hunter2007matplotlib] with blitting) even while the backend waits for hardware triggers. Numpy [@harris2020numpy] is utilised throughout this pipeline for efficient array manipulation and data validation during real-time updates.
 * **Data Integrity:** Experimental data integrity is prioritised through a "write on acquisition" strategy. Data is structured using `pandas` [@pandas2025] and is saved to a CSV file immediately after every acquisition point, preventing data loss in the event of a power failure or program/system crash.
 
 ### Hardware Abstraction Layer
 
 PICA utilises **PyVISA** [@grecco2023pyvisa] to abstract the low-level communication protocols (GPIB, USB, Ethernet). The software implements a strict initialisation routine:
 1. **Connection Verification:** A built-in "VISA Instrument Scanner" queries the bus (`*IDN?`) to map the connected instrument addresses.
-2.  **State Sanitization:** To eliminate the influence of all previous experiments, any stored data, cache in buffers, and existing settings or configurations, the instruments are explicitly reset, thereby providing a clean initial state before each measurement.
+2.  **Instrument Reset Protocol:** To eliminate the influence of all previous experiments, any stored data, cache in buffers, and existing settings or configurations, the instruments are explicitly reset, thereby providing a clean initial state before each measurement.
 3.  **Graceful Shutdown:** A "Safety Shutdown Routine" logic ensures that sources are ramped down to zero and heaters are disabled safely, even if the software is interrupted unexpectedly.
 
 ### Operational Transparency