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arxiv: 2604.18962 · v1 · submitted 2026-04-21 · ❄️ cond-mat.supr-con

AngstromPro: A versatile software for massive N-dimensional STM data management, visualization and in-depth analysis

Huiyu Zhao , Jiahao Yan , Catherine Dawson , Haitao Yang , Hong-Jun Gao This is my paper

Pith reviewed 2026-05-10 02:06 UTC · model grok-4.3

classification ❄️ cond-mat.supr-con
keywords STM data analysisPython softwaremodular architecturescanning tunneling microscopydata visualizationreproducibilityopen-source toolN-dimensional data
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The pith

AngstromPro offers a modular Python platform that unifies management, visualization, and analysis of large STM datasets while supporting custom extensions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents AngstromPro as an open-source software built to address fragmented workflows in scanning tunneling microscopy data handling. Its design centers on a top-level module that oversees global variables alongside sub-modules each equipped with local variable lists, allowing organized step-by-step processing for N-dimensional datasets. Built-in functions cover common STM tasks such as background subtraction, lattice correction, and sub-atomic registration, while the separation of graphical interface from core algorithms supports readability and user customization. If the architecture works as described, researchers gain a single environment for maintaining processing histories and rapidly developing new methods without starting from scattered tools.

Core claim

AngstromPro consists of a main module managing a Global Variables List and a list of sub-modules, each with its own Local Variables List to keep workspaces organized. Sub-modules handle specific tasks like the Multiple 2D Images Visualizer and Analyzer, and the system embeds standard STM algorithms including Lawler-Fujita correction and perfect lattice correction. The architecture deliberately isolates the graphical user interface from data processing routines to improve maintainability, and it accommodates users ranging from those using only built-in features to developers adding entirely new modules.

What carries the argument

The modular architecture with a top-level Global Variables List and per-sub-module Local Variables Lists that organizes STM workflows and separates interface code from processing algorithms.

If this is right

  • Detailed processing histories become standard, allowing any analysis step to be inspected or repeated exactly.
  • New STM-specific algorithms can be added as sub-modules without altering the core variable management system.
  • Built-in routines for background subtraction and lattice correction become available to all users in a consistent format.
  • Developers gain a ready structure for testing custom functions on large N-dimensional datasets.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same variable-list approach could be adapted for data from related techniques such as atomic force microscopy.
  • Integration with existing Python libraries for scientific computing would allow hybrid workflows that combine STM data with other measurements.
  • Widespread use might create shared repositories of processing histories that serve as community benchmarks for analysis methods.

Load-bearing premise

The modular architecture and included STM algorithms will produce measurable gains in efficiency and reproducibility for actual users, even though no benchmarks, comparisons, or user studies are reported.

What would settle it

A side-by-side trial in which experienced STM users complete identical analysis tasks with AngstromPro versus their current collection of scripts and tools, showing no reduction in time or error rate, would falsify the claimed improvements.

Figures

Figures reproduced from arXiv: 2604.18962 by Catherine Dawson, Haitao Yang, Hong-Jun Gao, Huiyu Zhao, Jiahao Yan.

Figure 1
Figure 1. Figure 1: Core framework and data handling in AngstromPro. Top-level window Module & Variable Manager comprise of Modules List and Global Variables List. All functional modules are registered and organized within the Modules List. Each functional module has its own Local Variables List and exchange data via the Global Variables List. The specific functions of each module are accessible in the right region of the mod… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of AngstromPro’s workflow and module architecture. The Data Browser provides snapshots previews of dataset stored locally in various file formats, including those generated by the data acquisition system. The data corresponding to the selected snapshot is encapsulated in a variable structure with its name, associated information, and process history, then added to the Global Variable List. From th… view at source ↗
Figure 3
Figure 3. Figure 3: Detailed functional modules and ecosystem vision in AngstromPro. Specific functional modules are designed for data acquisition, visualization, analysis and simulation, forming a complete ecosystem for STM. Modules & Variables Manager serves as the top-level module, organizing all other functional modules and facilitating data interaction between them. Design details of Data Browser and Multiple 2D Images V… view at source ↗
Figure 4
Figure 4. Figure 4: General data processing procedure: background subtract, perfect Lattice and Lawler-Fujita correction. (a) shows the raw data of NbSe2 topography. (b), (c) and (d) represent the sequential application of linear background subtraction, perfect lattice operation, and the Lawler-Fujita algorithm to (a). (e)-(h) is the Fast Fourier Transform (FFT) of (a)-(d).The dash line in (f) indicates the horizontal directi… view at source ↗
Figure 5
Figure 5. Figure 5: Results of data processing using 2D Lock-In technique (a) is a simulated square lattice topography with a 4𝑎0 CDW, which has a phase shift at the centre enclosed in the red frame. (b) is the FFT of (a) and the red circle shows the CDW points we choose for 2D lock-in. (c) and (d) shows the amplitude map and phase map of the lock-in points. (a) (b) (c) (d) (e) (f) 0 pm 69 pm 0 pm 68 pm -448 m 855 m -430 m 96… view at source ↗
Figure 6
Figure 6. Figure 6: Processed data result following sub-atomic precision registration (a) and (b) are two consecutively obtained topography. (c) is the Cross-Correlation between (a) and (b). The three yellow x-marked 𝑥1 , 𝑥2 and 𝑥3 in (a) is register points and 𝑥 ′ 1 , 𝑥 ′ 2 and 𝑥 ′ 3 in (b) is register reference points. (d) is the registered and then cropped image of (a). (e) is the cropped image of (b) using the same crop p… view at source ↗
Figure 7
Figure 7. Figure 7: The graphical user interface of Image2U3 Widget menu Variables Dock Widget Plot 1D Dock Widget Options menu Preference File Menu Make Movie From Export Main to Image Auxiliary to Image Main to Clipboard Auxiliary to Clipboard Main Auxiliary Analysis menu Fourier Transform R-Map Gap-Map 2D Lock-in Cross-Correlation Amplitude Map Phase Map Cross-Correlation Statistical Correlation Simulate menu Generate Curv… view at source ↗
Figure 8
Figure 8. Figure 8: The menu of Image2U3 H. Zhao et al.: Preprint submitted to Elsevier Page 18 of 14 [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The user graphical interface of Data Browser [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The user graphical interface of Multiple 1D Curves Visualizer & Analyzer H. Zhao et al.: Preprint submitted to Elsevier Page 19 of 14 [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The user graphical interface of Data Simulator H. Zhao et al.: Preprint submitted to Elsevier Page 20 of 14 [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
read the original abstract

We present AngstromPro, a versatile, modular and open-source software built on Python for managing, visualizing and analyzing large datasets acquired via Scanning Tunneling Microscopes (STM). Its robust architecture features a top-level module that manages a Global Variables List and a sub-modules List. Each sub-module, equipped with its own Local Variables List to maintain a tidy workspace, can be tailored for specific tasks, including built-in modules like the Multiple 2D Images Visualizer and Analyzer. These modules support step-by-step data processing and extensibility for custom algorithms and functions. AngstromPro's design supports a wide range of users, from those relying on built-in tools to developers creating custom algorithms or extending the platform with new modules. In its implementation, AngstromPro separates graphical user interface components from data processing algorithms, a strategy that enhances code readability, maintainability, and extensibility. The embedded algorithms reflect commonly adopted and recently developed approaches in STM data analysis, including background subtraction, perfect lattice correction, Lawler-Fujita correction, and sub-atomic precision registration, along with additional standard data processing routines. By consolidating fragmented STM workflows, maintaining detailed processing histories, and providing a flexible platform for customization, AngstromPro enhances both the efficiency, reliability, and reproducibility of STM data analysis, while enabling the rapid development of new methods and modules.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript presents AngstromPro, an open-source Python software for managing, visualizing, and analyzing large N-dimensional STM datasets. It describes a modular architecture with a top-level module handling a Global Variables List and sub-modules list, each sub-module maintaining its own Local Variables List. Built-in capabilities include a Multiple 2D Images Visualizer and Analyzer supporting step-by-step processing, separation of GUI components from data-processing algorithms, and embedded STM routines such as background subtraction, perfect lattice correction, Lawler-Fujita correction, and sub-atomic precision registration. The authors claim that consolidating fragmented workflows, maintaining detailed processing histories, and offering customization flexibility thereby enhances efficiency, reliability, and reproducibility of STM data analysis while enabling rapid development of new methods and modules.

Significance. A unified, extensible platform for STM data handling addresses a genuine practical need in the field as datasets grow in size and complexity. The described separation of GUI from algorithms, use of local/global variable scoping, and inclusion of both standard and recently developed STM-specific routines represent sound software-engineering choices that could support maintainability and community extensions. If the claimed workflow consolidation and reproducibility benefits are realized in practice, the tool could reduce reliance on ad-hoc scripts and multiple disconnected packages, with particular value for superconductivity and condensed-matter STM studies.

major comments (1)
  1. [Abstract] Abstract: the assertion that AngstromPro 'enhances both the efficiency, reliability, and reproducibility of STM data analysis' is presented as a direct consequence of the modular architecture and built-in routines, yet the manuscript supplies no timing benchmarks, reproducibility metrics (e.g., inter-user variance on identical datasets), comparisons against Gwyddion/WSxM/custom Python scripts, or user-study data to substantiate the improvement. This leaves the central practical claim unsupported by evidence.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the significance of AngstromPro. We address the single major comment below and will revise the manuscript to incorporate the suggestion.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that AngstromPro 'enhances both the efficiency, reliability, and reproducibility of STM data analysis' is presented as a direct consequence of the modular architecture and built-in routines, yet the manuscript supplies no timing benchmarks, reproducibility metrics (e.g., inter-user variance on identical datasets), comparisons against Gwyddion/WSxM/custom Python scripts, or user-study data to substantiate the improvement. This leaves the central practical claim unsupported by evidence.

    Authors: We agree that the current abstract presents the benefits as a direct outcome without supporting quantitative data such as benchmarks, metrics, or user studies. The claims stem from the described architecture (e.g., processing history tracking, GUI-algorithm separation, and modularity), which is designed to address common pain points in STM workflows. However, as a software presentation manuscript, we did not include empirical comparisons. To address this concern, we will revise the abstract to qualify the language (e.g., replacing 'enhances' with 'is designed to enhance' or 'supports improved') and add a short discussion section outlining the rationale for expected benefits with illustrative examples from the built-in routines. No new benchmarks will be added, as they would require a separate validation study beyond the scope of this work. revision: yes

Circularity Check

0 steps flagged

No circularity: purely descriptive software paper with no derivations, predictions, or fitted quantities

full rationale

The manuscript presents a software tool for STM data management and analysis. Its central statements describe architecture (global/local variable lists, sub-module separation, GUI/algorithm decoupling) and list included routines (background subtraction, Lawler-Fujita correction, sub-atomic registration). No equations, no parameter fitting, no predictions of numerical outcomes, and no derivation chain exist that could reduce to the inputs by construction. The claim that the design 'enhances efficiency, reliability, and reproducibility' is an unverified assertion rather than a mathematical or logical reduction; it does not constitute circularity under the defined patterns. The paper is self-contained as a descriptive account and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a software tool description paper; no free parameters, mathematical axioms, or new physical entities are introduced.

pith-pipeline@v0.9.0 · 5557 in / 1036 out tokens · 35110 ms · 2026-05-10T02:06:19.560339+00:00 · methodology

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Reference graph

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