b1500-powermeter-rollover

v1.0.1 suspicious
4.0
Medium Risk

Keysight B1500A + Thorlabs PM100D synchronized LIV sweep with automatic optical rollover detection (CUSUM, EWMA, rolling average, regression)

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal signs of typical security risks, but its low activity and single contributor raise concerns about its legitimacy and potential supply-chain attack.

  • Suspiciously low activity
  • Single contributor
Per-check LLM notes
  • Network: No network calls suggest normal behavior for a package focused on local power meter functionality.
  • Shell: No shell executions indicate the package is likely not executing external commands which could be a security risk.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating no immediate risk to secrets or credentials.
  • Metadata: Suspicious low activity and single contributor indicate potential risk.

📦 Package Quality Overall: Low (2.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4539 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 4 commits in vvvvvero/b1500_powermeter_LIV_rollover
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Single contributor with only 4 commit(s) — possibly throwaway account
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Veronica Gao Zhan" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with b1500-powermeter-rollover 
Create a Python-based mini-application that leverages the 'b1500-powermeter-rollover' package to perform advanced Light-I-V (LIV) sweeps on semiconductor devices. Your application will integrate a Keysight B1500A semiconductor parameter analyzer with a Thorlabs PM100D power meter to conduct synchronized measurements. The goal is to detect optical rollovers during the sweep process using sophisticated algorithms such as Cumulative Summation (CUSUM), Exponentially Weighted Moving Average (EWMA), rolling averages, and linear regression. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Ensure your development environment includes Python, the 'b1500-powermeter-rollover' package, and necessary libraries like numpy and matplotlib.
2. **Connect Devices**: Write code to connect the Keysight B1500A and Thorlabs PM100D via their respective APIs.
3. **Configure Sweep Parameters**: Allow users to input parameters for the LIV sweep such as voltage range, current compliance, and measurement points.
4. **Perform Synchronized Measurement**: Implement a function to start the LIV sweep, collecting data from both devices simultaneously.
5. **Detect Rollovers**: Utilize the 'b1500-powermeter-rollover' package to analyze collected data and identify any optical rollovers using CUSUM, EWMA, rolling averages, and regression techniques.
6. **Visualize Results**: Display the sweep results graphically, highlighting detected rollovers.
7. **Report Generation**: Create a feature to generate comprehensive reports summarizing the sweep data and findings.
8. **User Interface**: Develop a simple GUI using PyQt or Tkinter to make the application more user-friendly.

Suggested Features:
- Real-time data visualization during the sweep process.
- Export options for data and reports.
- Support for multiple devices and configurations.
- Error handling and logging for robustness.

💬 Discussion Feed

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