aiopvxs

v0.3.0 safe
4.0
Medium Risk

Python asyncio API to the PVXS libraries

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators based on the checks performed, with no detected network calls, shell executions, obfuscation, or credential harvesting attempts. However, the metadata risk score is elevated due to the lack of detailed maintainer information and community engagement.

  • No network calls detected
  • No shell execution patterns detected
  • Lack of community engagement and maintainer details
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no suspicious system command invocations.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's profile lacks detail, and the repository shows no community engagement.

📦 Package Quality Overall: Low (3.4/10)

○ 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 (8364 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
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 41 commits in m2es3h/aiopvxs
  • Two distinct contributors found

🔬 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

Email domain looks legitimate: gmail.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://code.visualstudio.com/docs/python/testing
⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aiopvxs 
Create a real-time data acquisition and visualization tool using Python's asyncio and the 'aiopvxs' library. This application will connect to a PVXS server to retrieve live sensor data, process it asynchronously, and display the results in a dynamic graph. Here are the steps and features you need to implement:

1. **Setup Environment**: Ensure your development environment has Python 3.7+ installed along with 'aiopvxs', 'matplotlib', and 'asyncio'. 
2. **Connection Management**: Implement a class `PVXSServerConnector` that handles connecting to the PVXS server, maintaining the connection, and disconnecting gracefully. Utilize 'aiopvxs' to manage these operations.
3. **Data Retrieval**: Within the same class, add methods to periodically fetch sensor data from the server. Use asyncio's scheduling capabilities to ensure efficient data polling without blocking other processes.
4. **Data Processing**: Develop asynchronous functions to process the retrieved data. These could include filtering out noise, calculating averages over time intervals, or identifying anomalies based on thresholds.
5. **Visualization**: Integrate 'matplotlib' to create a real-time updating plot that reflects the processed data. The graph should update dynamically as new data comes in.
6. **User Interface**: Design a simple command-line interface where users can specify which sensors they want to monitor, set thresholds for alerts, and choose the type of processing applied to the data.
7. **Alert System**: Implement an alert mechanism that triggers notifications when the processed data exceeds predefined thresholds. Notifications can be simple print statements or more sophisticated like sending emails or SMS.
8. **Testing & Documentation**: Write unit tests to validate each component of your application works as expected. Additionally, provide clear documentation on how to install dependencies, run the application, and customize its behavior.

This project aims to demonstrate the power of asyncio for handling concurrent tasks efficiently while showcasing 'aiopvxs' as a robust tool for interacting with PVXS servers.

💬 Discussion Feed

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