PySDKit

v0.4.24 safe
4.0
Medium Risk

A Python library for signal decomposition algorithms with a unified interface.

🤖 AI Analysis

Final verdict: SAFE

The package PySDKit v0.4.24 presents a low risk profile with no detected network calls, shell executions, obfuscations, or credential risks. However, a non-HTTPS link in the metadata slightly elevates the metadata risk score.

  • No network calls detected
  • Non-HTTPS link in metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The presence of a non-HTTPS link raises concerns, but no typosquatting or other severe flags are detected.

🔬 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://aquador.vovve.net/IEMD/
✓ Git Repository History

Repository wwhenxuan/PySDKit appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "whenxuan, changewam, josefinez, Yuan Feng, Wentong Zhao, JacktheFowler, Deeksha Manjunath" 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 PySDKit 
Create a mini-application called 'SignalAnalyzer' that leverages the PySDKit package to analyze audio signals from .wav files. The application should allow users to upload an audio file, select a signal decomposition algorithm provided by PySDKit (such as Empirical Mode Decomposition or Wavelet Transform), and visualize the decomposed components. Additionally, include the following features:

1. A user-friendly graphical interface built using Tkinter.
2. An option to save the decomposed signal components as separate .wav files.
3. Real-time visualization of the original and decomposed signals using matplotlib.
4. Detailed documentation explaining the installation process, usage, and limitations of the application.

Utilize PySDKit's unified interface to seamlessly switch between different decomposition methods, ensuring that the application remains flexible and easy to extend with future algorithms.