LaplaceX

v0.1.0 safe
1.0
Low Risk

Industrial-grade Laplace transform library: stable forward and inverse transforms via logarithmic-plane wavelet decomposition

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity or unnecessary risks given its described functionality.

  • No network or shell risks detected.
  • No obfuscation or credential harvesting attempts.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting legitimate usage.

🔬 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 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Vozmishchev Artem" 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 LaplaceX 
Create a mini-application called 'SignalAnalyzer' that leverages the 'LaplaceX' package to perform advanced signal processing tasks. This application will allow users to upload a time-domain signal file (such as a .wav audio file), apply Laplace transforms to analyze its frequency components, and visualize the results. Here are the key steps and features for your project:

1. **User Interface**: Design a simple yet intuitive GUI using Tkinter or Streamlit where users can select a file from their local system.
2. **File Handling**: Ensure the application supports various file formats such as .wav, .txt, etc., for input signals.
3. **Laplace Transform Application**: Utilize LaplaceX to perform both forward and inverse Laplace transforms on the uploaded signal data. The application should decompose the signal into its frequency components and reconstruct it if necessary.
4. **Visualization**: Implement visualization tools within the application to display the original signal, transformed signal in the Laplace domain, and reconstructed signal. Use libraries like Matplotlib for plotting.
5. **Detailed Analysis**: Offer detailed analysis reports that include metrics like amplitude, phase shift, and other relevant parameters derived from the Laplace transform of the signal.
6. **Saving Results**: Provide options for users to save the analyzed data and visualizations as files on their local machine.
7. **Educational Component**: Include brief explanations and interactive tutorials about Laplace transforms and their applications in signal processing within the application.

This project aims to showcase the power of LaplaceX in real-world signal analysis and provide a user-friendly tool for both professionals and students interested in signal processing.