🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network or shell activities detected. The low maintainer activity and metadata quality are concerning but not conclusive of malicious intent.

Low risk in all technical categories.
Concerns about metadata quality and maintainer activity.

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.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: Low risk due to lack of suspicious flags, but concern over low maintainer activity and metadata quality.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

1 test file(s) detected (e.g. test_basic.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (3513 chars)

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

○ Low Type Annotations 1.0

No type annotations detected

No type annotations, py.typed marker, or stub files detected

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 49 commits in jovan-AIcoder/AI-Based-Fourier-Analysis
Single author but highly active (49 commits)

🔬 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

Repository jovan-AIcoder/AI-Based-Fourier-Analysis appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author "Jovan" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aifourier

Develop a user-friendly mini-application that leverages the 'aifourier' Python package to perform advanced Fourier analysis on time-series data. This application will enable users to upload their own datasets, visualize the original time-series data alongside its Fourier transform, and analyze the frequency components of the data. Here are the key steps and features for building this application:

1. **Setup Environment**: Ensure all necessary packages including 'aifourier', 'numpy', 'pandas', 'matplotlib', and 'flask' are installed.
2. **Data Input Interface**: Create a simple web interface where users can upload CSV files containing time-series data.
3. **Data Preprocessing**: Implement functions to preprocess the uploaded data, handling any missing values and normalizing the dataset if needed.
4. **Fourier Transform**: Use the 'aifourier' package to compute the Fourier transform of the preprocessed data. Highlight how 'aifourier' employs sinusoidal neural networks for this process.
5. **Visualization**: Develop visualizations that display both the original time-series data and its Fourier transform side-by-side. Include interactive elements like sliders to adjust parameters affecting the Fourier analysis.
6. **Frequency Analysis**: Provide tools within the application for analyzing specific frequency components of the transformed data. Allow users to select and zoom into particular frequencies to explore their characteristics more closely.
7. **Export Results**: Enable users to export both the analyzed data and visualizations as downloadable files.
8. **Documentation & User Guide**: Prepare comprehensive documentation explaining how to use the application effectively, including tips on interpreting Fourier analysis results.

This project aims to showcase the capabilities of 'aifourier' in practical applications, making complex Fourier analysis accessible to a broader audience.