🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network calls, shell execution, and obfuscation but shows signs of low maintenance and potential lack of transparency in metadata, raising suspicion.

Low metadata maintenance
Potential lack of transparency

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 detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
Metadata: The package shows signs of low maintenance and potential lack of transparency, which could indicate risk.

🔬 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 MicroWavelet

Create a mini-application named 'MicrolensAnomalyHunter' using the Python package 'MicroWavelet'. This application will serve as a tool for astronomers and researchers to detect anomalies in microlensing light curves. Microlensing light curves are essential in astronomy for studying the gravitational lensing effect caused by stars and other celestial bodies.

The application should have the following core functionalities:
1. **Data Input**: Allow users to upload CSV files containing time-series data of microlensing light curves. The application should validate the input data format to ensure it contains time stamps and corresponding brightness measurements.
2. **Data Visualization**: Display the uploaded light curve data in a user-friendly chart format, allowing users to visually inspect the data before proceeding with anomaly detection.
3. **Anomaly Detection**: Utilize the Continuous Wavelet Transform (CWT) method provided by the 'MicroWavelet' package to analyze the light curve data and identify potential anomalies. Users should be able to select different wavelets and scales for more refined analysis.
4. **Anomaly Highlighting**: Mark detected anomalies on the light curve plot for easy identification and further investigation.
5. **Report Generation**: Automatically generate a report summarizing the findings, including details of detected anomalies, their timestamps, and any relevant statistical information about the data.

Suggested additional features:
- **Real-time Data Streaming Integration**: Enable the application to connect to real-time data streams from telescopes or astronomical observatories.
- **User Interface Customization**: Provide options for users to customize the appearance of charts and reports.
- **Collaboration Features**: Allow multiple users to collaborate on the same dataset, sharing insights and annotations.
- **Educational Resources**: Include tutorials and guides explaining the basics of microlensing and anomaly detection techniques.

Utilize the 'MicroWavelet' package's core capabilities to perform the Continuous Wavelet Transform on the uploaded data, which helps in identifying patterns and anomalies that might not be visible through simple visual inspection. Ensure that the application provides clear documentation and examples to help users understand how to use 'MicroWavelet' effectively.