ae-picker

v0.2.1 safe
4.0
Medium Risk

First-arrival time picking for acoustic emission and microseismic waveforms

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of malicious activity such as network calls, shell execution, or credential harvesting. However, the metadata risk score is elevated due to the maintainer's new or inactive account and lack of detailed author information.

  • Low network and shell execution risks
  • No obfuscation or credential harvesting detected
  • Elevated metadata risk due to maintainer's profile
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution detected, indicating the package does not attempt to execute system commands, reducing risk of unauthorized access or data exfiltration.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has a new or inactive account with limited package history and missing author information.

🔬 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

All external links appear legitimate

✓ Git Repository History

Repository wamriewdan/ae_arrival_picker appears legitimate

⚠ 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 ae-picker 
Create a Python-based mini-application named 'SeismicWaveAnalyzer' that leverages the 'ae-picker' package to analyze seismic data. This application should be designed to help researchers and engineers quickly identify and extract first-arrival times from acoustic emission and microseismic waveforms. Here are the steps and features your application should include:

1. **Data Importation**: Allow users to import seismic waveform data in common formats such as .txt, .csv, or .dat files. Ensure that the imported data can be visualized on a basic plot for quality assurance.
2. **First-Arrival Time Detection**: Utilize the 'ae-picker' package to automatically detect the first arrival times of waves within the imported data. Provide options for users to adjust parameters like threshold values and detection sensitivity to fine-tune the results.
3. **Manual Adjustment Tool**: Implement a feature that allows manual adjustment of detected first-arrival times if necessary. This could be done through zooming into specific sections of the waveform or adjusting detected points directly on the plot.
4. **Report Generation**: Automatically generate a report summarizing the analysis results, including a list of all detected first-arrival times, any adjustments made, and a brief statistical summary of the findings.
5. **Visualization Enhancements**: Improve the visualization of waveforms by adding annotations for detected first-arrival times and allowing users to customize plot aesthetics like color schemes and marker styles.
6. **Export Options**: Enable users to export the analyzed data and plots in various formats such as PDF, PNG, or CSV for further analysis or presentation purposes.
7. **User Interface**: Develop a simple yet intuitive graphical user interface (GUI) using libraries like Tkinter or PyQt to make the application accessible to non-programmers.

By following these guidelines, you will create a powerful yet user-friendly tool that significantly simplifies the process of analyzing seismic data.