AI Analysis
Final verdict: SAFE
The package AeroViz v0.3.4 presents minimal risks based on the analysis notes provided. It lacks any network calls, shell executions, obfuscations, or credential harvesting activities.
- No network calls detected
- No shell execution patterns detected
- No obfuscation patterns detected
- No credential harvesting patterns detected
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer's author information is incomplete and the account seems new or inactive, raising some concerns but not definitive proof of malice.
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 Alex870521/AeroViz appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 AeroViz
Develop a mini-application called 'AirQualityVisualizer' that leverages the AeroViz Python package to process and visualize aerosol data from various atmospheric instruments. This application will serve as a tool for researchers and environmental scientists to better understand air quality data collected from different sources. Here’s a detailed step-by-step guide on how to build this application: 1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including AeroViz, pandas, matplotlib, and seaborn. 2. **Data Ingestion**: Implement functionality to read data from multiple sources such as SMPS, APS, AE33, TEOM, Nephelometer, and XRF using AeroViz’s read functions. Ensure data is ingested in a standardized format for further processing. 3. **Data Quality Control (QC)**: Integrate AeroViz’s QC tools to clean the imported data, removing any anomalies or errors that might affect analysis accuracy. 4. **Data Analysis**: Use AeroViz to perform basic statistical analyses on the cleaned data. This includes calculating mean, median, mode, standard deviation, etc., for each instrument type. 5. **Visualization**: Create interactive plots and charts using matplotlib and seaborn to visualize the analyzed data. Include options to compare data across different instruments and time periods. 6. **User Interface**: Develop a simple GUI using Tkinter where users can upload their data files, select which instruments’ data they want to analyze, and choose from predefined visualizations. 7. **Export Options**: Allow users to export their visualized data as images or CSV files for further use or sharing. 8. **Documentation & Testing**: Write comprehensive documentation detailing how to use the application and its functionalities. Conduct thorough testing to ensure all features work as expected. Suggested Features: - Real-time data plotting during QC and analysis phases. - Customizable plot styles based on user preferences. - Support for exporting visualizations in high-resolution formats like PNG and PDF. - Integration of machine learning models (using scikit-learn) to predict future air quality trends based on historical data.