AI Analysis
The package shows no signs of malicious activity such as network calls, shell executions, or credential harvesting. The metadata risk is slightly elevated due to the maintainer having only one package, but this alone does not indicate a supply-chain attack.
- No network calls detected
- Maintainer has only one package
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands that could pose a risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which might indicate a new or less active account but does not strongly suggest malicious intent.
Package Quality Overall: Low (4.2/10)
No test suite detected
No test files or test-runner configuration detected
Some documentation present
Brief PyPI description (472 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
No type annotations detected
No type annotations, py.typed marker, or stub files detected
Active multi-contributor project
14 unique contributor(s) across 100 commits in ARM-DOE/pyartActive community — 5 or more distinct contributors
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: anl.gov>
All external links appear legitimate
Repository ARM-DOE/pyart appears legitimate
1 maintainer concern(s) found
Author "Jonathan Helmus" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a radar data visualization tool using the Python ARM Radar Toolkit (Py-ART). This tool will allow users to upload radar data files, process them using Py-ART's functionalities, and visualize the processed data as radar plots. Here’s a detailed breakdown of the steps and features you need to implement: 1. **Data Upload**: Allow users to upload radar data files (commonly in NetCDF format) through a user-friendly interface. 2. **Data Processing**: Utilize Py-ART to clean and enhance the radar data. This includes applying calibration, removing noise, and filtering out unwanted data points. 3. **Visualization**: Implement a feature to display the processed radar data as interactive radar plots. Users should be able to zoom in/out, pan across the plot, and select different parameters to visualize (e.g., reflectivity, velocity). 4. **Export Options**: Provide options for users to export their visualized data as image files or save it back into a NetCDF file for further analysis. 5. **Documentation and Help**: Include comprehensive documentation and a help section explaining how to use each feature, common issues faced while working with radar data, and how Py-ART helps in processing these datasets. Utilize Py-ART's core features such as 'correct_attenuation', 'calibrate_radar', and 'filter_noisy_data' to ensure the data is accurate and usable for scientific analysis. The goal is to create a robust, user-friendly tool that simplifies the complex task of radar data processing and visualization.
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