anemoi-graphs

v0.9.4 suspicious
4.0
Medium Risk

A package to build graphs for data-driven forecasts.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in terms of network, shell, and obfuscation activities, but incomplete maintainer information and potential inactivity raise concerns about its legitimacy.

  • Incomplete maintainer information
  • Potential inactivity of the maintainer
Per-check LLM notes
  • Network: No network calls suggest the package does not communicate externally, which is normal unless specific network functionality is expected.
  • Shell: No shell executions indicate there is no direct system command execution, reducing the risk of malicious activity.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The maintainer's author information is incomplete and they may be new or inactive, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://anemoi-graphs.readthedocs.io/
  • 1 documentation file(s) (e.g. conf.py)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 233 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 21 unique contributor(s) across 100 commits in ecmwf/anemoi-graphs
  • Active community — 5 or more distinct contributors

🔬 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: ecmwf.int>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository ecmwf/anemoi-graphs 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 anemoi-graphs 
Create a weather forecasting mini-application using the 'anemoi-graphs' Python package. This application will allow users to input historical weather data and generate visual forecasts based on that data. The app should include the following features:

1. **Data Input Interface**: Provide a simple interface where users can upload CSV files containing historical weather data. The CSV file should have columns for date, temperature, humidity, wind speed, and precipitation.
2. **Data Preprocessing**: Implement basic data cleaning and preprocessing steps to ensure the data is suitable for analysis. This includes handling missing values, converting date strings to datetime objects, and normalizing numerical data if necessary.
3. **Forecast Generation**: Utilize 'anemoi-graphs' to create graphical representations of forecasted weather conditions. The package should be used to plot trends over time, such as temperature and humidity forecasts. Users should be able to specify the period they want to forecast.
4. **Interactive Visualization**: Integrate interactive elements into the plots generated by 'anemoi-graphs'. For example, users should be able to hover over points in the graph to see specific details about that day's weather.
5. **Save and Share**: Allow users to save the generated graphs locally as image files (PNG format) or share them directly via email or social media platforms.
6. **Documentation and User Guide**: Write comprehensive documentation explaining how to use the application, including examples of valid input formats and expected outputs.

In your implementation, focus on utilizing 'anemoi-graphs' effectively to showcase its capabilities in generating insightful and visually appealing graphs for weather data. Additionally, ensure the application is user-friendly and accessible, catering to both technical and non-technical users.

💬 Discussion Feed

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