anemoi-transform

v0.4.0 safe
4.0
Medium Risk

A package to hold various data transformation functions to support training of ML models on ECMWF data.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with no network or shell risks detected. While there are some indications of potential code obfuscation and metadata issues, these do not strongly suggest a supply-chain attack.

  • Obfuscation risk observed
  • Incomplete author information and non-HTTPS license link
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 execute system commands.
  • Obfuscation: The code patterns suggest an attempt to handle generic types from the typing module, which could be part of normal type handling but might also indicate obfuscation techniques.
  • Credentials: No clear signs of credential harvesting were found in the provided code snippets.
  • Metadata: The author's information is incomplete and the license link is non-HTTPS, which raises some concerns but does not strongly indicate malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://anemoi-transform.readthedocs.io/
  • 2 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (1913 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 233 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

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

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • __origin__", None) is getattr(__import__("typing"), "Optional", None): args = getattr(annotation,
  • __origin__", None) is getattr(__import__("typing"), "Union", None): # If Union, check for NoneType
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository ecmwf/anemoi-transform 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-transform 
Create a weather forecasting mini-app using Python that leverages the 'anemoi-transform' package for preprocessing ECMWF (European Centre for Medium-Range Weather Forecasts) data. Your goal is to develop a user-friendly tool that allows users to input specific geographical coordinates and dates, then outputs a forecast based on historical weather patterns. Here's a step-by-step guide on how to build this app:

1. **Setup Environment**: Start by setting up your Python environment. Ensure you have installed the necessary packages including 'anemoi-transform', 'pandas', 'numpy', and any other dependencies required.
2. **Data Acquisition**: Integrate functionality within the app to fetch historical weather data from an available API or dataset source. This data will include temperature, precipitation, wind speed, and direction for the specified location and time period.
3. **Preprocessing with anemoi-transform**: Utilize the 'anemoi-transform' package to clean and transform the raw weather data into a format suitable for machine learning model training. Focus on applying relevant transformations such as normalization, feature scaling, and handling missing values.
4. **Model Integration**: Although the primary focus is on data preprocessing, consider integrating a simple predictive model using scikit-learn or another library of your choice. This model will use the preprocessed data to make basic forecasts.
5. **User Interface**: Develop a simple command-line interface (CLI) or a graphical user interface (GUI) using libraries like PyQt or Tkinter to allow users to input their desired location and date range. Display the processed data and predictions in an understandable format.
6. **Visualization**: Implement basic visualization capabilities to graphically represent the weather data and forecasts. Use matplotlib or seaborn for plotting.
7. **Documentation and Testing**: Write comprehensive documentation explaining each step of the process and how the app works. Conduct thorough testing to ensure accuracy and reliability of the data processing and prediction functionalities.

Suggested Features:
- Interactive date picker for selecting start and end dates of interest.
- Geographical map integration to visually highlight the selected location.
- Real-time updates or alerts for significant weather changes.
- Option to save and export the processed data and forecasts.

By following these steps, you'll create a valuable tool for anyone interested in understanding and predicting weather conditions based on historical data.

💬 Discussion Feed

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