anemoi-datasets

v0.5.37 suspicious
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its execution of shell commands and incomplete maintainer metadata, suggesting potential misuse or lack of experience. However, there are no clear signs of malicious intent or obfuscation.

  • High shell risk
  • Incomplete maintainer metadata
Per-check LLM notes
  • Network: The use of network calls to fetch data is common and expected in datasets packages, but the specific implementation should be reviewed for potential misuse.
  • Shell: Executing shell commands can pose significant risks if not properly sanitized or intended for legitimate purposes, especially since it allows external command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has an incomplete profile and a single package, which could indicate a less experienced or potentially suspicious actor.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://anemoi-datasets.readthedocs.io/
  • 7 documentation file(s) (e.g. xarray-kerchunk.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

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

Active multi-contributor project

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

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • import requests r = requests.get(self.url + "/" + key, timeout=10) if r.status_code
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • ecp {path} {local_name}") subprocess.check_call(["ecp", path, local_name]) return local_name # (C) Copy
✓ 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-datasets 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-datasets 
Create a weather prediction mini-app using the 'anemoi-datasets' package, which specializes in handling ECMWF (European Centre for Medium-Range Weather Forecasts) data for machine learning tasks. Your goal is to develop a tool that not only fetches and processes historical weather data but also trains a simple model to predict future weather conditions based on past patterns. Here’s a step-by-step guide to building this app:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have the latest version of 'anemoi-datasets' installed along with other necessary libraries such as numpy, pandas, scikit-learn, and matplotlib for visualization.

2. **Data Fetching**: Use 'anemoi-datasets' to fetch historical weather data from the ECMWF database. Explore different datasets available within the package and select one that suits your project's scope, such as temperature records over the last decade.

3. **Data Preprocessing**: Once you have fetched the data, preprocess it using 'anemoi-datasets'. This involves cleaning the data, handling missing values, and possibly normalizing the dataset for better performance when training models.

4. **Exploratory Data Analysis (EDA)**: Conduct EDA using matplotlib or seaborn to visualize trends and patterns within the dataset. This step is crucial for understanding the underlying dynamics of the weather data and for choosing appropriate features for your model.

5. **Model Training**: Train a simple machine learning model (e.g., linear regression, decision tree, or random forest) using the preprocessed data. Utilize 'anemoi-datasets' functions to split the dataset into training and testing sets efficiently.

6. **Model Evaluation**: Evaluate the trained model's performance using appropriate metrics like RMSE (Root Mean Squared Error) or MAE (Mean Absolute Error). Visualize the predictions against actual data points to understand the model's accuracy.

7. **Prediction Interface**: Develop a simple user interface (UI) where users can input parameters (such as date and location) and receive predicted weather conditions. This UI could be a basic web application using Flask or a command-line interface (CLI).

8. **Documentation and Deployment**: Document all steps taken during development, including setup instructions, usage examples, and troubleshooting tips. Consider deploying your application on a platform like Heroku or AWS if applicable.

Throughout the project, leverage 'anemoi-datasets' for its specialized functions related to ECMWF data handling and preprocessing. This will ensure that your app is robust and capable of dealing with complex weather data efficiently.