atmolidarpy

v0.2.0 suspicious
5.0
Medium Risk

Standalone atmospheric lidar data processing package

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential signs of legitimate functionality but has notable risks such as executing external programs and incomplete metadata. These factors raise suspicion without clear evidence of malicious intent.

  • Executing external programs using os.system
  • Incomplete maintainer's author information
Per-check LLM notes
  • Network: The network calls to an API endpoint and downloading files seem related to fetching model data which is expected if the package deals with atmospheric or environmental data processing.
  • Shell: Executing external programs using os.system can pose a risk as it allows arbitrary code execution. This should be reviewed carefully to ensure there's no unintended or malicious behavior.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer's author name is missing or very short, and they appear to be new or inactive which raises some concerns.

📦 Package Quality Overall: Medium (5.6/10)

◈ 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://jabravoaranda.github.io/lidarpy/
  • Detailed PyPI description (4221 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 179 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 70 commits in jabravoaranda/lidarpy
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • ime) response_filename = requests.get( f"{API_URL}/model-files", params={**base_qu
  • r the given day") file = requests.get(response_filename[0]["downloadUrl"]) nc = Dataset("ecmwf
  • hour response_filename = requests.get( f"{API_URL}/model-files", params={**base_qu
  • and pressure data file = requests.get(response_filename[0]["downloadUrl"]) nc = Dataset(output
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • nts=True, exist_ok=True) os.system(f"python {GHK_program} {ini_path} {output_dir.absolute()}")
✓ 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 jabravoaranda/lidarpy 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 atmolidarpy 
Create a fully-functional mini-application called 'LidarDataAnalyzer' using the Python package 'atmolidarpy'. This application will serve as a tool for researchers and atmospheric scientists to process and analyze atmospheric lidar data. The application should include the following core functionalities:

1. **Data Importation**: Allow users to upload atmospheric lidar data files (e.g., .csv, .txt). Ensure the application supports common file formats used in atmospheric science.
2. **Data Visualization**: Implement a feature that visualizes the imported data in real-time using matplotlib or a similar library. This visualization should include both raw data plots and processed data plots.
3. **Data Processing**: Utilize 'atmolidarpy' to perform essential atmospheric lidar data processing tasks such as background subtraction, signal-to-noise ratio calculation, and atmospheric correction.
4. **Analysis Tools**: Provide tools for analyzing the processed data, including calculating the mean, median, standard deviation of the data points, and identifying anomalies or outliers.
5. **Report Generation**: Enable users to generate detailed reports summarizing their analysis. These reports should include visualizations, statistical summaries, and any significant findings from the analysis.
6. **User Interface**: Develop a simple yet intuitive graphical user interface (GUI) using Tkinter or PyQt to make the application accessible to non-technical users.
7. **Documentation**: Write comprehensive documentation explaining how to use each feature of the application, along with examples of typical use cases in atmospheric research.

The application should be designed to be modular, allowing for easy updates and expansions in the future. Additionally, ensure that the application is well-documented and includes error handling to manage unexpected inputs or issues.

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