atmorad-py

v0.2.5.post1 suspicious
4.0
Medium Risk

A 3D monte carlo simulation of Atmospheric Radiative Transfer

πŸ€– AI Analysis

Final verdict: SUSPICIOUS

The package is mostly benign with low risks in most categories, but the detection of potential shell execution and missing metadata details raise some concerns.

  • Detection of shell execution
  • Missing author details and no linked GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is low risk.
  • Shell: Detection of shell execution suggests the package might run external commands, which could be benign but should be reviewed for potential misuse.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has some minor red flags, such as missing author details and no linked GitHub repository, but there's no strong evidence of malicious intent.

πŸ“¦ Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present β€” 4 test file(s) found

  • Test runner config found: conftest.py
  • 4 test file(s) detected (e.g. conftest.py)
β—ˆ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (17529 chars)
β—‹ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
β—ˆ Medium Type Annotations 5.0

Partial type annotation coverage

  • 109 type-annotated function signatures detected in source
β—‹ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked β€” contributor count unavailable

πŸ”¬ Heuristic Checks

βœ“ Outbound Network Calls

No suspicious network call patterns found

βœ“ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • s(example_path): result = subprocess.run( [sys.executable, str(example_path)], capture_output
βœ“ Credential Harvesting

No credential harvesting patterns detected

βœ“ Typosquatting

No typosquatting candidates detected

βœ“ Registered Email Domain

Email domain looks legitimate: kdabr.com>

βœ“ Suspicious Page Links

All external links appear legitimate

βœ“ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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)
βœ“ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

πŸ’‘ AI App Starter Prompt

Use this prompt to build a project with atmorad-py 
Your task is to develop a user-friendly desktop application that simulates atmospheric radiative transfer using the 'atmorad-py' Python package. This application will allow users to input various atmospheric conditions and observe the effects on radiative transfer through visualizations and numerical outputs. Here are the steps and features you need to implement:

1. **Setup**: Install necessary packages including 'atmorad-py', 'matplotlib' for plotting, and 'tkinter' for the GUI.
2. **User Interface**: Create a simple GUI where users can input parameters such as altitude, temperature, pressure, and humidity. Include sliders and text boxes for easy manipulation.
3. **Simulation Engine**: Utilize 'atmorad-py' to run Monte Carlo simulations based on the user's inputs. Ensure the simulation accurately reflects changes in the atmosphere.
4. **Visualization**: Display the results of the simulations through interactive plots showing the radiative fluxes and energy distribution. Allow users to toggle between different types of plots.
5. **Data Export**: Implement functionality to export simulation data and plots as CSV files and images respectively.
6. **Help Section**: Add a help section explaining the significance of each parameter and how they affect radiative transfer.
7. **Customization Options**: Offer advanced users the ability to customize certain aspects of the Monte Carlo simulation, like number of iterations and random seed.
8. **Real-World Examples**: Provide pre-defined scenarios representing different atmospheric conditions (e.g., clear sky, polluted air) for quick testing.

This project aims to make complex atmospheric science accessible and engaging for both educational and research purposes.

πŸ’¬ Discussion Feed

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