atmoflux

v1.1.0 suspicious
4.0
Medium Risk

Atmospheric and energy flux analysis library by Telluris Labs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks across most categories but has incomplete metadata with an absent maintainer's name and possible inactivity, raising suspicion about its legitimacy.

  • Incomplete metadata
  • Possibly inactive maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, indicating no immediate risk of command injection or similar attacks.
  • 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 and they appear to be new or inactive, which raises some concern but not enough to conclude malice.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 14 test file(s) found

  • 14 test file(s) detected (e.g. test_aerosols.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5208 chars)
○ 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

  • 85 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 98 commits in Telluris-Labs/atmoflux
  • Two distinct contributors found

🔬 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: tellurislabs.io>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Telluris-Labs/atmoflux 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 atmoflux 
Develop a climate monitoring application called 'ClimateWatcher' using the Python package 'atmoflux'. This application will serve as a tool for researchers and environmentalists to analyze atmospheric conditions and energy fluxes over time. The goal is to create a user-friendly interface where users can input geographical coordinates and select a date range to retrieve historical data on temperature, humidity, wind speed, solar radiation, and other relevant atmospheric parameters. The application should then process this data using 'atmoflux' to calculate key metrics such as net radiation, latent heat flux, sensible heat flux, and total energy balance at the surface. Additionally, it should provide visualizations of these metrics through graphs and charts. Here are the steps to follow:

1. **Setup Environment**: Ensure you have Python installed along with necessary packages like 'atmoflux', pandas, matplotlib, and any others needed for data manipulation and visualization.
2. **Data Input Interface**: Create a simple web-based form where users can enter location details and specify a date range. This can be done using Flask or Django for backend processing.
3. **Data Retrieval**: Integrate functionality to fetch historical weather data from APIs like OpenWeatherMap or similar services based on user inputs.
4. **Processing with 'atmoflux'**: Use 'atmoflux' to analyze the retrieved data. Focus on calculating the aforementioned atmospheric fluxes and balances.
5. **Visualization**: Implement graphical representations of the processed data. For example, plot time series graphs showing changes in temperature, humidity, and energy fluxes over the selected period.
6. **Report Generation**: Allow users to generate comprehensive reports summarizing their findings, including textual descriptions alongside visual elements.
7. **Testing & Deployment**: Thoroughly test the application for accuracy and usability. Once satisfied, deploy it on a cloud platform like Heroku or AWS for public access.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!