EnFROSP

v0.3.2 suspicious
5.0
Medium Risk

EnMAP Fast Retrieval Of Snow Properties

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risk in terms of network calls, shell execution, and obfuscation but has some metadata red flags including missing maintainer information and no linked Git repository, raising concerns about its origin and maintenance.

  • Missing maintainer information
  • No linked Git repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package has some red flags such as missing maintainer information and no linked Git repository, which could indicate potential issues.

🔬 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: gfz.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ 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)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with EnFROSP 
Your task is to develop a Python-based mini-application that leverages the 'EnFROSP' package to analyze and visualize snow properties using data from the EnMAP satellite. This application will be useful for researchers, environmental scientists, and meteorologists who need quick insights into snow conditions over large geographic areas.

### Application Overview
The application should provide a user-friendly interface where users can input geographical coordinates (latitude and longitude) and select a date range. The application will then retrieve relevant EnMAP data through the EnFROSP package, process it to extract key snow property metrics (such as snow water equivalent, snow depth, and albedo), and display these metrics both numerically and visually on a map.

### Core Features
1. **Data Input Interface**: Users should be able to enter start and end dates, along with latitude and longitude coordinates.
2. **Data Retrieval**: Utilize EnFROSP to fetch EnMAP satellite data corresponding to the provided date range and location.
3. **Snow Property Analysis**: Process the retrieved data to calculate and display key snow properties like snow water equivalent (SWE), snow depth, and albedo.
4. **Visualization**: Provide visual representations of the analyzed data, including maps showing the spatial distribution of snow properties.
5. **Export Options**: Allow users to export the results in CSV format for further analysis.
6. **Error Handling**: Implement robust error handling to manage issues such as invalid input, missing data, and processing errors.
7. **Documentation**: Include clear documentation explaining how to use the application and how each feature works.

### Implementation Steps
1. **Setup Environment**: Ensure you have Python installed along with necessary libraries such as EnFROSP, pandas, matplotlib, and geopandas.
2. **User Interface Design**: Develop a simple command-line interface (CLI) or a basic GUI using Tkinter for ease of use.
3. **Data Retrieval Logic**: Write functions to interact with EnFROSP for fetching EnMAP data based on user inputs.
4. **Analysis Functions**: Create functions to process the fetched data and compute the desired snow properties.
5. **Visualization Module**: Integrate plotting capabilities to show the data on maps and other visual formats.
6. **Export Functionality**: Implement functionality to save the results to a CSV file.
7. **Testing and Validation**: Test your application thoroughly with different sets of input data to ensure accuracy and reliability.
8. **Final Documentation**: Prepare comprehensive documentation detailing installation, usage, and troubleshooting steps.

By completing this project, you will not only gain practical experience with Python and satellite data analysis but also contribute a valuable tool to the scientific community interested in studying snow properties.