arcope

v0.1.11 suspicious
5.0
Medium Risk

Integration package bridging ARC crop parameter retrieval with SCOPE radiative transfer simulations

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation and has a low-activity repository with an inactive maintainer, raising concerns about its legitimacy and potential for supply-chain attacks.

  • Unusual import patterns indicating possible obfuscation
  • Low repository activity and inactive maintainer
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate risk of unauthorized system command execution.
  • Obfuscation: The code uses unusual import patterns which may indicate an attempt to bypass detection or analysis, suggesting potential malicious intent.
  • Credentials: No suspicious patterns related to credential harvesting were found.
  • Metadata: The repository's low activity and the maintainer's new/inactive account suggest potential risk.

📦 Package Quality Overall: Medium (6.4/10)

✦ High Test Suite 9.0

Test suite present — 21 test file(s) found

  • Test runner config found: conftest.py
  • 21 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://marcyin.github.io/ARCOPE/
  • Detailed PyPI description (11885 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 248 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 41 commits in MarcYin/ARCOPE
  • Single author but highly active (41 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • s string.""" try: __import__(module_name) except Exception as exc: return f"missing:{exc}
  • e__ = lambda *args, **kwargs: __import__("torch") except AttributeError: pass return
✓ 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: ucl.ac.uk>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 arcope 
Create a Python-based mini-application that integrates agricultural data analysis with atmospheric simulation. This tool will utilize the 'arcope' package to bridge crop parameter retrieval from Agricultural Remote Sensing Crop Parameter Retrieval Environment (ARC) with radiative transfer simulations provided by Spectral Characterization and Observation of Plant Ecosystems (SCOPE). Your goal is to develop a user-friendly application that allows researchers and farmers to input specific crop parameters and environmental conditions, then receive detailed atmospheric simulation results and potential impact assessments on crop health and yield.

### Core Features:
1. **User Interface**: Develop a simple GUI or command-line interface where users can input crop type, location, and environmental factors such as soil moisture, temperature, and solar radiation.
2. **Data Processing**: Utilize the 'arcope' package to process these inputs, retrieving relevant crop parameters from ARC and simulating atmospheric conditions using SCOPE's radiative transfer models.
3. **Result Visualization**: Display the simulation results graphically, showing how different atmospheric conditions might affect crop growth and health over time.
4. **Report Generation**: Automatically generate a report summarizing the simulation outcomes, including potential impacts on crop yield and suggestions for mitigation strategies.
5. **Integration with External Data Sources**: Optionally, allow the application to fetch real-time or historical weather data from external sources to enhance the accuracy of simulations.

### Implementation Steps:
1. **Setup Environment**: Install necessary packages including 'arcope', 'matplotlib' for visualization, and any other required libraries.
2. **Design User Interface**: Choose between a graphical or command-line interface based on target users and their preferences.
3. **Implement Data Input Handling**: Code functionality for users to input specific details about their crops and environment.
4. **Process Inputs with 'arcope'**: Use 'arcope' to process the input data, retrieving necessary crop parameters and setting up the SCOPE simulation scenarios.
5. **Run Simulations**: Execute the radiative transfer simulations using the processed data.
6. **Visualize Results**: Create visual representations of the simulation outcomes, highlighting key variables like photosynthesis rates, water stress levels, and growth stages.
7. **Generate Reports**: Automate the creation of detailed reports based on the simulation outputs, providing actionable insights for users.
8. **Test and Refine**: Conduct thorough testing with various datasets and refine the application based on feedback.
9. **Documentation**: Write comprehensive documentation explaining how to use the application, its features, and limitations.

By following these steps and utilizing the 'arcope' package effectively, your mini-application will become a valuable tool for both academic research and practical agricultural applications.

💬 Discussion Feed

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