🤖 AI Analysis

Final verdict: SAFE

The package appears safe with minimal risks identified. The primary concern is minor code obfuscation, which does not strongly indicate malicious intent.

No network calls or shell executions detected.
Potential code obfuscation noted but inconclusive evidence of malintent.

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires network interaction for its functionality.
Shell: No shell executions detected, indicating no immediate risk of unauthorized system command execution.
Obfuscation: The '# type: ignore' comments and minor syntax errors suggest potential code obfuscation but could also be due to development or testing oversight.
Credentials: No clear patterns indicating credential harvesting were found.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

# type: ignore model.eval() # type: ignore return model @classmetho

✓ 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: openclimatefix.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with PVNet_summation

Your task is to create a mini-application that leverages the 'PVNet_summation' Python package to process and visualize photovoltaic (solar panel) network data. This application will be designed to help solar energy professionals and enthusiasts analyze the performance of different solar panel configurations under various environmental conditions.

Step 1: Data Input
- Users should be able to input or upload CSV files containing photovoltaic network data. Each row represents a different configuration of solar panels, with columns including details such as number of panels, orientation, tilt angle, and weather conditions (e.g., irradiance).

Step 2: Data Processing
- Utilize the 'PVNet_summation' package to calculate the total power output for each configuration based on the provided data. Ensure the calculations account for factors like shading, temperature effects, and panel efficiency.

Step 3: Visualization
- Implement a graphical user interface (GUI) using a library like PyQt or Tkinter to display the results. Visualize the data through graphs showing power output over time, comparisons between different configurations, and other relevant metrics.

Suggested Features:
- Include a feature to simulate different weather scenarios (cloudy, sunny, partially cloudy) and observe their impact on power output.
- Allow users to save the processed data and visualizations as PDF reports for further analysis.
- Provide tooltips and explanations within the GUI to help users understand the significance of each data point and calculation.

How to Use 'PVNet_summation':
- Import the necessary functions from the 'PVNet_summation' package to perform summation operations specific to photovoltaic networks. These operations could include calculating total power output, optimizing panel configurations, and more. Ensure your code comments clearly document where and how the package is being utilized.