🤖 AI Analysis

Final verdict: SAFE

The package exhibits low risks across all critical areas such as network and shell execution, with no signs of obfuscation or credential harvesting. While there are minor concerns about its maintenance status, these do not rise to the level of indicating malicious activity or a supply-chain attack.

Low network and shell risk
No obfuscation or credential harvesting
Potential low maintenance effort

Per-check LLM notes

Network: No network calls suggest normal behavior for a visualization library.
Shell: No shell executions indicate there is no immediate risk of executing system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows some signs of low maintenance effort, but there's no clear evidence of malicious intent.

🔬 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: riverbankcomputing.com

✓ 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 "Riverbank Computing Limited" 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 PyQtDataVisualization-Qt5

Create a real-time financial dashboard application using Python and the PyQtDataVisualization-Qt5 library. This application will fetch live stock market data from a public API and display it visually through various charts and graphs. The goal is to provide users with an interactive way to monitor and analyze their investments in real-time.

Steps:
1. Set up your development environment with Python, PyQtDataVisualization-Qt5, and any necessary libraries for web requests.
2. Design the user interface with a main window that includes widgets for selecting stocks, viewing charts, and displaying real-time data.
3. Implement functionality to fetch live stock prices and other relevant financial metrics from a public API such as IEX Cloud or Alpha Vantage.
4. Use PyQtDataVisualization-Qt5 to create dynamic visualizations like line charts, candlestick charts, and volume histograms to represent the stock price movements over time.
5. Add features like tooltips on hover to show detailed information about specific data points.
6. Include a feature to save chart images as PNG files for future reference.
7. Ensure the application updates the displayed data every minute to reflect the latest available information.
8. Test the application thoroughly to ensure all features work correctly and the UI remains responsive during real-time updates.

Suggested Features:
- Allow users to input multiple stock symbols and view them simultaneously.
- Implement a settings panel where users can customize chart colors and styles.
- Provide historical data comparison features to overlay different time periods on the same chart.
- Include basic technical analysis indicators such as moving averages or relative strength index (RSI).
- Offer alerts for significant changes in stock prices or trading volumes.

How PyQtDataVisualization-Qt5 is Utilized:
- The PyQtDataVisualization-Qt5 package will be primarily used to create the graphical representations of the stock data. Specifically, you'll use its charting capabilities to plot line graphs showing the evolution of stock prices, candlestick charts to illustrate opening, closing, high, and low prices over a period, and volume histograms to represent the trading volume associated with each price point. Additionally, you'll leverage PyQtDataVisualization-Qt5’s interactivity features to allow users to interact with these charts directly within the application.