adctoolbox

v0.8.3 suspicious
6.0
Medium Risk

ADC Testing and Analysis Toolkit

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal direct risks but exhibits signs of low maintainer activity and poor metadata quality, which raises concerns about its overall trustworthiness.

  • Low network and shell execution risks
  • Poor metadata quality and low maintainer activity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
  • Shell: No shell executions detected, reducing the likelihood of executing arbitrary commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, which may indicate a lack of transparency and could be a red flag.

🔬 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

No author email provided

✓ 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 adctoolbox 
Create a mini-application called 'ADCAnalyzer' using Python's 'adctoolbox' package. This application will serve as a comprehensive tool for testing and analyzing Analog-to-Digital Converters (ADCs). Here are the detailed steps and features you need to implement:

1. **Setup Environment**: Begin by setting up your development environment. Install Python and ensure 'adctoolbox' is installed via pip.

2. **User Interface**: Design a simple yet intuitive command-line interface (CLI) where users can input their ADC specifications such as resolution, sample rate, and type of signal (e.g., sine wave, square wave).

3. **Signal Generation**: Utilize 'adctoolbox' to generate test signals based on user inputs. Ensure that the signals cover various scenarios such as full-scale, mid-scale, and low-level signals.

4. **ADC Simulation**: Simulate the ADC conversion process using 'adctoolbox'. Allow users to specify the noise level and other parameters affecting the conversion accuracy.

5. **Data Collection**: Collect the digital output data from the simulated ADC conversions. Store these outputs for further analysis.

6. **Analysis Tools**: Implement analysis tools within 'adctoolbox' to evaluate the performance of the ADC. Calculate metrics like Signal-to-Noise Ratio (SNR), Effective Number of Bits (ENOB), and Total Harmonic Distortion (THD).

7. **Visualization**: Use matplotlib or any preferred library to visualize the test signals, ADC responses, and analysis results. Provide plots that clearly show the quality of the ADC conversion.

8. **Report Generation**: Automatically generate a report summarizing all the tests performed, including graphs and key performance indicators (KPIs). Save this report in PDF format.

9. **Customization Options**: Offer customization options to adjust settings for different types of ADCs or specific testing requirements.

10. **Documentation**: Write clear documentation explaining how to use each feature of the ADCAnalyzer application, along with examples and explanations of the underlying principles.

Ensure the application is robust, well-documented, and easy to use, providing valuable insights into ADC performance through practical testing and analysis.

💬 Discussion Feed

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