🤖 AI Analysis

Final verdict: SAFE

The package has minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks. The only notable concern is incomplete maintainer information.

Incomplete maintainer information
No network calls
No shell execution
No obfuscation patterns
No credential harvesting patterns

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, reducing risk of unauthorized command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer's author information is incomplete, suggesting a potential lack of transparency or new/inactive account status.

🔬 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository MartinPdeS/DeepPeak appears legitimate

⚠ 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 DeepPeak

Create a real-time peak detection and trace analysis application using the 'DeepPeak' Python package. This application will be particularly useful for scientists and engineers working with time-series data from experiments such as chromatography, spectroscopy, or any other field where precise peak detection is crucial.

Step 1: Set up the project environment. Ensure you have Python installed along with the necessary libraries including DeepPeak. Use virtual environments for better management of dependencies.

Step 2: Design a user-friendly interface. The application should allow users to upload their 1D signal data files (CSV or similar formats). Provide options for real-time data streaming if possible.

Step 3: Implement real-time peak detection using DeepPeak's capabilities. Allow users to adjust parameters such as noise threshold, peak width, and sensitivity to fine-tune the detection process according to their specific needs.

Step 4: Develop trace analysis features. Utilize DeepPeak to analyze the detected peaks, providing information such as peak height, area under the curve, and peak-to-peak distance. Offer visual representations like graphs and charts to help interpret the results.

Step 5: Integrate dilution-series tooling provided by DeepPeak. Enable users to analyze how peak characteristics change with varying concentrations, which is essential for calibration curves and similar analyses.

Suggested Features:
- Interactive sliders and input fields for adjusting peak detection parameters.
- Exportable reports summarizing peak analysis results.
- A feature to save and load analysis configurations for repeatable experiments.
- Real-time visualization of the signal and detected peaks as data streams in.

How to utilize DeepPeak:
- For peak detection, use DeepPeak's functions to identify significant peaks in the uploaded or streamed data.
- For trace analysis, apply DeepPeak's analytical tools to extract meaningful metrics from the detected peaks.
- For dilution-series analysis, leverage DeepPeak's specialized functionalities to understand how peaks behave across different concentration levels.