🤖 AI Analysis

Final verdict: SAFE

The package accrqa is assessed as safe with a low risk score. It does not engage in network activities or obfuscation, and there are no signs of credential harvesting. The primary concern is the metadata risk due to the maintainer's single package on PyPI.

No network calls
Expected shell execution during installation
No obfuscation or credential risks
Single package from maintainer increases metadata risk

Per-check LLM notes

Network: No network calls detected.
Shell: Shell execution appears to be related to building and installing the package using CMake, which is expected behavior for packages that require compilation.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has only one package on PyPI, which may indicate a new or less active account.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

print(cmake_args) subprocess.check_call( ["cmake", ext.sourcedir] + cmake_args, cwd=self
.build_temp ) subprocess.check_call( ["cmake", "--build", "."] + build_args, cwd=sel
.build_temp ) subprocess.check_call( ["cmake", "--install", "."] + ["--config", cfg]

✓ 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

Repository KAdamek/AccRQA appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Karel Adamek, Jan Novotny, Radim Panis, Marwan Norbert" 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 accrqa

Create a Python-based mini-application that leverages the 'accrqa' package to perform Recurrence Quantification Analysis (RQA) on time series data. This application will be designed to help researchers and analysts understand the dynamics of complex systems through visualizations and statistical measures derived from RQA. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including 'accrqa', 'numpy', 'pandas', and 'matplotlib'.
2. **Data Input**: Design a user-friendly interface for importing time series data either as CSV files or directly inputting arrays.
3. **Parameter Configuration**: Allow users to configure parameters such as embedding dimension, time delay, radius, and window size which are crucial for RQA calculations.
4. **RQA Calculation**: Utilize 'accrqa' to calculate RQA measures on the provided time series data based on user-defined parameters. Ensure to implement both fixed and variable recurrence threshold options.
5. **Visualization**: Implement functions to visualize the recurrence plot and other relevant plots like distribution of diagonal and vertical line lengths.
6. **Statistical Outputs**: Display key RQA metrics such as recurrence rate, determinism, entropy, laminarity, and trapping time.
7. **Saving Results**: Provide options for saving the results and visualizations in formats like PNG, PDF, or as a CSV file containing all calculated statistics.
8. **Documentation & Help**: Include comprehensive documentation and tooltips within the application to guide users through each step of the process.

This application aims to streamline the process of performing RQA, making it accessible for those without deep expertise in the underlying mathematics.