FracDimPy

v0.1.4 safe
1.0
Low Risk

A comprehensive Python package for fractal dimension calculation and multifractal analysis

🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit any signs of malicious behavior or supply-chain attacks. It has no network calls, shell executions, obfuscations, or credential risks.

  • No network calls detected.
  • No shell execution patterns detected.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command invocations.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.

🔬 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 score 3.0

Suspicious email domain flags: Very short email domain: qq.com>

  • Very short email domain: qq.com>
✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Kecoya/FracDimPy 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 FracDimPy 
Develop a mini-application named 'FractalExplorer' that leverages the 'FracDimPy' Python package to analyze and visualize fractal dimensions of various datasets. This application should allow users to upload their own data or select from predefined datasets for analysis. The core functionalities of 'FractalExplorer' include calculating the fractal dimension of the dataset using different methods supported by 'FracDimPy', performing multifractal analysis, and visualizing the results through interactive plots. Additionally, 'FractalExplorer' should provide options to customize the parameters of the fractal dimension calculations and multifractal analysis, such as changing the box-counting method, selecting the range of scales for analysis, and adjusting the visualization settings.

Here are the detailed steps and features for 'FractalExplorer':
1. **Data Input**: Allow users to upload CSV files containing time series data or spatial coordinates, or choose from a set of predefined datasets included in the application.
2. **Fractal Dimension Calculation**: Implement functionality to calculate the fractal dimension using at least two different methods provided by 'FracDimPy', such as the box-counting method and the correlation dimension method. Display the calculated fractal dimension on the screen along with a confidence interval if possible.
3. **Multifractal Analysis**: Provide an option to perform multifractal analysis on the selected dataset using 'FracDimPy'. Show the multifractal spectrum and other relevant statistics.
4. **Visualization**: Create interactive plots that allow users to explore the fractal properties of their data visually. Include options to zoom, pan, and adjust plot parameters.
5. **Parameter Customization**: Enable users to customize the parameters of the fractal dimension calculations and multifractal analysis, including the scale range, number of iterations, and other relevant settings.
6. **Results Export**: Allow users to export the calculated fractal dimensions, multifractal spectra, and plots as PDFs or high-resolution images.
7. **User Interface**: Design a clean and intuitive user interface using a web framework like Flask or Dash for Python, ensuring easy navigation and accessibility for non-technical users.
8. **Documentation**: Provide detailed documentation on how to use 'FractalExplorer', including examples of input data formats, parameter settings, and interpretation of results.

By utilizing 'FracDimPy', 'FractalExplorer' aims to make advanced fractal analysis accessible and understandable to a broader audience, enabling users to gain insights into the complexity and self-similarity of their data.