InsarViz

v3.2.1 suspicious
4.0
Medium Risk

Python package for analysis of (InSar) data cubes

🤖 AI Analysis

Final verdict: SUSPICIOUS

While the package shows minimal signs of malicious activity, the incomplete author information and lack of a GitHub repository for such a specialized tool raise some concerns about its origin and maintainability.

  • Incomplete author information
  • No associated GitHub repository
Per-check LLM notes
  • Network: The observed network calls seem to be part of normal functionality, fetching metadata and images.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The package has no associated GitHub repository and the author information is incomplete, which raises some concerns but does not strongly indicate malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • yield 0.0 with urllib.request.urlopen(url) as preview_file: image = Image.
  • try: with urllib.request.urlopen(self.meta_url) as meta_file: for
  • bservableList() with urllib.request.urlopen(f"{self.base_url}/backup_product.json") as f:
  • _baselines = { } with urllib.request.urlopen(f"{self.base_url}/baseline.rsc") as f: f
  • self._tile_url req = urllib.request.Request(tile_url, headers = { "User-Agent": "Ins
  • try: image = urllib.request.urlopen(req).read() with tile_file.open(mode="wb
✓ 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: univ-grenoble-alpes.fr>

✓ 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 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 InsarViz 
Create a fully-functional mini-application using the 'InsarViz' Python package that enables users to analyze InSAR data cubes. The app should allow users to upload their own InSAR data cube files (e.g., HDF5, NetCDF), visualize the data in various ways, perform basic statistical analyses, and export results. Here’s a detailed breakdown of the steps and features:

1. **User Interface**: Design a clean, user-friendly interface that allows users to easily navigate through different functionalities.
2. **Data Upload**: Implement functionality to let users upload their InSAR data cubes. Ensure that the application supports common file formats like HDF5 and NetCDF.
3. **Data Visualization**: Utilize InsarViz to display the uploaded data in multiple views such as 2D maps, 3D surfaces, and time-series plots. Each visualization should be interactive, allowing users to zoom, pan, and adjust color scales.
4. **Statistical Analysis**: Provide tools for performing basic statistical analyses on the data, including calculating mean, median, standard deviation, and identifying outliers.
5. **Export Results**: Enable users to save their visualizations and analysis results in various formats (e.g., PNG, PDF, CSV).
6. **Documentation**: Include comprehensive documentation that explains how to use each feature and provides examples of InSAR data analysis workflows.

This project will showcase the capabilities of InsarViz in real-world applications and provide valuable insights into InSAR data interpretation.