PyFLASH-analysis

v0.1.4 suspicious
5.0
Medium Risk

ImageJ confocal microscopy data processing and analysis pipeline

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential misuse due to shell execution capabilities and lacks engagement in its repository, suggesting possible unreliability.

  • Shell execution detected
  • Low repository engagement and maintainer history
Per-check LLM notes
  • Network: No network calls detected, which is not necessarily suspicious.
  • Shell: Shell execution detected might be for legitimate purposes like running Streamlit apps, but requires further investigation to ensure it's not being misused.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
  • Metadata: The repository's low engagement and lack of maintainer history suggest potential unreliability.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • raise SystemExit( subprocess.call( [sys.executable, "-m", "streamlit", "run", app
✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 PyFLASH-analysis 
Develop a mini-application named 'MicroscopeMaster' that leverages the PyFLASH-analysis package to streamline image processing and analysis for confocal microscopy data. This application should serve as a user-friendly interface for researchers to upload their confocal microscopy images, apply various preprocessing techniques, perform quantitative analyses, and visualize results.

Step 1: Setup the Environment
- Ensure Python is installed on the system.
- Install PyFLASH-analysis using pip.
- Set up a virtual environment for the project.

Step 2: User Interface Design
- Create a simple GUI using Tkinter or PyQt5.
- Include options for file uploads, dropdown menus for selecting different analysis types, and buttons for initiating processes.

Step 3: Data Preprocessing
- Implement functions to load images from the uploaded files.
- Use PyFLASH-analysis to preprocess images, such as background subtraction, noise reduction, and contrast enhancement.

Step 4: Quantitative Analysis
- Utilize PyFLASH-analysis to calculate key metrics like intensity distribution, particle size, and density of features within the images.
- Provide options to customize parameters for these calculations based on user inputs.

Step 5: Visualization
- Integrate matplotlib or similar libraries to display processed images alongside raw ones.
- Visualize calculated metrics in graphs and charts for better interpretation.

Suggested Features:
- Save processed images and analysis results to disk.
- Allow users to compare multiple images side-by-side.
- Include documentation and examples for common use cases.
- Offer advanced settings for power users to tweak underlying algorithms.

How to Utilize PyFLASH-analysis:
- For loading and preprocessing images, use PyFLASH-analysis functions to handle image data efficiently.
- Apply PyFLASH-analysis tools for specific tasks such as segmentation, feature extraction, and statistical analysis.
- Leverage PyFLASH-analysis capabilities for generating reports and visualizations tailored to scientific research.