aind-labtracks-service-client

v0.3.3 safe
3.0
Low Risk

aind-labtracks-service

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories analyzed, with no indications of malicious activities. However, concerns about the maintainer's history and the absence of a git repository link slightly elevate the metadata risk.

  • No network calls detected.
  • No shell execution patterns.
  • No obfuscation or credential harvesting.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: Low risk due to lack of suspicious elements, but concerns about maintainer history and git repository link absence suggest potential low effort or inactive project.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • 9 test file(s) detected (e.g. test_default_api.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 37 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 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: openapitools.org

✓ 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 "OpenAPI Generator community" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aind-labtracks-service-client 
Create a Python-based mini-application that leverages the 'aind-labtracks-service-client' package to manage and visualize experimental data from laboratory tracks. This application will serve as a powerful tool for researchers to track, analyze, and visualize animal behavior studies efficiently.

**Application Features:**
1. **Data Retrieval:** Implement functionality to fetch experiment data from the lab tracks service using the 'aind-labtracks-service-client'. This includes metadata such as experiment ID, date, and subject details.
2. **Data Analysis:** Integrate basic statistical analysis tools within the app to process the retrieved data. This could include calculating average behavior metrics, identifying outliers, or performing trend analysis over time.
3. **Visualization:** Develop a user-friendly interface where users can visualize the analyzed data through graphs and charts. Consider including options like line plots, bar charts, and scatter plots for different types of data visualization.
4. **Customization Options:** Allow users to customize their data visualization preferences, such as selecting specific time ranges, choosing which metrics to display, and adjusting chart styles.
5. **Export Functionality:** Enable users to export visualized data into common file formats like CSV, Excel, or PDF for further analysis or record-keeping.

**Steps to Build the Application:**
1. Set up your development environment with Python installed along with the necessary libraries including 'aind-labtracks-service-client'.
2. Use the 'aind-labtracks-service-client' to authenticate and connect to the lab tracks service. Ensure you handle API keys securely.
3. Write functions to retrieve specific datasets based on user input or predefined criteria. Utilize pandas for efficient data handling and manipulation.
4. Implement statistical analysis methods using scipy or similar packages to process the retrieved data. Focus on providing meaningful insights relevant to behavioral studies.
5. Design the user interface using a library like PyQt or Streamlit, ensuring it is intuitive and easy to navigate. Incorporate interactive elements to allow real-time adjustments to visualizations.
6. Add customization options allowing users to tailor their experience according to their needs. For example, they should be able to select which metrics to display or adjust color schemes.
7. Finally, implement the export functionality allowing users to save their visualized data in various formats. Make sure to validate the output files before saving them.

This project aims to streamline the workflow of researchers dealing with large volumes of behavioral data, making it easier for them to focus on analysis rather than data management.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!