array-api-extra

v0.10.3 safe
3.0
Low Risk

Extra array functions built on top of the array API standard.

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network calls, shell execution, obfuscation, and credential harvesting. However, there are minor concerns regarding metadata quality.

  • Missing author information
  • Single package on PyPI
Per-check LLM notes
  • Network: No network calls detected, which is normal for a utility library.
  • Shell: No shell execution detected, which aligns with the expected behavior for a non-system administration Python package.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The package shows some red flags such as missing author information and a single package on PyPI, but no typosquatting or suspicious HTTPS links were found.

📦 Package Quality Overall: Medium (7.0/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 10 test file(s) detected (e.g. test_run_deps.py)
◈ Medium Documentation 7.0

Some documentation present

  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (18904 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 386 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in data-apis/array-api-extra
  • Active community — 5 or more distinct contributors

🔬 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: gmail.com>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.vanderplas.com/
  • Non-HTTPS external link: http://steppi.github.io
✓ Git Repository History

Repository data-apis/array-api-extra 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 array-api-extra 
Create a mini-app called 'ArrayStats' that leverages the Python package 'array-api-extra' to perform advanced statistical analysis on multi-dimensional arrays. This app should be designed to help users understand complex data sets through visualizations and statistical metrics. Here are the steps and features you need to implement:

1. **Setup**: Begin by setting up a Python environment and installing necessary packages including 'numpy', 'matplotlib', and 'array-api-extra'.
2. **Data Input**: Allow users to input their data as a multi-dimensional array. This could be done via a file upload (CSV, Excel) or manual entry.
3. **Statistical Analysis**: Utilize 'array-api-extra' to calculate various statistical measures such as mean, median, mode, standard deviation, variance, etc., across different dimensions of the array. Ensure that these calculations are optimized for performance and accuracy.
4. **Visualization**: Implement visualization tools using 'matplotlib' to display histograms, scatter plots, box plots, etc., based on the statistical data provided. These visualizations should help in understanding the distribution and correlation within the data.
5. **Interactive Features**: Add interactive elements like sliders or dropdowns to allow users to filter and analyze specific subsets of their data dynamically.
6. **Report Generation**: Include functionality to generate a report summarizing key statistics and visualizations. This report should be exportable as a PDF or HTML document.
7. **Error Handling & Validation**: Ensure robust error handling and data validation to prevent crashes and provide meaningful feedback when invalid data is entered.

In this project, 'array-api-extra' will play a crucial role in enhancing the computational efficiency and statistical capabilities of the app. Its functions should be showcased through efficient and accurate computation of statistical measures on large datasets.

💬 Discussion Feed

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