argovisHelpers

v0.0.37 safe
4.0
Medium Risk

Helper functions to consume and parse information from University of Colorado's Argovis API.

🤖 AI Analysis

Final verdict: SAFE

The package appears generally safe with no immediate signs of malicious activity. However, there are some areas that warrant further attention, such as the legitimacy of network endpoints and the low level of package metadata.

  • Low shell, obfuscation, and credential risks
  • Moderate network and metadata risks
Per-check LLM notes
  • Network: The network calls appear to be fetching version information and possibly downloading additional resources, which is not inherently suspicious but should be reviewed for the legitimacy of the endpoints.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author has only one package and lacks PyPI classifiers, indicating potential low activity or effort.

📦 Package Quality Overall: Low (3.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. tests.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (1090 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ 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 score 3.0

Found 2 network call pattern(s)

  • I try: response = requests.get(endpoint) response.raise_for_status() # Raise an ex
  • : vsn = '-1' dl = requests.get(apiroot.rstrip('/') + '/' + route.lstrip('/'), params = opti
✓ 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

No author email provided

✓ 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 "Katie Mills" 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 argovisHelpers 
Create a Python-based mini-application that leverages the 'argovisHelpers' package to visualize and analyze oceanographic data from the University of Colorado's Argovis API. This application should allow users to interactively explore various parameters such as temperature, salinity, and depth at different locations and times.

Step 1: Setup your development environment by installing Python and the 'argovisHelpers' package.

Step 2: Design a user-friendly command-line interface where users can input specific queries about the data they want to retrieve from the Argovis API. Users should be able to specify parameters like location coordinates, time periods, and the types of data they are interested in (e.g., temperature, salinity).

Step 3: Utilize the 'argovisHelpers' package to handle the API calls, parsing of returned JSON data, and conversion into a format suitable for analysis and visualization.

Step 4: Implement basic data analysis functionalities within the application, allowing users to calculate statistics such as mean, median, and standard deviation for the selected data points.

Step 5: Integrate a simple visualization component that can display the retrieved data in graphical formats such as line charts or heatmaps based on user selection. Ensure that these visualizations are interactive, enabling users to zoom in/out, pan across the graph, and highlight specific data points.

Suggested Features:
- Support for multiple query types (point queries, region queries)
- Ability to save and export visualizations as image files
- Historical data comparison feature
- Basic anomaly detection for unexpected data patterns

Your application should demonstrate proficiency in utilizing the 'argovisHelpers' package's core functionalities while providing a robust, user-centric experience.

💬 Discussion Feed

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