AI Analysis
Final verdict: SAFE
The package appears to be legitimate with low risks across multiple categories. While the metadata risk score is elevated due to the maintainer's inactivity and lack of community engagement, there is no evidence of malicious activity.
- Low network, shell, obfuscation, and credential risks.
- Elevated metadata risk due to new or inactive maintainer and lack of community engagement.
Per-check LLM notes
- Network: The observed network calls seem legitimate for fetching data from NOAA, indicating the package is likely intended for weather data analysis.
- 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 maintainer seems new or inactive, and the repository lacks community engagement.
Package Quality Overall: Low (3.6/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (17801 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
5.0
Partial type annotation coverage
124 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
5.0
Limited contributor diversity
1 unique contributor(s) across 100 commits in SDerrode/awesomepkfSingle author but highly active (100 commits)
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
SSTv5) from NOAA …") with urllib.request.urlopen(NINO_URL, timeout=30) as r: nino_text = r.reng SOI from NOAA …") with urllib.request.urlopen(SOI_URL, timeout=30) as r: soi_text = r.read
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
score 2.5
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Derrode Stéphane" 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 awesomepkf
Create a mini-application that utilizes the 'awesomepkf' Python package to demonstrate state estimation techniques using Pairwise Kalman Filters (PKF), Ensemble Pairwise Kalman Filters (EPKF), Unscented Pairwise Kalman Filters (UPKF), and Particle Pairwise Filters (PPF). Your application should include the following features: 1. User-friendly interface allowing input of system parameters such as state transition matrix, observation matrix, initial state estimates, and noise covariance matrices. 2. Ability to switch between different filter types (PKF, EPKF, UPKF, PPF) based on user selection. 3. Interactive visualizations showing the evolution of state estimates over time and comparing results from different filters. 4. A feature to simulate noisy measurements and apply the selected filter to estimate the true state. 5. Output of final state estimates and performance metrics such as mean squared error. Utilize the 'awesomepkf' package to handle the complex mathematical computations involved in each filtering method, ensuring accurate and efficient state estimation. Your goal is to create a tool that not only demonstrates the capabilities of these advanced filtering techniques but also serves as an educational resource for users interested in understanding the nuances of pairwise state estimation.
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