asymptotics

v0.2.0 safe
4.0
Medium Risk

A symbolic perturbation theory toolkit built on SymPy

🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit any significant risks such as network calls, shell executions, or credential harvesting. The metadata risk suggests some caution due to potential new or inactive maintainer activity, but overall, it appears safe.

  • No network calls or shell executions detected.
  • Metadata risk indicates potential new or inactive maintainer activity.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution detected, which is expected and indicates no immediate signs of malicious activity.
  • Obfuscation: The observed patterns appear to be related to code formatting and evaluation methods rather than malicious obfuscation.
  • Credentials: No evidence of credential harvesting or secret handling is present in the provided code snippets.
  • Metadata: The package shows signs of potential new or inactive maintainer activity with limited repository engagement.

📦 Package Quality Overall: Low (4.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (22204 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

  • 69 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 19 commits in saadgroup/asymptotics
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • der=3) >>> sol.show() >>> sol.eval(eps=0.1) # float >>> sol.compare_
  • >> import numpy as np >>> sol.eval(eps=0.1, at=np.linspace(0, 40, 500)) # ndarray >>> sol.comp
  • plain text in terminal. sol.eval(eps, at=None, params=None) Evaluate expansion as a NumPy
  • , filename=filename) def eval(self, eps, at=None, params=None): """ Evalua
  • -------- >>> x = sol.eval(eps=0.1) >>> x = sol.eval(eps=[0.1, 0.2, 0.3])
  • (eps=0.1) >>> x = sol.eval(eps=[0.1, 0.2, 0.3]) """ from asymptotics.ev
✓ 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: utah.edu>

✓ 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 asymptotics 
Develop a Python-based mini-application that leverages the 'asymptotics' package to analyze the asymptotic behavior of complex functions. This application will serve as a tool for mathematicians, physicists, and engineers to explore the limiting behavior of mathematical expressions as variables approach certain values, such as infinity or zero.

The application should include the following features:
1. User-friendly interface to input mathematical expressions.
2. Ability to specify the variable(s) of interest and the point at which the asymptotic analysis is performed (e.g., approaching infinity).
3. Computation of asymptotic expansions using the 'asymptotics' package, providing both the leading terms and higher-order approximations if requested.
4. Visualization of the original function and its asymptotic approximation on a graph, allowing users to visually compare the two.
5. Detailed report generation, including LaTeX-formatted mathematical expressions of the asymptotic expansions and relevant notes on the accuracy and limitations of the approximations.
6. Option to save results to a file for further analysis or documentation.

Utilize the 'asymptotics' package to handle the heavy lifting of symbolic computation and perturbation theory. Ensure that the application guides users through the process of selecting appropriate parameters for accurate asymptotic analysis, and provides meaningful feedback in case of errors or ambiguities in the input expressions.

💬 Discussion Feed

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