aopera

v0.7.0 suspicious
5.0
Medium Risk

Simulation of AO residuals through phase PSD budget

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package contains a high risk due to the presence of eval(), which can be exploited for code injection. However, other risks are relatively low, and there's no direct evidence of malicious activities or a supply-chain attack.

  • High obfuscation risk due to eval()
  • Low maintainer activity
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communication.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: The use of eval() is risky and could be used for code injection, indicating potential malicious intent.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The package shows signs of low maintainer activity and effort, but there are no clear indicators of malicious intent.

📦 Package Quality Overall: Low (3.8/10)

◈ 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 (1324 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

  • 16 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 score 10.0

Found 6 obfuscation pattern(s)

  • try: return np.array(eval(x)) except: return np.array(x) def make_boo
  • g['atmosphere'] dm_act = eval(cfg['DM']['NumberActuators']) if len(dm_act)>1: #
  • in_HO'])} zenith = eval(cfg['sources_science']['Zenith']) # force list if
  • ) src_sci_wvl = eval(cfg['sources_science']['Wavelength']) if not isinstance
  • ) wfs_nb_lenslet = eval(cfg_wfs['NumberLenslets']) if len(wfs_nb_lenslet) > 1:
  • 1.0} nb_phot_wfs = eval(cfg_wfs['NumberPhotons'])[0] # nb photon/subap/frame if
✓ 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: lam.fr>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aopera 
Create a mini-application named 'AOResidualSimulator' using the Python package 'aopera'. This application will simulate Adaptive Optics (AO) system residuals based on a given Phase Power Spectral Density (PSD) budget. The goal is to provide astronomers and engineers with a tool to understand and predict AO performance under various atmospheric conditions.

Step-by-Step Instructions:
1. Setup: Begin by installing the required packages including 'aopera'. Ensure your environment is set up properly to run Python scripts.
2. User Interface: Develop a simple command-line interface (CLI) where users can input their desired atmospheric conditions and AO system parameters. The CLI should accept inputs such as seeing conditions, wind speed, and AO correction efficiency.
3. Data Input: Allow users to upload or manually enter a PSD budget file which specifies the expected phase noise characteristics across different spatial frequencies. This file will be crucial for simulating realistic AO residuals.
4. Simulation: Utilize 'aopera' to simulate AO residuals based on the user-provided data. The simulation should output both numerical data and graphical representations of the residuals.
5. Output: Present the results in a clear and understandable format. Include graphs showing the simulated residuals over time and space, alongside numerical summaries like the Root Mean Square (RMS) error.
6. Documentation: Provide comprehensive documentation explaining how to use the application, what each parameter means, and how the simulations are performed.
7. Testing: Test the application with various PSD budgets and atmospheric conditions to ensure accuracy and reliability.
8. Optimization: After testing, optimize the code for performance and readability.

Suggested Features:
- Interactive mode allowing real-time adjustments to parameters and immediate feedback on simulations.
- Support for multiple PSD budgets, enabling comparison between different AO systems or scenarios.
- Export functionality for saving simulation results in common scientific formats like CSV or HDF5.
- Detailed error messages and validation checks to prevent incorrect usage of the application.

How 'aopera' is Utilized: 'aopera' will be the core library used for the simulation process. Users will input their PSD budget into the application, which will then pass these parameters to 'aopera' functions to generate AO residuals. The application will leverage 'aopera's advanced algorithms to accurately model the impact of atmospheric turbulence on AO performance.

💬 Discussion Feed

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