AI Analysis
Final verdict: SAFE
The package appears to have legitimate use cases, with moderate risks from network and shell command usage that do not strongly suggest malicious intent.
- moderate network risk
- high shell risk but likely for legitimate purposes
- low obfuscation and credential risks
Per-check LLM notes
- Network: Network requests appear to be used for fetching external resources, which is common but should be verified.
- Shell: Use of shell commands like curl for external resource fetching could indicate legitimate functionality but also poses higher risk due to potential misuse.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author has only one package, which might indicate a new or less active account.
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
DGS10" try: req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})"Mozilla/5.0"}) with urllib.request.urlopen(req, timeout=timeout) as resp: raw = res
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 10.0
Found 6 shell execution pattern(s)
he): try: subprocess.run(["curl", "-s", "--max-time", str(timeout),size(cache) < 10_000: subprocess.run(["curl", "-sL", "--max-time", str(timeout),d, start, stop, step) subprocess.run(["curl", "-sL", "--max-time", "120", url,th.exists(cache_csv): subprocess.run(["curl", "-sL", "--max-time", str(timeout),cmd += [url, "-o", cache] subprocess.run(cmd, check=True) if not os.path.exists(cache) or os.path.getsize(wav) < 1000: subprocess.run(["ffmpeg", "-y", "-i", ogg, "-ac", "1",
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
Repository sulcantonin/FastLSQ appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Antonin Sulc" 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 FastLSQ
Create a Python-based mini-application that solves partial differential equations (PDEs) using the FastLSQ package. This application should serve as an educational tool for students and researchers interested in numerical methods for PDEs. The application will demonstrate the power of one-shot solving via Fourier features, which allows for rapid computation of solutions without iterative refinement. Additionally, it will showcase the use of exact analytical derivatives, rank-revealing solvers, and learnable anisotropic bandwidths, all of which contribute to the efficiency and accuracy of the solution process. The application should support both CPU and GPU computations, providing users with the flexibility to choose based on their hardware capabilities. ### Key Features: - **User Interface**: Develop a simple GUI where users can input the coefficients of the PDE they wish to solve, specify boundary conditions, and select computational settings such as precision and solver type. - **Solver Selection**: Allow users to choose between different solvers supported by FastLSQ, including options for CPU and GPU execution. - **Visualization**: Implement real-time visualization of the solution to the PDE, allowing users to see the effect of changing parameters. - **Documentation and Help**: Provide comprehensive documentation within the application that explains each feature and parameter, guiding users through the process of solving PDEs with FastLSQ. - **Example Problems**: Include several example problems that users can run to understand the application's capabilities and limitations. ### Utilizing FastLSQ: - **Setup**: Begin by installing the FastLSQ package and ensuring that the necessary dependencies for CPU/GPU computations are met. - **Input Handling**: Use FastLSQ's API to handle user inputs, converting them into the appropriate format required for solving PDEs. - **Solving Process**: Leverage FastLSQ's core functionalities to solve the PDE, making use of its exact analytical derivatives and rank-revealing solvers to ensure accuracy and efficiency. - **Result Processing**: Post-processing of results should include normalization and conversion to a format suitable for visualization. - **Visualization Integration**: Integrate visualization tools like Matplotlib or Plotly to display the solution dynamically. ### Development Steps: 1. **Environment Setup**: Set up the development environment with Python, FastLSQ, and visualization libraries. 2. **Core Functionality**: Develop the core functionality for solving PDEs using FastLSQ. 3. **GUI Development**: Create a GUI using a library like Tkinter or PyQt for user interaction. 4. **Testing and Debugging**: Test the application thoroughly, focusing on both the correctness of the PDE solutions and the usability of the GUI. 5. **Documentation and Deployment**: Document the application and prepare it for deployment, possibly as a standalone executable.