abipy

v1.0.0 safe
3.0
Low Risk

Python package to automate ABINIT calculations and analyze the results.

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The package abipy v1.0.0 shows minimal risk with no network or shell risks identified. While there are non-secure links noted in the metadata, these alone do not suggest a supply-chain attack.

  • No network calls detected
  • No shell execution patterns detected
  • Non-secure links in metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Metadata: The package has some non-secure links but no other suspicious flags.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

⚠ Typosquatting score 3.0

Possible typosquat of: scipy

  • "abipy" is 2 edit(s) from "scipy"
✓ Registered Email Domain

Email domain looks legitimate: uclouvain.be>

⚠ Suspicious Page Links score 10.0

Found 16 suspicious link(s) on the package page

  • Non-HTTPS external link: http://abinit.github.io/abipy
  • Non-HTTPS external link: http://abinit.github.io/abipy/gallery/index.html
  • Non-HTTPS external link: http://abinit.github.io/abipy/flow_gallery/index.html
  • Non-HTTPS external link: http://abinit.github.io/abipy/installation
  • Non-HTTPS external link: http://abinit.github.io/abipy/installation#anaconda-howto
  • Non-HTTPS external link: http://pymatgen.org/usage.html#setting-the-pmg-mapi-key-in-the-config-file
✓ Git Repository History

Repository abinit/abipy appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "M. Giantomassi and the AbiPy group" 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 abipy 
Create a mini-application that leverages the 'abipy' Python package to streamline the process of conducting ABINIT calculations for materials science research. This application will enable users to input the necessary parameters for their ABINIT simulations, execute these simulations automatically, and then analyze the output data in a user-friendly manner. Here’s a step-by-step guide on how to develop this application:

1. **Setup Environment**: Begin by setting up a Python environment with all required dependencies installed, including 'abipy'. Ensure that ABINIT itself is properly installed and accessible from your system.
2. **User Interface Design**: Develop a simple yet effective command-line interface (CLI) that allows users to specify the type of ABINIT calculation they wish to perform (e.g., electronic structure calculation, molecular dynamics), input files needed, and any other relevant parameters such as k-point mesh, pseudopotentials, etc.
3. **Input Generation**: Utilize 'abipy' to generate the necessary ABINIT input files based on user inputs. This includes handling complex tasks like generating appropriate K-points, managing pseudopotentials, and setting up the calculation environment.
4. **Calculation Execution**: Implement functionality within the application to submit these jobs to a local or remote computational resource. Use 'abipy' to monitor job status and handle job queuing if necessary.
5. **Output Analysis**: After the calculations complete, use 'abipy' to parse the output files and extract key information such as energy levels, band structures, and density of states. Provide visualizations where applicable.
6. **Report Generation**: Finally, compile all the extracted data into a comprehensive report that summarizes the findings of the simulation. This report should be easily readable and include both textual descriptions and graphical representations of the results.

Suggested Features:
- Support for multiple types of ABINIT calculations.
- Integration with popular computational resources (local, HPC clusters).
- Automated error detection and recovery during job execution.
- Advanced plotting capabilities for better visualization of results.
- Option to save and load previous projects for continued work.

By following these steps and incorporating the suggested features, you will create a powerful tool that significantly enhances the efficiency and effectiveness of ABINIT-based research.