PyDPEET

v0.3.1 safe
4.0
Medium Risk

Python package to read, unify, and convert battery measurement data from arbitrary battery cyclers to Parquet files. This package also provides functions to process, evaluate, and visualise the standardised data.

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories with no network calls, shell executions, or obfuscations detected. The only notable concern is the newness of the maintainers and lack of detailed metadata.

  • Low risk scores across all technical categories
  • New maintainers and incomplete metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: No shell execution patterns detected, indicating no direct command execution risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting the package does not pose a risk for stealing secrets or credentials.
  • Metadata: The maintainers appear new and the package lacks PyPI classifiers, indicating low effort or poor metadata quality.

🔬 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

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: eet.tu-berlin.de>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Anton Schlösser, Martin Otto, Alexander Günter Hinrichsen, Jan Kalisch, Daniel Schröder, Cataldo De Simone" 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 PyDPEET 
Create a mini-application called 'BatteryDataPro' that leverages the PyDPEET package to streamline the processing of battery cycler data. The application should allow users to upload raw battery measurement data from different sources, unify it into a standard format, and then visualize key metrics such as capacity retention, voltage profiles, and cycle efficiency. Additionally, the app should provide an option to export the processed data into Parquet files for further analysis.

Steps to create the application:
1. Set up a user-friendly interface where users can select and upload their raw battery data files.
2. Implement a function to use PyDPEET to read and unify the uploaded data into a standardized format.
3. Develop a feature within the application to process the unified data, calculating important metrics like cycle efficiency, voltage profile changes, and capacity retention over time.
4. Integrate visualization tools to display the calculated metrics in an intuitive manner, allowing users to compare different sets of data side-by-side.
5. Include an export function that allows users to save the processed data as Parquet files for future reference or further data science tasks.

Suggested Features:
- Data validation checks before processing to ensure the quality and consistency of the input.
- Interactive charts and graphs that update dynamically based on user selections.
- A summary report that highlights key insights from the processed data.
- Support for multiple file formats commonly used in battery testing.
- User authentication and data privacy features to protect sensitive information.