atlas-ftag-tools

v0.3.3 safe
4.0
Medium Risk

ATLAS Flavour Tagging Tools

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across most categories, with only minor concerns regarding network and shell activities. These do not strongly suggest malicious intent.

  • network calls present
  • unusual git commands
Per-check LLM notes
  • Network: The network calls seem to be fetching data from a remote server, which could be legitimate depending on the package's functionality.
  • Shell: Executing git commands can be part of version control operations, but the presence of 'git diff' and 'git tag' without clear context might indicate unusual behavior or potential for unauthorized access.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret or credential theft.
  • Metadata: The maintainer has only one package and lacks PyPI classifiers, indicating potential low effort or newness, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (5.6/10)

◈ 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 (3190 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

  • 109 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 13 unique contributor(s) across 100 commits in umami-hep/atlas-ftag-tools
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • id={taskid}&json" r = requests.get(url, timeout=10) r.raise_for_status() data =
  • fn).exists(): r = requests.get(url, timeout=10) r.raise_for_status()
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ss`. """ try: subprocess.check_output( ["git", "rev-parse", "--is-inside-work-tree", "
  • return try: subprocess.check_output( ["git", "diff", "--quiet", "--exit-code"],
  • -url", "origin"] origin = subprocess.check_output(cmd, cwd=path).decode("utf-8").strip() if upstream not i
  • _for_fork(path, upstream) subprocess.check_output(["git", "tag", tagname, "-m", msg], cwd=path) subprocess
  • me, "-m", msg], cwd=path) subprocess.check_output(["git", "push", "-q", "origin", "--tags"], cwd=path) def g
  • return None git_hash = subprocess.check_output( ["git", "rev-parse", "--short", "HEAD"], cw
✓ 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 umami-hep/atlas-ftag-tools appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Sam Van Stroud, Philipp Gadow, Alexander Froch" 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 atlas-ftag-tools 
Your task is to create a Python-based mini-application that leverages the 'atlas-ftag-tools' package to analyze simulated ATLAS particle physics data. This application will serve as a tool for physicists and students to better understand flavor tagging techniques used in high-energy physics experiments. Here’s a detailed breakdown of the project requirements and features:

1. **Project Overview**: Design an interactive command-line tool that allows users to load, preprocess, and analyze simulated ATLAS data related to flavor tagging. The application should provide insights into the performance metrics of different tagging algorithms.

2. **Features**:
   - **Data Loading**: Implement functionality to load simulated data from CSV or ROOT files commonly used in ATLAS experiments.
   - **Preprocessing**: Develop preprocessing steps that include normalization, feature selection, and handling missing values.
   - **Algorithm Selection**: Allow users to choose between multiple flavor tagging algorithms provided by the 'atlas-ftag-tools' package.
   - **Performance Analysis**: Compute and display key performance indicators such as efficiency, purity, and ROC curves for the selected algorithms.
   - **Visualization**: Integrate matplotlib or seaborn for plotting histograms, scatter plots, and ROC curves to visualize the data and algorithm performance.
   - **User Interface**: Create a user-friendly CLI interface using argparse or click library to guide users through the process.

3. **Utilizing 'atlas-ftag-tools' Package**:
   - Use 'atlas-ftag-tools' to implement flavor tagging algorithms. Familiarize yourself with the documentation to understand how to apply these algorithms on your preprocessed datasets.
   - Explore the package's capabilities for handling specific types of data relevant to flavor tagging, such as b-jet identification.
   - Ensure that the application can take advantage of any optimization or advanced features provided by 'atlas-ftag-tools'.

4. **Deliverables**:
   - A fully functional Python application.
   - Documentation explaining how to install and use the application.
   - Sample datasets and instructions on how they were prepared.
   - A report detailing the design decisions, challenges faced, and lessons learned during the development process.

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