aggregate

v0.30.1 suspicious
6.0
Medium Risk

Tools for creating and working with aggregate probability distributions.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package aggregate v0.30.1 is flagged as suspicious due to its high obfuscation risk from the use of eval(), despite no clear evidence of credential harvesting or other malicious activities.

  • High obfuscation risk due to usage of eval()
  • No evidence of credential harvesting
Per-check LLM notes
  • Obfuscation: The code uses eval() in various contexts which can be a vector for arbitrary code execution, indicating potential malicious obfuscation.
  • Credentials: No direct evidence of credential harvesting is present in the provided snippets.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_suite.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://aggregate.readthedocs.io/en/latest/
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (27181 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 12 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in mynl/aggregate
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 8.0

Found 4 obfuscation pattern(s)

  • try: bs = eval(args.bs) except: # noqa pass
  • ex']: tests_ex[c] = [eval(i) if type(i)==str else float(i) for i in tests_ex[c]]
  • assert float(a.name) == eval(t.replace('%', '/100').replace('exp', 'np.exp').replace('inf
  • v): v = eval(v) else: v = float(v)
✓ 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: convexrisk.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository mynl/aggregate appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 aggregate 
Your task is to develop a mini-application named 'ProbabilisticWeatherForecaster' that leverages the 'aggregate' Python package to predict weather conditions based on historical data. This application will allow users to input a date range and location, then it will simulate various weather outcomes based on historical probabilities. The goal is to showcase the power of probabilistic modeling in predicting future events using past data.

Step 1: Setup
- Install the necessary packages including 'aggregate', pandas, and numpy.
- Import these libraries at the beginning of your script.

Step 2: Data Collection
- Fetch historical weather data from a public API or use a dataset that includes temperature, precipitation, and wind speed over a certain period.
- Clean and preprocess the data to ensure it is ready for analysis.

Step 3: Modeling
- Use the 'aggregate' package to create probability distributions for each weather variable (temperature, precipitation, wind speed) based on historical data.
- Ensure that you consider different seasons and geographical locations to make the model more accurate.

Step 4: Simulation
- Implement a function that simulates weather conditions for a given date range based on the probability distributions created in Step 3.
- The simulation should output a set of possible weather scenarios for each day within the specified range.

Suggested Features:
- Allow users to specify the confidence level of their predictions.
- Provide visualizations of the predicted weather outcomes using matplotlib or seaborn.
- Include an option to save the results to a CSV file for further analysis.
- Consider adding a user-friendly interface using Flask or Streamlit to interact with the application.

How 'aggregate' is Utilized:
- 'aggregate' is crucial for defining the probability distributions that underpin the simulations. By utilizing its tools, you can accurately represent the variability in weather conditions seen historically, allowing for realistic and statistically sound predictions.