atomica

v1.32.0 safe
3.0
Low Risk

Toolbox for compartment-based dynamic systems with costing and optimization

🤖 AI Analysis

Final verdict: SAFE

The package does not exhibit any immediate security risks such as network calls or shell executions. However, the metadata lacks critical information like the maintainer's name and a GitHub repository, which slightly increases suspicion.

  • No network calls detected
  • No shell execution patterns detected
  • Lack of maintainer's name and GitHub repository
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access for functionality.
  • Shell: No shell execution patterns detected, indicating no immediate risk of command injection or backdoor activities.
  • Metadata: The package shows some red flags due to the lack of a maintainer's name and GitHub repository, but there are no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 31 test file(s) found

  • Test runner config found: conftest.py
  • 31 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • 3 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4891 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

  • 191 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • op_names = pop_names def eval(self, model, baseline): # This is the main interface
  • objective += measurable.eval(model, baseline) return objective def _objective_f
  • , mode="eval") return eval(compiled_code) else: return plot_string def fo
  • }, **supported_functions} exec(compiled_code, namespace) return namespace["_fcn"], dep_list
  • ores=[])) compiled_code = compile(module, filename="<ast>", mode="exec") namespace = {"__builtins__": {}, **supported_function
  • """ model = pickle.loads(pickled_model) model.process() baselines = [
✓ 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: atomica.tools>

✓ 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 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 atomica 
Create a mini-application using the 'atomica' Python package to simulate the spread of a hypothetical infectious disease within a population over time. This application will serve as an educational tool for understanding the dynamics of disease transmission, the impact of various interventions, and cost-effectiveness analysis. Here’s a detailed breakdown of the project requirements:

1. **Define the Disease Model**: Utilize 'atomica' to define a compartmental model for your chosen disease. Consider at least four compartments such as Susceptible (S), Exposed (E), Infected (I), and Recovered (R). Incorporate parameters like infection rate, recovery rate, and incubation period.

2. **Simulate Scenarios**: Implement different scenarios based on varying levels of intervention effectiveness. For example, simulate the effects of vaccination programs, social distancing measures, and treatment availability. Use 'atomica' to run simulations under these conditions and observe changes in disease prevalence.

3. **Cost Analysis**: Integrate a costing module within your application to estimate the economic burden of the disease and the costs associated with each intervention. This will help in understanding the financial implications of different public health strategies.

4. **Optimization Module**: Employ the optimization capabilities of 'atomica' to find the most cost-effective strategy for controlling the disease. This could involve minimizing the total number of infections while staying within budget constraints.

5. **Visualization Tools**: Develop user-friendly visualizations to display simulation results, including graphs showing the evolution of disease prevalence over time and pie charts illustrating the distribution of individuals across different compartments.

6. **Interactive Interface**: Create an interactive interface where users can input their own parameters (e.g., initial population size, intervention efficacy) and see immediate feedback from the simulation. This will make the application more engaging and adaptable.

7. **Documentation and User Guide**: Provide comprehensive documentation explaining how to use the application, interpret the results, and customize the models. Include examples and case studies to illustrate key concepts.

By completing this project, you will gain hands-on experience with 'atomica', learn about dynamic system modeling, and contribute to the development of tools that can inform public health policy.

💬 Discussion Feed

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