repowisephysiomelinks/circulatory_autogen

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physiomelinks / circulatory_autogen

This project aims to automate the process of combining CellML modules into a system model, then doing parameter identification to calibrate to clinical data. Common use cases include generating coupled neuron models, generating neuron to cardiomyocyte models, generating patient specific 0D blood flow networks and more.

Python3github.com/physiomelinks/circulatory_autogen
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Files indexed
162
Symbols
933
Languages
8
Structure
Single

Languages

  • python46.3%
  • json24.7%
  • shell12.3%
  • markdown9.3%
  • cpp3.1%
  • yaml2.5%
  • makefile1.2%
  • toml0.6%

Explore physiomelinks/circulatory_autogen

Overview

Files, symbols, languages, packages, git intelligence

Dependency graph

Interactive view of how files import each other

Hotspots

Files with the most churn and co-change risk

Ownership

Bus-factor and per-file maintainer maps

Decisions

Architectural decisions extracted from commits and PRs

Dead code

Unreachable symbols and unused exports

AI docsPro

Module-by-module documentation generated from source

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Ask grounded questions over the indexed code

Entry points1

  • tutorial/docs/index.md

How physiomelinks/circulatory_autogen works

This project aims to automate the process of combining CellML modules into a system model, then doing parameter identification to calibrate to clinical data. Common use cases include generating coupled neuron models, generating neuron to cardiomyocyte models, generating patient specific 0D blood flow networks and more. This page is an auto-generated, always-fresh map of the physiomelinks/circulatory_autogen repository, written primarily in Python. Repowise indexes the source, parses every symbol, computes a dependency graph, mines git history for hotspots and ownership, and lifts the resulting architectural decisions into a wiki you can read or query through MCP.

The codebase has 162 source files, 933 symbols, and 8 languages. Git churn analysis flags 22 high-frequency files as hotspots — places where bugs, rewrites, and code review tend to concentrate. The dependency graph clusters into 106 tightly-coupled module communities.

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