zwitschi
e73a987d25
- Updated README.md to reflect new features and usage instructions. - Removed deprecated Dashboard.html component and integrated dashboard functionality directly into the main application. - Revised architecture documentation for clarity and added module map and request flow diagrams. - Enhanced maintenance model to include equipment association and cost tracking. - Updated requirements.txt to include new dependencies (httpx, pandas, numpy). - Improved consumption, maintenance, production, and reporting routes with better validation and response handling. - Added unit tests for maintenance and production routes, ensuring proper CRUD operations and validation. - Enhanced reporting service to calculate and return detailed summary statistics. - Redesigned Dashboard.html for improved user experience and integrated Chart.js for visualizing simulation results.
45 lines
2.0 KiB
Markdown
45 lines
2.0 KiB
Markdown
# Architecture Overview
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This overview complements `docs/architecture.md` with a high-level map of CalMiner's module layout and request flow.
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## Module Map
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- `main.py`: FastAPI entry point bootstrapping routers and middleware.
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- `models/`: SQLAlchemy declarative models for all database tables. Key modules:
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- `scenario.py`: central scenario entity with relationships to cost, consumption, production, equipment, maintenance, and simulation results.
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- `capex.py`, `opex.py`: financial expenditures tied to scenarios.
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- `consumption.py`, `production_output.py`: operational data tables.
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- `equipment.py`, `maintenance.py`: asset management models.
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- `routes/`: REST endpoints grouped by domain (scenarios, parameters, costs, consumption, production, equipment, maintenance, reporting, simulations, UI).
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- `services/`: business logic abstractions. `reporting.py` supplies summary statistics; `simulation.py` hosts the Monte Carlo extension point.
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- `middleware/validation.py`: request JSON validation prior to hitting routers.
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- `templates/`: Jinja2 templates for UI (scenario form, parameter input, dashboard).
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## Request Flow
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```mermaid
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graph TD
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A[Browser / API Client] -->|HTTP| B[FastAPI Router]
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B --> C[Dependency Injection]
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C --> D[SQLAlchemy Session]
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B --> E[Service Layer]
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E --> D
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E --> F[Reporting / Simulation Logic]
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D --> G[PostgreSQL]
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F --> H[Summary Response]
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G --> H
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H --> A
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```
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## Dashboard Interaction
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1. User loads `/dashboard`, served by `templates/Dashboard.html`.
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2. Template fetches `/api/reporting/summary` with sample or user-provided simulation outputs.
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3. Response metrics populate the summary grid and Chart.js visualization.
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## Simulation Roadmap
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- Implement stochastic sampling in `services/simulation.py` (e.g., NumPy random draws based on parameter distributions).
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- Store iterations in `models/simulation_result.py` via `/api/simulations/run`.
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- Feed persisted results into reporting for downstream analytics and historical comparisons.
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