# Implementation Plan

This document outlines the MVP features and implementation steps for CalMiner.

Refer to the following for context alignment:

- System architecture: [docs/architecture.md](architecture.md)
- Development setup: [docs/development_setup.md](development_setup.md)

## Project Setup

1. Connect to PostgreSQL database with schema `calminer`.
2. Create and activate a virtual environment and install dependencies via `requirements.txt`.
3. Define environment variables in `.env`, including `DATABASE_URL`.
4. Configure FastAPI entrypoint in `main.py` to include routers.

## Feature: Scenario Management

### Implementation Steps

1. Create `models/scenario.py` for scenario CRUD.
2. Implement API endpoints in `routes/scenarios.py` (GET, POST, PUT, DELETE).
3. Write unit tests in `tests/unit/test_scenario.py`.
4. Build UI component `components/ScenarioForm.html`.

## Feature: Process Parameters

### Implementation Steps

1. Create `models/parameters.py` for process parameters.
2. Implement Pydantic schemas in `routes/parameters.py`.
3. Add validation middleware in `middleware/validation.py`.
4. Write unit tests in `tests/unit/test_parameter.py`.
5. Build UI component `components/ParameterInput.html`.

## Feature: Stochastic Variables

### Implementation Steps

1. Create `models/distribution.py` for variable distributions.
2. Implement API routes in `routes/distributions.py`.
3. Write Pydantic schemas and validations.
4. Write unit tests in `tests/unit/test_distribution.py`.
5. Build UI component `components/DistributionEditor.html`.

## Feature: Cost Tracking

### Implementation Steps

1. Create `models/capex.py` and `models/opex.py`.
2. Implement API routes in `routes/costs.py`.
3. Write Pydantic schemas for CAPEX/OPEX.
4. Write unit tests in `tests/unit/test_costs.py`.
5. Build UI component `components/CostForm.html`.

## Feature: Consumption Tracking

### Implementation Steps

1. Create models for consumption: `chemical_consumption.py`, `fuel_consumption.py`, `water_consumption.py`, `scrap_consumption.py`.
2. Implement API routes in `routes/consumption.py`.
3. Write Pydantic schemas for consumption data.
4. Write unit tests in `tests/unit/test_consumption.py`.
5. Build UI component `components/ConsumptionDashboard.html`.

## Feature: Production Output

### Implementation Steps

1. Create `models/production_output.py`.
2. Implement API routes in `routes/production.py`.
3. Write Pydantic schemas for production output.
4. Write unit tests in `tests/unit/test_production.py`.
5. Build UI component `components/ProductionChart.html`.

## Feature: Equipment Management

### Implementation Steps

1. Create `models/equipment.py` for equipment data.
2. Implement API routes in `routes/equipment.py`.
3. Write Pydantic schemas for equipment.
4. Write unit tests in `tests/unit/test_equipment.py`.
5. Build UI component `components/EquipmentList.html`.

## Feature: Maintenance Logging

### Implementation Steps

1. Create `models/maintenance.py` for maintenance events.
2. Implement API routes in `routes/maintenance.py`.
3. Write Pydantic schemas for maintenance logs.
4. Write unit tests in `tests/unit/test_maintenance.py`.
5. Build UI component `components/MaintenanceLog.html`.

## Feature: Monte Carlo Simulation Engine

### Implementation Steps

1. Implement Monte Carlo logic in `services/simulation.py`.
2. Persist results in `models/simulation_result.py`.
3. Expose endpoint in `routes/simulations.py`.
4. Write integration tests in `tests/unit/test_simulation.py`.
5. Build UI component `components/SimulationRunner.html`.

## Feature: Reporting / Dashboard

### Implementation Steps

1. Implement report calculations in `services/reporting.py`.
2. Add detailed and summary endpoints in `routes/reporting.py`.
3. Write unit tests in `tests/unit/test_reporting.py`.
4. Enhance UI in `components/Dashboard.html` with charts.

## MVP Feature Analysis (summary)

Goal: Identify core MVP features, acceptance criteria, and quick estimates.

Features:

- Scenario Management

  - Acceptance: create/read/update/delete scenarios; persist to DB; API coverage with tests.
  - Estimate: 3-5 days (backend + minimal UI).

- Parameter Input & Validation

  - Acceptance: define parameter schemas, validate inputs, surface errors to API/UI.
  - Estimate: 2-3 days.

- Monte Carlo Simulation Engine

  - Acceptance: run parameterised simulations, store results, ability to rerun with different seeds, basic progress reporting.
  - Estimate: 1-2 weeks (core engine + persistence).

- Reporting / Dashboard
  - Acceptance: display simulation outputs (NPV, IRR distributions), basic charts, export CSV.
  - Estimate: 4-7 days.

Edge cases to consider:

- Large simulation runs (memory / timeouts) — use streaming, chunking, or background workers.
- DB migration and schema versioning.
- Authentication/authorization for scenario access.

Next actionable items:

1. Break Scenario Management into sub-issues (models, routes, tests, simple UI).
2. Scaffold Parameter Input & Validation (models/parameters.py, middleware, routes, tests).
3. Prototype the simulation engine with a small deterministic runner and unit tests.
4. Scaffold Monte Carlo Simulation endpoints (`services/simulation.py`, `routes/simulations.py`, tests).
5. Scaffold Reporting endpoints (`services/reporting.py`, `routes/reporting.py`, front-end Dashboard, tests).
6. Add CI job for tests and coverage.