calminer/docs/implementation_plan.md

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
• Development setup: docs/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.