Refactor and enhance CalMiner application
- 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.
This commit is contained in:
1
.gitignore
vendored
1
.gitignore
vendored
@@ -41,3 +41,4 @@ logs/
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# SQLite database
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*.sqlite3
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test.db
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44
README.md
44
README.md
@@ -10,15 +10,15 @@ A range of features are implemented to support these functionalities.
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## Features
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- **Scenario Management**: The database supports different scenarios for what-if analysis, with parent-child relationships between scenarios.
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- **Monte Carlo Simulation**: The system can perform Monte Carlo simulations for risk analysis and probabilistic forecasting.
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- **Stochastic Variables**: The model handles uncertainty by defining variables with probability distributions.
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- **Cost Tracking**: It tracks capital (`capex`) and operational (`opex`) expenditures.
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- **Consumption Tracking**: It monitors the consumption of resources like chemicals, fuel, water, and scrap materials.
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- **Production Output**: The database stores production results, including tons produced, recovery rates, and revenue.
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- **Process Parameters**: It allows for defining and storing various parameters for different processes and scenarios.
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- **Equipment Management**: The system manages equipment and their operational data.
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- **Maintenance Logging**: It includes a log for equipment maintenance events.
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- **Scenario Management**: Manage multiple mining scenarios with independent parameter sets and outputs.
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- **Process Parameters**: Define and persist process inputs via FastAPI endpoints and template-driven forms.
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- **Cost Tracking**: Capture capital (`capex`) and operational (`opex`) expenditures per scenario.
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- **Consumption Tracking**: Record resource consumption (chemicals, fuel, water, scrap) tied to scenarios.
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- **Production Output**: Store production metrics such as tonnage, recovery, and revenue drivers.
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- **Equipment Management**: Register scenario-specific equipment inventories.
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- **Maintenance Logging**: Log maintenance events against equipment with dates and costs.
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- **Reporting Dashboard**: Surface aggregated statistics for simulation outputs with an interactive Chart.js dashboard.
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- **Monte Carlo Simulation (in progress)**: Services and routes are scaffolded for future stochastic analysis.
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## Architecture
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@@ -66,10 +66,36 @@ pip install -r requirements.txt
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uvicorn main:app --reload
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```
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## Usage Overview
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- **API base URL**: `http://localhost:8000/api`
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- **Key routes**:
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- `POST /api/scenarios/` create scenarios
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- `POST /api/parameters/` manage process parameters
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- `POST /api/costs/capex` and `POST /api/costs/opex` capture project costs
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- `POST /api/consumption/` add consumption entries
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- `POST /api/production/` register production output
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- `POST /api/equipment/` create equipment records
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- `POST /api/maintenance/` log maintenance events
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- `POST /api/reporting/summary` aggregate simulation results
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### Dashboard Preview
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1. Start the FastAPI server and navigate to `/dashboard` (served by `templates/Dashboard.html`).
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2. Use the "Load Sample Data" button to populate the JSON textarea with demo results.
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3. Select "Refresh Dashboard" to view calculated statistics and a distribution chart sourced from `/api/reporting/summary`.
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4. Paste your own simulation outputs (array of objects containing a numeric `result` property) to visualize custom runs.
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## Testing
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Testing guidelines and best practices are outlined in [docs/testing.md](docs/testing.md).
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To execute the unit test suite:
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```powershell
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pytest
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```
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## Database Objects
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The database is composed of several tables that store different types of information.
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@@ -1,25 +0,0 @@
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>CalMiner Dashboard</title>
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</head>
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<body>
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<h1>Simulation Results Dashboard</h1>
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<div id="report-summary"></div>
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<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
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<script>
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// TODO: fetch summary report and render charts
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async function loadReport() {
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const response = await fetch('/api/reporting/summary', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify([])
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});
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const data = await response.json();
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document.getElementById('report-summary').innerText = JSON.stringify(data);
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}
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loadReport();
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</script>
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</body>
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</html>
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@@ -2,40 +2,47 @@
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## Overview
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CalMiner is a web application for planning mining projects, estimating costs, returns, and profitability. It uses Monte Carlo simulations for risk analysis and supports multiple scenarios.
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CalMiner is a FastAPI application that collects mining project inputs, persists scenario-specific records, and surfaces aggregated insights. The platform targets Monte Carlo driven planning, with deterministic CRUD features in place and simulation logic staged for future work.
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## System Components
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- **Frontend**: Web interface for user interaction (to be defined).
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- **Backend**: Python API server (e.g., FastAPI) handling business logic.
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- **Database**: PostgreSQL.
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- **Configuration**: Environment variables and settings loaded via `python-dotenv` and stored in `config/` directory.
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- **Simulation Engine**: Python-based Monte Carlo runs and stochastic calculations.
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- **API Routes**: FastAPI routers defined in `routes/` for scenarios, simulations, consumptions, and reporting endpoints.
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- **FastAPI backend** (`main.py`, `routes/`): hosts REST endpoints for scenarios, parameters, costs, consumption, production, equipment, maintenance, simulations, and reporting. Each router encapsulates request/response schemas and DB access patterns.
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- **Service layer** (`services/`): houses business logic. `services/reporting.py` produces statistical summaries, while `services/simulation.py` provides the Monte Carlo integration point.
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- **Persistence** (`models/`, `config/database.py`): SQLAlchemy models map to PostgreSQL tables in schema `bricsium_platform`. Relationships connect scenarios to derived domain entities.
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- **Presentation** (`templates/`, `components/`): server-rendered views support data entry (scenario and parameter forms) and the dashboard visualization powered by Chart.js.
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- **Middleware** (`middleware/validation.py`): applies JSON validation before requests reach routers.
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- **Testing** (`tests/unit/`): pytest suite covering route and service behavior.
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## Data Flow
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## Runtime Flow
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1. User inputs scenario parameters via frontend.
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2. Backend validates and stores in database.
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3. Simulation engine runs Monte Carlo iterations using stochastic variables.
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4. Results stored in `simulation_result` table.
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5. Frontend displays outputs like NPV, IRR, EBITDA.
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1. Users navigate to form templates or API clients to manage scenarios, parameters, and operational data.
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2. FastAPI routers validate payloads with Pydantic models, then delegate to SQLAlchemy sessions for persistence.
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3. Simulation runs (placeholder `services/simulation.py`) will consume stored parameters to emit iteration results via `/api/simulations/run`.
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4. Reporting requests POST simulation outputs to `/api/reporting/summary`; the reporting service calculates aggregates (count, min/max, mean, median, percentiles, standard deviation).
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5. `templates/Dashboard.html` fetches summaries, renders metric cards, and plots distribution charts with Chart.js for stakeholder review.
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## Database Architecture
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## Data Model Highlights
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- Schema: `bricsium_platform`
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- Key tables include:
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- `scenario`: central entity describing a mining scenario; owns relationships to cost, consumption, production, equipment, and maintenance tables.
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- `capex`, `opex`: monetary tracking linked to scenarios.
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- `consumption`: resource usage entries parameterized by scenario and description.
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- `production_output`: production metrics per scenario.
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- `equipment` and `maintenance`: equipment inventory and maintenance events with dates/costs.
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- `simulation_result`: staging table for future Monte Carlo outputs (not yet populated by `run_simulation`).
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- `scenario` (scenario metadata and parameters)
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- `capex`, `opex` (capital and operational expenditures)
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- `chemical_consumption`, `fuel_consumption`, `water_consumption`, `scrap_consumption`
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- `production_output`, `equipment_operation`, `ore_batch`
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- `exchange_rate`, `simulation_result`
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Foreign keys secure referential integrity between domain tables and their scenarios, enabling per-scenario analytics.
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- Relationships: Foreign keys link scenarios to parameters, consumptions, and simulation results.
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## Integrations and Future Work
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## Next Steps
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- **Monte Carlo engine**: `services/simulation.py` will incorporate stochastic sampling (e.g., NumPy, SciPy) to populate `simulation_result` and feed reporting.
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- **Persistence of results**: `/api/simulations/run` currently returns in-memory results; next iteration should persist to `simulation_result` and reference scenarios.
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- **Authentication**: not yet implemented; all endpoints are open.
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- **Deployment**: documentation focuses on local development; containerization and CI/CD pipelines remain to be defined.
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- Define API endpoints.
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- Implement simulation logic.
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- Add authentication and user management.
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For extended diagrams and setup instructions reference:
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- [docs/development_setup.md](development_setup.md) — environment provisioning and tooling.
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- [docs/testing.md](testing.md) — pytest workflow and coverage expectations.
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- [docs/mvp.md](mvp.md) — roadmap and milestone scope.
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- [docs/implementation_plan.md](implementation_plan.md) — feature breakdown aligned with the TODO tracker.
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- [docs/architecture_overview.md](architecture_overview.md) — supplementary module map and request flow diagram.
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44
docs/architecture_overview.md
Normal file
44
docs/architecture_overview.md
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@@ -0,0 +1,44 @@
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# 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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@@ -1,4 +1,4 @@
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from sqlalchemy import Column, Integer, String, DateTime, ForeignKey, func
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from sqlalchemy import Column, Date, Float, ForeignKey, Integer, String
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from sqlalchemy.orm import relationship
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from config.database import Base
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@@ -7,11 +7,17 @@ class Maintenance(Base):
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__tablename__ = "maintenance"
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id = Column(Integer, primary_key=True, index=True)
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equipment_id = Column(Integer, ForeignKey("equipment.id"), nullable=False)
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scenario_id = Column(Integer, ForeignKey("scenario.id"), nullable=False)
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performed_at = Column(DateTime(timezone=True), server_default=func.now())
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details = Column(String, nullable=True)
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maintenance_date = Column(Date, nullable=False)
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description = Column(String, nullable=True)
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cost = Column(Float, nullable=False)
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equipment = relationship("Equipment")
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scenario = relationship("Scenario", back_populates="maintenance_items")
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def __repr__(self):
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return f"<Maintenance id={self.id} scenario_id={self.scenario_id} performed_at={self.performed_at}>"
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def __repr__(self) -> str:
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return (
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f"<Maintenance id={self.id} equipment_id={self.equipment_id} "
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f"scenario_id={self.scenario_id} date={self.maintenance_date} cost={self.cost}>"
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)
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@@ -3,6 +3,10 @@ uvicorn
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sqlalchemy
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psycopg2-binary
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python-dotenv
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httpx
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jinja2
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pandas
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numpy
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pytest
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pytest-cov
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jinja2
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pytest-httpx
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@@ -1,10 +1,13 @@
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy.orm import Session
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from typing import List, Optional
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from pydantic import BaseModel
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from fastapi import APIRouter, Depends, status
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from pydantic import BaseModel, PositiveFloat
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from sqlalchemy.orm import Session
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from config.database import SessionLocal
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from models.consumption import Consumption
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router = APIRouter(prefix="/api/consumption", tags=["Consumption"])
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@@ -16,22 +19,25 @@ def get_db():
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db.close()
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# Pydantic schemas
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class ConsumptionCreate(BaseModel):
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class ConsumptionBase(BaseModel):
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scenario_id: int
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amount: float
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amount: PositiveFloat
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description: Optional[str] = None
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class ConsumptionRead(ConsumptionCreate):
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class ConsumptionCreate(ConsumptionBase):
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pass
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class ConsumptionRead(ConsumptionBase):
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id: int
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class Config:
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orm_mode = True
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@router.post("/", response_model=ConsumptionRead)
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async def create_consumption(item: ConsumptionCreate, db: Session = Depends(get_db)):
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@router.post("/", response_model=ConsumptionRead, status_code=status.HTTP_201_CREATED)
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def create_consumption(item: ConsumptionCreate, db: Session = Depends(get_db)):
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db_item = Consumption(**item.dict())
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db.add(db_item)
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db.commit()
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@@ -40,5 +46,5 @@ async def create_consumption(item: ConsumptionCreate, db: Session = Depends(get_
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@router.get("/", response_model=List[ConsumptionRead])
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async def list_consumption(db: Session = Depends(get_db)):
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def list_consumption(db: Session = Depends(get_db)):
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return db.query(Consumption).all()
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@@ -1,11 +1,14 @@
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy.orm import Session
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from datetime import date
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from typing import List, Optional
|
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from pydantic import BaseModel
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from datetime import datetime
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|
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from fastapi import APIRouter, Depends, HTTPException, status
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from pydantic import BaseModel, PositiveFloat
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from sqlalchemy.orm import Session
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|
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from config.database import SessionLocal
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from models.maintenance import Maintenance
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router = APIRouter(prefix="/api/maintenance", tags=["Maintenance"])
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@@ -17,29 +20,75 @@ def get_db():
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db.close()
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|
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|
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# Pydantic schemas
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class MaintenanceCreate(BaseModel):
|
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class MaintenanceBase(BaseModel):
|
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equipment_id: int
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scenario_id: int
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details: Optional[str] = None
|
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maintenance_date: date
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description: Optional[str] = None
|
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cost: PositiveFloat
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|
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|
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class MaintenanceRead(MaintenanceCreate):
|
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class MaintenanceCreate(MaintenanceBase):
|
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pass
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|
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|
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class MaintenanceUpdate(MaintenanceBase):
|
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pass
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|
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|
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class MaintenanceRead(MaintenanceBase):
|
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id: int
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performed_at: datetime
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|
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class Config:
|
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orm_mode = True
|
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|
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|
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@router.post("/", response_model=MaintenanceRead)
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async def create_maintenance(item: MaintenanceCreate, db: Session = Depends(get_db)):
|
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db_item = Maintenance(**item.dict())
|
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db.add(db_item)
|
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def _get_maintenance_or_404(db: Session, maintenance_id: int) -> Maintenance:
|
||||
maintenance = db.query(Maintenance).filter(
|
||||
Maintenance.id == maintenance_id).first()
|
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if maintenance is None:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
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detail=f"Maintenance record {maintenance_id} not found",
|
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)
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return maintenance
|
||||
|
||||
|
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@router.post("/", response_model=MaintenanceRead, status_code=status.HTTP_201_CREATED)
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def create_maintenance(maintenance: MaintenanceCreate, db: Session = Depends(get_db)):
|
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db_maintenance = Maintenance(**maintenance.dict())
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db.add(db_maintenance)
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db.commit()
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db.refresh(db_item)
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return db_item
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db.refresh(db_maintenance)
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return db_maintenance
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||||
|
||||
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@router.get("/", response_model=List[MaintenanceRead])
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async def list_maintenance(db: Session = Depends(get_db)):
|
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return db.query(Maintenance).all()
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def list_maintenance(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)):
|
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return db.query(Maintenance).offset(skip).limit(limit).all()
|
||||
|
||||
|
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@router.get("/{maintenance_id}", response_model=MaintenanceRead)
|
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def get_maintenance(maintenance_id: int, db: Session = Depends(get_db)):
|
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return _get_maintenance_or_404(db, maintenance_id)
|
||||
|
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|
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@router.put("/{maintenance_id}", response_model=MaintenanceRead)
|
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def update_maintenance(
|
||||
maintenance_id: int,
|
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payload: MaintenanceUpdate,
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||||
db: Session = Depends(get_db),
|
||||
):
|
||||
db_maintenance = _get_maintenance_or_404(db, maintenance_id)
|
||||
for field, value in payload.dict().items():
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||||
setattr(db_maintenance, field, value)
|
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db.commit()
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db.refresh(db_maintenance)
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return db_maintenance
|
||||
|
||||
|
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@router.delete("/{maintenance_id}", status_code=status.HTTP_204_NO_CONTENT)
|
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def delete_maintenance(maintenance_id: int, db: Session = Depends(get_db)):
|
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db_maintenance = _get_maintenance_or_404(db, maintenance_id)
|
||||
db.delete(db_maintenance)
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db.commit()
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||||
|
||||
@@ -1,10 +1,13 @@
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from sqlalchemy.orm import Session
|
||||
from typing import List, Optional
|
||||
from pydantic import BaseModel
|
||||
|
||||
from fastapi import APIRouter, Depends, status
|
||||
from pydantic import BaseModel, PositiveFloat
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from config.database import SessionLocal
|
||||
from models.production_output import ProductionOutput
|
||||
|
||||
|
||||
router = APIRouter(prefix="/api/production", tags=["Production"])
|
||||
|
||||
|
||||
@@ -16,22 +19,25 @@ def get_db():
|
||||
db.close()
|
||||
|
||||
|
||||
# Pydantic schemas
|
||||
class ProductionOutputCreate(BaseModel):
|
||||
class ProductionOutputBase(BaseModel):
|
||||
scenario_id: int
|
||||
amount: float
|
||||
amount: PositiveFloat
|
||||
description: Optional[str] = None
|
||||
|
||||
|
||||
class ProductionOutputRead(ProductionOutputCreate):
|
||||
class ProductionOutputCreate(ProductionOutputBase):
|
||||
pass
|
||||
|
||||
|
||||
class ProductionOutputRead(ProductionOutputBase):
|
||||
id: int
|
||||
|
||||
class Config:
|
||||
orm_mode = True
|
||||
|
||||
|
||||
@router.post("/", response_model=ProductionOutputRead)
|
||||
async def create_production(item: ProductionOutputCreate, db: Session = Depends(get_db)):
|
||||
@router.post("/", response_model=ProductionOutputRead, status_code=status.HTTP_201_CREATED)
|
||||
def create_production(item: ProductionOutputCreate, db: Session = Depends(get_db)):
|
||||
db_item = ProductionOutput(**item.dict())
|
||||
db.add(db_item)
|
||||
db.commit()
|
||||
@@ -40,5 +46,5 @@ async def create_production(item: ProductionOutputCreate, db: Session = Depends(
|
||||
|
||||
|
||||
@router.get("/", response_model=List[ProductionOutputRead])
|
||||
async def list_production(db: Session = Depends(get_db)):
|
||||
def list_production(db: Session = Depends(get_db)):
|
||||
return db.query(ProductionOutput).all()
|
||||
|
||||
@@ -1,9 +1,11 @@
|
||||
from fastapi import APIRouter, HTTPException, Request
|
||||
from typing import Dict, Any
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from fastapi import APIRouter, HTTPException, Request, status
|
||||
from pydantic import BaseModel
|
||||
|
||||
from services.reporting import generate_report
|
||||
from sqlalchemy.orm import Session
|
||||
from config.database import SessionLocal
|
||||
from services.reporting import generate_report
|
||||
|
||||
|
||||
router = APIRouter(prefix="/api/reporting", tags=["Reporting"])
|
||||
|
||||
@@ -16,11 +18,53 @@ def get_db():
|
||||
db.close()
|
||||
|
||||
|
||||
@router.post("/summary", response_model=Dict[str, float])
|
||||
def _validate_payload(payload: Any) -> List[Dict[str, float]]:
|
||||
if not isinstance(payload, list):
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail="Invalid input format",
|
||||
)
|
||||
|
||||
validated: List[Dict[str, float]] = []
|
||||
for index, item in enumerate(payload):
|
||||
if not isinstance(item, dict):
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail=f"Entry at index {index} must be an object",
|
||||
)
|
||||
value = item.get("result")
|
||||
if not isinstance(value, (int, float)):
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_400_BAD_REQUEST,
|
||||
detail=f"Entry at index {index} must include numeric 'result'",
|
||||
)
|
||||
validated.append({"result": float(value)})
|
||||
return validated
|
||||
|
||||
|
||||
class ReportSummary(BaseModel):
|
||||
count: int
|
||||
mean: float
|
||||
median: float
|
||||
min: float
|
||||
max: float
|
||||
std_dev: float
|
||||
percentile_10: float
|
||||
percentile_90: float
|
||||
|
||||
|
||||
@router.post("/summary", response_model=ReportSummary)
|
||||
async def summary_report(request: Request):
|
||||
# Read raw JSON to handle invalid input formats
|
||||
data = await request.json()
|
||||
if not isinstance(data, list):
|
||||
raise HTTPException(status_code=400, detail="Invalid input format")
|
||||
report = generate_report(data)
|
||||
return report
|
||||
payload = await request.json()
|
||||
validated_payload = _validate_payload(payload)
|
||||
summary = generate_report(validated_payload)
|
||||
return ReportSummary(
|
||||
count=int(summary["count"]),
|
||||
mean=float(summary["mean"]),
|
||||
median=float(summary["median"]),
|
||||
min=float(summary["min"]),
|
||||
max=float(summary["max"]),
|
||||
std_dev=float(summary["std_dev"]),
|
||||
percentile_10=float(summary["percentile_10"]),
|
||||
percentile_90=float(summary["percentile_90"]),
|
||||
)
|
||||
|
||||
@@ -37,13 +37,16 @@ def get_db():
|
||||
|
||||
@router.post("/", response_model=ScenarioRead)
|
||||
def create_scenario(scenario: ScenarioCreate, db: Session = Depends(get_db)):
|
||||
print(f"Creating scenario with name: {scenario.name}")
|
||||
db_s = db.query(Scenario).filter(Scenario.name == scenario.name).first()
|
||||
if db_s:
|
||||
print(f"Scenario with name {scenario.name} already exists.")
|
||||
raise HTTPException(status_code=400, detail="Scenario already exists")
|
||||
new_s = Scenario(name=scenario.name, description=scenario.description)
|
||||
db.add(new_s)
|
||||
db.commit()
|
||||
db.refresh(new_s)
|
||||
print(f"Scenario with name {scenario.name} created successfully.")
|
||||
return new_s
|
||||
|
||||
|
||||
|
||||
@@ -18,9 +18,3 @@ async def scenario_form(request: Request):
|
||||
async def parameter_form(request: Request):
|
||||
"""Render the parameter input form."""
|
||||
return templates.TemplateResponse("ParameterInput.html", {"request": request})
|
||||
|
||||
|
||||
@router.get("/", response_class=HTMLResponse)
|
||||
async def dashboard(request: Request):
|
||||
"""Render the central dashboard page."""
|
||||
return templates.TemplateResponse("Dashboard.html", {"request": request})
|
||||
|
||||
@@ -1,15 +1,57 @@
|
||||
from typing import List, Dict
|
||||
from statistics import mean, median, pstdev
|
||||
from typing import Dict, Iterable, List, Union
|
||||
|
||||
|
||||
def generate_report(simulation_results: List[Dict[str, float]]) -> Dict[str, float]:
|
||||
"""
|
||||
Generate summary report from simulation results.
|
||||
def _extract_results(simulation_results: Iterable[Dict[str, float]]) -> List[float]:
|
||||
values: List[float] = []
|
||||
for item in simulation_results:
|
||||
if not isinstance(item, dict):
|
||||
continue
|
||||
value = item.get("result")
|
||||
if isinstance(value, (int, float)):
|
||||
values.append(float(value))
|
||||
return values
|
||||
|
||||
Args:
|
||||
simulation_results: List of dicts with 'iteration' and 'result'.
|
||||
|
||||
Returns:
|
||||
Dictionary with summary statistics (e.g., mean, median).
|
||||
"""
|
||||
# TODO: implement reporting logic (e.g., calculate mean, median, percentiles)
|
||||
return {}
|
||||
def _percentile(values: List[float], percentile: float) -> float:
|
||||
if not values:
|
||||
return 0.0
|
||||
sorted_values = sorted(values)
|
||||
if len(sorted_values) == 1:
|
||||
return sorted_values[0]
|
||||
index = (percentile / 100) * (len(sorted_values) - 1)
|
||||
lower = int(index)
|
||||
upper = min(lower + 1, len(sorted_values) - 1)
|
||||
weight = index - lower
|
||||
return sorted_values[lower] * (1 - weight) + sorted_values[upper] * weight
|
||||
|
||||
|
||||
def generate_report(simulation_results: List[Dict[str, float]]) -> Dict[str, Union[float, int]]:
|
||||
"""Aggregate basic statistics for simulation outputs."""
|
||||
|
||||
values = _extract_results(simulation_results)
|
||||
|
||||
if not values:
|
||||
return {
|
||||
"count": 0,
|
||||
"mean": 0.0,
|
||||
"median": 0.0,
|
||||
"min": 0.0,
|
||||
"max": 0.0,
|
||||
"std_dev": 0.0,
|
||||
"percentile_10": 0.0,
|
||||
"percentile_90": 0.0,
|
||||
}
|
||||
|
||||
summary: Dict[str, Union[float, int]] = {
|
||||
"count": len(values),
|
||||
"mean": mean(values),
|
||||
"median": median(values),
|
||||
"min": min(values),
|
||||
"max": max(values),
|
||||
"percentile_10": _percentile(values, 10),
|
||||
"percentile_90": _percentile(values, 90),
|
||||
}
|
||||
|
||||
summary["std_dev"] = pstdev(values) if len(values) > 1 else 0.0
|
||||
return summary
|
||||
|
||||
@@ -1,25 +1,240 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<head>
|
||||
<meta charset="UTF-8" />
|
||||
<title>CalMiner Dashboard</title>
|
||||
</head>
|
||||
<body>
|
||||
<style>
|
||||
body {
|
||||
font-family: Arial, sans-serif;
|
||||
margin: 2rem;
|
||||
background-color: #f4f5f7;
|
||||
color: #1f2933;
|
||||
}
|
||||
h1 {
|
||||
font-size: 2rem;
|
||||
margin-bottom: 1rem;
|
||||
}
|
||||
.summary-card {
|
||||
background: #ffffff;
|
||||
border-radius: 8px;
|
||||
box-shadow: 0 2px 6px rgba(0, 0, 0, 0.08);
|
||||
padding: 1.5rem;
|
||||
margin-bottom: 2rem;
|
||||
}
|
||||
.summary-grid {
|
||||
display: grid;
|
||||
gap: 1.5rem;
|
||||
grid-template-columns: repeat(auto-fit, minmax(160px, 1fr));
|
||||
}
|
||||
.metric {
|
||||
text-align: center;
|
||||
}
|
||||
.metric-label {
|
||||
font-size: 0.8rem;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.05em;
|
||||
color: #52606d;
|
||||
}
|
||||
.metric-value {
|
||||
font-size: 1.4rem;
|
||||
font-weight: bold;
|
||||
margin-top: 0.4rem;
|
||||
}
|
||||
#chart-container {
|
||||
background: #ffffff;
|
||||
border-radius: 8px;
|
||||
box-shadow: 0 2px 6px rgba(0, 0, 0, 0.08);
|
||||
padding: 1.5rem;
|
||||
}
|
||||
#error-message {
|
||||
color: #b91d47;
|
||||
margin-top: 1rem;
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<h1>Simulation Results Dashboard</h1>
|
||||
<div id="report-summary"></div>
|
||||
<div class="summary-card">
|
||||
<h2>Summary Statistics</h2>
|
||||
<div id="summary-grid" class="summary-grid"></div>
|
||||
<p id="error-message" hidden></p>
|
||||
</div>
|
||||
<div class="summary-card">
|
||||
<h2>Sample Results Input</h2>
|
||||
<p>
|
||||
Provide simulation outputs as JSON (array of objects containing the
|
||||
<code>result</code> field) and refresh the dashboard to preview metrics.
|
||||
</p>
|
||||
<textarea
|
||||
id="results-input"
|
||||
rows="6"
|
||||
style="width: 100%; font-family: monospace"
|
||||
></textarea>
|
||||
<div style="margin-top: 1rem; display: flex; gap: 0.5rem">
|
||||
<button id="load-sample" type="button">Load Sample Data</button>
|
||||
<button id="refresh-dashboard" type="button">Refresh Dashboard</button>
|
||||
</div>
|
||||
</div>
|
||||
<div id="chart-container">
|
||||
<h2>Result Distribution</h2>
|
||||
<canvas id="summary-chart" height="120"></canvas>
|
||||
</div>
|
||||
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
|
||||
<script>
|
||||
// TODO: fetch summary report and render charts
|
||||
async function loadReport() {
|
||||
const response = await fetch('/api/reporting/summary', {
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
body: JSON.stringify([])
|
||||
});
|
||||
const data = await response.json();
|
||||
document.getElementById('report-summary').innerText = JSON.stringify(data);
|
||||
const SUMMARY_FIELDS = [
|
||||
{ key: "mean", label: "Mean" },
|
||||
{ key: "median", label: "Median" },
|
||||
{ key: "min", label: "Min" },
|
||||
{ key: "max", label: "Max" },
|
||||
{ key: "std_dev", label: "Std Dev" },
|
||||
{ key: "percentile_10", label: "10th Percentile" },
|
||||
{ key: "percentile_90", label: "90th Percentile" },
|
||||
];
|
||||
|
||||
async function fetchSummary(results) {
|
||||
const response = await fetch("/api/reporting/summary", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify(results),
|
||||
});
|
||||
|
||||
if (!response.ok) {
|
||||
const message = await response.json();
|
||||
throw new Error(message.detail || "Failed to retrieve summary");
|
||||
}
|
||||
loadReport();
|
||||
|
||||
return response.json();
|
||||
}
|
||||
|
||||
function getResultsFromInput() {
|
||||
const textarea = document.getElementById("results-input");
|
||||
try {
|
||||
const parsed = JSON.parse(textarea.value || "[]");
|
||||
if (!Array.isArray(parsed)) {
|
||||
throw new Error("Input must be a JSON array");
|
||||
}
|
||||
return parsed;
|
||||
} catch (error) {
|
||||
throw new Error(`Invalid JSON input: ${error.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
function renderSummary(summary) {
|
||||
const grid = document.getElementById("summary-grid");
|
||||
grid.innerHTML = "";
|
||||
SUMMARY_FIELDS.forEach(({ key, label }) => {
|
||||
const value = summary[key] ?? 0;
|
||||
const metric = document.createElement("div");
|
||||
metric.className = "metric";
|
||||
metric.innerHTML = `
|
||||
<div class="metric-label">${label}</div>
|
||||
<div class="metric-value">${value.toFixed(2)}</div>
|
||||
`;
|
||||
grid.appendChild(metric);
|
||||
});
|
||||
}
|
||||
|
||||
let chartInstance = null;
|
||||
|
||||
function renderChart(summary) {
|
||||
const ctx = document.getElementById("summary-chart").getContext("2d");
|
||||
const dataPoints = [
|
||||
summary.min,
|
||||
summary.percentile_10,
|
||||
summary.median,
|
||||
summary.mean,
|
||||
summary.percentile_90,
|
||||
summary.max,
|
||||
].map((value) => Number(value ?? 0));
|
||||
|
||||
if (chartInstance) {
|
||||
chartInstance.destroy();
|
||||
}
|
||||
|
||||
chartInstance = new Chart(ctx, {
|
||||
type: "line",
|
||||
data: {
|
||||
labels: ["Min", "P10", "Median", "Mean", "P90", "Max"],
|
||||
datasets: [
|
||||
{
|
||||
label: "Result Summary",
|
||||
data: dataPoints,
|
||||
borderColor: "#2563eb",
|
||||
backgroundColor: "rgba(37, 99, 235, 0.2)",
|
||||
tension: 0.3,
|
||||
fill: true,
|
||||
},
|
||||
],
|
||||
},
|
||||
options: {
|
||||
plugins: {
|
||||
legend: { display: false },
|
||||
},
|
||||
scales: {
|
||||
y: {
|
||||
beginAtZero: true,
|
||||
},
|
||||
},
|
||||
},
|
||||
});
|
||||
}
|
||||
|
||||
function showError(message) {
|
||||
const errorElement = document.getElementById("error-message");
|
||||
errorElement.textContent = message;
|
||||
errorElement.hidden = false;
|
||||
}
|
||||
|
||||
function attachHandlers() {
|
||||
const loadSampleButton = document.getElementById("load-sample");
|
||||
const refreshButton = document.getElementById("refresh-dashboard");
|
||||
|
||||
const sampleData = JSON.stringify(
|
||||
[
|
||||
{ result: 18.2 },
|
||||
{ result: 22.1 },
|
||||
{ result: 30.4 },
|
||||
{ result: 25.7 },
|
||||
{ result: 28.3 },
|
||||
],
|
||||
null,
|
||||
2
|
||||
);
|
||||
|
||||
loadSampleButton.addEventListener("click", () => {
|
||||
document.getElementById("results-input").value = sampleData;
|
||||
});
|
||||
|
||||
refreshButton.addEventListener("click", async () => {
|
||||
try {
|
||||
const results = getResultsFromInput();
|
||||
const summary = await fetchSummary(results);
|
||||
renderSummary(summary);
|
||||
renderChart(summary);
|
||||
document.getElementById("error-message").hidden = true;
|
||||
} catch (error) {
|
||||
console.error(error);
|
||||
showError(error.message);
|
||||
}
|
||||
});
|
||||
|
||||
document.getElementById("results-input").value = sampleData;
|
||||
}
|
||||
|
||||
async function initializeDashboard() {
|
||||
try {
|
||||
attachHandlers();
|
||||
const initialResults = getResultsFromInput();
|
||||
const summary = await fetchSummary(initialResults);
|
||||
renderSummary(summary);
|
||||
renderChart(summary);
|
||||
} catch (error) {
|
||||
console.error(error);
|
||||
showError(error.message);
|
||||
}
|
||||
}
|
||||
|
||||
initializeDashboard();
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
@@ -29,7 +29,7 @@ def test_create_and_list_consumption():
|
||||
cons_payload = {"scenario_id": sid, "amount": 250.0,
|
||||
"description": "Monthly consumption"}
|
||||
resp2 = client.post("/api/consumption/", json=cons_payload)
|
||||
assert resp2.status_code == 200
|
||||
assert resp2.status_code == 201
|
||||
cons = resp2.json()
|
||||
assert cons["scenario_id"] == sid
|
||||
assert cons["amount"] == 250.0
|
||||
|
||||
@@ -1,8 +1,30 @@
|
||||
from fastapi.testclient import TestClient
|
||||
from main import app
|
||||
from config.database import Base, engine
|
||||
from uuid import uuid4
|
||||
|
||||
# Setup and teardown
|
||||
from fastapi.testclient import TestClient
|
||||
from sqlalchemy import create_engine
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
|
||||
from config.database import Base
|
||||
from main import app
|
||||
from routes import (
|
||||
consumption,
|
||||
costs,
|
||||
distributions,
|
||||
equipment,
|
||||
maintenance,
|
||||
parameters,
|
||||
production,
|
||||
reporting,
|
||||
scenarios,
|
||||
simulations,
|
||||
ui,
|
||||
)
|
||||
|
||||
SQLALCHEMY_DATABASE_URL = "sqlite:///./test.db"
|
||||
engine = create_engine(SQLALCHEMY_DATABASE_URL, connect_args={
|
||||
"check_same_thread": False})
|
||||
TestingSessionLocal = sessionmaker(
|
||||
autocommit=False, autoflush=False, bind=engine)
|
||||
|
||||
|
||||
def setup_module(module):
|
||||
@@ -13,29 +35,145 @@ def teardown_module(module):
|
||||
Base.metadata.drop_all(bind=engine)
|
||||
|
||||
|
||||
def override_get_db():
|
||||
db = TestingSessionLocal()
|
||||
try:
|
||||
yield db
|
||||
finally:
|
||||
db.close()
|
||||
|
||||
|
||||
app.dependency_overrides[maintenance.get_db] = override_get_db
|
||||
app.dependency_overrides[equipment.get_db] = override_get_db
|
||||
app.dependency_overrides[scenarios.get_db] = override_get_db
|
||||
app.dependency_overrides[distributions.get_db] = override_get_db
|
||||
app.dependency_overrides[parameters.get_db] = override_get_db
|
||||
app.dependency_overrides[costs.get_db] = override_get_db
|
||||
app.dependency_overrides[consumption.get_db] = override_get_db
|
||||
app.dependency_overrides[production.get_db] = override_get_db
|
||||
app.dependency_overrides[reporting.get_db] = override_get_db
|
||||
app.dependency_overrides[simulations.get_db] = override_get_db
|
||||
|
||||
app.include_router(maintenance.router)
|
||||
app.include_router(equipment.router)
|
||||
app.include_router(scenarios.router)
|
||||
app.include_router(distributions.router)
|
||||
app.include_router(ui.router)
|
||||
app.include_router(parameters.router)
|
||||
app.include_router(costs.router)
|
||||
app.include_router(consumption.router)
|
||||
app.include_router(production.router)
|
||||
app.include_router(reporting.router)
|
||||
app.include_router(simulations.router)
|
||||
|
||||
client = TestClient(app)
|
||||
|
||||
|
||||
def _create_scenario_and_equipment():
|
||||
scenario_payload = {
|
||||
"name": f"Test Scenario {uuid4()}",
|
||||
"description": "Scenario for maintenance tests",
|
||||
}
|
||||
scenario_response = client.post("/api/scenarios/", json=scenario_payload)
|
||||
assert scenario_response.status_code == 200
|
||||
scenario_id = scenario_response.json()["id"]
|
||||
|
||||
equipment_payload = {
|
||||
"scenario_id": scenario_id,
|
||||
"name": f"Test Equipment {uuid4()}",
|
||||
"description": "Equipment linked to maintenance",
|
||||
}
|
||||
equipment_response = client.post("/api/equipment/", json=equipment_payload)
|
||||
assert equipment_response.status_code == 200
|
||||
equipment_id = equipment_response.json()["id"]
|
||||
return scenario_id, equipment_id
|
||||
|
||||
|
||||
def _create_maintenance_payload(equipment_id: int, scenario_id: int, description: str):
|
||||
return {
|
||||
"equipment_id": equipment_id,
|
||||
"scenario_id": scenario_id,
|
||||
"maintenance_date": "2025-10-20",
|
||||
"description": description,
|
||||
"cost": 100.0,
|
||||
}
|
||||
|
||||
|
||||
def test_create_and_list_maintenance():
|
||||
# Create a scenario to attach maintenance
|
||||
resp = client.post(
|
||||
"/api/scenarios/", json={"name": "MaintScenario", "description": "maintenance scenario"}
|
||||
scenario_id, equipment_id = _create_scenario_and_equipment()
|
||||
payload = _create_maintenance_payload(
|
||||
equipment_id, scenario_id, "Create maintenance")
|
||||
|
||||
response = client.post("/api/maintenance/", json=payload)
|
||||
assert response.status_code == 201
|
||||
created = response.json()
|
||||
assert created["equipment_id"] == equipment_id
|
||||
assert created["scenario_id"] == scenario_id
|
||||
assert created["description"] == "Create maintenance"
|
||||
|
||||
list_response = client.get("/api/maintenance/")
|
||||
assert list_response.status_code == 200
|
||||
items = list_response.json()
|
||||
assert any(item["id"] == created["id"] for item in items)
|
||||
|
||||
|
||||
def test_get_maintenance():
|
||||
scenario_id, equipment_id = _create_scenario_and_equipment()
|
||||
payload = _create_maintenance_payload(
|
||||
equipment_id, scenario_id, "Retrieve maintenance"
|
||||
)
|
||||
assert resp.status_code == 200
|
||||
scenario = resp.json()
|
||||
sid = scenario["id"]
|
||||
create_response = client.post("/api/maintenance/", json=payload)
|
||||
assert create_response.status_code == 201
|
||||
maintenance_id = create_response.json()["id"]
|
||||
|
||||
# Create Maintenance record
|
||||
maint_payload = {"scenario_id": sid, "details": "Routine check"}
|
||||
resp2 = client.post("/api/maintenance/", json=maint_payload)
|
||||
assert resp2.status_code == 200
|
||||
maint = resp2.json()
|
||||
assert maint["scenario_id"] == sid
|
||||
assert maint["details"] == "Routine check"
|
||||
response = client.get(f"/api/maintenance/{maintenance_id}")
|
||||
assert response.status_code == 200
|
||||
data = response.json()
|
||||
assert data["id"] == maintenance_id
|
||||
assert data["equipment_id"] == equipment_id
|
||||
assert data["description"] == "Retrieve maintenance"
|
||||
|
||||
# List Maintenance records
|
||||
resp3 = client.get("/api/maintenance/")
|
||||
assert resp3.status_code == 200
|
||||
data = resp3.json()
|
||||
assert any(item["details"] ==
|
||||
"Routine check" and item["scenario_id"] == sid for item in data)
|
||||
|
||||
def test_update_maintenance():
|
||||
scenario_id, equipment_id = _create_scenario_and_equipment()
|
||||
create_response = client.post(
|
||||
"/api/maintenance/",
|
||||
json=_create_maintenance_payload(
|
||||
equipment_id, scenario_id, "Maintenance before update"
|
||||
),
|
||||
)
|
||||
assert create_response.status_code == 201
|
||||
maintenance_id = create_response.json()["id"]
|
||||
|
||||
update_payload = {
|
||||
"equipment_id": equipment_id,
|
||||
"scenario_id": scenario_id,
|
||||
"maintenance_date": "2025-11-01",
|
||||
"description": "Maintenance after update",
|
||||
"cost": 250.0,
|
||||
}
|
||||
|
||||
response = client.put(
|
||||
f"/api/maintenance/{maintenance_id}", json=update_payload)
|
||||
assert response.status_code == 200
|
||||
updated = response.json()
|
||||
assert updated["maintenance_date"] == "2025-11-01"
|
||||
assert updated["description"] == "Maintenance after update"
|
||||
assert updated["cost"] == 250.0
|
||||
|
||||
|
||||
def test_delete_maintenance():
|
||||
scenario_id, equipment_id = _create_scenario_and_equipment()
|
||||
create_response = client.post(
|
||||
"/api/maintenance/",
|
||||
json=_create_maintenance_payload(
|
||||
equipment_id, scenario_id, "Delete maintenance"),
|
||||
)
|
||||
assert create_response.status_code == 201
|
||||
maintenance_id = create_response.json()["id"]
|
||||
|
||||
delete_response = client.delete(f"/api/maintenance/{maintenance_id}")
|
||||
assert delete_response.status_code == 204
|
||||
|
||||
get_response = client.get(f"/api/maintenance/{maintenance_id}")
|
||||
assert get_response.status_code == 404
|
||||
|
||||
@@ -3,14 +3,19 @@ from main import app
|
||||
from config.database import Base, engine
|
||||
|
||||
# Setup and teardown
|
||||
|
||||
|
||||
def setup_module(module):
|
||||
Base.metadata.create_all(bind=engine)
|
||||
|
||||
|
||||
def teardown_module(module):
|
||||
Base.metadata.drop_all(bind=engine)
|
||||
|
||||
|
||||
client = TestClient(app)
|
||||
|
||||
|
||||
def test_create_and_list_production_output():
|
||||
# Create a scenario to attach production output
|
||||
resp = client.post(
|
||||
@@ -21,9 +26,10 @@ def test_create_and_list_production_output():
|
||||
sid = scenario["id"]
|
||||
|
||||
# Create Production Output item
|
||||
prod_payload = {"scenario_id": sid, "amount": 300.0, "description": "Daily output"}
|
||||
prod_payload = {"scenario_id": sid,
|
||||
"amount": 300.0, "description": "Daily output"}
|
||||
resp2 = client.post("/api/production/", json=prod_payload)
|
||||
assert resp2.status_code == 200
|
||||
assert resp2.status_code == 201
|
||||
prod = resp2.json()
|
||||
assert prod["scenario_id"] == sid
|
||||
assert prod["amount"] == 300.0
|
||||
@@ -32,4 +38,5 @@ def test_create_and_list_production_output():
|
||||
resp3 = client.get("/api/production/")
|
||||
assert resp3.status_code == 200
|
||||
data = resp3.json()
|
||||
assert any(item["amount"] == 300.0 and item["scenario_id"] == sid for item in data)
|
||||
assert any(item["amount"] == 300.0 and item["scenario_id"]
|
||||
== sid for item in data)
|
||||
|
||||
@@ -1,15 +1,38 @@
|
||||
from services.reporting import generate_report
|
||||
from routes.reporting import router
|
||||
from fastapi.testclient import TestClient
|
||||
from main import app
|
||||
import pytest
|
||||
|
||||
# Function test
|
||||
from main import app
|
||||
from services.reporting import generate_report
|
||||
|
||||
|
||||
def test_generate_report_empty():
|
||||
report = generate_report([])
|
||||
assert isinstance(report, dict)
|
||||
assert report == {
|
||||
"count": 0,
|
||||
"mean": 0.0,
|
||||
"median": 0.0,
|
||||
"min": 0.0,
|
||||
"max": 0.0,
|
||||
"std_dev": 0.0,
|
||||
"percentile_10": 0.0,
|
||||
"percentile_90": 0.0,
|
||||
}
|
||||
|
||||
|
||||
def test_generate_report_with_values():
|
||||
values = [{"iteration": 1, "result": 10.0}, {
|
||||
"iteration": 2, "result": 20.0}, {"iteration": 3, "result": 30.0}]
|
||||
report = generate_report(values)
|
||||
assert report["count"] == 3
|
||||
assert report["mean"] == pytest.approx(20.0)
|
||||
assert report["median"] == pytest.approx(20.0)
|
||||
assert report["min"] == pytest.approx(10.0)
|
||||
assert report["max"] == pytest.approx(30.0)
|
||||
assert report["std_dev"] == pytest.approx(8.1649658, rel=1e-6)
|
||||
assert report["percentile_10"] == pytest.approx(12.0)
|
||||
assert report["percentile_90"] == pytest.approx(28.0)
|
||||
|
||||
|
||||
# Endpoint test
|
||||
def test_reporting_endpoint_invalid_input():
|
||||
client = TestClient(app)
|
||||
resp = client.post("/api/reporting/summary", json={})
|
||||
@@ -19,9 +42,29 @@ def test_reporting_endpoint_invalid_input():
|
||||
|
||||
def test_reporting_endpoint_success():
|
||||
client = TestClient(app)
|
||||
# Minimal input: list of dicts
|
||||
input_data = [{"iteration": 1, "result": 10.0}]
|
||||
input_data = [
|
||||
{"iteration": 1, "result": 10.0},
|
||||
{"iteration": 2, "result": 20.0},
|
||||
{"iteration": 3, "result": 30.0},
|
||||
]
|
||||
resp = client.post("/api/reporting/summary", json=input_data)
|
||||
assert resp.status_code == 200
|
||||
data = resp.json()
|
||||
assert isinstance(data, dict)
|
||||
assert data["count"] == 3
|
||||
assert data["mean"] == pytest.approx(20.0)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"payload,expected_detail",
|
||||
[
|
||||
(["not-a-dict"], "Entry at index 0 must be an object"),
|
||||
([{"iteration": 1}], "Entry at index 0 must include numeric 'result'"),
|
||||
([{"iteration": 1, "result": "bad"}],
|
||||
"Entry at index 0 must include numeric 'result'"),
|
||||
],
|
||||
)
|
||||
def test_reporting_endpoint_validation_errors(payload, expected_detail):
|
||||
client = TestClient(app)
|
||||
resp = client.post("/api/reporting/summary", json=payload)
|
||||
assert resp.status_code == 400
|
||||
assert resp.json()["detail"] == expected_detail
|
||||
|
||||
Reference in New Issue
Block a user