#!/usr/bin/env python
# coding: utf-8

# # Workflow for a multi-regional energy system
# 
# In this application of the FINE framework, a multi-regional energy system is modeled and optimized.
# 
# All classes which are available to the user are utilized and examples of the selection of different parameters within these classes are given.
# 
# The workflow is structures as follows:
# 1. Required packages are imported and the input data path is set
# 2. An energy system model instance is created
# 3. Commodity sources are added to the energy system model
# 4. Commodity conversion components are added to the energy system model
# 5. Commodity storages are added to the energy system model
# 6. Commodity transmission components are added to the energy system model
# 7. Commodity sinks are added to the energy system model
# 8. The energy system model is optimized
# 9. Selected optimization results are presented
# 

# 1. Import required packages and set input data path

import FINE as fn
import pandas as pd

def test_miniSystem():
    locations = {'loc1','loc2'}
    numberOfTimeSteps = 365
    hoursPerTimeStep = 24
    commodities = {'electricity'}
    commodityUnitDict = {'electricity': r'GW$_{el}$'}

    esM = fn.EnergySystemModel(locations=locations, commodities=commodities, numberOfTimeSteps=numberOfTimeSteps,
                            commodityUnitsDict=commodityUnitDict, hoursPerTimeStep=hoursPerTimeStep,
                            costUnit='Euro', lengthUnit='km', verboseLogLevel=0)

    costTS = pd.DataFrame([[(j%5)*(i+1) for i in range(len(locations))]
                        for j in range(numberOfTimeSteps)], columns=['loc1','loc2'])

    esM.add(fn.Source(esM=esM, name='Electricity purchase', commodity='electricity',
                    hasCapacityVariable=False, commodityCostTimeSeries=costTS))

    revenueTS = pd.DataFrame([[(j%5)*(i+1) for i in range(len(locations))]
                        for j in range(numberOfTimeSteps)], columns=['loc1','loc2'])
            
    demandTS = pd.DataFrame([[i+1 for i in range(len(locations))]
                        for j in range(numberOfTimeSteps)], columns=['loc1','loc2'])
            
    esM.add(fn.Sink(esM=esM, name='Electricity demand', commodity='electricity',operationRateFix=demandTS,
            hasCapacityVariable=False, commodityRevenueTimeSeries=costTS))

    esM.optimize(timeSeriesAggregation=False, solver = 'glpk')

    summary = esM.getOptimizationSummary("SourceSinkModel", outputLevel=2)
    assert summary.loc[('Electricity demand','TAC','[Euro/a]'),'loc1'] == 730
    assert summary.loc[('Electricity demand','TAC','[Euro/a]'),'loc2'] == 2920
    assert summary.loc[('Electricity purchase','TAC','[Euro/a]'),'loc1'] == -730
    assert summary.loc[('Electricity purchase','TAC','[Euro/a]'),'loc2'] == -2920

    esM.cluster(numberOfTypicalPeriods=5, numberOfTimeStepsPerPeriod=1)

    esM.optimize(timeSeriesAggregation=True, solver = 'glpk')

    summary = esM.getOptimizationSummary("SourceSinkModel", outputLevel=2)
    assert summary.loc[('Electricity demand','TAC','[Euro/a]'),'loc1'] == 730
    assert summary.loc[('Electricity demand','TAC','[Euro/a]'),'loc2'] == 2920
    assert summary.loc[('Electricity purchase','TAC','[Euro/a]'),'loc1'] == -730
    assert summary.loc[('Electricity purchase','TAC','[Euro/a]'),'loc2'] == -2920
