import FINE as fn
import pandas as pd
from matplotlib import pyplot as plt


# Energiesystem:
sink_1 = "sink_1_commodity"
source_1 = "source_1_commodity"
source_2 = "source_2_commodity"
conversion_1 =["source_1_commodity","sink_1_commodity"]
conversion_2 =["source_2_commodity","sink_1_commodity"]


## Aufbau EMS

locations = {'location01'}

commodityUnitDict = {'sink_1_commodity': 'kW_el',
                     'source_1_commodity': 'kW_el',
                     'source_2_commodity': 'kW_el',
                     }

commodities = {'sink_1_commodity',
               'source_1_commodity',
               'source_2_commodity'
               }


esM = fn.EnergySystemModel(locations={"location01"},
                            commodities=commodities,
                            numberOfTimeSteps=8760,
                            commodityUnitsDict=commodityUnitDict,
                            hoursPerTimeStep=1,
                            costUnit='1e Euro',
                            lengthUnit='km',
                            verboseLogLevel=0)


## Sinks

#sink_1
esM.add(fn.Sink(esM=esM,
                name='sink_1',
                commodity=sink_1,
                hasCapacityVariable=False,
                operationRateFix=pd.read_excel("DataForExample/sink_1(2).xlsx"),
                ),
        )


## sources

#source_1
esM.add(fn.Source(esM=esM,
                  name='source_1',
                  commodity=source_1,
                  hasCapacityVariable=False,
                  commodityCost=0.1914421286
                  )
        )

#source_2
investPerCapacity=1000
esM.add(fn.Source(esM=esM,
                  name='source_2',
                  commodity=source_2,
                  hasCapacityVariable=True,
                  capacityMax= 1000,
                  operationRateMax=pd.read_excel("DataForExample/PV_1.xlsx"),
                  investPerCapacity=investPerCapacity,
                  opexPerCapacity= investPerCapacity * 0.015,
                  interestRate=0.04,
                  economicLifetime=25))



## Conversions

#conversion_source_1
esM.add(fn.Conversion(esM=esM,
                      name='conversion_1',
                      physicalUnit='kW_el',
                      commodityConversionFactors={source_1:-1, sink_1:1},
                      hasCapacityVariable=False))


#conversion_source_2
esM.add(fn.Conversion(esM=esM,
                      name='conversion_2',
                      physicalUnit='kW_el',
                      commodityConversionFactors={source_2:-1, sink_1:1},
                      hasCapacityVariable=False))








# 6. Results
##results

esM.cluster(numberOfTypicalPeriods=7)
esM.optimize(timeSeriesAggregation=True, solver='gurobi')
srcSnkSummary = esM.getOptimizationSummary("SourceSinkModel", outputLevel=1)
print(srcSnkSummary)
convSummary = esM.getOptimizationSummary("ConversionModel", outputLevel=1)
print(convSummary)

# Read the data from the Excel files
sink_data = pd.read_excel("DataForExample/sink_1(2).xlsx")
# Read the data from the Excel files
pv_data = pd.read_excel("DataForExample/PV_1.xlsx")
#Get the first column name as the data column
sink_data_col = sink_data.columns[0]
pv_data_col = pv_data.columns[0]

# Create the plots
plt.figure(figsize=(16, 12))

# Plot sink data separately
plt.subplot(3, 1, 1)
plt.plot(sink_data.index, sink_data[sink_data_col], label='Sink Operation Rate', color='red')
plt.xlabel('Time Index')
plt.ylabel('Operation Rate (kW_el)')
plt.title('Sink Operation Rate Over Time')
plt.legend()

# Plot PV data separately
plt.subplot(3, 1, 2)
plt.plot(pv_data.index, pv_data[pv_data_col], label='PV Operation Rate', color='green')
plt.xlabel('Time Index')
plt.ylabel('Operation Rate (kW_el)')
plt.title('PV Operation Rate Over Time')
plt.legend()

# Plot the two graphs overlapping
plt.subplot(3, 1, 3)
plt.plot(sink_data.index, sink_data[sink_data_col], label='Sink Operation Rate', color='blue')
plt.plot(pv_data.index, pv_data[pv_data_col], label='PV Operation Rate', color='green')
plt.xlabel('Time Index')
plt.show()