from integrated.Modules.Core.Handlers.FileHandler import FileHandler
from integrated.Modules.Hydrology.IHACRES.IHACRES import IHACRES
import pandas as pd
if __name__ == '__main__':
ClimateData = FileHandler().loadCSV('test_data/climate/Echuca.txt',
columns=[0, 7, 9],
skiprows=41,
delimiter=r'\s*',
engine='python') #
ClimateData.index = pd.to_datetime(ClimateData[ClimateData.columns[0]],
format='%Y%m%d')
ClimateData.columns = ['Date', 'rainfall', 'ET']
RRmodel = IHACRES(ClimateData, 50)
prev_storage = 50
inflow = 100
irrig_out = 25
evap = 10
local_inflow = 10
base_flow = 5
deep_drainage = 10
storage_coef = 0.5
ET_f = 1.1
#Test flow calculation
print RRmodel.calcFlow(prev_storage, inflow, irrig_out, evap, local_inflow,
base_flow, deep_drainage, storage_coef, ET_f)
max_separation=getAssetParam(asset_table, gauge, 'Event_gap'), \
min_duration=getAssetParam(asset_table, gauge, 'Event_dur') )
#End envIndex()
# ## Function to calculate median/total flow/baseflow levels (annual), if total, fun=sum.
# flowsum <- function (x,fun=median,...){
# summaryby <- as.POSIXlt(time(x))$year + 1900
# flowsummary <- aggregate(x, by=summaryby, FUN = fun, na.rm=TRUE)
# return(round(flowsummary,2))
# }
### Script starts here ###
FileHandle = FileHandler()
#Read in input data
## Read in input data
# read in all breakpoint files from 1900 til end of file
date_range = ["1900-01-01", None]
indexes = FileHandle.importFiles("Inputs/index", ext=".csv", walk=False)
# read in asset table
#This could be set as class attribute as it is used as a global in the R script
asset_table = FileHandle.loadCSV("Inputs/ctf_dss.csv")
# read in weights
weightall = FileHandle.loadCSV("Inputs/index/weight/weight.csv")
#Tired of having columns split over several lines
pd.set_option('max_colwidth', 5000)
pd.set_option('display.width', 5000)
TestFarm = FarmInfo(**FarmConfig.BASE_FARM.getParams())
test_farm_area = 100 #100 Ha
print "Processing {farm_name}".format(farm_name=TestFarm.name)
print "--------------------------"
print "Setting up historical data for Echuca area"
print "--------------------------"
#Real data for Echuca
ClimateData = FileHandler().loadCSV('test_data/climate/climate.csv', columns=['Date', '406265_rain', '406265_evap'], index_col=0, dayfirst=True, parse_dates=True)
ClimateData.index.name = 'Date'
ClimateData.columns = ['rainfall', 'ET']
Climate = Climate(data=ClimateData, low_rainfall_thres_mm=400, high_rainfall_thres_mm=500)
SurfacePolicy = SurfaceWaterPolicy(Climate)
#Determine water source costs
#TODO: Separate this out!
sw_yearly = FarmConfig.sw_costs['Campaspe']['yearly_costs']
sw_seasonal = FarmConfig.sw_costs['Campaspe']['water_costs']
sw_yearly_costs = sw_yearly.loc[['service', 'service_point', 'service_point_local_read', 'surface_drainage_service'], :].sum()['Amount ($/Point)']
#12ML/Day irrigation system capacity, price simulator says to divide entitlement with 100 = 4.35
sw_yearly_costs += sw_yearly.loc[['infrastructure_access (ML/Day)'], :].sum()['Amount ($/Point)'] * 0 #4.35
