import os
import numpy as np
if __name__ == '__main__':
#import os
#os.chdir('C:\\UserData\\fub\\work09\\MDB')
from integrated.Modules.Core.Handlers.FileHandler import FileHandler
from integrated.Modules.Ecology.PlatypusFlow import PlatypusFlow
# from integrated.Modules.Ecology.SpeciesFlow import SpeciesFlow
import pandas as pd
import datetime
from datetime import timedelta
#import flow dev data
FileHandle = FileHandler()
#Paths to files
#dev_data_path = "integrated/Modules/Ecology/Inputs"
dev_data_path = "Inputs"
# Read in flow data
#flow_data_path = "integrated/Modules/Ecology/Inputs/Hydrology/sce1/406201.csv" #201,202, 265
flow_data_path = dev_data_path+"/Hydrology/sce1/406201.csv"
flow_data = FileHandle.loadCSV(flow_data_path, index_col="Date", parse_dates=True, dayfirst=True)
flow_data[flow_data<0] = np.nan
flow_col = "Flow"
#import environmental flow requirement data
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")
#main_hydro_dev.py
if __name__ == '__main__':
import numpy as np
import pandas as pd
from integrated.Modules.Core.Handlers.FileHandler import FileHandler
from Hydrology import Hydrology
import datetime
#Set up the file handler
FileHandle = FileHandler()
#Import all the files from the data directory
data_path = "data"
data = FileHandle.importFiles(data_path, ext=".csv", walk=False, dayfirst=True)
data = data["data"] #Remove parent directory from listing
#Set node_ids as index where possible
for name, df in data.iteritems():
try:
df.index = df['node']
except KeyError as e:
#Doesn't have node column
#Safe to skip
continue
#End try
#End for
from integrated.Modules.Core.Handlers.FileHandler import FileHandler
from integrated.Modules.Hydrology.Routing.Routing import Routing
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"]
WaterRouter = Routing(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 WaterRouter.calcFlow(
prev_storage, inflow, irrig_out, evap, local_inflow, base_flow, deep_drainage, storage_coef, ET_f
)
import os
import numpy as np
if __name__ == '__main__':
#import os
#os.chdir('C:\\UserData\\fub\\work09\\MDB')
from integrated.Modules.Core.Handlers.FileHandler import FileHandler
from integrated.Modules.Ecology.PlatypusFlow import PlatypusFlow
# from integrated.Modules.Ecology.SpeciesFlow import SpeciesFlow
import pandas as pd
import datetime
from datetime import timedelta
#import flow dev data
FileHandle = FileHandler()
#Paths to files
#dev_data_path = "integrated/Modules/Ecology/Inputs"
dev_data_path = "Inputs"
# Read in flow data
#flow_data_path = "integrated/Modules/Ecology/Inputs/Hydrology/sce1/406201.csv" #201,202, 265
flow_data_path = dev_data_path + "/Hydrology/sce1/406201.csv"
flow_data = FileHandle.loadCSV(flow_data_path,
index_col="Date",
parse_dates=True,
dayfirst=True)
flow_data[flow_data < 0] = np.nan
#flow_dev.py
if __name__ == '__main__':
#import os
#os.chdir('C:\\UserData\\fub\\work09\\MDB')
from integrated.Modules.Ecology.FlowSuitability import FlowSuitability
from integrated.Modules.Core.Handlers.FileHandler import FileHandler
import pandas as pd
FileHandle = FileHandler()
#Paths to files
dev_data_path = "Integrated/Modules/Ecology/Inputs"
#dev_data_path = "Inputs"
# Read in flow data
scenarios = [dev_data_path+"/Hydrology/sce1"]#, dev_data_path+"/Hydrology/sce2"]
date_range = ["1900-01-01", None]
# Read in index data
# NOTE: Left most column will be used as the DataFrame index
indexes = FileHandle.importFiles(dev_data_path+"/Ecology/index", ext=".csv", index_col=0, walk=False)
indexes = indexes["index"] #Remove parent folder listing from Dict, as this is unneeded
# 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(dev_data_path+"/Ecology/Water_suitability_param.csv")
eco_assets = ['A2','A4','A5']
name='ASR',
num_years=20,
storage_capacity_ML=200,
storage_cost_per_ML=0,
capital_cost_per_ML_at_02_per_day=700, #TESTING, REMOVE WHEN DONE
cost_capital=0,
maintenance_rate=0.07,
capture_pump_cost_ratio=0.6,
pump_cost_dollar_per_ML=35,
ClimateVariables=ParameterSet(surface_evap_rate=0.4),
# WaterSources=WaterSources(water_source={'flood_harvest': 200})
)
### Import data from files ###
DataHandle = FileHandler()
#Water Costs
#Groundwater
gw_costs = DataHandle.importFiles('WaterSources/data/costs/groundwater', index_col=0, skipinitialspace=True, walk=True)
sw_costs = DataHandle.importFiles('WaterSources/data/costs/surface_water', index_col=0, skipinitialspace=True, walk=True)
#Irrigations
irrigation_data = {}
irrigation_files = DataHandle.importFiles('Irrigations/data', index_col=0, skipinitialspace=True)
irrigation_params = {}
for folder in irrigation_files:
for irrigation_name in irrigation_files[folder]:
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)
#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
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[
#flow_dev.py
if __name__ == '__main__':
#import os
#os.chdir('C:\\UserData\\fub\\work09\\MDB')
from integrated.Modules.Ecology.FlowSuitability import FlowSuitability
from integrated.Modules.Core.Handlers.FileHandler import FileHandler
import pandas as pd
FileHandle = FileHandler()
#Paths to files
dev_data_path = "Integrated/Modules/Ecology/Inputs"
#dev_data_path = "Inputs"
# Read in flow data
scenarios = [dev_data_path + "/Hydrology/sce1"
] #, dev_data_path+"/Hydrology/sce2"]
date_range = ["1900-01-01", None]
# Read in index data
# NOTE: Left most column will be used as the DataFrame index
indexes = FileHandle.importFiles(dev_data_path + "/Ecology/index",
ext=".csv",
index_col=0,
walk=False)
indexes = indexes[
"index"] #Remove parent folder listing from Dict, as this is unneeded