# 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")
# Set up additional parameters:
# Set up weight for groundwater index
gweight = 0.2
#scenarios = FileHandle.getFolders('Inputs/')
scenarios = ["Inputs/Hist"] #FileHandle.getFolders('Inputs/')
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
#Create a Hydrology Object
Hydro = Hydrology(data)
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]:
irrigation_data[irrigation_name] = irrigation_files[folder][irrigation_name]
temp_data = irrigation_data[irrigation_name]['Best Guess'].to_dict()
temp_data['irrigation_name'] = irrigation_name
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']
# read in weights
weights = indexes["weights"]
#change headers to lowercase to make it consistent with other csvs
weights.columns = [x.lower() for x in weights.columns]
# Set up additional parameters:
# Set up weight for groundwater index
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']
# read in weights
weights = indexes["weights"]
#change headers to lowercase to make it consistent with other csvs
weights.columns = [x.lower() for x in weights.columns]