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
#eflow_req_path = "integrated/Modules/Ecology/Inputs/Ecology/Platypus_flow_req.csv"
eflow_req_path = dev_data_path+"/Ecology/Platypus_flow_req.csv"
eflow_req = FileHandle.loadCSV(eflow_req_path)
# minimum duration requirements for low flow index
summerlowday = 120
winterlowday = 60 #winter low is limiting the low flow index -> u/c required
# inputs for food index and dispersal index
durations = {
### 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/')
# For DSS, can use RRGMS only as a minimum.
specieslist = [
"RRGMS", "RRGRR", "BBMS", "BBRR", "LGMS", "LGRR", "WCMS", "WCRR"
]
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
gw_weight = 0.4
sw_weight = 0.6
# For DSS, can use RRGMS only as a minimum.
specieslist = ["RRGMS","RRGRR"]
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
#eflow_req_path = "integrated/Modules/Ecology/Inputs/Ecology/Platypus_flow_req.csv"
eflow_req_path = dev_data_path + "/Ecology/Platypus_flow_req.csv"
eflow_req = FileHandle.loadCSV(eflow_req_path)
# minimum duration requirements for low flow index
summerlowday = 120
winterlowday = 60 #winter low is limiting the low flow index -> u/c required
# inputs for food index and dispersal index
temp_data['irrigation_name'] = irrigation_name
irrigation_params[irrigation_name] = ParameterSet(**temp_data)
globals()[irrigation_name+"_params"] = irrigation_params[irrigation_name]
temp_params = irrigation_params[irrigation_name].getParams()
temp_params['name'] = irrigation_name
globals()[irrigation_name] = IrrigationPractice(**temp_params)
#End for
#End for
#Pumping System
DieselPump = DataHandle.loadCSV('PumpingSystems/data/shallow.csv', index_col=0, skipinitialspace=True)
DieselPump = Pumps(name='Diesel Pump', **DieselPump['Best Guess'].to_dict())
NoPump = DataHandle.loadCSV('PumpingSystems/data/no_pump.csv', index_col=0, skipinitialspace=True)
NoPump = Pumps(name='No Pump', **NoPump['Best Guess'].to_dict())
#Crops
crop_data_files = DataHandle.importFiles('Crops/data/variables', walk=True, index_col=0, skipinitialspace=True)
crop_data = {}
crop_params = {}
for folder in crop_data_files:
for crop_name in crop_data_files[folder]:
crop_data[crop_name] = crop_data_files[folder][crop_name]
temp_data = crop_data[crop_name]['Best Guess'].to_dict()
temp_data['crop_name'] = crop_name
] #, 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
gw_weight = 0.4
sw_weight = 0.6
# For DSS, can use RRGMS only as a minimum.
specieslist = ["RRGMS", "RRGRR"]
