LOONE_Q.py

# -*- coding: utf-8 -*-
"""
Created on Sun Jul 18 18:44:37 2021

@author: osama
"""

import os
import pandas as pd
from datetime import datetime
import numpy as np
from calendar import monthrange  
from Model_Config import Model_Config 
Working_Path = Model_Config.Working_Path
os.chdir('%s'%Working_Path) 
from Pre_defined_Variables import Pre_defined_Variables 
from Model_variables import M_var
from LO_FNs import LO_FNs
from Stg_Sto_Ar import Stg_Sto_Ar 
from LONINO_FNs import LONINO_FNs
from Dec_Tree_FNs import Dec_Tree_FNs
from WCA_Stages_Cls import WCA_Stages_Cls
from Additional_Fncs import Add_Fn
from THC_Class import THC_Class
from df_WSMs import WSMs
from Data import Data
from Trib_HC import Trib_HC

def LOONE_Q(P_1,P_2,S77_DV,S308_DV,TP_Lake_S): 
    print("LOONE Q Module is Running!")
    # Based on the defined Start and End year, month, and day on the Pre_defined_Variables File, Startdate and enddate are defined. 
    year, month, day = map(int, Pre_defined_Variables.startdate_entry)
    startdate = datetime(year, month, day).date() 
    year, month, day = map(int, Pre_defined_Variables.startdate_entry)
    begdateCS = datetime(year, month, day).date()
    year, month, day = map(int, Pre_defined_Variables.enddate_entry)
    enddate = datetime(year, month, day).date()
        
#############################################################################
    if Model_Config.Sim_type == 0 or Model_Config.Sim_type == 1:
        WSMs()
        df_WSMs = pd.read_csv('./Data/df_WSMs.csv')
    else:
        df_WSMs = pd.read_csv('./Data/df_WSMs.csv')
    #The Following Code interpolates daily LOSA demand from weekly data for 6 differnet datasets where the user defines the LOSA demand that will be used based on a Code (1:6).
    #Set time frame for model run
    date_rng_2 = pd.date_range(start=startdate, end = enddate, freq= 'D')
    #Create a data frame with a date column
    Water_dmd = pd.DataFrame(date_rng_2, columns =['date'])
    
    N = []
    Wk = []
    #Generate a count list
    for i in Water_dmd['date']:
        if i.month == 1 and i.day == 1:
            n = 0
        else:
            n = n + 1
        N.append(n)
    Water_dmd['count'] = N
    #Calculate the week number for all rows in the data frame
    for i in Water_dmd['count']:
        if i > 363:
            J = 52
        else:
            J = int(i/7)+1
        Wk.append(J)
    Water_dmd['Week_num'] = Wk
    dd = [] #daily demand
    #Calculate daily water demand
    for i in Water_dmd['Week_num']:
        D = ((Data.Weekly_dmd['C%s'%Pre_defined_Variables.Code].iloc[i-1])/7)*(Pre_defined_Variables.Multiplier/100)
        dd.append(D)
    Water_dmd['Daily_demand'] = dd
    ##############################################################################################
    #Determine Tributary Hydrologic Conditions
    TC_LONINO_df = Trib_HC()
    #Determine WCA Stages
    WCA_Stages_df = WCA_Stages_Cls(TC_LONINO_df)
    #A dataframe to determine eachday's season (Months 11,12,1,2 are Season 1, Months 3,4,5 are season 2, Months 6,7 are season 3, Months 8,9,10 are season 4 )
    date_rng_5 = pd.date_range(start = startdate, end = enddate, freq ='D')
    Seasons = pd.DataFrame(date_rng_5, columns =['date'])
    Seas_Count = len(Seasons.index)
    for i in range(Seas_Count):
        if Seasons['date'].iloc[i].month > 2 and Seasons['date'].iloc[i].month < 6:
            S = 2
        elif Seasons['date'].iloc[i].month > 5 and Seasons['date'].iloc[i].month < 8:
            S = 3
        elif Seasons['date'].iloc[i].month > 7 and Seasons['date'].iloc[i].month < 11:
            S = 4
        else:
            S = 1
        M_var.Daily_Seasons[i] = S
        M_var.Mon[i] = Seasons['date'].iloc[i].month
    Seasons['Season'] = M_var.Daily_Seasons 
    Seasons['Month'] = M_var.Mon
##################################################################################################################   
    #This following Script runs the main model daily simulations.
    date_rng_6 = pd.date_range(start='12/30/%d'%(Pre_defined_Variables.startyear-1), end = enddate, freq= 'D')
    LO_Model = pd.DataFrame(date_rng_6, columns =['date'])
    LO_Model['Net_Inflow'] = Data.NetInf_Input['Netflows_acft']
    n_rows = len(LO_Model.index)
    LO_Model['LOSA_dmd_SFWMM'] = Data.SFWMM_W_dmd['LOSA_dmd'] * (Pre_defined_Variables.Mult_LOSA/100)
    LO_Model['C44RO'] = Data.C44_Runoff['C44RO']
    ##################################
    DecTree_df = pd.DataFrame(date_rng_5, columns = ['Date'])
    DecTree_df['Zone_B_MetFcast'] = TC_LONINO_df['LONINO_Seasonal_Classes']
    #Create a dataframe that includes Monthly Mean Basin Runoff & BaseFlow-Runoff & Runoff-Baseflow (cfs)
    date_rng_11 = pd.date_range(start=startdate, end = enddate, freq= 'MS')
    date_rng_11d = pd.date_range(start=startdate, end = enddate, freq= 'D')
    date_rng_11d.name = 'Date'
    Basin_RO = pd.DataFrame(date_rng_11, columns =['date'])
    #Baseflows
    Outlet1_baseflow = Data.S77_RegRelRates['Zone_D0'].iloc[0]   
    Outlet2_baseflow = Data.S80_RegRelRates['Zone_D0'].iloc[0]
    #Calculta number of months in the timeseries data.
    num_B_R = len(Basin_RO.index)
    BS_C43RO = np.zeros(num_B_R)
    BS_C44RO = np.zeros(num_B_R)
    C44RO_SLTRIB = np.zeros(num_B_R)
    C44RO_BS = np.zeros(num_B_R)
    Num_days = np.zeros(num_B_R)
    for i in range(num_B_R) :
        Num_days[i] = monthrange(Basin_RO['date'].iloc[i].year, Basin_RO['date'].iloc[i].month)[1] #no. of days in each time step month.
        BS_C43RO[i] = max(0, (Outlet1_baseflow - Data.C43RO['C43RO'].iloc[i]))
        BS_C44RO[i] = max(0, (Outlet2_baseflow - Data.C44RO['C44RO'].iloc[i]))
        C44RO_SLTRIB[i] = BS_C44RO[i] + Data.SLTRIB['SLTRIB_cfs'].iloc[i]
        C44RO_BS[i] = max(0, Data.C44RO['C44RO'].iloc[i] - Outlet2_baseflow)*Num_days[i]
    Basin_RO['Ndays'] = Num_days
    Basin_RO['C43RO'] = Data.C43RO['C43RO']
    Basin_RO['BS-C43RO'] = BS_C43RO
    Basin_RO['C44RO'] = Data.C44RO['C44RO']
    Basin_RO['BS-C44RO'] = BS_C44RO
    Basin_RO['SLTRIB'] = Data.SLTRIB['SLTRIB_cfs']
    Basin_RO['C44RO_SLTRIB'] = C44RO_SLTRIB
    Basin_RO['C44RO-BS'] = C44RO_BS
    LO_Model['C43RO'] = Data.C43RO_Daily['C43RO']
    S80avgL1 = Data.Pulses['S-80_L1_%s'%Pre_defined_Variables.Schedule].mean()
    S80avgL2 = Data.Pulses['S-80_L2_%s'%Pre_defined_Variables.Schedule].mean()
    S80avgL3 = Data.Pulses['S-80_L3_%s'%Pre_defined_Variables.Schedule].mean()
    S77avgL1 = Data.Pulses['S-77_L1_%s'%Pre_defined_Variables.Schedule].mean() #LORS
    S77avgL2 = Data.Pulses['S-77_L2_%s'%Pre_defined_Variables.Schedule].mean() #LORS
    S77avgL3 = Data.Pulses['S-77_L3_%s'%Pre_defined_Variables.Schedule].mean()
    Basin_RO = Basin_RO.set_index(['date'])
    Basin_RO.index = pd.to_datetime(Basin_RO.index)
    Basin_RO_Daily = Basin_RO.reindex(date_rng_11d, method='ffill')
    Basin_RO = Basin_RO.reset_index()
    VLOOKUP1 = Basin_RO_Daily['BS-C44RO']
    VLOOKUP1_c = [x for x in VLOOKUP1 if ~np.isnan(x)]
    ##################################################################################################################
    #This following script contains the logic and calculations for the proposed Lake Okeechobee Adaptive Protocol.
    AdapProt_df = pd.DataFrame(date_rng_5, columns = ['date'])
    #Calculate Late Dry Season (Apr-May) logic.
    Late_Dry_Season = []
    for i in AdapProt_df['date']:
        if i.month > 3 and i.month < 6:
            L = True
        else:
            L= False
        Late_Dry_Season.append(L)
    AdapProt_df['Late_Dry_Season'] = Late_Dry_Season
    AdapProt_df['Tributary Hydrologic Condition'] = TC_LONINO_df['Tributary_Condition']       
    #Define "Low Chance" 6/1 stg<11'
    if Pre_defined_Variables.Opt_Date_Targ_Stg ==1:
        Targ_Stg = Data.Targ_Stg_June_1st
    else:
        Targ_Stg = Data.Targ_Stg_May_1st
    
    Targ_Stg_df = pd.DataFrame(date_rng_5, columns = ['dates'])
    for i in range(len(Targ_Stg_df)):
        M_var.V10per[i] = Add_Fn.Replicate(Targ_Stg_df['dates'].iloc[i].year, Targ_Stg_df['dates'].iloc[i].timetuple().tm_yday,10,Targ_Stg)   
        M_var.V20per[i] = Add_Fn.Replicate(Targ_Stg_df['dates'].iloc[i].year, Targ_Stg_df['dates'].iloc[i].timetuple().tm_yday,20,Targ_Stg)
        M_var.V25per[i] = Add_Fn.Replicate(Targ_Stg_df['dates'].iloc[i].year, Targ_Stg_df['dates'].iloc[i].timetuple().tm_yday,25,Targ_Stg)
        M_var.V30per[i] = Add_Fn.Replicate(Targ_Stg_df['dates'].iloc[i].year, Targ_Stg_df['dates'].iloc[i].timetuple().tm_yday,30,Targ_Stg)   
        M_var.V40per[i] = Add_Fn.Replicate(Targ_Stg_df['dates'].iloc[i].year, Targ_Stg_df['dates'].iloc[i].timetuple().tm_yday,40,Targ_Stg) 
        M_var.V45per[i] = Add_Fn.Replicate(Targ_Stg_df['dates'].iloc[i].year, Targ_Stg_df['dates'].iloc[i].timetuple().tm_yday,45,Targ_Stg)
        M_var.V50per[i] = Add_Fn.Replicate(Targ_Stg_df['dates'].iloc[i].year, Targ_Stg_df['dates'].iloc[i].timetuple().tm_yday,50,Targ_Stg)   
        M_var.V60per[i] = Add_Fn.Replicate(Targ_Stg_df['dates'].iloc[i].year, Targ_Stg_df['dates'].iloc[i].timetuple().tm_yday,60,Targ_Stg)   

    V10per_c = [x for x in M_var.V10per if ~np.isnan(x)]
    V20per_c = [x for x in M_var.V20per if ~np.isnan(x)]
    V25per_c = [x for x in M_var.V25per if ~np.isnan(x)]
    V30per_c = [x for x in M_var.V30per if ~np.isnan(x)]
    V40per_c = [x for x in M_var.V40per if ~np.isnan(x)]
    V45per_c = [x for x in M_var.V45per if ~np.isnan(x)]
    V50per_c = [x for x in M_var.V50per if ~np.isnan(x)]
    V60per_c = [x for x in M_var.V60per if ~np.isnan(x)]
    Targ_Stg_df['10%'] = V10per_c
    Targ_Stg_df['20%'] = V20per_c
    Targ_Stg_df['25%'] = V25per_c
    Targ_Stg_df['30%'] = V30per_c
    Targ_Stg_df['40%'] = V40per_c
    Targ_Stg_df['45%'] = V45per_c
    Targ_Stg_df['50%'] = V50per_c
    Targ_Stg_df['60%'] = V60per_c
    
    # Outlet1_baseflow = Data.S77_RegRelRates['Zone_D0'].iloc[0]
    Outlet1_baseflow = 450 #cfs
    VLOOKUP2 = Basin_RO_Daily['BS-C43RO']
    VLOOKUP2_c = [x for x in VLOOKUP2 if ~np.isnan(x)]
####################################################################################################################
    M_var.Lake_Stage[0] = Pre_defined_Variables.begstageCS
    M_var.Lake_Stage[1] = Pre_defined_Variables.begstageCS
    M_var.DecTree_Relslevel[0] = np.nan
    M_var.DecTree_Relslevel[1] = np.nan
    if startdate.month == LO_Model['date'].iloc[2].month and startdate.day == LO_Model['date'].iloc[2].day:
        X1 = 'SimDay1'
    elif begdateCS.year == LO_Model['date'].iloc[2].year and begdateCS.month == LO_Model['date'].iloc[2].month and begdateCS.day == LO_Model['date'].iloc[2].day:
        X1 = 'CS start date'
    else:
        X1 = LO_Model['date'].iloc[2]
    M_var.DayFlags[2] = X1     
    StartStorage = Stg_Sto_Ar.stg2sto(Pre_defined_Variables.startstage,0)
    M_var.Storage[0] = StartStorage
    M_var.Storage[1] = StartStorage
    # Flood = np.zeros(n_rows, dtype = object)
    ##Here, I will insert the Storage Deviaiton Values as Input!
    Storage_dev = Data.Stroage_dev_df['DS_dev'] 
    #Create a Choose Function for AP Post Baseflow
    # if Pre_defined_Variables.Opt_AdapProt == 0:
    #     C = 450
    # elif Pre_defined_Variables.Opt_AdapProt == 1:
    #     C = Data.S77_RegRelRates['Zone_D0'].iloc[0]
    # Choose_1 = C
    Choose_1 = 450 #cfs
##############################################################################################################    
    M_var.Zone_Code[0] = LO_FNs.Zone_Code(M_var.Lake_Stage[0],df_WSMs['A'].iloc[0],df_WSMs['B'].iloc[0],df_WSMs['C'].iloc[0],df_WSMs['D3'].iloc[0],df_WSMs['D2'].iloc[0],df_WSMs['D1'].iloc[0],df_WSMs['D0'].iloc[0],df_WSMs['WSM1'].iloc[0])
    M_var.LO_Zone[0] = LO_FNs.LO_Zone(M_var.Zone_Code[0])
    for i in range(n_rows-2):
        M_var.WSM_Zone[i+2] = LO_FNs.WSM_Zone(M_var.Lake_Stage[i+1],df_WSMs.at[i+1, 'WSM4'],df_WSMs.at[i+1, 'WSM3'],df_WSMs.at[i+1, 'WSM2'],df_WSMs.at[i+1, 'WSM1'])
    #Calculate Daily Maximum Water Supply
    # Note that in LOSA_dmd we used (i) because this file starts from 1/1/2008 so i at this point =0.
    #Cutbacks are determined based on the WSM Zone. 
        M_var.Max_Supply[i+2] = LO_FNs.Max_Supply(M_var.WSM_Zone[i+2],Water_dmd.at[i, 'Daily_demand'],Pre_defined_Variables.Z1_cutback,Pre_defined_Variables.Z2_cutback,Pre_defined_Variables.Z3_cutback,Pre_defined_Variables.Z4_cutback)
    #Actual Daily Water supply
        M_var.LOSA_Supply[i+2] = LO_FNs.LOSA_Supply(M_var.WSM_Zone[i+2],LO_Model.at[i+2, 'LOSA_dmd_SFWMM'],M_var.Max_Supply[i+2],Pre_defined_Variables.Opt_LOSAws)
    # NetInflow - LOSA Supply
        M_var.NI_Supply[i+2] = LO_Model.at[i+2, 'Net_Inflow'] - M_var.LOSA_Supply[i+2]
    #TODO Note: for the pass statement, We will read the Daily Water supply from the SFWMM as an input.
    #Calculate the cutback where Cutback = Demand - Supply
        ctbk = LO_Model.at[i+2, 'LOSA_dmd_SFWMM'] - M_var.LOSA_Supply[i+2]        
        M_var.Cut_back[i+2] = ctbk
    #Calculate percentage of the demand that is not supplied for each day
        if LO_Model.at[i+2, 'LOSA_dmd_SFWMM'] == 0:
            DNS = 0
        else:
            DNS = (M_var.Cut_back[i+2] / LO_Model.at[i+2, 'LOSA_dmd_SFWMM'])*100
        M_var.Dem_N_Sup[i+2] = DNS
    # Calculate the Zone Code
    #Note that to calculate the Zone Code in Dec 31 2020 we needed the WSM and breakpoint zones in 1/1/2021!
    #Note Also that i = 0 in Stage indicates Dec 30 1964 while i = 0 in df_WSMs indicates Dec 31 1964!
        M_var.Zone_Code[i+1] = LO_FNs.Zone_Code(M_var.Lake_Stage[i+1],df_WSMs.at[i+1, 'A'],df_WSMs.at[i+1, 'B'],df_WSMs.at[i+1, 'C'],df_WSMs.at[i+1, 'D3'],df_WSMs.at[i+1, 'D2'],df_WSMs.at[i+1, 'D1'],df_WSMs.at[i+1, 'D0'],df_WSMs.at[i+1, 'WSM1'])
    #Generate the Zone Column based on the corresponding Zone Code.
        M_var.LO_Zone[i+1] = LO_FNs.LO_Zone(M_var.Zone_Code[i+1])
        M_var.Zone_D_Trib[i] = Dec_Tree_FNs.Zone_D_Trib(TC_LONINO_df.at[i, 'Tributary_Condition'],Pre_defined_Variables.Opt_NewTree)
        M_var.Zone_D_stage[i] = Dec_Tree_FNs.Zone_D_stage(M_var.Lake_Stage[i+1],df_WSMs.at[i, 'C-b'])
        M_var.Zone_D_Seas[i] = Dec_Tree_FNs.Zone_D_Seas(TC_LONINO_df.at[i, 'LONINO_Seasonal_Classes'],M_var.Zone_D_Trib[i],Pre_defined_Variables.Opt_NewTree)
        M_var.Zone_D_MSeas[i] = Dec_Tree_FNs.Zone_D_MSeas(TC_LONINO_df.at[i, 'LONINO_MultiSeasonal_Classes'])
        M_var.Zone_D_Branch_Code[i] = M_var.Zone_D_Trib[i]*1000 + M_var.Zone_D_stage[i]*100 + M_var.Zone_D_Seas[i]*10 + M_var.Zone_D_MSeas[i]*1
        M_var.Zone_D_Rel_Code[i] = Dec_Tree_FNs.Zone_D_Rel_Code(M_var.Zone_D_Branch_Code[i],Pre_defined_Variables.Opt_DecTree)  
        M_var.Zone_C_Trib[i] = Dec_Tree_FNs.Zone_C_Trib(TC_LONINO_df.at[i, 'Tributary_Condition'],Pre_defined_Variables.Opt_NewTree)
        M_var.Zone_C_Seas[i] = Dec_Tree_FNs.Zone_C_Seas(TC_LONINO_df.at[i, 'LONINO_Seasonal_Classes'],Pre_defined_Variables.Opt_NewTree)  
        M_var.Zone_C_MSeas[i] = Dec_Tree_FNs.Zone_C_MSeas(TC_LONINO_df.at[i, 'LONINO_MultiSeasonal_Classes'])
        M_var.Zone_C_MetFcast[i] = Dec_Tree_FNs.Zone_C_MetFcast(M_var.Zone_C_Seas[i],TC_LONINO_df.at[i, 'LONINO_Seasonal_Classes'],Pre_defined_Variables.Zone_C_MetFcast_Indicator)
        M_var.Zone_C_Branch_Code[i] = M_var.Zone_C_Trib[i]*1000 + M_var.Zone_C_MetFcast[i]*100 + M_var.Zone_C_Seas[i]*10 + M_var.Zone_C_MSeas[i]*1
        M_var.Zone_C_Rel_Code[i] = Dec_Tree_FNs.Zone_C_Rel_Code(M_var.Zone_C_Branch_Code[i],Pre_defined_Variables.Opt_DecTree)   
        M_var.Zone_B_Trib[i] = Dec_Tree_FNs.Zone_B_Trib(TC_LONINO_df.at[i, 'Tributary_Condition'],Pre_defined_Variables.Opt_NewTree)
        M_var.Zone_B_Stage[i] = Dec_Tree_FNs.Zone_B_Stage(M_var.Lake_Stage[i+1],Seasons.at[i, 'Season'])
        M_var.Zone_B_Seas[i] = Dec_Tree_FNs.Zone_B_Seas(TC_LONINO_df.at[i, 'LONINO_Seasonal_Classes'])
        M_var.Zone_B_Branch_Code[i] = M_var.Zone_B_Trib[i]*1000 + M_var.Zone_B_Stage[i]*100 + DecTree_df.at[i, 'Zone_B_MetFcast']*10 + M_var.Zone_B_Seas[i]*1
        M_var.Zone_B_Rel_Code[i] = Dec_Tree_FNs.Zone_B_Rel_Code(M_var.Zone_B_Branch_Code[i],Pre_defined_Variables.Opt_DecTree)
        M_var.DecTree_Relslevel[i+2] = LO_FNs.DecTree_Relslevel(M_var.Zone_Code[i+1],M_var.Zone_D_Rel_Code[i],M_var.Zone_C_Rel_Code[i],M_var.Zone_B_Rel_Code[i])
        if i >= 3:
            if startdate.month == LO_Model.at[i, 'date'].month and startdate.day == LO_Model.at[i, 'date'].day and (Pre_defined_Variables.CSflag == 0 or startdate.year == LO_Model.at[i, 'date'].year):
                X2 = 'SimDay1'
            else:
                X2 = LO_Model.at[i, 'date'].date()
            M_var.DayFlags[i] = X2
        M_var.PlsDay[i+2] = LO_FNs.PlsDay(M_var.DayFlags[i+2],M_var.DecTree_Relslevel[i+2],Pre_defined_Variables.PlsDay_Switch)
        M_var.Release_Level[i+2] = LO_FNs.Release_Level(M_var.Release_Level[i+1],M_var.Lake_Stage[i+1],TC_LONINO_df.at[i, 'Tributary_Condition'],M_var.PlsDay[i+2],M_var.Zone_Code[i+1],M_var.DecTree_Relslevel[i+2],Pre_defined_Variables.MaxQstgTrigger)
        if i >= 6:
            dh = M_var.Lake_Stage[i+1] - M_var.Lake_Stage[i-6]
            M_var.dh_7days[i+1] = dh
        M_var.ZoneCodeminus1Code[i+1] = LO_FNs.ZoneCodeminus1Code(M_var.Zone_Code[i+1],df_WSMs.at[i+1, 'WSM1'],df_WSMs.at[i+1, 'D0'],df_WSMs.at[i+1, 'D1'],df_WSMs.at[i+1, 'D2'],df_WSMs.at[i+1, 'D3'],df_WSMs.at[i+1, 'C'],df_WSMs.at[i+1, 'B'],df_WSMs.at[i+1, 'A'])
        M_var.ZoneCodeCode[i+1] = LO_FNs.ZoneCodeCode(M_var.Zone_Code[i+1],df_WSMs.at[i+1, 'WSM1'],df_WSMs.at[i+1, 'D0'],df_WSMs.at[i+1, 'D1'],df_WSMs.at[i+1, 'D2'],df_WSMs.at[i+1, 'D3'],df_WSMs.at[i+1, 'C'],df_WSMs.at[i+1, 'B'],df_WSMs.at[i+1, 'A'])
        M_var.Fraction_of_Zone_height[i+1] = LO_FNs.Fraction_of_Zone_height(M_var.Zone_Code[i+1],M_var.Lake_Stage[i+1],M_var.ZoneCodeminus1Code[i+1],M_var.ZoneCodeCode[i+1])
        M_var.ReLevelCode_1[i+2] = LO_FNs.ReLevelCode_1(M_var.Release_Level[i+2],Pre_defined_Variables.dstar_D1,Pre_defined_Variables.dstar_D2,Pre_defined_Variables.dstar_D3,Pre_defined_Variables.dstar_C,Pre_defined_Variables.dstar_B)
        M_var.ReLevelCode_2[i+2] = LO_FNs.ReLevelCode_2(M_var.Release_Level[i+2],Pre_defined_Variables.astar_D1,Pre_defined_Variables.astar_D2,Pre_defined_Variables.astar_D3,Pre_defined_Variables.astar_C,Pre_defined_Variables.astar_B)
        M_var.ReLevelCode_3_S80[i+2] = LO_FNs.ReLevelCode_3_S80(M_var.Release_Level[i+2],Pre_defined_Variables.bstar_S80_D1,Pre_defined_Variables.bstar_S80_D2,Pre_defined_Variables.bstar_S80_D3,Pre_defined_Variables.bstar_S80_C,Pre_defined_Variables.bstar_S80_B)
        M_var.Outlet2DS_Mult[i+2] = LO_FNs.Outlet2DS_Mult(Seasons.at[i, 'Season'],Seasons.at[i, 'Month'],M_var.dh_7days[i+1],M_var.ReLevelCode_1[i+2],M_var.Fraction_of_Zone_height[i+1],M_var.ReLevelCode_2[i+2],M_var.ReLevelCode_3_S80[i+2],Pre_defined_Variables.Opt_QregMult)
        M_var.Outlet2DS_Mult_2[i+2] = LO_FNs.Outlet2DS_Mult_2(LO_Model.at[i+2, 'date'].month,LO_Model.at[i+2, 'date'].day,M_var.PlsDay[i+2],M_var.Outlet2DS_Mult[i+2-M_var.PlsDay[i+2]],M_var.Outlet2DS_Mult[i+2],Pre_defined_Variables.Opt_QregMult)        
        M_var.Outlet2DSRS[i+2] = LO_FNs.Outlet2DSRS(M_var.Release_Level[i+2],Data.S80_RegRelRates.at[0, 'Zone_D1'],S80avgL1,Data.Pulses.at[M_var.PlsDay[i+2]-1 if M_var.PlsDay[i+2]-1>=0 else len(Data.Pulses)-1, 'S-80_L1_%s'%Pre_defined_Variables.Schedule],M_var.Outlet2DS_Mult_2[i+2],Data.CE_SLE_turns.at[LO_Model.at[i+2, 'date'].year-Pre_defined_Variables.startyear, 'SLEturn'],Data.S80_RegRelRates.at[0, 'Zone_D2'],S80avgL2,Data.Pulses.at[M_var.PlsDay[i+2]-1 if M_var.PlsDay[i+2]-1>=0 else len(Data.Pulses)-1, 'S-80_L2_%s'%Pre_defined_Variables.Schedule],Data.S80_RegRelRates.at[0, 'Zone_D3'],S80avgL3,Data.Pulses.at[M_var.PlsDay[i+2]-1 if M_var.PlsDay[i+2]-1>=0 else len(Data.Pulses)-1, 'S-80_L3_%s'%Pre_defined_Variables.Schedule],Data.S80_RegRelRates.at[0, 'Zone_C'],Data.S80_RegRelRates.at[0, 'Zone_B'],Data.S80_RegRelRates.at[0, 'Zone_A'])
        M_var.Outlet2USRG1[i+2] = max(0,M_var.Outlet2DSRS[i+2]-LO_Model.at[i+2, 'C44RO'])
        M_var.Sum_Outlet2USRG1[i+2] = LO_FNs.Sum_Outlet2USRG1(LO_Model.at[i+2, 'date'].day,M_var.Outlet2USRG1[i+2])
        M_var.Outlet2DSBS[i+2] = LO_FNs.Outlet2DSBS(M_var.Release_Level[i+2],M_var.Sum_Outlet2USRG1[i+2],VLOOKUP1_c[i],Outlet2_baseflow,Pre_defined_Variables.Option_S80Baseflow)
        M_var.Outlet2USBK[i+2] = LO_FNs.Outlet2USBK(M_var.Lake_Stage[i+1],df_WSMs.at[i+1, 'D1'],M_var.Outlet2USRG[i+1],LO_Model.at[i+2, 'C44RO'],Data.SFWMM_Daily_Outputs.at[i+2, 'S308BK'],Pre_defined_Variables.Opt_S308,Pre_defined_Variables.S308BK_Const,Pre_defined_Variables.S308_BK_Thr)
        M_var.ROeast[i+2] = LO_Model.at[i+2, 'C44RO'] - M_var.Outlet2USBK[i+2]
        M_var.Outlet2USBS[i+2] = LO_FNs.Outlet2USBS(M_var.Outlet2DSBS[i+2],M_var.Outlet2USRG1[i+2],M_var.ROeast[i+2],Pre_defined_Variables.Option_S80Baseflow)
        M_var.Sum_Outlet2USBK[i+2] = LO_FNs.Sum_Outlet2USBK(LO_Model.at[i+2, 'date'].day,M_var.Outlet2USBK[i+2])
        M_var.Outlet2USRG_Code[i+2] = LO_FNs.Outlet2USRG_Code(M_var.Outlet2USRG1[i+2],M_var.Outlet2USBS[i+2],Data.SFWMM_Daily_Outputs.at[i+2, 'S308RG'],Data.SFWMM_Daily_Outputs.at[i+2, 'STEST'],Pre_defined_Variables.Option_RegS77S308)
        if Model_Config.Sim_type == 0:
            M_var.Outlet2USRG[i+2] = LO_FNs.Outlet2USRG(M_var.Outlet2USRG_Code[i+2],Data.SFWMM_Daily_Outputs.at[i+2, 'S308RG'],Data.SFWMM_Daily_Outputs.at[i+2, 'STEST'],Pre_defined_Variables.Opt_S308,Pre_defined_Variables.S308RG_Const)
        else:
            if M_var.Lake_Stage[i+1] >= 18:
                M_var.Outlet2USRG[i+2] = 7200
            elif M_var.Lake_Stage[i+1] <= 8:
                M_var.Outlet2USRG[i+2] = 0
            elif (TP_Lake_S[i] <= P_1) and (date_rng_6[i+2].month in [1,2,3,4,11,12]):
                M_var.Outlet2USRG[i+2] = S308_DV[(date_rng_6[i+2].month)-1]
            elif (TP_Lake_S[i] <= P_2) and (date_rng_6[i+2].month in [5,6,7,8,9,10]):
                M_var.Outlet2USRG[i+2] = S308_DV[(date_rng_6[i+2].month)-1]
            else:
                M_var.Outlet2USRG[i+2] = 0
        M_var.Outlet2DS[i+2] = LO_FNs.S80(M_var.ROeast[i+2],M_var.Outlet2USRG[i+2],Data.SFWMM_Daily_Outputs.at[i+2, 'S80'],Pre_defined_Variables.S80_Const)
        M_var.ReLevelCode_3_S77[i+2] = LO_FNs.ReLevelCode_3_S77(M_var.Release_Level[i+2],Pre_defined_Variables.bstar_S77_D1,Pre_defined_Variables.bstar_S77_D2,Pre_defined_Variables.bstar_S77_D3,Pre_defined_Variables.bstar_S77_C,Pre_defined_Variables.bstar_S77_B)
        M_var.Outlet1US_Mult[i+2] = LO_FNs.Outlet1US_Mult(Seasons.at[i, 'Season'],Seasons.at[i, 'Month'],M_var.dh_7days[i+1],M_var.ReLevelCode_1[i+2],M_var.Fraction_of_Zone_height[i+1],M_var.ReLevelCode_2[i+2],M_var.ReLevelCode_3_S77[i+2],Pre_defined_Variables.Opt_QregMult)
        M_var.Outlet1US_Mult_2[i+2] = LO_FNs.Outlet1US_Mult_2(LO_Model.at[i+2, 'date'].month,LO_Model.at[i+2, 'date'].day,M_var.PlsDay[i+2],M_var.Outlet1US_Mult[i+2-M_var.PlsDay[i+2]],M_var.Outlet1US_Mult[i+2],Pre_defined_Variables.Opt_QregMult)
        M_var.Outlet1USRS[i+2] = LO_FNs.Outlet1USRS(M_var.Release_Level[i+2],Data.S77_RegRelRates.at[0, 'Zone_D1'],S77avgL1,Data.Pulses.at[M_var.PlsDay[i+2]-1 if M_var.PlsDay[i+2]-1>=0 else len(Data.Pulses)-1, 'S-77_L1_%s'%Pre_defined_Variables.Schedule],M_var.Outlet1US_Mult_2[i+2],LO_Model.at[i+2, 'C43RO'],Data.CE_SLE_turns.at[LO_Model.at[i+2, 'date'].year-Pre_defined_Variables.startyear, 'CEturn'],Data.S77_RegRelRates.at[0, 'Zone_D2'],S77avgL2,Data.Pulses.at[M_var.PlsDay[i+2]-1 if M_var.PlsDay[i+2]-1>=0 else len(Data.Pulses)-1, 'S-77_L2_%s'%Pre_defined_Variables.Schedule],M_var.Zone_Code[i+1],Data.S77_RegRelRates.at[0, 'Zone_D3'],S77avgL3,Data.Pulses.at[M_var.PlsDay[i+2]-1 if M_var.PlsDay[i+2]-1>=0 else len(Data.Pulses)-1, 'S-77_L3_%s'%Pre_defined_Variables.Schedule],Data.S77_RegRelRates.at[0, 'Zone_C'],Data.S77_RegRelRates.at[0, 'Zone_B'],Data.S77_RegRelRates.at[0, 'Zone_A'],Pre_defined_Variables.Opt_Outlet1DSRG)
        M_var.Sum_Outlet1USRS[i+2] = LO_FNs.Sum_Outlet1USRS(LO_Model.at[i+2, 'date'].day,M_var.Outlet1USRS[i+2])
        M_var.Outlet1USBK[i+2] = LO_FNs.Outlet1USBK(M_var.Lake_Stage[i+1],M_var.Outlet1USRS[i+2],M_var.Outlet1USBSAP[i+1],M_var.Outlet1USEWS[i+1],LO_Model.at[i+2, 'C43RO'],Data.SFWMM_Daily_Outputs.at[i+2, 'S77BK'],Pre_defined_Variables.Outlet1USBK_Switch,Pre_defined_Variables.Outlet1USBK_Threshold)
        M_var.ROwest[i+2] = LO_Model.at[i+2, 'C43RO'] - M_var.Outlet1USBK[i+2]
        M_var.Outlet1DSBS[i+2] = LO_FNs.Outlet1DSBS(M_var.Release_Level[i+2],M_var.Sum_Outlet1USRS[i+2],VLOOKUP2_c[i],Outlet1_baseflow,Pre_defined_Variables.Option_S77Baseflow)
        M_var.Outlet1USBS[i+2] = LO_FNs.Outlet1USBS(M_var.Outlet1DSBS[i+2],M_var.Outlet1USRS[i+2],M_var.ROwest[i+2],Pre_defined_Variables.Option_S77Baseflow)
        #Define THC Class Normal or above
        if i < (n_rows-2):
            M_var.Post_Ap_Baseflow[i] = THC_Class(i,M_var.THC_Class_normal_or_above,M_var.Lake_O_Stage_AP,M_var.Lake_O_Schedule_Zone,M_var.LStgCorres,M_var.LowChance_Check,M_var.Outlet1USRS_AP,M_var.Outlet1USBS_AP,
              M_var.Outlet1USRS_Pre_AP_S77_Baseflow,M_var.Forecast_D_Sal,M_var.n30d_mavg,M_var.n30davgForecast,M_var.LORS08_bf_rel,M_var.LDS_LC6_1,M_var.S_O,M_var.All_4,
              M_var.Sabf,M_var.Swbf,M_var.Swbu,M_var.All_4andStage,M_var.All_4andStagein,M_var.P_AP_BF_Stg,M_var.Logic_test_1,M_var.Post_Ap_Baseflow,M_var.Outlet1USRSplusPreAPS77bsf,
              M_var.AndEstNeedsLakeWater,M_var.AndLowChance61stagelessth11,M_var.ATHCnora,M_var.Choose_PAPEWS_1,M_var.Choose_PAPEWS_2,M_var.Post_AP_EWS,
              M_var.Post_AP_Baseflow_EWS_cfs,AdapProt_df,M_var.Lake_Stage,M_var.Zone_Code,df_WSMs,Targ_Stg_df,M_var.Outlet1USRS,M_var.Outlet1USBS,Data.Estuary_needs_water,
              Choose_1,M_var.WSM_Zone)['Post_Ap_Baseflow']
            M_var.Post_AP_EWS[i] = THC_Class(i,M_var.THC_Class_normal_or_above,M_var.Lake_O_Stage_AP,M_var.Lake_O_Schedule_Zone,M_var.LStgCorres,M_var.LowChance_Check,M_var.Outlet1USRS_AP,M_var.Outlet1USBS_AP,
              M_var.Outlet1USRS_Pre_AP_S77_Baseflow,M_var.Forecast_D_Sal,M_var.n30d_mavg,M_var.n30davgForecast,M_var.LORS08_bf_rel,M_var.LDS_LC6_1,M_var.S_O,M_var.All_4,
              M_var.Sabf,M_var.Swbf,M_var.Swbu,M_var.All_4andStage,M_var.All_4andStagein,M_var.P_AP_BF_Stg,M_var.Logic_test_1,M_var.Post_Ap_Baseflow,M_var.Outlet1USRSplusPreAPS77bsf,
              M_var.AndEstNeedsLakeWater,M_var.AndLowChance61stagelessth11,M_var.ATHCnora,M_var.Choose_PAPEWS_1,M_var.Choose_PAPEWS_2,M_var.Post_AP_EWS,
              M_var.Post_AP_Baseflow_EWS_cfs,AdapProt_df,M_var.Lake_Stage,M_var.Zone_Code,df_WSMs,Targ_Stg_df,M_var.Outlet1USRS,M_var.Outlet1USBS,Data.Estuary_needs_water,
              Choose_1,M_var.WSM_Zone)['Post_AP_EWS']
        M_var.Outlet1USBSAP[i+2] = LO_FNs.Outlet1USBSAP(M_var.Outlet1USBS[i+2],M_var.Post_Ap_Baseflow[i],Pre_defined_Variables.Opt_AdapProt)
        M_var.Outlet1USEWS[i+2] = LO_FNs.Outlet1USEWS(M_var.Post_AP_EWS[i],Data.SFWMM_Daily_Outputs.at[i+2, 'CAEST'],Pre_defined_Variables.Outlet1USEWS_Switch,Pre_defined_Variables.Opt_AdapProt)
        if Model_Config.Sim_type == 0:
            M_var.Outlet1USREG[i+2] = LO_FNs.Outlet1USREG(M_var.Outlet1USRS[i+2],M_var.Outlet1USBSAP[i+2],Data.SFWMM_Daily_Outputs.at[i+2, 'S77RG'],Pre_defined_Variables.Outlet1USREG_Switch,Pre_defined_Variables.Option_RegS77S308)
        else:
            if M_var.Lake_Stage[i+1] >= 18:
                M_var.Outlet1USREG[i+2] = 7800
            elif M_var.Lake_Stage[i+1] <= 8:
                M_var.Outlet1USREG[i+2] = 0
            elif (TP_Lake_S[i] <= P_1) and (date_rng_6[i+2].month in [1,2,3,4,11,12]):
                M_var.Outlet1USREG[i+2] = S77_DV[(date_rng_6[i+2].month)-1]
            elif (TP_Lake_S[i] <= P_2) and (date_rng_6[i+2].month in [5,6,7,8,9,10]):
                M_var.Outlet1USREG[i+2] = S77_DV[(date_rng_6[i+2].month)-1]
            else:
                M_var.Outlet1USREG[i+2] = 0
        M_var.Outlet1DS[i+2] = LO_FNs.Outlet1DS(M_var.Outlet1USREG[i+2],M_var.Outlet1USEWS[i+2],M_var.ROwest[i+2],Data.SFWMM_Daily_Outputs.at[i+2, 'S79'],Pre_defined_Variables.Outlet1DS_Switch)
        M_var.TotRegEW[i+2] = (M_var.Outlet1USREG[i+2] + M_var.Outlet2USRG[i+2])*1.9835
        M_var.Choose_WCA[i+2] = LO_FNs.Choose_WCA(Data.SFWMM_Daily_Outputs.at[i+2, 'RegWCA'],Pre_defined_Variables.Option_RegWCA,Pre_defined_Variables.Constant_RegWCA)
        M_var.RegWCA[i+2] = min(Pre_defined_Variables.MaxCap_RegWCA , Pre_defined_Variables.Multiplier_RegWCA*M_var.Choose_WCA[i+2])
        M_var.Choose_L8C51[i+2] = LO_FNs.Choose_L8C51(Data.SFWMM_Daily_Outputs.at[i+2, 'RegL8C51'],Pre_defined_Variables.Option_RegL8C51,Pre_defined_Variables.Constant_RegL8C51)
        M_var.RegL8C51[i+2] = min(Pre_defined_Variables.MaxCap_RegL8C51 , Pre_defined_Variables.Multiplier_RegL8C51*M_var.Choose_L8C51[i+2])
        M_var.TotRegSo[i+2] = (M_var.RegWCA[i+2] + M_var.RegL8C51[i+2]) * 1.9835
        M_var.Stage2ar[i+2] = Stg_Sto_Ar.stg2ar(M_var.Lake_Stage[i+1],0)
        M_var.Stage2marsh[i+2] = Stg_Sto_Ar.stg2mar(M_var.Lake_Stage[i+1],0)
        M_var.RF[i+2] = Data.RF_Vol.at[i+2, 'RF_acft']
        M_var.ET[i+2] = LO_FNs.ET(Data.SFWMM_Daily_Outputs.at[i+2, 'et_dry'],M_var.Stage2ar[i+2],Data.SFWMM_Daily_Outputs.at[i+2, 'et_litoral'],M_var.Stage2marsh[i+2],Data.SFWMM_Daily_Outputs.at[i+2, 'et_open'],Data.ET_Vol.at[i+2, 'ETVol_acft'],Pre_defined_Variables.ET_Switch)
        M_var.Choose_WSA_1[i+2] = LO_FNs.Choose_WSA_1(df_WSMs.at[i+2, 'WSM1'],Pre_defined_Variables.Opt_WSA,Pre_defined_Variables.WSAtrig2,Pre_defined_Variables.WSAoff2)   
        M_var.Choose_WSA_2[i+2] = LO_FNs.Choose_WSA_2(df_WSMs.at[i+2, 'WSM1'],Pre_defined_Variables.Opt_WSA,Pre_defined_Variables.WSAtrig1,Pre_defined_Variables.WSAoff1)    
        M_var.WSA_MIA[i+2] = LO_FNs.WSA_MIA(WCA_Stages_df.at[i, 'Are WCA stages too low?'],TC_LONINO_df.at[i, 'LONINO_Seasonal_Classes'],M_var.Lake_Stage[i+1],M_var.Choose_WSA_1[i+2],Data.EAA_MIA_RUNOFF.at[i, 'MIA'],Data.EAA_MIA_RUNOFF.at[i, 'S3PMP'],M_var.Choose_WSA_2[i+2],Pre_defined_Variables.Opt_WSA,Pre_defined_Variables.WSA_THC,Pre_defined_Variables.MIAcap2,Pre_defined_Variables.MIAcap1)
        M_var.WSA_NNR[i+2] = LO_FNs.WSA_NNR(WCA_Stages_df.at[i, 'Are WCA stages too low?'],TC_LONINO_df.at[i, 'LONINO_Seasonal_Classes'],M_var.Lake_Stage[i+1],M_var.Choose_WSA_1[i+2],Data.EAA_MIA_RUNOFF.at[i, 'NNR'],Data.EAA_MIA_RUNOFF.at[i, 'S2PMP'],M_var.Choose_WSA_2[i+2],Pre_defined_Variables.Opt_WSA,Pre_defined_Variables.WSA_THC,Pre_defined_Variables.NNRcap2,Pre_defined_Variables.NNRcap1)
        M_var.DSto[i+2] = M_var.NI_Supply[i+2] + M_var.RF[i+2] - M_var.ET[i+2] + 1.9835*(M_var.Outlet2USBK[i+2]\
                     + M_var.Outlet1USBK[i+2] + M_var.WSA_MIA[i+2] + M_var.WSA_NNR[i+2]\
                     - M_var.Outlet1USEWS[i+2]) - M_var.TotRegEW[i+2] - M_var.TotRegSo[i+2] + Storage_dev[i+2]
        M_var.Storage[i+2] = LO_FNs.Storage(M_var.DayFlags[i+2],M_var.Storage[i],StartStorage,M_var.Storage[i+1],M_var.DSto[i+2])
        M_var.Lake_Stage[i+2] = LO_FNs.Lake_Stage(Stg_Sto_Ar.stg2sto(M_var.Storage[i+2],1),Data.SFWMM_Daily_Outputs.at[i+2, 'EOD Stg(ft,NGVD)'],Pre_defined_Variables.Option_Stage)
        # if M_var.Lake_Stage[i+2] >= 18:
        #     Flood[i+2] = 1
        # else:
        #     Flood[i+2] = 0
        LO_Model['Cutback'] = M_var.Cut_back
        LO_Model['Stage'] = M_var.Lake_Stage
        LO_Model['Storage'] = M_var.Storage
        LO_Model['S77_Q'] = M_var.Outlet1USREG
        LO_Model['S308_Q'] = M_var.Outlet2USRG
        LO_Model['S77EW'] = M_var.Outlet1USEWS
        LO_Model['TotRegEW'] = M_var.TotRegEW
        LO_Model['TotRegSo'] = M_var.TotRegSo
        
    return[LO_Model]