GenInputMO.py

import time
import pandas as pd
import os
import sys

TimePeriod = int(sys.argv[1])

def BMP_Prep(TimePeriod):
    file = open('BMPsInfo.csv')
    file.readline()
    Table = []
    while True:
        l = file.readline()
        if l == '':
            break
        l = l.strip('\n')
        l = l.split(',')
        Table.append([int(l[0]), l[2], float(l[3]), float(l[4]),
                      float(l[5])])  # float(l[3] is N reduction cap, float(l[4]) is P reduction cap
    file.close()

    def FindBMPs(Source, LU_code):
        L = []
        for i in range(len(Source)):
            if Table[i][0] == LU_code:
                L.append(i)
        return L
        # ------------------------------------------------------------

    file = open('NetworkInfo.csv')
    filep = open('Net_Data_old.csv', 'w+')
    BMP = open('BMP_Tech.csv', 'w+')
    BMP.write('BMPs,Cost,P_LB,N_LB,P_UB,N_UB\n')

    file.readline()
    while True:
        l = file.readline()

        if l == '':
            break
        lp = l.strip('\n')
        lp = lp.split(',')

        ''' In code below, lp[ ] and lp[ ] indexing is where the code must be changed in order to incorporate the time index.'''

        LU_codes = [i for i in lp[4 + 2 * TimePeriod].split(' ') if (i.strip('.0')).isdigit()]
        #print(LU_codes)
        LU_Areas = [i for i in lp[5 + 2 * TimePeriod].split(' ') if (i.strip('.0')).isdigit()]
        # LU_percent = [i for i in lp[8].split(' ') if (i.strip('.0').isdigit())]  #####
        ####Table1.append[int(lp[0], int(lp[6], int(lp[8])))]
        if len(LU_codes) > 0:
            l = l.strip('\n')
            l = l + ','
            for i in range(len(LU_codes)):

                List = FindBMPs(Table, int(float(LU_codes[i])))
                for j in List:
                    l = l + ' ' + Table[j][1] + '_' + lp[0]

                    # print('Table j1', Table[j][1])
                    # print('lp0', lp[0])
                    # print('Table j3', Table[j][3])
                    # print('lp percent area', lp[6 + 2 * TimePeriod])
                    # print('Table j2', Table[j][2])
                    # print('Table j4', Table[j][4])
                    # print('LU area', LU_Areas[i])
                    #print("*******************************")
                    # BMP.write(Table[j][1] + '_' + lp[0] + ',' + str(Table[j][4] * float(LU_Areas[i])) + ',' + str(
                    #     Table[j][3] * float(lp[6 + 2 * TimePeriod])) + ',' + str(
                    #     Table[j][2] * float(lp[6 + 2 * TimePeriod + 1])) + ',' + str(
                    #     Table[j][3] * float(lp[6 + 2 * TimePeriod])) + ',' + str(
                    #     Table[j][2] * float(lp[6 + 2 * TimePeriod + 1])) + '\n')
                    BMP.write(
                        Table[j][1] + '_' + lp[0] + ',' + str(
                            Table[j][4] * float(LU_Areas[i]))+ ',' + str(Table[j][3] * float(lp[6 + 2 * TimePeriod])) + ',' + str(
                            Table[j][2] * float(lp[6 + 2 * TimePeriod+1])) + ',' + str(Table[j][3] * float(lp[6 + 2 * TimePeriod])) + ',' + str(
                            Table[j][2] * float(lp[6 + 2 * TimePeriod+1]))+'\n')
                    ## Table[j][3] is phosphorous redn cap, Table[j][2] is nitrogen redn cap, Table[j][4] is cost
                    ## Both P and N red cap is multiplied by percent area of dominant land use (within total Reach Area).
            l = l + '\n'

        filep.write(l)

    BMP.close()
    filep.close()
    file.close()

BMP_Prep(TimePeriod)


def SplittingNodeCreation(TimePeriod):
    # Specify the file path for the CSV file
    file_path = 'Net_Data_old.csv'

    # List of column indices to be deleted (0-based index) #different for single and multiobjective
    columns_to_delete = [-5,-4,-3,-2]
    # For example, the script above deletes the 2nd last, 3rd last, and 4th last columns for multiobjective optimization case

    # Read the CSV file into a DataFrame without header
    df = pd.read_csv(file_path, header=None)

    # Drop the specified columns using the column indices
    df.drop(df.columns[columns_to_delete], axis=1, inplace=True)

    # Save the updated DataFrame back to the same CSV file
    df.to_csv(file_path, index=False, header=False)

    #time.sleep(1)
    while not os.path.exists('Net_Data_old.csv'):
        time.sleep(1)

    file = open('Net_Data_old.csv')
    filep = open('Net_Data_split.csv', 'w+')
    splitNodeIncomings = {}
    while True:
        l = file.readline()

        if l == '':
            break
        lp = l.strip('\n')
        lp = lp.split(',')
        splitRatio = lp[3]
        splitRatio = splitRatio.split(' ')
        ccc = False
        if len(splitRatio) <= 1:
            filep.write(l)
        else:
            splitNodeIncomings[lp[0]] = lp[1]
            for iii in range(2 * TimePeriod):
                if float(lp[4 + iii]) > 0 or float(lp[4 + iii]) < 0:
                    ccc = True
                    if ccc == True:
                        break
            if ccc == True:
                filep.write(lp[0] + '_s,' + lp[1] + ',' + lp[0] + ',,')
                #print("nodes is", lp[0])
                for iii in range(2 * TimePeriod):
                    if float(lp[4 + iii]) > 0 or float(lp[4 + iii]) < 0:
                        filep.write(lp[4 + iii] + ',')
                    else:
                        filep.write('0,')
                filep.write(lp[4 + 2 * TimePeriod] + '\n')
                filep.write(lp[0] + ',' + lp[0] + '_s' + ',' + lp[2] + ',' + lp[3] + ',')
                for iii in range(2 * TimePeriod):
                    filep.write('0,')
                filep.write(lp[4 + 2 * TimePeriod] + '\n')
            else:
                filep.write(l)
    filep.close()
    file.close()
    keys = []
    values = []
    ingoings_list = []
    for key, value in splitNodeIncomings.items():
        keys.append(key)
        values.append(value.split(' '))
    for i in range(len(values)):
        ingoings_list += values[i]
    #print(ingoings_list)

    #time.sleep(1)
    while not os.path.exists('Net_Data_split.csv'):
        time.sleep(1)
        pass

    file = open('Net_Data_split.csv')
    filep = open('Net_Data_split2.csv', 'w+')
    while True:
        l2 = file.readline()
        if l2 == '':
            break
        lp2 = l2.strip('\n')
        lp2 = lp2.split(',')

        if lp2[0] not in ingoings_list:
            # print('True')
            filep.write(l2)
        else:
            if lp2[0] not in keys:  ##because the ingoings lp[2] might have multiple outgoings as well. write code for else condition
                # print("True")
                filep.write(lp2[0] + ',' + lp2[1] + ',' + lp2[2] + '_s,' + lp2[3] + ',')
                for iii in range(2 * TimePeriod):
                    filep.write(lp2[4 + iii] + ',')
                filep.write(lp2[4 + 2 * TimePeriod] + '\n')
            else:
                abc = []
                temp_outgoing = lp2[2].split(' ')
                print(temp_outgoing)
                for items in temp_outgoing:
                    if items in keys:
                        abc.append(items + '_s')
                    else:
                        abc.append(items)
                outgoing = " ".join(abc)
                filep.write(lp2[0] + ',' + lp2[1] + ',' + outgoing + ',' + lp2[3] + ',')   #lp2[2] + '_s,'
                for iii in range(2 * TimePeriod):
                    filep.write('0,')
                filep.write(lp2[4 + 2 * TimePeriod] + '\n')

    file.close()
    filep.close()

SplittingNodeCreation(TimePeriod)

while not os.path.exists('Net_Data_split2.csv'):
    pass

def Prep_Net(TimePeriod):
    file = open('Net_Data_split2.csv')
    filep = open('Net_Data.csv', 'w+')
    """ writing the header of the Net_Data file """
    filep.write('Reach,Ingoings,Outgoings,Split Ratio,')
    for iii in range(TimePeriod):
        filep.write('P_{}'.format(iii) + ',')
    for iii in range(TimePeriod):
        filep.write('N_{}'.format(iii) + ',')
    filep.write('BMPs\n')

    l = file.readline()
    filep.write(l)

    counter = 0
    while True:
        l = file.readline()
        if l == '':
            break

        lp = l.split(',')
        """lp[6] for single timeperiod changes to lp[4+2*Timeperiod]"""
        BMPs = lp[4 + 2 * TimePeriod].strip('\n')
        BMPs = BMPs.split(' ')
        for j in range(len(BMPs)):
            BMPs[j] = BMPs[j].strip(' ')

        ii = 0
        while ii < len(BMPs):
            if BMPs[ii] == '':
                del (BMPs[ii])
            else:
                ii += 1

        InNodes = lp[1].strip('\n')
        InNodes = InNodes.split(' ')
        for j in range(len(InNodes)):
            InNodes[j] = InNodes[j].strip(' ')

        ii = 0
        while ii < len(InNodes):
            if InNodes[ii] == '':
                del (InNodes[ii])
            else:
                ii += 1

        """ Making an separate array of Phosphorous and Nitrogen loading each for user input TimePeriods. """
        phosphorousTimed = []
        nitrogenTimed = []
        for iii in range(TimePeriod):
            phosphorousTimed.append(lp[4 + iii])
            nitrogenTimed.append(lp[4 + TimePeriod + iii])

        if len(BMPs) >= 1 and len(InNodes) >= 1:
            """lp[4] means Phosphorous loading at that Node"""
            jjj = False
            for iii in range(2 * TimePeriod):
                if float(lp[4 + iii]) < 0:
                    jjj = True
            if jjj == True:
                filep.write(lp[0] + ',')
                filep.write(lp[1] + ' ' + lp[0] + '_0' + ' ' + lp[0] + '_a,')
                filep.write(lp[2] + ',')
                filep.write(lp[3] + ',')
                for iii in range(2 * TimePeriod):
                    # if float(lp[4+iii]) <= 0:
                    filep.write('0,')
                filep.write('\n')
                # else:
                # filep.write(lp[4+iii]+',')
                # for iii in range(TimePeriod):
                # if float(lp[4+TimePeriod+iii]) <= 0:
                # filep.write('0,')
                # else:
                # filep.write(lp[4+TimePeriod+iii]+',')

                filep.write(lp[0] + '_0,,' + lp[0] + ',,')
                for iii in range(2 * TimePeriod):
                    if float(lp[4 + iii]) <= 0:
                        filep.write('0,')
                    else:
                        filep.write(lp[4 + iii] + ',')
                filep.write(lp[4 + 2 * TimePeriod])  #### removed \n from here.

                filep.write(lp[0] + '_a,,' + lp[0] + ',,')
                # print("lp[0]+_a is", lp[0]+'_a')

                for iii in range(2 * TimePeriod):

                    if float(lp[4 + iii]) <= 0:
                        filep.write(lp[4 + iii] + ',')
                    else:
                        filep.write('0,')
                filep.write('\n')

            else:
                filep.write(lp[0] + ',')
                filep.write(lp[1] + ' ' + lp[0] + '_0,')
                filep.write(lp[2] + ',')
                filep.write(lp[3] + ',')
                for iii in range(2 * TimePeriod):
                    filep.write('0,')
                filep.write('\n')

                filep.write(lp[0] + '_0,,' + lp[0] + ',,')
                for iii in range(2 * TimePeriod):
                    filep.write(lp[4 + iii] + ',')
                filep.write(lp[4 + 2 * TimePeriod])  ##removed \n from here previously : filep.write(lp[4 + 2 * TimePeriod] + '\n')
        else:
            jjj = False
            #print("node is:", lp[0] )
            for iii in range(2 * TimePeriod):
                if float(lp[4 + iii]) < 0:
                    jjj = True
            if jjj == True:
                filep.write(lp[0] + ',')
                if lp[1] == '':
                    filep.write(lp[0] + '_a,')
                else:
                    filep.write(lp[0] + '_a' + ' ' + lp[1] + ',')
                filep.write(lp[2] + ',')
                filep.write(lp[3] + ',')
                for iii in range(2 * TimePeriod):
                    if float(lp[4 + iii]) <= 0:
                        filep.write('0,')
                    else:
                        filep.write(lp[4 + iii] + ',')
                filep.write('\n')
                filep.write(lp[0] + '_a,,' + lp[0] + ',,')
                for iii in range(2 * TimePeriod):
                    if float(lp[4 + iii]) <= 0:
                        filep.write(lp[4 + iii] + ',')
                    else:
                        filep.write('0,')
                filep.write('\n')

            else:
                filep.write(l)

        counter += 1
    #    if counter == 5:
    #        break

    filep.close()
    file.close()

    try:
        os.remove('Net_data_old.csv')
        os.remove('Net_Data_split.csv')
        os.remove('Net_Data_split2.csv')
    except:
        print("No such file in the directory")

Prep_Net(TimePeriod)