def readfromxls(self,fileMain,params,scenario):
#def readfromxls(self,fileMain,fileRen,fileDem,params,scenario):
global m
# Create DataFrame from Excel file
xls = pd.ExcelFile(fileMain)
m.commodities = Validation.ValidateCommoditySheet(xls.parse('Commodity', convert_float=False))
def proc_read(xls_file):
"""
reads process sheet from the input file, loading the appropriate evrys values.
Args:
- xls: main excel sheet
Returns:
evrys-compatible pandas DataFrame with process data
Note:
supports just single input, and single output plus CO2.
If fed with MIMO data, last value will be the considered one
"""
import pandas as pd
import numpy as np
import math
from datetime import datetime
#imports Process-Commodity sheet, which contains the informations needed
# for the creation of the evrys-compatible dataframe
com = xls_file.parse('Process-Commodity', convert_float=False)
expected_column_labels_2 = ['Process', 'Commodity', 'Direction', 'ratio', 'ratio-min']
Validation.CheckColumnNames(expected_column_labels_2, com, "Process-Commodity")
#adds required evrys columns
new_com_columns=['CoIn','CoOut','ratio_in','ratio_out','ratio_co2','ratio_in_min','ratio_out_min','eff','eff_min','cotwo']
new_com = pd.DataFrame(columns=new_com_columns)
new_com_rows=set(com['Process'])
#adds string value to create dummy entries in the dataframe, which will be later
# changed for the appropriate values
for i in range(len(new_com_columns)):
this_column = new_com.columns[i]
new_com[this_column] = ['n']*len(new_com_rows)
new_com['Process']=new_com_rows
new_com.set_index('Process', inplace=True)
#adds the values which are already available from the excel sheet,
# taking into consideration the direction of the process
for i in range(len(com)):
comprocess=com['Process'][i]
if com['Direction'][i]=='In':
new_com.at[comprocess,'CoIn']=com['Commodity'][i]
#com.at[i,'CoOut']='Elec'
new_com.at[comprocess,'ratio_in']=com['ratio'][i]
new_com.at[comprocess,'ratio_in_min']=com['ratio-min'][i]
elif com['Direction'][i]=='Out' and com['Commodity'][i]!='CO2':
#com.at[i,'CoIn']='Elec'
new_com.at[comprocess,'CoOut']=com['Commodity'][i]
new_com.at[comprocess,'ratio_out']=com['ratio'][i]
new_com.at[comprocess,'ratio_out_min']=com['ratio-min'][i]
elif com['Direction'][i]=='Out' and com['Commodity'][i]=='CO2':
new_com.at[comprocess, 'ratio_co2']=com['ratio'][i]
else:
print 'ValueError'
#calculates the values which are needed for evrys from the values previously
# loaded
for row in list(new_com.index):
new_com.at[row, 'eff']=new_com['ratio_out'][row]/new_com['ratio_in'][row]
if new_com['ratio_co2'][row]=='n':
new_com.at[row,'cotwo']=0
else:
new_com.at[row,'cotwo']=new_com['ratio_co2'][row]/new_com['ratio_in'][row]
in_bool=math.isnan(new_com['ratio_in_min'][row])
out_bool=math.isnan(new_com['ratio_out_min'][row])
if in_bool==False and out_bool==False:
new_com.at[row, 'eff_min']=new_com['ratio_out_min'][row]/new_com['ratio_in_min'][row]
elif in_bool==True and out_bool==False:
new_com.at[row, 'eff_min']=new_com['ratio_out_min'][row]/new_com['ratio_in'][row]
elif in_bool==False and out_bool==True:
new_com.at[row, 'eff_min']=new_com['ratio_out'][row]/new_com['ratio_in_min'][row]
else:
new_com.at[row, 'eff_min']=new_com['ratio_out'][row]/new_com['ratio_in'][row]
new_com.drop(['ratio_in','ratio_out','ratio_in_min','ratio_out_min','ratio_co2'], axis=1, inplace=True)
#loads the Process sheet, which is the one with the required evrys structure
pro = xls_file.parse('Process', convert_float=False)
class modelData(object):
#def __init__(self,fileMain,fileRen,fileDem,params,scenario):
def __init__(self,fileMain,params,scenario):
self.dbco = ws.add_database()
self.dbprocess = ws.add_database()
self.dbtransport = ws.add_database()
self.dbstorage = ws.add_database()
self.dbdsm = ws.add_database()
self.dbsites = ws.add_database()
self.dbtime = ws.add_database()
self.dbflags = ws.add_database()
self.dbPTDF = ws.add_database()
self.dbDCDF = ws.add_database()
self.dbPSDF = ws.add_database()
print "added database"
def readfromxls(self,fileMain,params,scenario):
#def readfromxls(self,fileMain,fileRen,fileDem,params,scenario):
global m
# Create DataFrame from Excel file
xls = pd.ExcelFile(fileMain)
m.commodities = Validation.ValidateCommoditySheet(xls.parse('Commodity', convert_float=False))
def proc_read(xls_file):
"""
reads process sheet from the input file, loading the appropriate evrys values.
Args:
- xls: main excel sheet
Returns:
evrys-compatible pandas DataFrame with process data
Note:
supports just single input, and single output plus CO2.
If fed with MIMO data, last value will be the considered one
"""
import pandas as pd
import numpy as np
import math
from datetime import datetime
#imports Process-Commodity sheet, which contains the informations needed
# for the creation of the evrys-compatible dataframe
com = xls_file.parse('Process-Commodity', convert_float=False)
expected_column_labels_2 = ['Process', 'Commodity', 'Direction', 'ratio', 'ratio-min']
Validation.CheckColumnNames(expected_column_labels_2, com, "Process-Commodity")
#adds required evrys columns
new_com_columns=['CoIn','CoOut','ratio_in','ratio_out','ratio_co2','ratio_in_min','ratio_out_min','eff','eff_min','cotwo']
new_com = pd.DataFrame(columns=new_com_columns)
new_com_rows=set(com['Process'])
#adds string value to create dummy entries in the dataframe, which will be later
# changed for the appropriate values
for i in range(len(new_com_columns)):
this_column = new_com.columns[i]
new_com[this_column] = ['n']*len(new_com_rows)
new_com['Process']=new_com_rows
new_com.set_index('Process', inplace=True)
#adds the values which are already available from the excel sheet,
# taking into consideration the direction of the process
for i in range(len(com)):
comprocess=com['Process'][i]
if com['Direction'][i]=='In':
new_com.at[comprocess,'CoIn']=com['Commodity'][i]
#com.at[i,'CoOut']='Elec'
new_com.at[comprocess,'ratio_in']=com['ratio'][i]
new_com.at[comprocess,'ratio_in_min']=com['ratio-min'][i]
elif com['Direction'][i]=='Out' and com['Commodity'][i]!='CO2':
#com.at[i,'CoIn']='Elec'
new_com.at[comprocess,'CoOut']=com['Commodity'][i]
new_com.at[comprocess,'ratio_out']=com['ratio'][i]
new_com.at[comprocess,'ratio_out_min']=com['ratio-min'][i]
elif com['Direction'][i]=='Out' and com['Commodity'][i]=='CO2':
new_com.at[comprocess, 'ratio_co2']=com['ratio'][i]
else:
print 'ValueError'
#calculates the values which are needed for evrys from the values previously
# loaded
for row in list(new_com.index):
new_com.at[row, 'eff']=new_com['ratio_out'][row]/new_com['ratio_in'][row]
if new_com['ratio_co2'][row]=='n':
new_com.at[row,'cotwo']=0
else:
new_com.at[row,'cotwo']=new_com['ratio_co2'][row]/new_com['ratio_in'][row]
in_bool=math.isnan(new_com['ratio_in_min'][row])
out_bool=math.isnan(new_com['ratio_out_min'][row])
if in_bool==False and out_bool==False:
new_com.at[row, 'eff_min']=new_com['ratio_out_min'][row]/new_com['ratio_in_min'][row]
elif in_bool==True and out_bool==False:
new_com.at[row, 'eff_min']=new_com['ratio_out_min'][row]/new_com['ratio_in'][row]
elif in_bool==False and out_bool==True:
new_com.at[row, 'eff_min']=new_com['ratio_out'][row]/new_com['ratio_in_min'][row]
else:
new_com.at[row, 'eff_min']=new_com['ratio_out'][row]/new_com['ratio_in'][row]
new_com.drop(['ratio_in','ratio_out','ratio_in_min','ratio_out_min','ratio_co2'], axis=1, inplace=True)
#loads the Process sheet, which is the one with the required evrys structure
pro = xls_file.parse('Process', convert_float=False)
#Check the column labels
expected_column_labels = ['Site', 'Process', 'inst-cap', 'cap-lo', 'cap-up', 'max-grad',
'min-fraction', 'inv-cost', 'fix-cost', 'var-cost', 'wacc', 'y',
'area-per-cap', 'act-up', 'on-off', 'start-cost', 'reserve-cost', 'ru',
'rd', 'rumax', 'rdmax', 'cotwo', 'detail', 'lambda', 'heatmax',
'maxdeltaT', 'heatupcost', 'su', 'sd', 'hotstart', 'pdt', 'pot',
'prepow', 'pretemp', 'preheat', 'prestate', 'precaponline', 'year']
Validation.CheckColumnNames(expected_column_labels, pro ,'Process')
#adds the required new columns to the Process dataframe
new_columns=['CoIn','CoOut','eff','eff_min','cotwo']
for column in new_columns:
pro[column]=np.nan
pro[column]=pro[column].fillna('n')
#adds the correct values to the new dataframe, considering the fact that
# in this new dataframe, process can appear multiple times, while in the
# previous data structure this didn't happen. Processes appeared just
# one time each.
for proc in new_com.index:
for title in new_columns:
n_appar=len(pro[title][proc])
if n_appar==1:
pro.at[proc, title]=new_com[title][proc]
else:
serie_ind=[proc]*n_appar
serie_cont=[new_com[title][proc]]*n_appar
serie=pd.Series(serie_cont, index=serie_ind)
pro.at[proc, title]=serie
pro.reset_index(inplace=True)
pro.set_index(['Site','Process','CoIn','CoOut'], inplace=True)
return pro
