#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Nov 5 12:28:38 2020 @author: ryanoreilly #################### Diff between version 4 and 5 are the file locations and dACV6 instead of dACV7. dACV6 is the latest version Diff between version 8 and 9 is that 9 references the new HP which referenced the long-run COP for NO, LT, and TR #################### The objective of this file is to: 1) Expand data set to represent hourly energy consumption 2) create time stamp 3) cast into IAMC format 4) test Example from: https://github.com/openENTRANCE/nomenclature/commit/7591988c8d41dec8a0f2252a711de838beb168e7 | **model** | **scenario** | **region** | **variable** | **unit** | **subannual** | **2015** | **2020** | **2025** | |-------------|---------------------|------------|----------------|----------|-------------------|---------:|---------:|---------:| | GENeSYS-MOD | Societal Commitment | Europe | Primary Energy | GJ/y | 01-01 00:00+01:00 | 7.99 | 7.50 | ... | | ... | ... | ... | ... | ... | ... | ... | ... | ... | | **model** | **scenario** | **region** | **variable** | **unit** | **subannual** | **2015** | **2020** | **2025** | |-------------|---------------------|------------|----------------|----------|-------------------|---------:|---------:|---------:| | GENeSYS-MOD | Societal Commitment | Europe | Primary Energy | GJ/y | 01-01 00:00+01:00 | 7.99 | 7.50 | ... | | ... | ... | ... | ... | ... | ... | ... | ... | ... | previous: TF_CS1_BE1-Theoretical TF_CS1_BE2-Centrally planned new: TF_CS1_BE1-Full TF_CS1_BE2-Realistic NEW VERSION V2: updated versions of P_WM, P_DW, and P_TD are used; V2 """ ### BEGIN - global options ### ## Import packages import pandas as pd ## necessary data analysis package import numpy as np ## necessary data analysis package from datetime import date,timedelta,datetime # for getting today's date import time import json import os from smtplib import SMTP_SSL ## for sending emails #pip install pyam-iamc import pyam from pyam import IamDataFrame #pip install git+https://github.com/openENTRANCE/nomenclature #import nomenclature import pint #pyam dependency #import matplotlib #pyam dependency #import seaborn #pyam dependency #import six #pyam dependency #import requests # for API ### Set options os.chdir('I:/Projekte/OpenEntrance - WV0173/Durchführungsphase/WP6/CS1/OE_data_analysis/openEntrance/data/theoretical potential/') # Display 6 columns for viewing purposes pd.set_option('display.max_columns', 6) # Reduce decimal points to 2 pd.options.display.float_format = '{:,.2f}'.format ### END - global options ### Load in data d_WM = pd.read_csv('./d_WMV6.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_WH = pd.read_csv('./d_WHV7.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_TD = pd.read_csv('./d_TDV6.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_SH = pd.read_csv('./d_SHV7.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_RF = pd.read_csv('./d_RFV5.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_FR = pd.read_csv('./d_FRV5.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_EV = pd.read_csv('./d_EVV7.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_EVfit55 = pd.read_csv('./d_EVV6_fit55V2.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_DW = pd.read_csv('./d_DWV6.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_AC = pd.read_csv('./d_ACV8.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_HP = pd.read_csv('./d_HPV4.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# d_CP = pd.read_csv('./d_CPV6.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_WM = pd.read_csv('./p_WMV6.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_WH = pd.read_csv('./p_WHV7.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_SH = pd.read_csv('./p_SHV7.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_EV = pd.read_csv('./p_EVV7.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_EVfit55 = pd.read_csv('./p_EVV6_fit55V2.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_DW = pd.read_csv('./p_DWV6.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_AC = pd.read_csv('./p_ACV8.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_TD = pd.read_csv('./p_TDV6.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_RF = pd.read_csv('./p_RFV5.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_FR = pd.read_csv('./p_FRV5.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_CP = pd.read_csv('./p_CPV6.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# p_HP = pd.read_csv('./p_HPV4.csv',sep=",",encoding = "ISO-8859-1", header=0, index_col=False)# # sort all dataframes d_WM = d_WM.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_WH = d_WH.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_TD = d_TD.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_SH = d_SH.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_RF = d_RF.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_FR = d_FR.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_EV = d_EV.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_EVfit55 = d_EVfit55.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_DW = d_DW.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_AC = d_AC.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_CP = d_CP.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) d_HP = d_HP.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_WM = p_WM.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_WH = p_WH.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_SH = p_SH.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_EV = p_EV.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_EVfit55 = p_EVfit55.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_DW = p_DW.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_AC = p_AC.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_TD = p_TD.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_RF = p_RF.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_FR = p_FR.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_CP = p_CP.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) p_HP = p_HP.sort_values(['nutscode', 'TIME','hour'],ignore_index=True) # p_Device = P_device - d_device; the electricity available in hour t = maximum dispatchable load - hourly demand p_WM = p_WM.iloc[0:p_WM.shape[0],1:34]-d_WM.iloc[0:d_WM.shape[0],1:34] # WM p_WH = p_WH.iloc[0:p_WH.shape[0],1:34]-d_WH.iloc[0:d_WH.shape[0],1:34] # WH p_SH = p_SH.iloc[0:p_SH.shape[0],1:34]-d_SH.iloc[0:d_SH.shape[0],1:34] # SH p_EV = p_EV.iloc[0:p_EV.shape[0],1:34]-d_EV.iloc[0:d_EV.shape[0],1:34] # EV p_EVfit55 = p_EVfit55.iloc[0:p_EVfit55.shape[0],1:34]-d_EVfit55.iloc[0:d_EVfit55.shape[0],1:34] # EV p_DW = p_DW.iloc[0:p_DW.shape[0],1:34]-d_DW.iloc[0:d_DW.shape[0],1:34] # DW p_AC = p_AC.iloc[0:p_AC.shape[0],1:34]-d_AC.iloc[0:d_AC.shape[0],1:34] # AC p_TD = p_TD.iloc[0:p_TD.shape[0],1:34]-d_TD.iloc[0:d_TD.shape[0],1:34] # TD p_RF = p_RF.iloc[0:p_RF.shape[0],1:34]-d_RF.iloc[0:d_RF.shape[0],1:34] # RF p_FR = p_FR.iloc[0:p_FR.shape[0],1:34]-d_FR.iloc[0:d_FR.shape[0],1:34] # FR p_CP = p_CP.iloc[0:p_CP.shape[0],1:34]-d_CP.iloc[0:d_CP.shape[0],1:34] # CP p_HP = p_HP.iloc[0:p_HP.shape[0],1:34]-d_HP.iloc[0:d_HP.shape[0],1:34] # HP p_WM['country'] = d_WM['country'] p_WH['country'] = d_WH['country'] p_SH['country'] = d_SH['country'] p_EV['country'] = d_EV['country'] p_EVfit55['country'] = d_EVfit55['country'] p_DW['country'] = d_DW['country'] p_AC['country'] = d_AC['country'] p_TD['country'] = d_TD['country'] p_RF['country'] = d_RF['country'] p_FR['country'] = d_FR['country'] p_CP['country'] = d_CP['country'] p_HP['country'] = d_HP['country'] for year in range(2018-2018,2050+1-2018): yr = p_AC.columns[year] d_neg = p_AC[p_AC[yr]<0] print(yr,len(d_neg)) # WASHING MACHINE WILL BE USED AS TEMPLATE # WASHING MACHINE DEMENAD d_WM['unit'] = 'MW' d_WM['model'] = 'Flexibilities 2.2' d_WM['scenario'] = 'TF_CS1_BE1-Full' d_WM['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Washing Machine' d_WM['region'] = d_WM['nutscode'] # CREATION OF TIMESTAMP/ SUBANNUAL #month d_WM['month'] = "" d_WM['h'] = "" for i in range(0,d_WM.shape[0]): print(i) d_WM['month'][i] = d_WM['TIME'].loc[i][5:7] d_WM['h'][i] = "%.2d" % d_WM['hour'][i] # create timestamp to be used for all dfs since they are all in the same format d_WM['subannual'] = (d_WM['month']+'-'+'01'+' '+d_WM['h']+':00+01:00') timestamp = list(d_WM['subannual']) d_WM=d_WM.drop(['hour','nutscode','TIME','h','month','Unnamed: 0'],axis=1) # remove redundant variable nutscode = d_WM['region'] # WASHING MACHINE PINCREASE p_WM['unit'] = 'MW' p_WM['model'] = 'Flexibilities 2.2' p_WM['scenario'] = 'TF_CS1_BE1-Full' p_WM['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Washing Machine' p_WM['region'] = nutscode p_WM['subannual'] = timestamp # WATER HEATER DEMAND d_WH['unit'] = 'MW' d_WH['model'] = 'Flexibilities 2.2' d_WH['scenario'] = 'TF_CS1_BE1-Full' d_WH['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Water Heater' d_WH['region'] = nutscode d_WH['subannual'] = timestamp d_WH=d_WH.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # WATER HEATER PINCREASE p_WH['unit'] = 'MW' p_WH['model'] = 'Flexibilities 2.2' p_WH['scenario'] = 'TF_CS1_BE1-Full' p_WH['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Water Heater' p_WH['region'] = nutscode p_WH['subannual'] = timestamp # TUMBLE DRIER DEMAND d_TD['unit'] = 'MW' d_TD['model'] = 'Flexibilities 2.2' d_TD['scenario'] = 'TF_CS1_BE1-Full' d_TD['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Dryer' d_TD['region'] = nutscode d_TD['subannual'] = timestamp d_TD=d_TD.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # TUMBLE DRIER PINCREASE p_TD['unit'] = 'MW' p_TD['model'] = 'Flexibilities 2.2' p_TD['scenario'] = 'TF_CS1_BE1-Full' p_TD['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Dryer' p_TD['region'] = nutscode p_TD['subannual'] = timestamp # STORAGE HEATER DEMAND d_SH['unit'] = 'MW' d_SH['model'] = 'Flexibilities 2.2' d_SH['scenario'] = 'TF_CS1_BE1-Full' d_SH['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Storage Heater' d_SH['region'] = nutscode d_SH['subannual'] = timestamp d_SH=d_SH.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # STORAGE HEATER PINCREASE p_SH['unit'] = 'MW' p_SH['model'] = 'Flexibilities 2.2' p_SH['scenario'] = 'TF_CS1_BE1-Full' p_SH['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Storage Heater' p_SH['region'] = nutscode p_SH['subannual'] = timestamp # Combine refrigerator and Freezer # REFRIGERATOR/FREEZER DEMAND d_RF=d_RF.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable d_RF=d_FR.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable d_RF_FR = d_RF+d_FR d_RF_FR['country'] = d_RF['country'] d_RF_FR['unit'] = 'MW' d_RF_FR['model'] = 'Flexibilities 2.2' d_RF_FR['scenario'] = 'TF_CS1_BE1-Full' d_RF_FR['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Refrigeration' d_RF_FR['region'] = nutscode d_RF_FR['subannual'] = timestamp d_RF_FR=d_RF_FR.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable del d_RF del d_FR # REFRIGERATOR/FREEZER PINCREASE p_RF_FR = p_RF+p_FR p_RF_FR['country'] = p_RF['country'] p_RF_FR['unit'] = 'MW' p_RF_FR['model'] = 'Flexibilities 2.2' p_RF_FR['scenario'] = 'TF_CS1_BE1-Full' p_RF_FR['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Refrigeration' p_RF_FR['region'] = nutscode p_RF_FR['subannual'] = timestamp del p_RF del p_FR # ELECTRIC VEHICLE DEMAND d_EV['unit'] = 'MW' d_EV['model'] = 'Flexibilities 2.2' d_EV['scenario'] = 'TF_CS1_BE1-Full' d_EV['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Electric Vehicle' d_EV['region'] = nutscode d_EV['subannual'] = timestamp d_EV=d_EV.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # ELECTRIC VEHICLE PINCREASE p_EV['unit'] = 'MW' p_EV['model'] = 'Flexibilities 2.2' p_EV['scenario'] = 'TF_CS1_BE1-Full' p_EV['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Electric Vehicle' p_EV['region'] = nutscode p_EV['subannual'] = timestamp # ELECTRIC VEHICLE DEMAND - fit55 d_EVfit55['unit'] = 'MW' d_EVfit55['model'] = 'Flexibilities 2.2' d_EVfit55['scenario'] = 'TF_CS1_BE1-Full' d_EVfit55['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Electric Vehicle_55' d_EVfit55['region'] = nutscode d_EVfit55['subannual'] = timestamp d_EVfit55=d_EVfit55.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # ELECTRIC VEHICLE PINCREASE - fit55 p_EVfit55['unit'] = 'MW' p_EVfit55['model'] = 'Flexibilities 2.2' p_EVfit55['scenario'] = 'TF_CS1_BE1-Full' p_EVfit55['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Electric Vehicle_55' p_EVfit55['region'] = nutscode p_EVfit55['subannual'] = timestamp # DISH WASHER DEMAND d_DW['unit'] = 'MW' d_DW['model'] = 'Flexibilities 2.2' d_DW['scenario'] = 'TF_CS1_BE1-Full' d_DW['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Dish Washer' d_DW['region'] = nutscode d_DW['subannual'] = timestamp d_DW=d_DW.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # DISH WASHER PINCREASE p_DW['unit'] = 'MW' p_DW['model'] = 'Flexibilities 2.2' p_DW['scenario'] = 'TF_CS1_BE1-Full' p_DW['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Dish Washer' p_DW['region'] = nutscode p_DW['subannual'] = timestamp # AIR CONDITIONING DEMAND d_AC['unit'] = 'MW' d_AC['model'] = 'Flexibilities 2.2' d_AC['scenario'] = 'TF_CS1_BE1-Full' d_AC['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Air Conditioning' d_AC['region'] = nutscode d_AC['subannual'] = timestamp d_AC=d_AC.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # AIR CONDITIONING PINCREASE p_AC['unit'] = 'MW' p_AC['model'] = 'Flexibilities 2.2' p_AC['scenario'] = 'TF_CS1_BE1-Full' p_AC['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Air Conditioning' p_AC['region'] = nutscode p_AC['subannual'] = timestamp # FRY5 has NAs for d_EV and p_EV; need to remove in order to validate dataframe d_EVfit55 = d_EVfit55.dropna() p_EVfit55 = p_EVfit55.dropna() d_EV = d_EV.dropna() p_EV = p_EV.dropna() # CIRCULATION PUMP DEMAND d_CP['unit'] = 'MW' d_CP['model'] = 'Flexibilities 2.2' d_CP['scenario'] = 'TF_CS1_BE1-Full' d_CP['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Circulation Pump' d_CP['region'] = nutscode d_CP['subannual'] = timestamp d_CP=d_CP.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # CIRCULATION PUMP PINCREASE p_CP['unit'] = 'MW' p_CP['model'] = 'Flexibilities 2.2' p_CP['scenario'] = 'TF_CS1_BE1-Full' p_CP['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Circulation Pump' p_CP['region'] = nutscode p_CP['subannual'] = timestamp # Heat Pump demand d_HP['unit'] = 'MW' d_HP['model'] = 'Flexibilities 2.2' d_HP['scenario'] = 'TF_CS1_BE1-Full' d_HP['variable'] = 'Demand Response|Maximum Reduction|Load Shifting|Electricity|Residential|Heat Pump' d_HP['region'] = nutscode d_HP['subannual'] = timestamp d_HP = d_HP.drop(['hour','nutscode','TIME','Unnamed: 0'],axis=1) # remove redundant variable # Heat PUMP PINCREASE p_HP['unit'] = 'MW' p_HP['model'] = 'Flexibilities 2.2' p_HP['scenario'] = 'TF_CS1_BE1-Full' p_HP['variable'] = 'Demand Response|Maximum Dispatch|Load Shifting|Electricity|Residential|Heat Pump' p_HP['region'] = nutscode p_HP['subannual'] = timestamp final = d_AC.append(p_AC,ignore_index=True) # AC can only be delayed not advanced final = final.append(d_DW,ignore_index=True) final = final.append(p_DW,ignore_index=True) final = final.append(d_EV,ignore_index=True) final = final.append(p_EV,ignore_index=True) # can only be delayed final = final.append(d_EVfit55,ignore_index=True) # can only be delayed final = final.append(p_EVfit55,ignore_index=True) final = final.append(d_RF_FR,ignore_index=True) final = final.append(p_RF_FR,ignore_index=True) final = final.append(d_SH,ignore_index=True) # energy from STORAGE HEATER can not be advanced final = final.append(p_SH,ignore_index=True) final = final.append(d_TD,ignore_index=True) final = final.append(p_TD,ignore_index=True) final = final.append(d_WH,ignore_index=True) # energy from WATER HEATER can not be advanced final = final.append(p_WH,ignore_index=True) final = final.append(d_WM,ignore_index=True) final = final.append(p_WM,ignore_index=True) final = final.append(d_CP,ignore_index=True) final = final.append(p_CP,ignore_index=True) final = final.append(d_HP,ignore_index=True) final = final.append(p_HP,ignore_index=True) del(d_AC) del(p_AC) del(d_DW) del(p_DW) del(d_EV) del(p_EV) del(d_SH) del(p_SH) del(d_TD) del(p_TD) del(d_WH) del(p_WH) del(d_WM) del(p_WM) del(d_RF_FR) del(p_RF_FR) del(d_CP) del(p_CP) del(d_HP) del(p_HP) # ============================================================================= # country aggregated - separate devices # ============================================================================= final_country = final.groupby(['country','subannual','variable'], as_index = False).sum() final_country = final_country.sort_values(['country','variable','subannual']) final_country['unit'] = 'MW' final_country['model'] = 'Flexibilities 2.2' final_country['scenario'] = 'TF_CS1_BE1-Full' final_country.columns = ['region', 'subannual', 'variable', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025', '2026', '2027', '2028', '2029', '2030', '2031', '2032', '2033', '2034', '2035', '2036', '2037', '2038', '2039', '2040', '2041', '2042', '2043', '2044', '2045', '2046', '2047', '2048', '2049', '2050', 'unit', 'model', 'scenario'] final_country = final_country[ ['model', 'scenario', 'region', 'variable', 'unit', 'subannual', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025', '2026', '2027', '2028', '2029', '2030', '2031', '2032', '2033', '2034', '2035', '2036', '2037', '2038', '2039', '2040', '2041', '2042', '2043', '2044', '2045', '2046', '2047', '2048', '2049', '2050']] final_country.to_csv(r'./Full_potential.V9.country.csv', index = False) ################################################# # nuts 2 ################################################# # rearrange columns #| **model** | **scenario** | **region** | **variable** | **unit** | **subannual** | **2015** | **2020** | **2025** | list(final) final = final[ ['model', 'scenario', 'region', 'variable', 'unit', 'subannual', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025', '2026', '2027', '2028', '2029', '2030', '2031', '2032', '2033', '2034', '2035', '2036', '2037', '2038', '2039', '2040', '2041', '2042', '2043', '2044', '2045', '2046', '2047', '2048', '2049', '2050']] #validate df iamcdf = pyam.IamDataFrame(final) final.to_csv(r'./Full_potential.V9.csv', index = False) ''' # ============================================================================= # country aggregate and variable aggregate - this no longer works because of the two EV scenarios # ============================================================================= # aggregate all countries final_agg = final_country.groupby(['subannual','variable'], as_index = False).sum() final_agg.to_csv(r'./Full_potential.V.Agg.csv', index = False) ''' """ # iamc validate df iamcdf = pyam.IamDataFrame(df)