elzoneplot.py

#TODO
#Make conspumptionfraction visible in description aswell? No only in plot?

from matplotlib import pyplot as plt
import requests
import arrow
import pandas as pd
import sys, getopt, math, os.path
from datetime import datetime

#year = datetime.now().year
maxYear = 2025
zone = ''
groupby = 'HOUR'
output = ''
flows = False
consumptionFraction = 100

def main(argv):
	global year
	global zone
	global groupby
	global output
	global flows
	global consumptionFraction

	try:
		opts, args = getopt.getopt(argv,"hy:z:g:o:fc:",["year=","zone=","groupby=","output=","flows=","consumptionfraction="])
	except getopt.GetoptError as err:
		print(err)
		sys.exit(1)

	if not opts:
		print('Usage:')
		print('elzoneplot.py -y <2007-', maxYear, '> -z <SE|SE1|SE2|SE3|SE4> -g <MONTH|WEEK|DAY|HOUR(default)> -o <plot|std|oFile> -f -c <50-100(default)>', sep='')
		sys.exit(1)
	for opt, arg in opts:
		if opt == '-h':
			print('Usage:')
			print('elzoneplot.py -y <2007-', maxYear, '> -z <SE|SE1|SE2|SE3|SE4> -g <MONTH|WEEK|DAY|HOUR(default)> -o <plot|std|oFile> -f -c <50-100(default)>', sep='')
			sys.exit(1)
		elif opt in ("-y", "--year"):
			arg = int(arg)
			if arg >= 2007 and arg <= maxYear:
				year = arg
#			elif  arg == '':
#				zone = 'SE'
			else:
				print('Specified year ', arg, ' is not within range 2007 - ', maxYear, sep='')
				sys.exit()

		elif opt in ("-z", "--zone"):
			arg=arg.upper()
			if arg == 'SE' or arg == 'SE1' or arg == 'SE2' or arg == 'SE3' or arg == 'SE4':
				zone = arg
#			elif  arg == '':
#				zone = 'SE'
			else:
				print('Not a known zone: ', arg)
				sys.exit()
		elif opt in ("-g", "--group"):
			arg=arg.upper()
			if arg == 'MONTH' or arg == 'WEEK' or arg == 'DAY' or arg == 'HOUR':
				groupby = arg
			elif arg == '':
				groupby = 'HOUR'
			else:
				print('Not a known groupby command: ', arg)
				sys.exit()
		elif opt in ("-o", "--output"):
			if arg.lower() == 'std':
				output = 'STDOUT'
			elif arg.lower() == 'plot':
				output = 'PLOT'
			else:
				output = arg
		elif opt in ("-f", "--flows"):
			flows = True
			if zone == 'SE':
				print('Cant include domestic flows for zone SE')
				sys.exit()
		elif opt in ("-c", "--consumptionfraction"):
			arg = int(arg)
			if arg >= 50 and arg <= 100:
				consumptionFraction = arg
			else:
				print('Specified value ', arg, ' is not within range 50 - 100', sep='')
				sys.exit()


	print('Year=', year, ', Zone=', zone, ', Groupby=', groupby, ', Output=', output, ', Flows=', flows, ', Consumption Fraction=', consumptionFraction, sep='')

	# Get data from file "Statistik per elområde och timme, 2022.xlsx"
	# Statistics available at https://www.svk.se/om-kraftsystemet/kraftsystemdata/elstatistik/

	urlDict={
		2007:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden_2007.xls',
		2008:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden2008.xls',
		2009:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden2009.xls',
		2010:{
			0:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2010-01-till-06.xls',
			1:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2010-07-till-12.xls'
		},
		2011:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2011-01-12.xls',
		2012:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2012-01-12.xls',
		2013:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2013-01-12.xls',
		2014:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2014-01-12.xls',
		2015:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/statistik-per-timme-och-elomrade-2015.xls',
		2016:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2016-01-12.xls',
		2017:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2017-01-12.xls',
		2018:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2018-01-12.xls',
		2019:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2019-01-12.xls',
		2020:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2020-01-12.xls',
		2021:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2021-01-12.xls',
		2022:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2022-01-12.xls',
		2023:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2023-01-12.xls',
		2024:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2024-01-12_.xls',
		2025:'https://www.svk.se/siteassets/1.om-kraftsystemet/kraftsystemdata/statistik/elomrade-och-timme/arkiverade/timvarden-2025-01-12.xls',
	}

	#This dict contains the indexes for the consumption columns by year
	consumptionDict={
		2007:{
			'SE1':[2,6,10,14,18],
			'SE2':[3,7,11,15,19],
			'SE3':[4,8,12,16,20],
			'SE4':[5,9,13,17,21]
		},
		2008:{
			'SE1':[2,6,10,14,18],
			'SE2':[3,7,11,15,19],
			'SE3':[4,8,12,16,20],
			'SE4':[5,9,13,17,21]
		},
		2009:{
			'SE1':[2,6,10,14,18],
			'SE2':[3,7,11,15,19],
			'SE3':[4,8,12,16,20],
			'SE4':[5,9,13,17,21]
		},
		2010:{
			0:{
				'SE1':[2,6,10,14,18],
				'SE2':[3,7,11,15,19],
				'SE3':[4,8,12,16,20],
				'SE4':[5,9,13,17,21]
			},
			1:{
				'SE1':[1,  8,12,35,39],	#No Ospec. förbrukning
				'SE2':[2,5,9,13,36,40],
				'SE3':[3,6,10,14,37,41],
				'SE4':[4,7,11,15,38,42]
			}
		},
		2011:{
			'SE1':[1,  8,32,36],	#No Ospec. förbrukning
			'SE2':[2,5,9,33,37],
			'SE3':[3,6,10,34,38],
			'SE4':[4,7,11,35,39]
		},
		2012:{
			'SE1':[1,5,9,34,38],
			'SE2':[2,6,10,35,39],
			'SE3':[3,7,11,36,40],
			'SE4':[4,8,12,37,41]
		},
		2013:{
			'SE1':[1,5,9,33,37],
			'SE2':[2,6,10,34,38],
			'SE3':[3,7,11,35,39],
			'SE4':[4,8,12,36,40]
		},
		2014:{
			'SE1':[1,5,9,33,37],
			'SE2':[2,6,10,34,38],
			'SE3':[3,7,11,35,39],
			'SE4':[4,8,12,36,40]
		},
		2015:{
			'SE1':[1,5,9,33,37],
			'SE2':[2,6,10,34,38],
			'SE3':[3,7,11,35,39],
			'SE4':[4,8,12,36,40]
		},
		2016:{
			'SE1':[1,5,9,34,38],
			'SE2':[2,6,10,35,39],
			'SE3':[3,7,11,36,40],
			'SE4':[4,8,12,37,41]
		},
		2017:{
			'SE1':[1,5,9,34,38],
			'SE2':[2,6,10,35,39],
			'SE3':[3,7,11,36,40],
			'SE4':[4,8,12,37,41]
		},
		2018:{
			'SE1':[1,5,30,34],
			'SE2':[2,6,31,35],
			'SE3':[3,7,32,36],
			'SE4':[4,8,33,37]
		},
		2019:{
			'SE1':[1,5,9,13,38,42],
			'SE2':[2,6,10,14,39,43],
			'SE3':[3,7,11,15,40,44],
			'SE4':[4,8,12,16,41,45]
		},
		2020:{
			'SE1':[1,5,9,13,38,42],
			'SE2':[2,6,10,14,39,43],
			'SE3':[3,7,11,15,40,44],
			'SE4':[4,8,12,16,41,45]
		},
		2021:{
			'SE1':[1,5,9,13,38,42],
			'SE2':[2,6,10,14,39,43],
			'SE3':[3,7,11,15,40,44],
			'SE4':[4,8,12,16,41,45]
		},
		2022:{
			'SE1':[1,5,9,13,38,42],
			'SE2':[2,6,10,14,39,43],
			'SE3':[3,7,11,15,40,44],
			'SE4':[4,8,12,16,41,45]
		},
		2023:{
			'SE1':[1,5,9,13,38,42],
			'SE2':[2,6,10,14,39,43],
			'SE3':[3,7,11,15,40,44],
			'SE4':[4,8,12,16,41,45]
		},
		2024:{
			'SE1':[1,5,9,13,38,42],
			'SE2':[2,6,10,14,39,43],
			'SE3':[3,7,11,15,40,44],
			'SE4':[4,8,12,16,41,45]
		},
		2025:{
			'SE1':[1,5,9,13,17,46,50],	#, energy storage added
			'SE2':[2,6,10,14,18,47,51],
			'SE3':[3,7,11,15,19,48,52],
			'SE4':[4,8,12,16,20,49,53]
		}
	}

	#This dict contains the indexes for the production columns by year
	productionDict={
		2007:{
			'SE1':[22,26,30,34,   38],	#No nuclear
			'SE2':[23,27,31,35,   39],	#No nuclear
			'SE3':[24,28,32,36,40,42],
			'SE4':[25,29,33,37,41,43]
		},
		2008:{
			'SE1':[22,26,30,34,   38],	#No nuclear
			'SE2':[23,27,31,35,   39],	#No nuclear
			'SE3':[24,28,32,36,40,42],
			'SE4':[25,29,33,37,41,43]
		},
		2009:{
			'SE1':[22,26,30,34,   38],	#No nuclear
			'SE2':[23,27,31,35,   39],	#No nuclear
			'SE3':[24,28,32,36,40,42],
			'SE4':[25,29,33,37,41,43]
		},
		2010:{
			0:{
				'SE1':[22,26,30,34,   38],	#No nuclear
				'SE2':[23,27,31,35,   39],	#No nuclear
				'SE3':[24,28,32,36,40,42],
				'SE4':[25,29,33,37,41,43]
			},
			1:{
				'SE1':[16,20,25,   26,30,43],	#No nuclear
				'SE2':[17,21,26,   27,32,44],	#No nuclear
				'SE3':[18,22,27,24,28,33,45],
				'SE4':[19,23,28,25,29,34,46]
			}
		},
		2011:{
			'SE1':[   15,19,   24,28,40],	#No Ospec. produktion, no nuclear
			'SE2':[12,16,20,   25,29,41],		#No nuclear
			'SE3':[13,17,21,23,26,30,42],
			'SE4':[14,18,22,   27,31,43]	#No nuclear
		},
		2012:{
			'SE1':[   16,20,   26,30,42],	#No Ospec. produktion, no nuclear
			'SE2':[13,17,21,   27,31,43],	#No nuclear
			'SE3':[14,18,22,24,28,32,44],
			'SE4':[15,19,23,25,29,33,45]
		},
		2013:{
			'SE1':[   16,20,   25,29,41],	#No Ospec. produktion, no nuclear
			'SE2':[13,17,21,   26,30,42],	#No nuclear
			'SE3':[14,18,22,24,27,31,43],
			'SE4':[15,19,23,   28,32,44]	#No nuclear
		},
		2014:{
			'SE1':[   16,20,   25,29,41],	#No Ospec. produktion, no nuclear
			'SE2':[13,17,21,   26,30,42],	#No nuclear
			'SE3':[14,18,22,24,27,31,43],
			'SE4':[15,19,23,   28,32,44]	#No nuclear
		},
		2015:{
			'SE1':[   16,20,   25,29,41],	#No Ospec. produktion, no nuclear
			'SE2':[13,17,21,   26,30,42],	#No nuclear
			'SE3':[14,18,22,24,27,31,43],
			'SE4':[15,19,23,   28,32,44]	#No nuclear
		},
		2016:{
			'SE1':[13,17,21,   26,30,42],	#No nuclear
			'SE2':[14,18,22,   27,31,43],	#No nuclear
			'SE3':[15,19,23,25,28,32,44],
			'SE4':[16,20,24,   29,33,45]	#No nuclear
		},
		2017:{
			'SE1':[13,17,21,   26,30,42],	#No nuclear
			'SE2':[14,18,22,   27,31,43],	#No nuclear
			'SE3':[15,19,23,25,28,32,44],
			'SE4':[16,20,24,   29,33,45]	#No nuclear
		},
		2018:{
			'SE1':[9, 13,17,   22,26],	#No nuclear
			'SE2':[10,14,18,   23,27],	#No nuclear
			'SE3':[11,15,19,21,24,28],
			'SE4':[12,16,20,   25,29]	#No nuclear
		},
		2019:{
			'SE1':[17,21,25,   30,34],	#No nuclear
			'SE2':[18,22,26,   31,35],	#No nuclear
			'SE3':[19,23,27,29,32,36],
			'SE4':[20,24,28,   33,37]	#No nuclear
		},
		2020:{
			'SE1':[17,21,25,   30,34],	#No nuclear
			'SE2':[18,22,26,   31,35],	#No nuclear
			'SE3':[19,23,27,29,32,36],
			'SE4':[20,24,28,   33,37]	#No nuclear
		},
		2021:{
			'SE1':[17,21,25,   30,34],	#No nuclear
			'SE2':[18,22,26,   31,35],	#No nuclear
			'SE3':[19,23,27,29,32,36],
			'SE4':[20,24,28,   33,37]	#No nuclear
		},
		2022:{
			'SE1':[17,21,25,   30,34],	#No nuclear
			'SE2':[18,22,26,   31,35],	#No nuclear
			'SE3':[19,23,27,29,32,36],
			'SE4':[20,24,28,   33,37]	#No nuclear
		},
		2023:{
			'SE1':[17,21,25,   30,34],	#No nuclear
			'SE2':[18,22,26,   31,35],	#No nuclear
			'SE3':[19,23,27,29,32,36],
			'SE4':[20,24,28,   33,37]	#No nuclear
		},
		2024:{
			'SE1':[17,21,25,   30,34,38],	#No nuclear, energy storage added
			'SE2':[18,22,26,   31,35,39],	#No nuclear, energy storage added
			'SE3':[19,23,27,29,32,36,40],	#			, energy storage added
			'SE4':[20,24,28,   33,37,41]	#No nuclear, energy storage added
		},
		2025:{
			'SE1':[21,25,29,   34,38,42],	#No nuclear, energy storage added
			'SE2':[22,26,30,   35,39,43],	#No nuclear, energy storage added
			'SE3':[23,27,31,33,36,40,44],	#			, energy storage added
			'SE4':[24,28,32,   37,41,45]	#No nuclear, energy storage added
		}
	}

	#This dict specifies a list of how many rows to skip before reaching the actual data by year
	#We start at 1, because 0 holds the header
	skipRowsDict={
		2007:[1],
		2008:[1],
		2009:[1],
		2010:{
			0:[1,2],
			1:[1,2,3,4]
		},
		2011:[1,2,3,4],
		2012:[1,2,3,4],
		2013:[1,2,3,4],
		2014:[1,2,3,4],
		2015:[1,2,3,4],
		2016:[1,2,3,4],
		2017:[1,2,3,4],
		2018:[1,2,3,4],
		2019:[1,2,3,4],
		2020:[1,2,3,4],
		2021:[1,2,3,4],
		2022:[1,2,3,4],
		2023:[1,2,3,4],
		2024:[1,2,3,4],
		2025:[1,2,3,4]
	}

	def getDataframe(fname, year, i=-1):
		if not os.path.isfile(fname):
			try:
				if i == -1:
					print('Fetching url: ',urlDict[year])
					r = requests.get(urlDict[year])
				else:
					print('Fetching url: ',urlDict[year][i])
					r = requests.get(urlDict[year][i])
				r.raise_for_status()
			except requests.exceptions.RequestException as e:
				raise SystemExit(e)
			print('Saving file to: ',fname)
			open(fname, 'wb').write(r.content)

		#Cant use skiprows at a fixed value since data starts at different indexes between different files
		#However, it looks to be consistent that the data starts at the row below first complete empty row.
		if i == -1:
			df = pd.read_excel(fname, header=0, skiprows=skipRowsDict[year])
		else:
			df = pd.read_excel(fname, header=0, skiprows=skipRowsDict[year][i])
		print('Reading excel to dataframe: ',fname)

		#Get value from first column of 1th row.
		dotCount=str(df.iloc[:, 0].iloc[0]).count('.')
		dashCount=str(df.iloc[:, 0].iloc[0]).count('-')
		colonCount=str(df.iloc[:, 0].iloc[0]).count(':')
		df2 = pd.DataFrame()
		if dotCount == 0 and dashCount == 0 and colonCount == 0:
			df2['datetime'] = pd.to_datetime(df.iloc[:, 0], format="%Y%m%d", dayfirst=True) + pd.to_timedelta((df.iloc[:, 1] / 100), unit='h')
		elif (dotCount == 2 and dashCount == 0 and colonCount == 1) or (dotCount == 0 and dashCount == 2 and colonCount == 2):
#			df2['datetime'] = pd.to_datetime(df.iloc[:, 0], format="%d.%m.%Y %H:%M")
			df2['datetime'] = pd.to_datetime(df.iloc[:, 0], dayfirst=True)
		else:
			print('Unknwon date format: ',str(df.iloc[:, 0].iloc[0]))
			sys.exit()

		df2['timestamp'] = pd.to_datetime(df2['datetime']).astype(int)/ 10**9
		df2['year'] = df2['datetime'].dt.isocalendar().year
		df2['week'] = df2['datetime'].dt.isocalendar().week

		#This is needed for year 2010 which has two .xls for same year
		if i == -1:
			df2['consumption_SE1']=df.iloc[:, consumptionDict[year]['SE1']].sum(axis=1,numeric_only=True)
			df2['consumption_SE2']=df.iloc[:, consumptionDict[year]['SE2']].sum(axis=1,numeric_only=True)
			df2['consumption_SE3']=df.iloc[:, consumptionDict[year]['SE3']].sum(axis=1,numeric_only=True)
			df2['consumption_SE4']=df.iloc[:, consumptionDict[year]['SE4']].sum(axis=1,numeric_only=True)
			df2['consumption_SE']=df2['consumption_SE1']+df2['consumption_SE2']+df2['consumption_SE3']+df2['consumption_SE4']
			
			df2['production_SE1']=df.iloc[:, productionDict[year]['SE1']].sum(axis=1,numeric_only=True)
			df2['production_SE2']=df.iloc[:, productionDict[year]['SE2']].sum(axis=1,numeric_only=True)
			df2['production_SE3']=df.iloc[:, productionDict[year]['SE3']].sum(axis=1,numeric_only=True)
			df2['production_SE4']=df.iloc[:, productionDict[year]['SE4']].sum(axis=1,numeric_only=True)
			df2['production_SE']=df2['production_SE1']+df2['production_SE2']+df2['production_SE3']+df2['production_SE4']
		else:
			df2['consumption_SE1']=df.iloc[:, consumptionDict[year][i]['SE1']].sum(axis=1,numeric_only=True)
			df2['consumption_SE2']=df.iloc[:, consumptionDict[year][i]['SE2']].sum(axis=1,numeric_only=True)
			df2['consumption_SE3']=df.iloc[:, consumptionDict[year][i]['SE3']].sum(axis=1,numeric_only=True)
			df2['consumption_SE4']=df.iloc[:, consumptionDict[year][i]['SE4']].sum(axis=1,numeric_only=True)
			df2['consumption_SE']=df2['consumption_SE1']+df2['consumption_SE2']+df2['consumption_SE3']+df2['consumption_SE4']
			
			df2['production_SE1']=df.iloc[:, productionDict[year][i]['SE1']].sum(axis=1,numeric_only=True)
			df2['production_SE2']=df.iloc[:, productionDict[year][i]['SE2']].sum(axis=1,numeric_only=True)
			df2['production_SE3']=df.iloc[:, productionDict[year][i]['SE3']].sum(axis=1,numeric_only=True)
			df2['production_SE4']=df.iloc[:, productionDict[year][i]['SE4']].sum(axis=1,numeric_only=True)
			df2['production_SE']=df2['production_SE1']+df2['production_SE2']+df2['production_SE3']+df2['production_SE4']
				
		df2['balance_SE']=df2['consumption_SE'] + df2['production_SE']
		df2['balance_SE1']=df2['consumption_SE1'] + df2['production_SE1']
		df2['balance_SE2']=df2['consumption_SE2'] + df2['production_SE2']
		df2['balance_SE3']=df2['consumption_SE3'] + df2['production_SE3']
		df2['balance_SE4']=df2['consumption_SE4'] + df2['production_SE4']

		df2['balance_with_' + str(consumptionFraction) + '_consumption_SE']=(df2['consumption_SE']*(consumptionFraction/100)) + df2['production_SE']
		df2['balance_with_' + str(consumptionFraction) + '_consumption_SE1']=(df2['consumption_SE1']*(consumptionFraction/100)) + df2['production_SE1']
		df2['balance_with_' + str(consumptionFraction) + '_consumption_SE2']=(df2['consumption_SE2']*(consumptionFraction/100)) + df2['production_SE2']
		df2['balance_with_' + str(consumptionFraction) + '_consumption_SE3']=(df2['consumption_SE3']*(consumptionFraction/100)) + df2['production_SE3']
		df2['balance_with_' + str(consumptionFraction) + '_consumption_SE4']=(df2['consumption_SE4']*(consumptionFraction/100)) + df2['production_SE4']

		if flows:
			#Read flowdata from csv
			fname='flows/svkstats_flowHour_' + str(year) +'_SE.csv'
			url='https://github.com/skorpi0n/elzoneplot/flows/' + fname
			if not os.path.isfile(fname):
				print('Flow data was not found locally, trying to fetch it remotely')
				try:
					print('Fetching url: ', url)
					r = requests.get(url)
					r.raise_for_status()
					print('Saving flows file to: ', fname)
					open(fname, 'wb').write(r.content)
				except requests.exceptions.RequestException as e:
					if r.status_code == 404:
						print('ERROR: No flow data is available for year ' + str(year))
						sys.exit(1)
					else:
						print('ERROR:')
						raise SystemExit(e)

			csv_df = pd.read_csv(fname)

			#csv_df values are in KW, so we make them to MW to fit df2
			df2['domestic_export_SE1']=(csv_df['SE1_SE2'] + (csv_df['FI_SE1']*-1) + (csv_df['NO4_SE1']*-1)) / 1000
			df2['domestic_export_SE2']=((csv_df['SE1_SE2']*-1) + csv_df['SE2_SE3'] + (csv_df['NO3_SE2']*-1) + (csv_df['NO4_SE2']*-1)) / 1000
			df2['domestic_export_SE3']=((csv_df['SE2_SE3']*-1) + csv_df['SE3_SE4'] + (csv_df['DK1_SE3']*-1) + (csv_df['FI_SE3']*-1) + (csv_df['NO1_SE3']*-1)) / 1000
			df2['domestic_export_SE4']=((csv_df['SE3_SE4']*-1) + csv_df['SE4_DK2'] + csv_df['SE4_PL'] + (csv_df['DE_SE4']*-1) + (csv_df['LT_SE4']*-1)) / 1000

			#We can always use the column "balance_with_" for balance since it either defaults to 100% if not used
			df2['balance_after_domestic_export_SE1']= df2['balance_with_' + str(consumptionFraction) + '_consumption_SE1'] + (df2['domestic_export_SE1']*-1)
			df2['balance_after_domestic_export_SE2']= df2['balance_with_' + str(consumptionFraction) + '_consumption_SE2'] + (df2['domestic_export_SE2']*-1)
			df2['balance_after_domestic_export_SE3']= df2['balance_with_' + str(consumptionFraction) + '_consumption_SE3'] + (df2['domestic_export_SE3']*-1)
			df2['balance_after_domestic_export_SE4']= df2['balance_with_' + str(consumptionFraction) + '_consumption_SE4'] + (df2['domestic_export_SE4']*-1)
		else:
			df2['domestic_export_SE1']=''
			df2['domestic_export_SE2']=''
			df2['domestic_export_SE3']=''
			df2['domestic_export_SE4']=''
	
			df2['balance_after_domestic_export_SE1']=''
			df2['balance_after_domestic_export_SE2']=''
			df2['balance_after_domestic_export_SE3']=''
			df2['balance_after_domestic_export_SE4']=''

		return df2

	if year <= 2009:
		df3 = getDataframe('Statistik per elområde och timme, ' + str(year) + '.xlsx', year, -1)
	elif year == 2010:
		#Year 2010 consists of 2 separate files which we concatenate into one
		dfa3 = getDataframe('Statistik per elområde och timme, ' + str(year) + '_01-till-06.xlsx', year, 0)
		dfb3 = getDataframe('Statistik per elområde och timme, ' + str(year) + '_07-till-12.xlsx', year, 1)
		df3 = pd.concat([dfa3, dfb3],ignore_index=True)
	elif year >= 2011 and year <= datetime.now().year:
		df3 = getDataframe('Statistik per elområde och timme, ' + str(year) + '.xlsx', year, -1)
	else:
		sys.exit('Something went wrong')

	colAggDict={
		'datetime':'first',
		'year':'first',
		'week':'first',
		'balance_SE':'sum',
		'balance_SE1':'sum',
		'balance_SE2':'sum',
		'balance_SE3':'sum',
		'balance_SE4':'sum',
		'consumption_SE':'sum',
		'consumption_SE1':'sum',
		'consumption_SE2':'sum',
		'consumption_SE3':'sum',
		'consumption_SE4':'sum',
		'production_SE':'sum',
		'production_SE1':'sum',
		'production_SE2':'sum',
		'production_SE3':'sum',
		'production_SE4':'sum'
	}
	
	groupLyDict={
		'hour':'hourly',
		'day':'daily',
		'week':'weekly',
		'month':'monthly'
	}
	
	dotSizeDict={
		'hour':20,
		'day':80,
		'week':600,
		'month':2000
	}

	#This sets the x and y position of each point
	if groupby == '' or groupby == 'HOUR':
		df4=df3[[
			'datetime', 'year', 'week',
			'balance_SE', 'balance_SE1', 'balance_SE2', 'balance_SE3', 'balance_SE4',
			'consumption_SE', 'consumption_SE1', 'consumption_SE2', 'consumption_SE3', 'consumption_SE4',
			'production_SE', 'production_SE1', 'production_SE2', 'production_SE3', 'production_SE4',
			'domestic_export_SE1', 'domestic_export_SE2', 'domestic_export_SE3', 'domestic_export_SE4',
			'balance_after_domestic_export_SE1', 'balance_after_domestic_export_SE2', 'balance_after_domestic_export_SE3', 'balance_after_domestic_export_SE4',
			'balance_with_' + str(consumptionFraction) + '_consumption_SE', 'balance_with_' + str(consumptionFraction) + '_consumption_SE1', 'balance_with_' + str(consumptionFraction) + '_consumption_SE2', 'balance_with_' + str(consumptionFraction) + '_consumption_SE3', 'balance_with_' + str(consumptionFraction) + '_consumption_SE4'
		]].copy()
		df4['hour']=df4.index
		df4['y']=df4['hour'].apply(lambda x: int(x/120))
		df4['x']=df4['hour'].apply(lambda x: int(x%120))
	elif groupby == 'DAY':
		df4=df3.groupby([pd.Grouper(key='datetime', freq='D')], as_index=False).agg(colAggDict)
		df4['hour']=df4.index
		df4['y']=df4['hour'].apply(lambda x: int(x/20))
		df4['x']=df4['hour'].apply(lambda x: int(x%20))
	elif groupby == 'WEEK':
		df4=df3.groupby([pd.Grouper(key='datetime', freq='W')], as_index=False).agg(colAggDict)
		df4['hour']=df4.index
		df4['y']=df4['hour'].apply(lambda x: int(x/7))
		df4['x']=df4['hour'].apply(lambda x: int(x%7))
	elif groupby == 'MONTH':
		df4=df3.groupby([pd.Grouper(key='datetime', freq='M')], as_index=False).agg(colAggDict)
		df4['hour']=df4.index
		df4['y']=df4['hour'].apply(lambda x: int(x/3))
		df4['x']=df4['hour'].apply(lambda x: int(x%3))

	selfSufficiency=math.ceil(df4['production_' + zone].sum()/abs(df4['consumption_' + zone].sum())*1000)/10

	#If balance should include domestic flows, use another balance column for the plot and other description
	if flows:
		balanceCol='balance_after_domestic_export_'
		plotDescStr = '\n'.join((
			r'Production: ' + str(math.ceil(df4['production_' + zone].sum())) + ' MWh | Consumption: ' + str(math.ceil(abs(df4['consumption_' + zone].sum()))) + ' MWh | Balance: ' + str(math.ceil(df4['balance_' + zone].sum())) + ' MWh',
			r'Domestic export: ' + str(math.ceil(df4['domestic_export_' + zone].sum())) + ' MWh | Foreign export: ' + str(math.ceil(df4['balance_after_domestic_export_' + zone].sum())) + ' MWh | Self-sufficiency: ' + str(selfSufficiency) + '%'
		))
	elif consumptionFraction != 100:
		balanceCol='balance_with_' + str(consumptionFraction) + '_consumption_'
		plotDescStr = '\n'.join((
			r'Production: ' + str(math.ceil(df4['production_' + zone].sum())) + ' MWh | Consumption: ' + str(math.ceil(abs(df4['consumption_' + zone].sum()))) + ' MWh',
			r'Balance: ' + str(math.ceil(df4[balanceCol + zone].sum())) + ' MWh | Self-sufficiency: ' + str(selfSufficiency) + '%'
		))
	else:
		balanceCol='balance_'
		plotDescStr = '\n'.join((
			r'Production: ' + str(math.ceil(df4['production_' + zone].sum())) + ' MWh | Consumption: ' + str(math.ceil(abs(df4['consumption_' + zone].sum()))) + ' MWh',
			r'Balance: ' + str(math.ceil(df4[balanceCol + zone].sum())) + ' MWh | Self-sufficiency: ' + str(selfSufficiency) + '%'
		))

	#Colorize a column based on value of balance in zone
	export_color = 'orange'
	import_color = 'grey'
	df4['color'] = df4[balanceCol + zone].apply(lambda x: import_color if x < 0 else export_color)
	importLabel='Import (' + str((df4[balanceCol + zone] < 0).sum()) + ' ' + groupby.lower() + 's)'
	exportLabel='Export (' + str((df4[balanceCol + zone] >= 0).sum()) + ' ' + groupby.lower() + 's)'

	print('Production: ' + str(math.ceil(df4['production_' + zone].sum())) + ' MWh')
	print('Consumption: ' + str(math.ceil(abs(df4['consumption_' + zone].sum()))) + ' MWh')
	print('Balance: ' + str(math.ceil(df4['balance_' + zone].sum())) + ' MWh')
	print('Self-sufficient: ' + str(selfSufficiency) + '%')

	if year == datetime.now().year:
		title='NOTE! Data only to ' + str(df4['datetime'].iloc[-1]) + '\nZone ' + str(zone) + ' ' + str(year) + ' (' + groupLyDict[groupby.lower()] + ')'
	else:
		title='Zone ' + str(zone) + ' ' + str(year) + ' (' + groupLyDict[groupby.lower()] + ')'

	#These dataframes are just dummys to create labels for export/orange and import/grey
	#Another solution would have been to split the main dataframe by color-value, but that caused colors with no data to not be displayed
	importLabel_df = pd.DataFrame([[0, 0, import_color]], columns=['x', 'y', 'color'])
	exportLabel_df = pd.DataFrame([[0, 0, export_color]], columns=['a', 'b', 'color'])

	#Plot the data
	ax = importLabel_df.plot(kind='scatter', x='x', y='y', c='color', label=importLabel)
	exportLabel_df.plot(kind='scatter', x='a', y='b', c='color', label=exportLabel, ax=ax)
	df4.plot(kind='scatter', x='x', y='y', c='color', title=title, ax=ax, figsize=(7, 6), s = dotSizeDict[groupby.lower()])

	#Hide irrelevant names on axes.
	ax.axes.get_xaxis().set_visible(False)
	ax.axes.get_yaxis().set_visible(False)

	#Add disclaimer when flows are used to include domestic export/import
	if flows:
		font = {'color':'black','weight':'bold','size': 10}
		box = {'facecolor': 'red','edgecolor': 'green','boxstyle': 'round', 'alpha':0.2}
		flowsDiscStr = 'DISCLAIMER! Domestic export/import is included by using\nESTIMATED flows taken from\nSvenska Kraftnät - Kontrollrummet\nThis causes a mix of actual and estimated results!'
		plt.text(60, 45, '\n' + flowsDiscStr +'\n', ha = 'center', va = 'center', fontdict=font, bbox=box)
		print('\n' + flowsDiscStr + '\n')

	#Add disclaimer when consumption is lowered by factor to see how close the selfsufficiency really is
	if consumptionFraction != 100:
		font = {'color':'black','weight':'bold','size': 10}
		box = {'facecolor': 'red','edgecolor': 'green','boxstyle': 'round', 'alpha':0.2}
		consumptionFractionDiscStr = 'DISCLAIMER! Zone consumption is ' + str(consumptionFraction) + '% of actual value\nThis only affect the plot!'
		plt.text(60, 25, '\n'+ consumptionFractionDiscStr + '\n', ha = 'center', va = 'center', fontdict=font, bbox=box)
		print('\n' + consumptionFractionDiscStr + '\n')

	#Add text to the bottom
	plt.text(0.51, -0.015, plotDescStr, ha = 'center', va = 'top', transform = ax.transAxes)

	sourceStr = 'Source: svk.se > Statistik per elområde och timme. Made with https://github.com/skorpi0n/elzoneplot'
	plt.text(0.5, -0.1, sourceStr, fontsize=8, ha = 'center', va = 'top', transform = ax.transAxes)

	ax.figure.savefig('elzoneplot_' + str(zone) + '_' + str(year) + '_' + groupLyDict[groupby.lower()] + '.png')
	print('Saved plot to: ' + 'elzoneplot_' + str(zone) + '_' + str(year) + '_' + groupLyDict[groupby.lower()] + '.png')

	if output == 'STDOUT':
#		pd.set_option('display.max_columns', None)
		pd.set_option('display.max_rows', None)
		print(df4)
	elif output == 'PLOT':
		#Show plot
		plt.show()
	elif output != '':
		print('Saving dataframe to: ', output, sep='')
		df4.to_csv(output, sep='\t', encoding='utf-8')

if __name__ == "__main__":
	main(sys.argv[1:])