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Hent data om dit elektricitetsforbrug fra Ørsted med Python

Dette lille Python-program, genererer CSV-filer med dit elektricitetsforbrugsdata fra Ørsted (tidligere DONG), hvis du har en fjernaflæst måler. Du kan fx bruge programmet, hvis du har lyst til at holde øje med dit forbrug i et Excel-dokument.

Det færdige program

# -*- coding: utf-8 -*-
# Author: Morten Helmstedt. E-mail: helmstedt@gmail.com
""" This program gets your Ørsted electricity consumption data and saves it to CSV"""

import requests 
from datetime import datetime
from datetime import date
from datetime import timedelta

# USER ACCOUNT AND PERIOD DATA. SHOULD BE EDITED FOR YOUR NEEDS #

# User account credentials
user = ''	#E-mail address
password = ''		#Password

# Start date and date today used for consumption data
startdate = '2019-01-01'
today = date.today()
enddate = datetime.strftime(today, '%Y-%m-%d')

# API LOGIN #
url = 'https://api.obviux.dk/v2/authenticate'

headers = {
	'X-Customer-Ip': '0.0.0.0'
	}
	
credentials = {
	'customer': user,
	'password': password
	}

request = requests.post(url, headers=headers, json=credentials)
response = request.json()

# Save data for further API requests
external_id = response['external_id']
headers['Authorization'] = response['token']

# GET EAN #
url = 'https://api.obviux.dk/v2/deliveries'
request = requests.get(url, headers=headers)
response = request.json()

# Assuming only one Ørsted agreement, save ean value for further API queries for that agreement
# In case of more than one agreement, loop through list and save values instead
ean = response[0]['ean']

# API CALL #
base_url = 'https://capi.obviux.dk/v1/consumption/customer/'
id_ean = external_id + '/ean/' + ean + '/'

# There's limits on periods for each type of consumption data, so we create lists of periods
startdate_datetime = datetime.strptime(startdate, '%Y-%m-%d')
enddate_datetime = datetime.strptime(enddate, '%Y-%m-%d')

# The number of days for each period
days_for_type = {
	"hourly": 15,
	"daily": 370,
	"weekly": 420,
	"monthly": 1860,
	"yearly": 1830	
	}

def get_consumption_data(type):
	# Loop that returns a list of periods (dates) to request
	periods = []
	loop = True	
	start_of_periods = startdate_datetime
	days = days_for_type[type]

	while loop == True:
		start = datetime.strftime(start_of_periods, '%Y-%m-%d')
		end = start_of_periods + timedelta(days=days)
		# Loop ends and replaces end value if the calculated end date is later than what the user is looking for
		if end > enddate_datetime:
			end = enddate
			loop = False
		# Every weekly period ends on a Sunday and next starts on Monday
		elif type == "weekly" and not end.weekday() == 6:
			correction = end.weekday() + 1
			end -= timedelta(days=correction)
			end = datetime.strftime(end, '%Y-%m-%d')
			start_of_periods += timedelta(days=days + 1 - correction)
		# All months end on last day of month and next period starts the 1st of next month
		elif type == "monthly":
			day_in_month = end.day
			end -= timedelta(days=day_in_month)
			start_of_periods = end + timedelta(days=1)
			end = datetime.strftime(end, '%Y-%m-%d')
		# All yearly periods end on 31st December and next period starts next year January 1st
		elif type == "yearly":
			start_of_periods = datetime.strptime(str(end.year)+"-01-01", '%Y-%m-%d')
			end = str(end.year-1) + "-12-31"
		# Covers hourly and daily periods and weeks ending on Sunday
		else:
			end = datetime.strftime(end, '%Y-%m-%d')
			start_of_periods += timedelta(days=days + 1)
		periods.append([start, end])

	# API requests to cover requested periods
	url = base_url + id_ean + type
	responses = []
	for period in periods:
		params = {
			'from': period[0],
			'to': period[1]
			}
		request = requests.get(url, headers=headers, params=params)
		response = request.json()
		responses.append(response)

	# Write responses to CSV
	if type == "hourly":
		output = "date;hour;amount\n"
	elif type == "weekly":
		output = "date;weeknum;amount\n"
	else:
		output = "date;amount\n"
	for entry in responses:
		for data in entry['data']:
			if data['consumptions']:
				for d in data['consumptions']:
					start = datetime.strptime(d['start'], '%Y-%m-%dT%H:%M:%S.%f%z')
					start_in_timezone = datetime.astimezone(start).strftime("%Y-%m-%d %H:%M")
					amount = str(d['kWh']).replace(".",",")
					if type == "hourly":
						hour = datetime.astimezone(start).strftime("%H")
						output += start_in_timezone + ";" + hour + ";" + amount + "\n"
					elif type == "weekly":
						weeknum = datetime.astimezone(start).strftime("%V")
						output += start_in_timezone + ";" + weeknum + ";" + amount + "\n"
					else:
						output += start_in_timezone + ";" + amount + "\n"
		# Special case for year when change is done from manual to automatic consumption reading
		if type == "yearly":
			if entry['readings']:
				for ent in entry['readings']:
					if ent['readings']:
						for e in ent['readings']:
							if e['consumption']:
								date = e['startdate'] + " 00:00"
								amount = str(e['consumption']).replace(".",",")
								output += date + ";" + amount + "\n"
	filename = type + ".csv"
	with open(filename, "w", encoding='utf8') as fout:
		fout.write(output)

# Loop to cycle through all consumption endpoints
endpoints = ["hourly", "daily", "weekly", "monthly", "yearly"]
for endpoint in endpoints:
	get_consumption_data(endpoint)