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test.py
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import entsoeAPI as e
import pandas as pd
from entsoe import EntsoePandasClient as entsoePandas
from entsoe import EntsoeRawClient as eRaw
import os
def generateIntialFileName(options,type):
f = options["country"]+"-"+options["start"]+"-"+options["end"]+"-"+type
return f
def saveHistoricalActualData(options):
fname = generateIntialFileName(options,"actual")
try:
#logging.info("[Start] Getting : "+ fname)
#start_time = time.time()
data = e.getActualRenewableValues(options)
#end_time = time.time()
#time_taken = end_time - start_time
fname = fname+"-"+str(data["duration"])
#logging.info("[Stop] Getting : "+ fname+" ; Took : "+str(round(time_taken,2))+" s")
data["data"].to_csv("./rawData/"+fname+".csv")
except Exception as error :
print(error)
#logging.error("[Stop][Error] Getting :"+fname,exc_info=True)
# saveHistoricalActualData({"start":"202204010000","end":"202204200000","country":"FR","interval60":True})
# saveHistoricalActualData({"start":"202201010000","end":"202301010000","country":"FR","interval60":True})
def getAPIToken():
variable_name = "ENTSOE_TOKEN"
value = os.environ.get(variable_name)
if value is None:
raise ValueError(f"The required environment variable '{variable_name}' is not set.")
return value
def checkActualFranc():
oneDay = pd.Timedelta(days=1)
#endDayPlus1 = pd.Timestamp(options["end"], tz='UTC') + oneDay
client1 = entsoePandas(api_key=getAPIToken())
data1 = client1.query_generation("FR", start=pd.Timestamp("202204150000", tz='UTC'), end=pd.Timestamp("202204170000", tz='UTC'),psr_type=None)
data1.to_csv("./manualDownloads/france2022-actual-entsoe-py.csv")
# data2 = client1.query_generation("FR", start=pd.Timestamp("202201010000",tz="Europe/Berlin"), end=pd.Timestamp("202301010000",tz="Europe/Berlin"),psr_type=None)
# data2.to_csv("./manualDownloads/test/france2022-actual-entsoe-1.csv")
def check1():
client = eRaw(api_key=getAPIToken())
xml_string = client.query_generation("FR", start=pd.Timestamp("202204160000", tz='UTC'), end=pd.Timestamp("202204170000", tz='UTC'))
with open('outfile.xml', 'w') as f:
f.write(xml_string)
# check1()
# checkActualFranc()
def checkValueFrance2022():
manual = pd.read_csv("./manualDownloads/test/FR-1.csv")
api = pd.read_csv("./manualDownloads/test/FR-2.csv")
result = pd.concat([manual, api], axis=1)
result.to_csv("./manualDownloads/test/france2022-actual-final.csv")
# checkValueFrance2022()
# data = e.entsoe_getActualGenerationDataPerProductionType({"country":"FR","start":"202204150000","end":"202204170000"})
# print(data)
# data = e.entsoe_getDayAheadAggregatedGeneration({"start":"202305010000","end":"202305150000","country":"FR","interval60":False})
# print(data)
# data1 = e.entsoe_getDayAheadGenerationForecastsWindSolar({"start":"202007010000","end":"202007250000","country":"FR","interval60":False})
# print(data1["data"])
# data1["data"].to_csv("./test/sample.csv")
# data1 = e.entsoe_getDayAheadGenerationForecastsWindSolar({"start":"202004040000","end":"202004060000","country":"LV","interval60":True})
# print(data1)
# data1 = e.getRenewableForecast({"start":"202305100000","end":"202305200000","country":"GR","interval60":True})
# print(data1)
# def refineData(options,data1):
# durationMin = (data1.index[1] - data1.index[0]).total_seconds() / 60
# timeStampsUTC = util_countIntervals(options["start"],options["end"],durationMin)
# logging.info(" Row count : Fetched = "+str(len(data1))+" , Required = "+str(timeStampsUTC["count"]))
# logging.info(" Duration : "+str(durationMin))
# totalAverageValue = data1.mean().fillna(0).round().astype(int)
# data1['startTimeIndex'] = data1.index.strftime('%Y%m%d%H%M')
# data1['startTimeIndex'] = pd.to_datetime(data1['startTimeIndex'])
# data1.to_csv("./test/"+"testrefine1"+".csv")
# start_time = data1.index.min()
# end_time = data1.index.max()
# expected_timestamps = pd.date_range(start=start_time, end=end_time, freq=f"{durationMin}T")
# expected_df = pd.DataFrame(index=expected_timestamps)
# missing_indices = expected_df.index.difference(data1.index)
# logging.info(" Missing values ("+str(len(missing_indices))+") :"+str(missing_indices))
# for index in missing_indices:
# logging.info(" Missing value: "+str(index))
# rows_same_day = data1[ data1.index.date == index.date()]
# if len(rows_same_day)>0 :
# avg_val = rows_same_day.mean().fillna(0).round().astype(int)
# avg_type = "average day value "+ str(rows_same_day.index[0].date())+" "
# else:
# avg_val = totalAverageValue
# avg_type = "whole data average "
# logging.info(" replaced with "+avg_type+" : "+' '.join(avg_val.astype(str)))
# new_row = pd.DataFrame([avg_val], columns=data1.columns, index=[index])
# data1 = pd.concat([data1, new_row])
# # prev_index = index - dur
# # next_index = index + dur
# # avg_val = (data1.loc[prev_index]+data1.loc[next_index])/2
# # logging.info(" previous value: " + ' '.join(data1.loc[prev_index].astype(str)))
# # logging.info(" previous value: " + ' '.join(data1.loc[next_index].astype(str)))
# # logging.info(" average value: " + ' '.join(avg_val.astype(str)))
# # new_row = pd.DataFrame([avg_val], columns=data1.columns, index=[index])
# # data1 = pd.concat([data1, new_row])
# data1.sort_index(inplace=True)
# data1.to_csv("./test/"+"testrefine2"+".csv")
# print(data1)
# d = pd.DataFrame({"start": timeStampsUTC["startBin"] ,"end": timeStampsUTC["endBin"]})
# d.to_csv("./test/datetest.csv")
# data1["startTime"] = timeStampsUTC["startBin"]
# data1["endTime"] = timeStampsUTC["endBin"]
# return data1