date = []
data = pd.read_csv('dataexport_20200925T050912.csv')
#data -> Raining & Snow & Basel Temperature [2 m elevation corrected] mean & cloud_cover_low
for i, d in enumerate((data["location"])):
if i >= 9:
date.append([
d[:-5],
float(data["Basel.9"][i]),
float(data["Basel.2"][i]),
float(data["Basel.14"][i])
]) # to remove T0000 things [:-5]
##### AI file
AI = AI.AI_parsing(date)
AI_done = AI.loop_parsing()
temp_KNN = AI_done[0]
rain_KNN = AI_done[1]
claud_KNN = AI_done[2]
##### input file
protect1 = int(date[0][0])
protect2 = int(date[-1][0])
day_input = input.dates(protect1, protect2)
day_output = day_input.daysBefore()
#print(day_output)
### class_predic file
AX = class_predic.Weather_prediction(temp_KNN, rain_KNN, claud_KNN, date)
predic_dato = AX.predict_weather_for(day_output)
