try:

time_df = pd.DataFrame(time_list, columns=['timestamp']).set_index(['timestamp'])

locations = session.query(old_obj.gid).distinct(old_obj.gid).all()

locations = sorted([loc[0] for loc in locations])

for loc in locations:

data = session.query(old_obj.timestamp, *features).filter(old_obj.gid == loc)\

.order_by(old_obj.timestamp).all()

df = pd.DataFrame(data, columns=['timestamp'] + features).set_index(['timestamp'])

df = df.loc[~df.index.duplicated(keep='first')] # remove the potential duplicates in index

df = df.join(time_df, how='right').sort_index()

# df['wind_bearing'] = df['wind_bearing'].apply(lambda x: x - 360 if x > 180 else x)

inter_data = df.interpolate(method='linear').reset_index()

obj_results = [target_obj(gid=loc, timestamp=dt[0], data=dt[1:]) for dt in inter_data.values.tolist()]

session.add_all(obj_results)

session.commit()

print('Location {} has finished. {} records has been generated.'.format(loc, len(inter_data)))

return

except Exception as e:

print(e)

meo_features = ['temperature', 'dew_point', 'humidity', 'pressure', 'wind_speed', 'wind_bearing',

'cloud_cover', 'visibility']

max_time = session.query(func.max(old_meo_obj.timestamp)).scalar().strftime('%Y-%m-%d %H:%M:%S')

min_time = session.query(func.min(old_meo_obj.timestamp)).scalar().strftime('%Y-%m-%d %H:%M:%S')

tz = pytz.timezone('America/Los_Angeles')

time_df = pd.date_range(start=min_time, end=max_time, freq='1H')

time_list = sorted(list(set([tz.localize(x) for x in time_df])))

print(len(time_list))

""" !!! Be careful, create table would overwrite the original table """

create_table(target_meo_obj)