bigdata = pd.DataFrame(columns=['datetime', 'cloud_mean'])
bigraining = {}
biggroupofclouds = {}
bigdata = pd.concat([bigdata, df])
bigdata.sort_values('datetime', inplace=True)
bigdata.drop_duplicates('datetime', inplace=True)
bigdata.reset_index(drop=True, inplace=True)
rained = {df.loc[0, 'datetime'].strftime('%Y-%m-%d %H:%M'): rain}
bigraining.update(rained)
clouding = {
df.loc[0, 'datetime'].strftime('%Y-%m-%d %H:%M'): cloud_group
}
biggroupofclouds.update(clouding)
# Saving New Values
bigdata.to_pickle('data.pkl')
processor.dicttopickle(bigraining, 'raining.p')
processor.dicttopickle(biggroupofclouds, 'groupofclouds.p')
logger.info('Data Saved till {}'.format(minute))
except Exception as e:
logger.info(
'Error for image: {0} _while processing due to: {1}'.format(
image_name, str(e)))
df = pd.DataFrame(columns=['datetime', 'cloud_mean'])
current_time = (date - timedelta(minutes=360)).time()
if current_time < datetime(2018, 1, 1, 18, 30).time():
df.loc[0, :] = datetime.combine(date.date(), current_time), centre
else:
df.loc[0, :] = datetime.combine((date.date() - timedelta(days=1)),
current_time), centre
df['datetime'] = pd.to_datetime(df['datetime'])
df['datetime'] = df['datetime'].apply(lambda x: x.tz_localize(
