data = df[start_p:stop_p].astype('float32')
# data['Day'] = data.index.dayofyear #add day
data = data.interpolate(limit=30000000, limit_direction='both').astype(
'float32') #interpolate neighbor first, for rest NA fill with mean()
data[target] = data[target].shift(-out_t_step)
data.dropna(inplace=True)
return data
#--------------------------------------------------------#
loading = instant_data()
df, mode = loading.hourly_instant(), 'hour'
# df,mode = loading.daily_instant(),'day'
st = 'CPY012'
target, start_p, stop_p, host_path = station_sel(st, mode)
if mode == 'hour': n_past, n_future = 24 * 6, 72
elif mode == 'day': n_past, n_future = 60, 30
n_pca = 4
split_date = '2016-11-01'
#----------------------------------------#
save_path = host_path + 'ML_svr/'
if not os.path.exists(save_path):
os.makedirs(save_path)
###########################################
def call_data():
loading = instant_data()
df, mode = loading.hourly_instant(), 'hour'
import matplotlib.pyplot as plt
from tqdm import tqdm
from DLtools.Data import instant_data, check_specific_col, station_sel
from DLtools.MachineLearning import tsplot
def scope_data(data):
global start_p, stop_p
data = data[start_p:stop_p]
# data = del_less_col(data)
return data
##############################
st, mode = 'CPY012', 'day'
target, start_p, stop_p, _ = station_sel(st, mode)
##############################
loading = instant_data()
df_r = scope_data(loading.df_r)
df_w = scope_data(loading.df_w)
df_wet = scope_data(loading.df_wet)
df_dam = scope_data(loading.df_d)
# df_day=loading.daily_instant()
# df_hour =loading.hourly_instant()
df_solar = df_wet[check_specific_col(df_wet, 'solar')]
df_rain1h = df_wet[check_specific_col(df_wet, 'rain1h')]
df_temp = df_wet[check_specific_col(df_wet, 'temp')]
df_press = df_wet[check_specific_col(df_wet, 'press')]
df_humid = df_wet[check_specific_col(df_wet, 'humid')]
