# sampled_data_FC1=sp.sampling(x1,gau_fil_data_FC1)
# sampled_data_FC2=sp.sampling(x2,gau_fil_data_FC2)
# print '\n Sample the data successfully!'
##interpolate the filtered data with nearest value
sampled_data_FC1=int_nea.interpolate_nearest(x1,gau_fil_data_FC1)
sampled_data_FC2=int_nea.interpolate_nearest(x2,gau_fil_data_FC2)
print '\n interpolate the data successfully!'
##save the sampled data
wsd.wri_sam_data(sampled_data_FC1,FC='FC1')
wsd.wri_sam_data(sampled_data_FC2,FC='FC2')
print ('\n save the sampled data successfully!')
elif switch_r_nr==1:
##read the sampled data, TO SAVE TIME
sampled_data_FC1=rsd.re_sam_data(FC='FC1')
sampled_data_FC2=rsd.re_sam_data(FC='FC2')
print ('\n read the sampled data successfully!')
else:
# ERR INIT switch_r_nr
print ('\n ERR INIT switch_r_nr \nswitch_r_nr must be 1 or 0 !!!')
# </editor-fold>
# test
plt.plot(sampled_data_FC1[:,1])
plt.plot(sampled_data_FC2[:,1])
plt.show()
## use the svr fit the FC1-FC2 and get the FC2 predict value
import numpy as np
import pandas as pd
import test_stationarity as test_sta
import matplotlib.pylab as plt
from matplotlib.pylab import rcParams
import read_sam_data as rsd
import pdb
import rloess
rcParams['figure.figsize']=15,6
dateparse=lambda dates:pd.datetime.strptime(dates,'%Y-%m')
date=pd.read_csv('/home/ycc/Documents/data/AirPassengers.csv',parse_dates="Month",index_col="Month",date_parser=dateparse)
sampled_FC1=rsd.re_sam_data('FC1')
sampled_FC2=rsd.re_sam_data('FC2')
# divide the data into time and value
sampled_FC1_value=sampled_FC1[:,1]
sampled_FC2_value=sampled_FC2[:,1]
sampled_FC1_time=sampled_FC1[:,0]
sampled_FC2_time=sampled_FC2[:,0]
# implement the rloess filter
FC2_value_filtered=rloess.lowess(sampled_FC2_time,sampled_FC2_value,f=0.9,iter=3)
#plt.plot(sampled_FC2_value)
#plt.plot(FC2_value_filtered)
#plt.show()
#!/usr/bin/env python
# coding=utf-8
import read_sam_data as rsd
import numpy as np
sampled_data_FC=rsd.re_sam_data(FC='FC2')
FT1_FC=sampled_data_FC[0,1]*(1-0.035)
FT2_FC=sampled_data_FC[0,1]*(1-0.040)
FT3_FC=sampled_data_FC[0,1]*(1-0.045)
FT4_FC=sampled_data_FC[0,1]*(1-0.050)
FT5_FC=sampled_data_FC[0,1]*(1-0.055)
FT1_flag=0 # flag bit, 0 means haven't searched the FT1, 1 means already searched the FT1
FT2_flag=0 # flag bit, 0 means haven't searched the FT1, 1 means already searched the FT1
FT3_flag=0 # flag bit, 0 means haven't searched the FT1, 1 means already searched the FT1
FT4_flag=0 # flag bit, 0 means haven't searched the FT1, 1 means already searched the FT1
FT5_flag=0 # flag bit, 0 means haven't searched the FT1, 1 means already searched the FT1
sampled_data_FC=sampled_data_FC[1100:,]
x_old=np.array([[0],[0]])
for x in sampled_data_FC:
if x[1] < FT1_FC and x_old[1] > FT1_FC and FT1_flag==0:
FT1_value=x
FT1_flag=1
if x[1] < FT2_FC and x_old[1] > FT2_FC and FT2_flag==0:
FT2_value=x
FT2_flag=1
if x[1] < FT3_FC and x_old[1] > FT3_FC and FT3_flag==0:
FT3_value=x
