# 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