__author__ = 'Newsteinwell' import numpy as np import matplotlib.pyplot as plt from scipy import interpolate from scipy.interpolate import interp1d import load_data as ld import gauss_filter as gf #sampling bandwidth=50 [FC1_data,x1]=ld.load_data_FC1() gau_fil_data=gf.smoothListGaussian(FC1_data,bandwidth) #cubic-spline # x=np.arange(0,2*np.pi+np.pi/4,2*np.pi/8) # y=np.sin(x) batches_len=2000 #Because the memory limit, we interpolated the whole data in batches, batches_len is the length of batches. start_num=np.arange(0,len(gau_fil_data),batches_len) xnew=[] ynew=[] for i in range(len(start_num)): print (i) x=x1[start_num[i]:start_num[i]+batches_len] y=gau_fil_data[start_num[i]:start_num[i]+batches_len] f2 = interp1d(x, y, kind='cubic') xnew=np.arange(x[0],x[len(x)-1],0.5) ynew.append(f2(xnew))
plt.show() pdb.set_trace() print '\n load data successfully!' ##remove raw data peak FC1_data = rm_p.rm_p(FC1_data) FC2_data = rm_p.rm_p(FC2_data) print '\n remove_peak successfully!' # pdb.set_trace() ##process the raw data with gauss filter gau_fil_data_FC1=gf.smoothListGaussian(FC1_data,degree=bandwidth) gau_fil_data_FC2=gf.smoothListGaussian(FC2_data,degree=bandwidth) print '\n Gauss filter the raw data successfully!' ##sampling the filtered data # 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')