__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')
