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data_cleaner.py
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import numpy as np
import peakutils
from numpy import genfromtxt
#v1: if noise found of size at least arg ms, removes noise
#arg=numero da medicao; arg1=nome do macaco; arg2=medi/musi/puzz/movi; arg3=min noise duration to remove
# def data_cleaner_v1(arg,arg1,arg2,arg3):
# my_data = genfromtxt('readings/'+arg1+'/'+arg2+'/'+arg1+'_'+arg2+'_'+arg+'.csv', delimiter=',')
#
# #aN_data (|N=[0,5]) is a list of length 700000 whose elements vary from 0 to 1018,
# a0_data = my_data[0::6].flatten().tolist()
# a1_data = my_data[1::6].flatten().tolist()
# a2_data = my_data[2::6].flatten().tolist()
# a3_data = my_data[3::6].flatten().tolist()
# a4_data = my_data[4::6].flatten().tolist()
# a5_data = my_data[5::6].flatten().tolist()
#
#
# #print "SIZE B4 CLEAN: ",len(a0_data)
#
# #a0_u_count=0 #saturated signal frequency - upper limit: >=1018
# #a0_l_count=0 #saturated signal frequency - lower limit: 0
# #a1_u_count=0
# #a1_l_count=0
#
# #for d in a0_data:
# # if d==0:
# # a0_l_count +=1
# # elif d>= 1018:
# # a0_u_count +=1
#
# count = 0
# aux = []
# indices = []
#
# for idx, val in enumerate(a0_data):
# if val == 0 or val >=1018:
# count+=1
# aux.append(idx)
# else:
# if count>=arg3:
# #print "single saturation's size= ", len(aux), aux
# indices+=aux
# count=0
# aux = []
#
# for idx, val in enumerate(a1_data):
# if val == 0 or val >=1018:
# count+=1
# aux.append(idx)
# else:
# if count>=arg3:
# #print "single saturation's size= ", len(aux), aux
# indices+=aux
# count=0
# aux = []
#
# a0_out = [i for j, i in enumerate(a0_data) if j not in indices]
# a1_out = [i for j, i in enumerate(a1_data) if j not in indices]
#
# #print "SIZE AFTER CLEANING NOISE: a0=",len(a0_out)," =a1= ",len(a1_out)
#
# fft_a0=numpy.square(numpy.absolute(numpy.fft.rfft(a0_out)))/1000
# fft_a1=numpy.square(numpy.absolute(numpy.fft.rfft(a1_out)))/1000
#
#
#
# csv_file = file('readings/'+arg1+'/'+arg2+'/'+arg1+'_'+arg2+'_'+arg+'_clean.csv', 'a')
# numpy.savetxt(csv_file, [fft_a0], delimiter=",", fmt="%.3e")
# numpy.savetxt(csv_file, [fft_a1], delimiter=",", fmt="%.3e")
# numpy.savetxt(csv_file, [a2_data], delimiter=",")
# numpy.savetxt(csv_file, [a3_data], delimiter=",")
# numpy.savetxt(csv_file, [a4_data], delimiter=",")
# numpy.savetxt(csv_file, [a5_data], delimiter=",")
#
# print("readings/"+arg1+"/"+arg2+"/"+arg1+"_"+arg2+"_" +arg+"_clean.csv")
#v1: if noise found of size at least arg ms, discards corresponding second
#arg=numero da medicao; arg1=nome do macaco; arg2=medi/musi/puzz/movi; arg3=min noise duration to remove
def data_cleaner_v2(arg,arg1,arg2,arg3):
my_data = genfromtxt('readings/'+arg1+'/'+arg2+'/'+arg1+'_'+arg2+'_'+arg+'.csv', delimiter=',')
#aN_data (|N=[0,5]) is a list of length 700000 whose elements vary from 0 to 1018,
a0_data = my_data[0::6]
a1_data = my_data[1::6]
a2_data = my_data[2::6].flatten()
a3_data = my_data[3::6].flatten().tolist()
a4_data = my_data[4::6].flatten().tolist()
a5_data = my_data[5::6].flatten().tolist()
# ppg_no_baseline = peakutils.baseline(a2_data, 2) # remove baseline from ppg signal
indexes = peakutils.indexes(a2_data, thres=max(0.5, 0.9 * max(a2_data)/1023), min_dist=500) # find its peak
# print 0.9 * max(a2_data) / 1023
# print indexes
for idx in indexes:
a2_data[idx]=1023
# fft_a2 = np.square(np.absolute(np.fft.rfft(a2_data)))/1000
# print "a2_data.shape=",a2_data.shape," a2_fft.shape=",fft_a2.shape
a2_data.tolist()
indices = []
idx = 0
for arr in a0_data:
count = 0
for num in arr:
if num == 0 or num >= 1018:
count += 1
elif count >= arg3:
indices.extend([idx])
break
idx += 1
idx = 0
for arr in a1_data:
count = 0
for num in arr:
if num == 0 or num >= 1018:
count += 1
elif count >= arg3:
indices.extend([idx])
break
idx += 1
aux=0
for num in list(set(indices)):
a0_data=np.delete(a0_data,(num-aux),0)
a1_data=np.delete(a1_data,(num-aux),0)
aux+=1
# a0_data=a0_data.shape
# a1_data=a1_data.shape
fft_a0=[]
for elmt in a0_data:
fft_a0=np.append(fft_a0,np.transpose(np.square(np.absolute(np.fft.rfft(elmt))))/1000)
fft_a1=[]
for elmt in a1_data:
fft_a1=np.append(fft_a1,np.transpose(np.square(np.absolute(np.fft.rfft((elmt)))))/1000)
csv_file = file('readings/'+arg1+'/'+arg2+'/'+arg1+'_'+arg2+'_'+arg+'_clean.csv', 'a')
# np.savetxt(csv_file, [a0_data.flatten().tolist()], delimiter=",", fmt="%d")
# np.savetxt(csv_file, [a1_data.flatten().tolist()], delimiter=",", fmt="%d")
np.savetxt(csv_file, [fft_a0], delimiter=",", fmt="%.3e")
np.savetxt(csv_file, [fft_a1], delimiter=",", fmt="%.3e")
np.savetxt(csv_file, [a2_data], delimiter=",", fmt="%d")
np.savetxt(csv_file, [a3_data], delimiter=",", fmt="%d")
np.savetxt(csv_file, [a4_data], delimiter=",", fmt="%d")
np.savetxt(csv_file, [a5_data], delimiter=",", fmt="%d")
print("readings/"+arg1+"/"+arg2+"/"+arg1+"_"+arg2+"_" +arg+"_clean.csv")
file_list = ['5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','22','23','24','25']
file_list_costa_puzz=['5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21','23','24','25']
file_list_ze_medi = ['3','4','5','6','7','8','9','10','11','12','13','15','16','17','18','19','21','22','23','24','25']
file_list_ze_else=['3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','21','22','23','24','25']
for arg in file_list:
data_cleaner_v2(arg, 'toni', 'musi',10)
data_cleaner_v2(arg, 'toni', 'movi',10)
data_cleaner_v2(arg, 'toni', 'puzz',10)
data_cleaner_v2(arg, 'costa', 'musi',10)
data_cleaner_v2(arg, 'costa', 'movi',10)
for arg in file_list_costa_puzz:
data_cleaner_v2(arg, 'costa', 'puzz',10)
print("---- COSTA & TONI OK ----")
# ZE MOVI,MUSI,PUZZ FALTA 20, VAI DO 3 AO 25
for arg in file_list_ze_else:
data_cleaner_v2(arg, 'ze', 'musi',10)
data_cleaner_v2(arg, 'ze', 'movi',10)
data_cleaner_v2(arg, 'ze', 'puzz',10)
print("---- ZE OK ----")
for arg in file_list:
data_cleaner_v2(arg, 'toni', 'medi',10)
data_cleaner_v2(arg, 'costa', 'medi',10)
print("---- COSTA & TONI DONE ----")
for arg in file_list_ze_medi:
data_cleaner_v2(arg, 'ze', 'medi',10)
print("---- ZE DONE ----")