from sklearn.cluster import KMeans
import functionclass as fc
import numpy as np
import matplotlib.pyplot as plt
import csv
file = 'Data/Datawalk.out'
filetime = 'Data/DataTimewalk.out'
t0 = 9.765625000e-12
c = 299792458
signaldata = fc.readfile(file)
timedata = fc.readfile(filetime)
yti = fc.alldifferentpoint(signaldata)
yi = yti[0]
print(yi, len(yi))
ymax = np.max(yti[1])
print(yti[1])
print(ymax, type(yti[1][0]))
print(len(yti[1]))
fc.addsaveamplituate(yti[1])
disy = []
for x in range(len(yi)):
disy.append((timedata[x][yi[x]] - t0) * c)
print(disy)
print(disy, type(disy))
# ulabel = u.labels_
# print('ulabel:',u.labels_)
#
# gh=[]
# for x in range(len(ulabel)-2):
# if ulabel[x]-ulabel[x+1]!=0 and ulabel[x+1]==ulabel[x+2]:
# gh.append(x)
# print(gh)
file = 'Data/Datastandsit5s.out'
filetime = 'Data/DataTimestandsit5s.out'
file1 = 'Data/Datarun.out'
filetime1 = 'Data/DataTimerun.out'
t0 = 9.765625000e-12
c = 299792458
signaldata = fc.readfile(file)
timedata = fc.readfile(filetime)
signaldata1 = fc.readfile(file1)
timedata1 = fc.readfile(filetime1)
#
yi = fc.alldifferentpoint(signaldata)[1]
#
print(yi, " before")
fc.drawpicture(yi, "data b4 'noise removing'")
# testd = fc.remove_nosie(yi) TODO remove noise
testd = yi
print(testd, " after")
#is DISY relative spatial loc change?
disy = []
#hmmmm looks fishey