string.append([xc,yc,zc])
else:
a=1
if (not string):
a=1
else:
allstrings.append(string)
#get the mean string from a group of strings
v1,v2,v3=getMeanString(allstrings)
vecn=[v1,v2,v3]
vecz=zip(*vecn)
vec_array=np.asarray(vecz)
#reparametrize the mean string
meanD,totD=dm_vec(vecz,args.Mout)
vecr1=reparam(vec_array,DIM,args.Mout,meanD)
vecr2=zip(*vecr1)
#############################################################
#Saving the reparametrized center of mass (COM) coordinates
##formating the COMs###
x=vecr2[0]
y=vecr2[1]
z=vecr2[2]
x1=[]
y1=[]
z1=[]
for i in range(args.Mout):
a= ("{0:.4f}".format(float(x[i])))
b= ("{0:.4f}".format(float(y[i])))
c= ("{0:.4f}".format(float(z[i])))
M=file_cols(args.INPUT) P1=load_data(args.INPUT,N,M) # estimate the initial string (P2) of size Min P2 = string_ini(P1,M,args.Min) Pini=string_ini(P1,M,args.Min) PiniT=zip(*Pini) # maximun distances of the data to the images of the string maxdistances=maxvec(P1,P2) # Estimate the mean of the voronoid cells P3, fail, free_energy, allpoints, closeP, closeI = voroid_mean(P1,P2,M,maxdistances, 0) # Reparametrization and smoothing of the centers of the voronoid cells dist_to_X2, totalD = dm_vec(P3,args.Min) Pin=np.asarray(P3) Plist=reparam(Pin,M,args.Min,dist_to_X2) #smooth Ps = smooth_DIM(Plist,args.smooth,M) Prep=zip(*Plist) PinT=zip(*Pin) PsT=zip(*Ps) # Iterations for N-dimensional case (3D visualization) ####################################################### if (len(args.CV2plot)==3): fig = plt.figure() ax = fig.add_subplot(111,projection='3d')
