def polymer_end_2_end(M, L, frames ):
hpy = h5py.File('polymer_stetching.hdf5','w')
delta = 1
P = M.cord_frames(['M'],frames)
V = M.cord_frames(['V'],frames)
polymers = P.shape[1]/(2*V.shape[1])
for count, k in enumerate(frames):
counter = 0
ps2pe = np.zeros(V.shape[1]*polymers)
c2pe = np.zeros(V.shape[1]*polymers)
c2ps = np.zeros(V.shape[1]*polymers)
for i in range(V.shape[1]):
for j in range(0,2*polymers,2):
ps2pe[counter] = points.dist(P[count][i*(2*polymers)+j],P[count][i*(2*polymers)+j+1],L[k])[0]
c2ps[counter] = points.dist(V[count][i],P[count][i*(2*polymers)+j],L[k])[0]
c2pe[counter] = points.dist(V[count][i],P[count][i*(2*polymers)+j+1],L[k])[0]
counter+=1
dset = hpy.create_dataset("%ips2pe"%k, data = ps2pe)
dset.attrs.create("N_atoms",V.shape[1]*polymers)
dset.attrs.create("L",L[k][0])
dset.attrs.create("n_frames",delta)
dset = hpy.create_dataset("%ic2ps"%k, data = c2ps)
dset.attrs.create("N_atoms",V.shape[1]*polymers)
dset.attrs.create("L",L[k][0])
dset.attrs.create("n_frames",delta)
dset = hpy.create_dataset("%ic2pe"%k, data = c2pe)
dset.attrs.create("N_atoms",V.shape[1]*polymers)
dset.attrs.create("L",L[k][0])
dset.attrs.create("n_frames",delta)
hpy.close()
def find_nearest(point, A, B, L):
smallA = 100
smallB = 100
typeA = 'G'
typeB = 'G'
for i in range(A.shape[0]):
d = points.dist(point,A[i],L)[0]
if d < smallB:
smallA = smallB
typeA =typeB
smallB = d
typeB = 'A'
else:
if smallA < d:
smallA = d
typeA = 'A'
for i in range(B.shape[0]):
d = points.dist(point,B[i],L)[0]
if d < smallB:
smallA = smallB
typeA =typeB
smallB = d
typeB = 'B'
else:
if smallA < d:
smallA = d
typeA = 'B'
return [typeA,typeB]
def find_neighbors(M,point,L,rcut=17):
neighbors = []
for i in range(len(M)):
if points.dist(M[i],point,L)[0]>1:
if points.dist(M[i],point,L)[0]<rcut:
neighbors.append(M[i])
return neighbors
def close_neighbors_point(M,point,L):
neighbors = False
lowest = 100
for i in range(M.shape[0]):
if dist(M[i],point,L)[0] != 0:
D = dist(M[i],point,L)[0]
if lowest > D:
lowest = D
neighbor = i
return neighbor, lowest
def find_nearest(point,CW,CV,L):
current = 100
for i in CW:
d = points.dist(point,i,L)[0]
if d < current:
current = d
new_point = i
for i in CV:
d = points.dist(point,i,L)[0]
if d < current:
current = d
new_point = i
return new_point
def find_neighbors(index, CW, CV, L, cut = 17):
point = find_lattice(index,CW,CV)
V_neighbors = []
W_neighbors = []
for i,j in enumerate(CV):
if points.dist(j,point,L)[0] < cut:
if points.dist(j,point,L)[0] > 1:
V_neighbors.append(i)
for i,j in enumerate(CW):
if points.dist(j,point,L)[0] < cut:
if points.dist(j,point,L)[0] > 1:
W_neighbors.append(i)
return V_neighbors, W_neighbors
def end_end_connected(M,L): #find the length of the polymer
## \brief end to end distance of hybridized vs nonhybridized polymers
#
# \returns average distance from start to end of polymer
# for hybridizations and free polymer
#
# \param M - ReadCord Class
# \param L
#
# V ----- A C k
# W ----- F G T
import MD.analysis.connections as con
try:
K=util.pickle_load('K.pkl')
T=util.pickle_load('T.pkl')
S=util.pickle_load('S.pkl')
except:
K=M.cord_auto(['K'])
T=M.cord_auto(['T'])
S=M.cord_auto(['M'])
util.pickle_dump(K,'K.pkl')
util.pickle_dump(T,'T.pkl')
util.pickle_dump(S,'S.pkl')
#get connections
con = _connections(M)
h_sum = []
f_sum = []
for k in range(len(con)):
print k
hybrid = []
free = []
#get the points that are not connected
for i in range(K.shape[1]):
if i in con[k][0]:
hybrid.append(points.dist(K[k][i],S[k][i],L[k])[0]+0.5)
else:
free.append(points.dist(K[k][i],S[k][i],L[k])[0]+0.5)
for i in range(T.shape[1]):
if i in con[k][1]:
hybrid.append(points.dist(T[k][i],S[k][i+K.shape[1]],L[k])[0]+0.5)
else:
free.append(points.dist(T[k][i],S[k][i+K.shape[1]],L[k])[0]+0.5)
print hybrid
h_sum.append(sum(hybrid)/len(hybrid))
f_sum.append(sum(free)/len(free))
f_sum[-1]= f_sum[-2]
h_sum[-1]= h_sum[-2]
x = range(S.shape[0])
pyplot.plot2(x,h_sum,x,f_sum,xlabel='timestep',ylabel='sigma',label1='hybridizations',
label2='free',save='free_hybrid_end_end_connections',showleg='true')
def close_2neighbors_point(M,point,L):
neighbors = []
vectors = []
lowest = 100
neighbor2 = 0
for i in range(M.shape[0]):
if dist(M[i],point,L)[0] != 0:
D = dist(M[i],point,L)[0]
if lowest > D:
lowest = D
neighbor = i
neighbor2 = neighbor
try:
return neighbor, neighbor2, lowest
except:
print "error No neighbors were found try increasing"
def find_int_dist(new_point, active_int,L):
current = 100
for i in range(len(active_int)):
d = points.dist(new_point,active_int[i][-1],L)[0]
if d < current:
current = d
return current
def draw_polymer(M,L, frames,NP=[1,2], rcut=1.0, step=5e4):
print "getting cors"
P = M.cord_frames(['M'],frames)
VW = M.cord_frames(['V','W'],frames)
print "finished getting cords"
ndna = P.shape[1]/VW.shape[1]
#distance from center to edge of cube
#draw edges of the cube
def write_line(fid,p1,p2):
s1 = "{%.2f %.2f %.2f}"%(p1[0],p1[1],p1[2])
s2 = "{%.2f %.2f %.2f}"%(p2[0],p2[1],p2[2])
fid.write(("draw arrow %s %s\n")%(s1,s2))
c = ['green','blue','red']
for count,k in enumerate(frames):
fid = open('polymer%i.tcl'%k,'w')
fid.write('proc vmd_draw_arrow {mol start end} {\n' +
'set middle [vecadd $start [vecscale 0.9 [vecsub $end'+
' $start]]]\n'+'graphics $mol cylinder $start $middle' +
' radius 0.15\n'+'graphics $mol cone $middle $end radius' +
' 0.25\n}\n')
#for i in range(VW.shape[1]):
for cc,i in enumerate(NP):
color = c[cc]
fid.write('draw color '+color+'\n')
for j in range(0,ndna,2):
d = points.dist(P[count][j+i*ndna],P[count][j+1+i*ndna],L[k])[1]
write_line(fid,P[count][j+i*ndna],P[count][j+i*ndna]+d)
fid.close()
def nearpart_remove():
#print out directory
fid = open('dna_2nuc.xyz','r')
N_atoms = fid.readline()
fid.readline()
M = fid.readlines()
V = []
count = 0
NP_center = []
#NP_center = index of center of particle
#V = numpy array of all particle positions
#index = "name of particle at each index'
index = []
for line in M:
index.append(line.split()[0])
V.append([float(line.split()[1]),float(line.split()[2]),float(line.split()[3])])
if line.split()[0] == 'V' or line.split()[0] == 'W':
NP_center.append(count)
count += 1
fid.close()
V = np.array(V)
Lx = 220
Ly = 110
Lz = 110
L = np.array([Lx, Ly, Lz])
print L
#the center of the partilcl
too_close = []
print NP_center
C = NP_center[0]
NP_size = NP_center[1] - NP_center[0]
for i in NP_center:
print i/NP_size
for j in NP_center:
if i != j:
if points.dist(V[i],V[j],L)[0]<12:
too_close.append([min(i,j),max(i,j)])
too_close = points.unique(too_close)
remove = []
for i in too_close:
remove.append(i[0])
#remove the NP which are too_close!
#to do and write the new dna.xyz file
print 'writing dna.xyz'
out = open('dna_close_remove.xyz','w')
out.write(('%i\n\n')%(V.shape[0]))
count = 0
for i in range(V.shape[0]):
if i in remove:
i+=1
if count>=V.shape[0]-1:
print 'breaking'
break
for j in range(NP_size):
count = i * NP_size + j
out.write(('%s %.2f %.2f %.2f\n')%(index[count],V[count][0],V[count][1],V[count][2]))
def connections(A,B,L,rcut=1.5):
counter=[]
for k in range(A.shape[0]):
count = []
print 'step',k
for i in range(A.shape[1]):
for j in range(B.shape[1]):
d = dist(A[k][i],B[k][j],L)[0]
if d<rcut:
count.append([i,j+A.shape[1]])
counter.append(count)
return counter
def connections_min_max(A,B,L,rcut=1.5,rmin=1):
counter=[]
for k in range(A.shape[0]):
count = []
print 'step',k
for i in range(A.shape[1]):
for j in range(B.shape[1]):
d = dist(A[k][i],B[k][j],L)[0]
if (d<rcut) & (d>rmin):
count.append([i,j+A.shape[1]])
counter.append(count)
return counter
def add_particles(self,M):
self.particle_list = np.zeros((M.shape[0]))
for i in range(M.shape[0]):
dsmall = 100
index = 0
for j in range(len(self.box)):
d = points.dist(M[i],self.box[j][0],self.L)[0]
if d < dsmall:
index = j
dsmall = d
self.box[index][2].append(i)
self.particle_list[i] = int(index)
def min_distance(A1,A2,L):
num=0
side1=0
side2=0
smallest=50
for i in range(A1.shape[0]):
for j in range(A2.shape[0]):
num = dist(A1[i],A2[j],L)[0]
if smallest > num:
smallest = num
side1 = i
side2 = j
return side1,side2
def end_end(V,W,M,L):
T=M.cord_auto(['T'])
K=M.cord_auto(['K'])
V=M.cord_auto(['V'])
W=M.cord_auto(['W'])
ndna = T.shape[1]/V.shape[1]
#K-V
#W-T
VK=[]
WT=[]
last = V.shape[0]
#find center-ssdna particle distance
for k in range(last-5,last):
for i in range(V.shape[1]):
for j in range(ndna):
VK.append(points.dist(V[k][i],K[k][j+i*ndna],L)[0]-3)
WT.append(points.dist(W[k][i],T[k][j+i*ndna],L)[0]-3)
hist_K,xK,max_k=histogram(VK,10)
hist_T,xT,max_T=histogram(WT,10)
plt.close()
pyplot.plot_bar(xK,hist_K,save='VK_distance')
plt.close()
pyplot.plot_bar(xT,hist_T,save='WT_distance')
plt.close()
#find the length of the polymer
KT=M.cord_auto(['K','T'])
S=M.cord_auto(['M'])
edge=[]
for k in range(last-5,last):
for i in range(S.shape[1]):
edge.append(points.dist(KT[k][i],S[k][i],L)[0])
hist_edge,xedge,max_edge=histogram(edge,50)
pyplot.plot_bar(xedge,hist_edge,save='polymer_length')
plt.close()
def __init__(self,L,delta=4):
#initialize the box
delta = L[0]/round(delta)
self.box = []
self.L = L
for i in np.arange(-L[0]/2.,L[0]/2.,delta):
for j in np.arange(-L[1]/2.,L[1]/2.,delta):
for k in np.arange(-L[2]/2.,L[2]/2.,delta):
self.box.append([np.array([i,j,k]),[],[]])
#add the box neighbors
for i in range(len(self.box)):
for j in range(len(self.box)):
if points.dist(self.box[i][0],self.box[j][0],L)[0] <= 3**0.5*delta:
self.box[i][1].append(j)
def place_int(new_point, active_int,list_index,L):
current = 100
for i in range(len(active_int)):
if i not in list_index:
d = points.dist(new_point,active_int[i][-1],L)[0]
if d < current:
current = d
index = i
try:
index
active_int[index].append(new_point)
list_index.append(index)
return index
except:
pass
def msd_r(A,lattice,L):
#First find the closest to the real coordinate
msd=np.zeros((A.shape[0],2))
msd+=L[0]+10
for i in range(A.shape[0]):
for j in range(lattice.shape[0]):
D=points.dist(A[i],lattice[j],L)[0]
#if the distance is shorter store the point and the distance
if abs(msd[i][1])>abs(D):
msd[i][0]=j
msd[i][1]=D
#find the total msd
MSD=0
for i in range(msd.shape[0]):
MSD+=msd[i][1]**2
MSD_total= MSD/A.shape[0]
print 'msd for choice',MSD_total
return MSD_total
def diff(A,L,step=50000*5):
#find the drift
print A.shape
Drift = drift_position(A,L)[0]
#find msd
num = A[0].shape[0]
print num
print num
frames=A.shape[0]
print "Calculating MSD"
MSD=np.zeros((frames-1))
r_sum = 0
for k in range(0,frames-1):
for i in range(num):
d = points.dist(A[0][i][0:3], A[k][i][0:3], L)
r_sum = abs(d[0]) + r_sum
MSD[k] = r_sum
x = np.arange(0, A.shape[0] * step - step, step)
return x,MSD
def drift_position(A,L):
num = A[0].shape[0]
print "Calculating drift of position"
D = np.zeros(((A.shape[0]-1),3))
Dsum = np.zeros(((A.shape[0]-1),1))
x_whole = 0; y_whole = 0; z_whole = 0
for k in range(A.shape[0] - 1):
x_sum = 0; y_sum = 0; z_sum = 0
for i in range(num):
d, dx = points.dist(A[k][i], A[k+1][i], L)
x_sum += dx[0]
y_sum += dx[1]
z_sum += dx[2]
D[k][0] = (x_sum / num + x_whole)
D[k][1] = (y_sum / num + y_whole)
D[k][2] = (z_sum / num + z_whole)
Dsum[k] = (((x_sum / num +x_whole)**2
+(y_sum / num + y_whole)**2
+(z_sum / num + z_whole)**2)**0.5)
x_whole = x_sum / num + x_whole
y_whole = y_sum / num + y_whole
z_whole = z_sum / num + z_whole
return D, Dsum
positions.append([x,y,z])
fid.close()
#Transform
L = np.array([50,50,50])
w = np.array([[1,0,0],[0,1,0],[0,0,1]])
V = np.array([0,0,0])
x_r = points.unit(np.array(orientations[1]))
y_r = points.unit(np.array(orientations[2]))
z_r = points.unit(np.array(orientations[3]))
v = np.array([x_r,y_r,z_r])
print points
R = points.reference_rotation(w,v)
location = []
for i in range(len(P)):
d = points.dist(V,P[i],L)[0]
c_r = points.unit(points.vector1d(V,P[i],L))
location.append(points.unit(np.dot(R,np.transpose(c_r)))*d)
#write out
positions.extend([0,0,0])
fid.open('cube_unit_cell.xyz','w')
fid.write('%i \n\n'%(len(positions)*len(location)))
for i in range(len(positions)):
for j in range(len(location)):
x = location[j][0] + positions[i][0]
y = location[j][1] + positions[i][1]
z = location[j][2] + positions[i][2]
fid.write('Z %.2f %.2f %.2f\n'%(x,y,z))
def find_defects_mod(CV,CW,VW,V,W,L,n_finish=1,n_start=0,delta=20,filters=0.2):
from MD.analysis.nearest_neighbor import ws_neighbors_point
from MD.analysis.nearest_neighbor import close_neighbors_point
#######
debug = False
x = np.arange(n_start,n_finish,delta)
lattice = []
for i in range(CV.shape[0]):
lattice.append(int(points.dist(CV[0],CV[i],L)[0]))
for i in range(CW.shape[0]):
lattice.append(int(points.dist(CV[0],CW[i],L)[0]))
points.unique(lattice)
lattice.sort()
print lattice
rmin = lattice[1] / 2.0
rmax = lattice[2] / 2.0 +1
#for interstitial
s = min(W.shape[1],V.shape[1])
#for vacancy
#s = max(W.shape[1],V.shape[1])
DW = np.zeros((len(x),CW.shape[0]))
DV = np.zeros((len(x),CV.shape[0]))
vac_count = np.zeros((len(x),1))
int_count = np.zeros((len(x),1))
fid = open('defects.txt','w')
master = [[],[]]
particle_frame = []
for index,k in enumerate(x):
print 'frame',k
# Identify the points on the bcc Lattice which belong to A and B type
#lets look at one point first
# Assign each point to its place on the lattice
#find the closest point
#####################
N = []
N_V = []
N_W = []
Vn = []
Wn = []
for i in V[k]:
if abs(i[0]) < L[0]:
Vn.append(i)
for i in W[k]:
if abs(i[0]) < L[0]:
Wn.append(i)
print 'Wn',len(Wn)
print 'Vn',len(Vn)
particle_frame.append(len(Wn)+len(Vn))
Wn = np.array(Wn)
Vn = np.array(Vn)
for i in range(CV.shape[0]):
N = ws_neighbors_point(Vn,CV[i],L,i,rmin=rmin,rmax=rmax)[0]
N_V.extend(N)
num = len(N)
if num == 0:
N2 = ws_neighbors_point(Wn,CV[i],L,i,rmin=rmin,rmax=rmax)[0]
N_W.extend(N2)
num = -len(N2)
DV[index][i] = num
###########################
for i in range(CW.shape[0]):
N = ws_neighbors_point(Wn,CW[i],L,i+W.shape[1],rmin=rmin,rmax=rmax)[0]
N_W.extend(N)
num = len(N)
if num == 0:
N2 = ws_neighbors_point(Vn,CW[i],L,i+V.shape[1],rmin=rmin,rmax=rmax)[0]
N_V.extend(N2)
num = -len(N2)
DW[index][i] = num
#find the atoms that haven't been placed on the lattice yet
IV = points.difference(range(Vn.shape[0]),N_V)
IW = points.difference(range(Wn.shape[0]),N_W)
##
if debug:
print 'atoms not added listed after first sorting'
print IV, IW
print DW[index]
print DV[index]
for i in IV:
#find closest lattice point
nw, dw = close_neighbors_point(CW,Vn[i],L)
nv, dv = close_neighbors_point(CV,Vn[i],L)
if dw <= dv:
#check to see if there is already an atom at that point
if DW[index][nw] == 1 or DW[index][nw] == -1:
if DV[index][nv] == 0:
DV[index][nv] = 1
N_V.extend([i])
else:
DW[index][nw] = -2
N_V.extend([i])
if DW[index][nw] == 0:
DW[index][nw] = -1
N_V.extend([i])
#check to see if there is already an atom at that point
else:
if DV[index][nv] == 1 or DV[index][nv] == -1:
#if there isn't one at the other point add it
if DW[index][nw] == 0:
DW[index][nw] = -1
N_V.extend([i])
else:
if DV[index][nv] == 1:
DV[index][nv] = 2
if DV[index][nv] == -1:
DV[index][nv] = -2
N_V.extend([i])
if DV[index][nv] == 0:
DV[index][nv] = 1
N_V.extend([i])
for i in IW:
nw, dw = close_neighbors_point(CW,Wn[i],L)
nv, dv = close_neighbors_point(CV,Wn[i],L)
if dv <= dw:
if DV[index][nv] == 1 or DV[index][nv] == -1:
if DW[index][nw] == 0:
DW[index][nw] = 1
N_W.extend([i])
else:
DV[index][nv] = -2
N_W.extend([i])
if DV[index][nv] == 0:
DV[index][nv] = -1
N_W.extend([i])
else:
if DW[index][nw] == 1 or DW[index][nw] == -1:
if DV[index][nv] == 0:
DV[index][nv] = -1
N_W.extend([i])
else:
DW[index][nw] = 2
N_W.extend([i])
if DW[index][nw] == 0:
DW[index][nw] = 1
N_W.extend([i])
#find the atoms that haven't been placed on the lattice yet
IV = points.difference(range(Vn.shape[0]),N_V)
IW = points.difference(range(Wn.shape[0]),N_W)
##
if debug:
print 'atoms not added list for debugging'
print IV, IW
print DW[index]
print DV[index]
print 'Defect list for lattice'
#print out the vacency, substitutions
def out_defect(A, index, fid, def_list, C=0):
for i in range(A.shape[1]):
if A[index][i] == 0:
pr = 'vacecy at '+ str(i+C)+ '\n'
try:
def_list[0].extend(i+C)
except:
def_list[0].append(i+C)
if debug:
print pr
fid.write(pr)
if A[index][i] == -1:
pr = 'substitution ' + str(i+C)+ '\n'
if debug:
print pr
fid.write(pr)
if A[index][i] == -2:
pr = 'interstitial ' + str(i + C)+ '\n'
try:
def_list[1].extend(i+C)
except:
def_list[1].append(i+C)
if debug:
print pr
fid.write(pr)
if A[index][i] == 2:
pr = 'interstitial ' + str(i + C)+ '\n'
try:
def_list[1].extend(i+C)
except:
def_list[1].append(i+C)
if debug:
print pr
fid.write(pr)
frame = 'Frame ' + str(k) + '\n'
fid.write(frame)
def_list = [[],[]]
out_defect(DV, index, fid, def_list)
out_defect(DW, index, fid, def_list, C = DV.shape[1])
if len(points.difference(def_list[0], master[0])) != 0:
vac_count[index] += len(points.difference(def_list[1],master[1]))
master[0] = def_list[0]
if len(points.difference(def_list[1],master[1])) != 0:
int_count[index] += len(points.difference(def_list[1],master[1]))
master[1] = def_list[1]
#find the atoms that haven't been placed on the lattice yet
IV = points.difference(range(V.shape[1]),N_V)
IW = points.difference(range(W.shape[1]),N_W)
# Identify the defects surrounding each point
#The total number of frames we are going to look at
# Find the number of defects in each frame
count = 0
substitutions = []
vacancies = []
intersticial = []
def count_def(A,check):
count = 0
for i in A:
if i == check:
count +=1
return count
def count_ldef(A,check):
count = 0
for i in A:
if i < check:
count +=1
return count
def count_adef(A,check):
count = 0
for i in A:
if abs(i) == check:
count +=1
return count
for k in range(DW.shape[0]):
#find the points that are nearest neighbor that are different
substitutions.append(count_ldef(DW[k],0)+count_ldef(DV[k],0))
vacancies.append(count_def(DW[k],0)+count_def(DV[k],0))
intersticial.append(count_adef(DW[k],2.0)+count_adef(DV[k],2.0))
print 'substitions'
print substitutions
print 'vacancies'
print vacancies
print 'interstitials'
print intersticial
util.pickle_dump(DV,'DV_s.pkl')
util.pickle_dump(DW,'DW_s.pkl')
util.pickle_dump([substitutions, vacancies, intersticial, x],'plot_def_s.pkl')
pyplot.plot3(x, substitutions, x, particle_frame, x, intersticial,
label1='substitutions',label2='number of particles',label3='intersticial',
save='defects_per_frame_surface',showleg=True)
#pyplot.plot(x, vac_count, save='defect_vac_diffusion_count')
#pyplot.plot(x, int_count, save='defect_int_diffusion_count')
return DV, DW, [substitutions, vacancies, intersticial, x]
def center_end_connected(M,L):
## \brief center to end distance of hybridized vs nonhybridized polymers
#
# \returns average distance from center of particle to end of polymer
# for hybridizations and free polymer
#
# \param M - ReadCord Class
# \param L
#
# V ----- A C k
# W ----- F G T
#find the length of the polymer
import MD.analysis.connections as con
try:
K=util.pickle_load('K.pkl')
T=util.pickle_load('T.pkl')
V=util.pickle_load('V.pkl')
W=util.pickle_load('W.pkl')
except:
V=M.cord_auto(['V'])
W=M.cord_auto(['W'])
K=M.cord_auto(['K'])
T=M.cord_auto(['T'])
util.pickle_dump(K,'K.pkl')
util.pickle_dump(T,'T.pkl')
util.pickle_dump(V,'V.pkl')
util.pickle_dump(W,'W.pkl')
#get connections
con = _connections(M)
h_sum = []
f_sum = []
if len(con) != K.shape[0]:
print len(con)
print K.shape
print 'connections and K have different lenghts'
asdf
for k in range(len(con)):
print k
hybrid = []
free = []
#get the points that are not connected
for i in range(K.shape[1]):
if i in con[k][0]:
hybrid.append(points.dist(K[k][i],V[k][i/(K.shape[1]/V.shape[1])],L[k])[0]+0.5)
else:
free.append(points.dist(K[k][i],V[k][i/(K.shape[1]/V.shape[1])],L[k])[0]+0.5)
for i in range(T.shape[1]):
if i in con[k][1]:
hybrid.append(points.dist(T[k][i],W[k][i/(T.shape[1]/W.shape[1])],L[k])[0]+0.5)
else:
free.append(points.dist(T[k][i],W[k][i/(T.shape[1]/W.shape[1])],L[k])[0]+0.5)
h_sum.append(sum(hybrid)/len(hybrid))
f_sum.append(sum(free)/len(free))
f_sum[-1]= f_sum[-2]
h_sum[-1]= h_sum[-2]
x = range(V.shape[0])
print 'number of DNA per NP'
print K.shape[1]/V.shape[1]
print T.shape[1]/W.shape[1]
pyplot.plot2(x,h_sum,x,f_sum,xlabel='timestep',ylabel='sigma',label1='hybridizations',
label2='free',save='free_hybrid_connections',showleg='true')
def ssDNA_gauss(M,VW, Z,L, frames, rcut=1.0, step=5e4):
try:
#V = util.pickle_load('V.pkl')
P = util.pickle_load('SSDNA.pkl')
except:
P = M.cord_auto(['K','T'])
util.pickle_dump(P,'SSDNA.pkl')
gauss_map = []
w = np.array([[1,0,0],[0,1,0],[0,0,1]])
ndna = P.shape[1]/VW.shape[1]
print P.shape
for k in frames:
location = []
print k
try:
for i in range(VW.shape[1]):
#we must rotate about a specific cube reference frame
if i in range(20):
V = VW[k][i]
x_r = points.unit(points.vector1d(V,Z[k][i*6+1],L[k]))
y_r = points.unit(points.vector1d(V,Z[k][i*6+2],L[k]))
z_r = points.unit(points.vector1d(V,Z[k][i*6+5],L[k]))
dd = points.dist(V,Z[k][i*6+5],L[k])[0]
v = np.array([x_r,y_r,z_r])
R = points.reference_rotation(v,w)
for j in range(ndna):
d = points.dist(V,P[k][j+i*ndna],L[k])[0]
c_r = points.unit(points.vector1d(V,P[k][j+i*ndna],L[k]))
location.append(points.unit(np.dot(R,np.transpose(c_r)))*d)
except:
print 'something went wrong here'
gauss_map.append(location)
#########
fid = open('gaussmap_dna.xyz','w')
max_gauss = 0
for k in gauss_map:
if len(k) > max_gauss:
max_gauss = len(k)
for k in range(len(gauss_map)):
fid.write('%i\n%i\n'%(max_gauss+4,frames[k]))
fid.write('E %.2f 0 0\n'%dd)
fid.write('E 0 %.2f 0\n'%dd)
fid.write('E 0 0 %.2f\n'%dd)
fid.write('V 0 0 0\n')
for i in gauss_map[k]:
fid.write('N %.3f %.3f %.3f\n'%(i[0],i[1],i[2]))
for i in range(max_gauss - len(gauss_map[k])):
fid.write('N 0 0 0\n')
fid.close()
###########
fid = open('gaussmap_dna_step.xyz','w')
max_gauss = 0
step = 10
for k in gauss_map:
if len(k) > max_gauss:
max_gauss = len(k)
max_gauss = max_gauss*step
for s in range(0,len(gauss_map)-step,step):
fid.write('%i\n\n'%(max_gauss+4))
fid.write('E 1 0 0\n')
fid.write('E 0 1 0\n')
fid.write('E 0 0 1\n')
fid.write('V 0 0 0\n')
count = 4
for k in range(s,s+step):
for i in gauss_map[k]:
fid.write('N %.3f %.3f %.3f\n'%(i[0],i[1],i[2]))
count+=1
for i in range(max_gauss+4 - count):
fid.write('N 0 0 0\n')
fid.close()
def polymer_gauss(M,VW, Z,L, frames, rcut=1.0, step=5e4):
#\brief find the gauss map of polymers surrounding NC
try:
#V = util.pickle_load('V.pkl')
P = util.pickle_load('M.pkl')
except:
P = M.cord_auto(['M'])
util.pickle_dump(P,'M.pkl')
A = VW.shape[1]/2
gauss_map = []
w = np.array([[1,0,0],[0,1,0],[0,0,1]])
ndna = P.shape[1]/VW.shape[1]
print P.shape
for k in frames:
location = []
print k
try:
for i in range(VW.shape[1]):
#we must rotate about a specific cube reference frame
if i in range(1):
V = VW[k][i]
x_r = points.unit(points.vector1d(V,Z[k][i*6+1],L[k]))
y_r = points.unit(points.vector1d(V,Z[k][i*6+2],L[k]))
z_r = points.unit(points.vector1d(V,Z[k][i*6+5],L[k]))
v = np.array([x_r,y_r,z_r])
R = points.reference_rotation(v,w)
for j in range(1,ndna,2):
d = points.dist(V,P[k][j+i*ndna],L[k])[0]
c_r = points.unit(points.vector1d(V,P[k][j+i*ndna],L[k]))
location.append(points.unit(np.dot(R,np.transpose(c_r)))*d)
except:
print 'something went wrong here'
gauss_map.append(location)
#########
#fid = open('gaussmap_polymer.xyz','w')
#max_gauss = 0
#for k in gauss_map:
# if len(k) > max_gauss:
# max_gauss = len(k)
#for k in range(len(gauss_map)):
# fid.write('%i\n%i\n'%(max_gauss+4,frames[k]))
# fid.write('E 1 0 0\n')
# fid.write('E 0 1 0\n')
# fid.write('E 0 0 1\n')
# fid.write('V 0 0 0\n')
# for i in gauss_map[k]:
# fid.write('N %.3f %.3f %.3f\n'%(i[0],i[1],i[2]))
# for i in range(max_gauss - len(gauss_map[k])):
# fid.write('N 0 0 0\n')
#fid.close()
###########
fid = open('gaussmap_polymers_total.xyz','w')
max_gauss = 0
for k in gauss_map:
if len(k) > max_gauss:
max_gauss = len(k)
max_gauss = max_gauss*len(gauss_map)
fid.write('%i\n\n'%(max_gauss+4))
fid.write('E 1 0 0\n')
fid.write('E 0 1 0\n')
fid.write('E 0 0 1\n')
fid.write('V 0 0 0\n')
count = 4
for k in range(len(gauss_map)):
for i in gauss_map[k]:
fid.write('N %.3f %.3f %.3f\n'%(i[0],i[1],i[2]))
count+=1
fid.close()
