def _conclude(self):
print("Concluding...")
results = np.vstack(self._results)
Mup = results[:, 0]
Mdw = results[:, 1]
zlimup = results[:, 2].flatten()
zlimdw = results[:, 3].flatten()
dim = np.vstack(results[:, 4])
N = self.N
df = Universe.empty(n_atoms=N,
n_residues=N,
atom_resindex=np.arange(N),
trajectory=True)
df.add_TopologyAttr('resname', ['O'] * N)
df.add_TopologyAttr('name', ['O'] * N)
df.add_TopologyAttr('resid', np.arange(N) + 1)
nframes = len(dim)
fac = np.zeros((nframes, N, 3))
df.load_new(fac, order='fac')
df.trajectory[0].dt = self.dt
for i, ts in enumerate(df.trajectory):
df.trajectory[i].dimensions = dim[i]
PLacyl = df.copy()
Deep = df.copy()
TGacyl = df.copy()
TGglyc = df.copy()
xxyy = []
for i in range(len(df.trajectory)):
xarray = np.arange(0, dim[i][0], self.dx)
yarray = np.arange(0, dim[i][1], self.dy)
xx, yy = np.meshgrid(xarray, yarray)
xxyy.append([xx, yy])
### PL acyl
PLacyls = []
for i, ts in enumerate(PLacyl.trajectory):
num = 0
bA = (1e3 <= Mup[i]) & (Mup[i] < 1e6)
PLacyls.append(bA.astype(int))
for x1, y1 in zip(xxyy[i][0][bA], xxyy[i][1][bA]):
PLacyl.atoms[num].position = np.array([y1, x1, zlimup[i]])
num += 1
bA = (1e3 <= Mdw[i]) & (Mdw[i] < 1e6)
PLacyls.append(bA.astype(int))
for x1, y1 in zip(xxyy[i][0][bA], xxyy[i][1][bA]):
PLacyl.atoms[num].position = np.array([y1, x1, zlimdw[i]])
num += 1
### Deep
Deeps = []
for i, ts in enumerate(Deep.trajectory):
num = 0
bA = (Mup[i] == 0)
Deeps.append(bA.astype(int))
for x1, y1 in zip(xxyy[i][0][bA], xxyy[i][1][bA]):
Deep.atoms[num].position = np.array([y1, x1, zlimup[i]])
num += 1
bA = (Mdw[i] == 0)
Deeps.append(bA.astype(int))
for x1, y1 in zip(xxyy[i][0][bA], xxyy[i][1][bA]):
Deep.atoms[num].position = np.array([y1, x1, zlimdw[i]])
num += 1
### TG glycerol
TGglycs = []
for i, ts in enumerate(TGglyc.trajectory):
num = 0
bA = (1 <= Mup[i]) & (Mup[i] < 1e3)
TGglycs.append(bA.astype(int))
for x1, y1 in zip(xxyy[i][0][bA], xxyy[i][1][bA]):
TGglyc.atoms[num].position = np.array([y1, x1, zlimup[i]])
num += 1
bA = (1 <= Mdw[i]) & (Mdw[i] < 1e3)
TGglycs.append(bA.astype(int))
for x1, y1 in zip(xxyy[i][0][bA], xxyy[i][1][bA]):
TGglyc.atoms[num].position = np.array([y1, x1, zlimdw[i]])
num += 1
### TG acyl
TGacyls = []
for i, ts in enumerate(TGacyl.trajectory):
num = 0
bA = (0 < Mup[i]) & (Mup[i] < 1)
TGacyls.append(bA.astype(int))
for x1, y1 in zip(xxyy[i][0][bA], xxyy[i][1][bA]):
TGacyl.atoms[num].position = np.array([y1, x1, zlimup[i]])
num += 1
bA = (0 < Mdw[i]) & (Mdw[i] < 1)
TGacyls.append(bA.astype(int))
for x1, y1 in zip(xxyy[i][0][bA], xxyy[i][1][bA]):
TGacyl.atoms[num].position = np.array([y1, x1, zlimdw[i]])
num += 1
def write(fname, u):
u.trajectory[-1]
u.atoms.write(fname + '.gro')
with mda.Writer(fname + '.xtc', u.atoms.n_atoms) as W:
for ts in u.trajectory:
W.write(u.atoms)
write('PLacyl', PLacyl)
write('Deep', Deep)
write('TGglyc', TGglyc)
write('TGacyl', TGacyl)
PD = PackingDefect()
PD.defect_size(PLacyls,
fname='PLacyl.dat',
nbins=self.nbins,
bin_max=self.bin_max)
PD.defect_size(Deeps,
fname='Deep.dat',
nbins=self.nbins,
bin_max=self.bin_max)
PD.defect_size(TGglycs,
fname='TGglyc.dat',
nbins=self.nbins,
bin_max=self.bin_max)
PD.defect_size(TGacyls,
fname='TGacyl.dat',
nbins=self.nbins,
bin_max=self.bin_max)
def _conclude(self):
print("Concluding...")
results = np.vstack(self._results)
Mup = results[:, 0]
Mdw = results[:, 1]
zlimup = results[:, 2].flatten()
zlimdw = results[:, 3].flatten()
dim = np.vstack(results[:, 4])
N = self.N
df = Universe.empty(n_atoms=N,
n_residues=N,
atom_resindex=np.arange(N),
residue_segindex=[0] * N,
trajectory=True)
df.add_TopologyAttr('resname', ['O'] * N)
df.add_TopologyAttr('name', ['O'] * N)
df.add_TopologyAttr('resid', np.arange(N) + 1)
nframes = len(dim)
fac = np.zeros((nframes, N, 3))
df.load_new(fac, order='fac')
df.trajectory[0].dt = self.dt
for i, ts in enumerate(df.trajectory):
df.trajectory[i].dimensions = dim[i]
defects = ['Deep', 'PLacyl', 'TGglyc', 'TGacyl']
defect_uni = {}
defect_clu = {}
for d in defects:
defect_uni[d] = df.copy()
for d in defects:
defect_clu[d] = []
defect_thr = {'Deep': 0, 'PLacyl': 1, 'TGglyc': 2, 'TGacyl': 3}
for d in defects:
for i, ts in enumerate(defect_uni[d].trajectory):
num = 0
bA = (Mup[i] == defect_thr[d])
defect_clu[d].append(bA.astype(int))
ind = np.where(bA)
xs = ind[1]
ys = ind[0]
for x1, y1 in zip(xs, ys):
pos = np.array([x1, y1, zlimup[i]])
defect_uni[d].atoms[num].position = pos
num += 1
bA = (Mdw[i] == defect_thr[d])
defect_clu[d].append(bA.astype(int))
ind = np.where(bA)
xs = ind[1]
ys = ind[0]
for x1, y1 in zip(xs, ys):
pos = np.array([x1, y1, zlimdw[i]])
defect_uni[d].atoms[num].position = pos
num += 1
### DEFECT LOCALIZATION
for d in defects:
u = defect_uni[d]
u.trajectory[-1]
u.atoms.write(self.prefix + d + '2.gro')
with mda.Writer(self.prefix + d + '2.xtc', u.atoms.n_atoms) as W:
for ts in u.trajectory:
W.write(u.atoms)
### DEFECT CLUSTER
PD = PackingDefect2()
for d in defects:
PD.defect_size(defect_clu[d],
fname=self.prefix + d + '2.dat',
nbins=self.nbins,
bin_max=self.bin_max)