def load_all(self, removedrift=False, showprogress=True):
"""Returns all positions from all trajectories"""
trajpos = [np.zeros([len(tr), 3]) for tr in self.trajs]
if showprogress:
pro = ProgressBar(self.size)
for t, name in self.enum():
pos = np.loadtxt(name, skiprows=2)
p2tr = self.p2tr(t)
for p, tr in enumerate(p2tr):
trajpos[tr][t - self.starts[tr]] = pos[p]
if showprogress:
pro.animate(t)
if removedrift:
#compute drift using all trajectories
drift = np.zeros((self.size,3))
nb = np.zeros((self.size), int)
for tr, start in zip(trajpos, self.starts):
drift[start+1:start + len(tr)] += tr[1:] - tr[:-1]
nb[start+1:start + len(tr)] += 1
drift /= np.maximum(1, nb)[:,None]
drift = np.cumsum(drift, axis=0)
#remove drift from each trajectory
for tr, start in enumerate(self.starts):
trajpos[tr] -= drift[start:start + len(trajpos[tr])]
return trajpos
def never_closer(self, L=3, mintrajlength=None, showprogress=True):
"""All the broken bonds (in term of trajectories) with a on graph distance that stays larger than L, associated with the last time they break"""
try:
ret = []
for t, name in self.enum('_L%d'%L, ext='nevc'):
if os.stat(name).st_size:
ret.append(np.loadtxt(name, dtype=int))
else:
ret.append(np.array((0,2), dtype=int))
except IOError:
pass
if showprogress:
pro = ProgressBar(self.size)
result = dict()
for t, name in self.enum(ext='bonds'):
try:
bonds1 = np.atleast_2d(np.loadtxt(name, dtype=int))
except UserWarning:
bonds1 = np.zeros([0,2], int)
p2tr1 = self.p2tr(t)
if t>0:
for path in broken_bonds_path(bonds0, bonds1, p2tr0, p2tr1):
if len(path)<=L:
continue
a, b = sorted([path[0], path[-1]])
if mintrajlength is not None and min(len(self.trajs[u]) for u in [a,b]) <= mintrajlength:
continue
result[(a,b)] = t #create or replace if was already broken before
#graph of the bonds between trajectories at t1
g = nx.Graph()
g.add_nodes_from(p2tr1)
g.add_edges_from(p2tr1[bonds1])
#remove all bonds that are closer than L at t1
existing = [[a,b] for a,b in result.iterkeys()]
for path in shortest_path(g, existing):
if len(path) <= L:
a,b = sorted([path[0], path[-1]])
result.pop((a,b),-1)
bonds0 = bonds1
p2tr0 = p2tr1
if showprogress:
pro.animate(t)
#translate as a list of arrays of bonds, an array per time
tdl = [
np.zeros([nbb, 2], int)
for nbb in np.histogram(result.values(), bins=np.arange(0, self.size+1))[0]
]
ns = np.zeros(self.size, int)
for bond, t in result.iteritems():
tdl[t][ns[t]] = bond
ns[t] += 1
#save a cache
for t, name in self.enum('_L%d'%L, ext='nevc'):
np.savetxt(name, tdl[t], fmt='%d')
return tdl
parser = argparse.ArgumentParser(description='Convert a HDF5 format compatible with trackpy.PandasHDFStoreSingleNode to coordinates and trajectory data.')
parser.add_argument('h5name', help='HDF5 file name')
parser.add_argument('--out', help='The folder where to save the data. By default the same name as HDF5 file, without extension.')
args = parser.parse_args()
h5name = args.h5name
print(h5name)
if args.out is None:
args.out = os.path.splitext(h5name)[0]
print('to %s'%args.out)
os.mkdir(args.out)
basename = os.path.join(args.out, os.path.split(os.path.basename(h5name))[0])
with tp.PandasHDFStoreSingleNode(h5name) as s:
nbzeros = len('%d'%(len(s)-1))
datfile = +'_t%0{:d}d.dat'.format(nbzeros)
p2trfile = +'_t%0{:d}d.p2tr'.format(nbzeros)
pro = ProgressBar(len(s))
for t, frame in s:
pos = np.vstack((np.zeros(2,3), frame.as_matrix(['x','y','z'])))
pos[0] = 1, len(pos)-2, 1
np.savetxt(datfile%t, pos, fmt='%g')
np.savetxt(p2trfile%t, frame.asmatrix(['particle'], fmt='%d')
#if t==0:
# linker = Linker(len(frame))
#else:
# linker.loadFrame(frame.asmatrix(['particle'])
pro.animate(t)
#TODO save the linker to a traj file
'The file.h5 where to save the data. By default the same name as trajectory file, one level below in the directory tree.'
)
args = parser.parse_args()
trname = args.trname
print(trname)
x = xp.Experiment(trname)
if args.out is None:
args.out = os.path.join(
os.path.split(x.path)[0],
os.path.splitext(x.trajfile)[0] + '.h5')
print('to %s' % args.out)
assert x.size < 2**15, "Too many time steps, use larger data types"
assert x.nb_trajs < 2**31, "Too many trajectories, use larger data types"
pro = ProgressBar(x.size)
with tp.PandasHDFStoreSingleNode(args.out) as s:
for t, name in x.enum():
pro.animate(t)
d = {
x: c
for x, c in zip(
'xyz',
np.loadtxt(name, skiprows=2, unpack=True,
dtype=np.float16))
}
d['frame'] = np.full(d['x'].shape, t, dtype=np.int16)
d['particle'] = x.p2tr(t).astype(np.int32)
s.put(DataFrame(d))
pro.animate(x.size)
