def calculate_variation(Input, resolution):
"""Calculates the variation in the activation Energy"""
filelist = bf.lookupfiles(path=Input['folder_snapshot_data'],
filechr='Wigner')
filenumbers = np.zeros((len(filelist)))
i = 0
for filename in filelist:
filepath = '%s%s' % (Input['folder_snapshot_data'], filename)
f = open(filepath, 'r')
xml.ReadHeader(f)
positions = xml.ReadCoordinates(f, Input['BASEparticles'])
f.close()
coordinates, potential = potential_slice(Input, positions, resolution)
if i == 0:
all_potential = potential.reshape((1,len(potential),len(potential)))
else:
all_potential = np.append(all_potential,
potential.reshape((1,len(potential),len(potential))),
axis=0)
filenumber = int(filename.strip('Wigner.').strip('.xml'))
filenumbers[i] = filenumber
if i == 0:
save_potential_field(Input, coordinates, potential, 'testje')
i += 1
sorted_filenumbers = np.argsort(filenumbers,axis=0)
sorted_potentials = all_potential[sorted_filenumbers]
average_potential = np.average(sorted_potentials,axis=0)
variance_potential = np.var(sorted_potentials,axis=0)
coordx, coordy = np.nonzero(np.less(variance_potential,100))
print len(coordx)
if len(coordx) > 0:
time = np.sort(sorted_filenumbers)*Input['dt']*Input['BASEtau']
#eplt.plotsliceheatmap(Input, coordinates, potential, positions, resolution, plotpart=False)
eplt.plot_energy_track(time, sorted_potentials[:,coordx[0],coordy[0]] - np.min(sorted_potentials[:,coordx[0],coordy[0]]))
def find_cell_volumes_folder(Input):
"""find cell volumes of a whole folder"""
positions_folder = '%spositions_voronoi/' % Input['systempath']
cell_volume_folder = '%svolumes_voronoi/' % Input['systempath']
os.mkdir(cell_volume_folder)
filelist = bf.lookupfiles(path=positions_folder, filechr='Wigner')
for file_ in filelist:
filename = positions_folder + file_
new_name = positions_folder + file_ + '.vol'
savename = cell_volume_folder + file_ + '.vol'
find_cell_volume(Input, filename)
os.rename(new_name, savename)
def find_cell_volumes_folder(Input):
"""find cell volumes of a whole folder"""
positions_folder = '%spositions_voronoi/' % Input['systempath']
cell_volume_folder = '%svolumes_voronoi/' % Input['systempath']
os.mkdir(cell_volume_folder)
filelist = bf.lookupfiles(path=positions_folder, filechr='Wigner')
for file_ in filelist:
filename = positions_folder + file_
new_name = positions_folder + file_ + '.vol'
savename = cell_volume_folder + file_ + '.vol'
find_cell_volume(Input, filename)
os.rename(new_name, savename)
def prepareforvoro(Input):
voronoi_folder = '%spositions_voronoi/' % Input['systempath']
os.mkdir(voronoi_folder)
filelist = bf.lookupfiles(path=Input['folder_snapshot_data'], filechr='Wigner')
for file_ in filelist:
filename = '%s%s' % (Input['folder_snapshot_data'], file_)
f = open(filename, 'r')
xml.ReadHeader(f)
Positions = xml.ReadCoordinates(f, Input['ALLparticles'])
f.close()
voronoiformat = np.concatenate((np.reshape(np.arange(1,Input['ALLparticles']+1),(-1,1)), Positions),axis=1)
savename = '%s%s' % (voronoi_folder, file_.strip('.xml'))
format_ = ['%6d','%.18e', '%.18e', '%.18e']
np.savetxt(savename, voronoiformat, fmt=format_, delimiter='\t')
def meanmsd_wholefolder(Input):
filelist = bf.lookupfiles(path=Input['folder_msd_data'], filechr='INTER')
path = '%s%s' % (Input['folder_msd_data'], filelist[0])
data = np.genfromtxt(path, delimiter='\t')
total_msd = np.zeros(len(data))
mean_msd = np.zeros((len(data),2))
mean_msd[:,0] = data[:,0]
for filename in filelist:
path = '%s%s' % (Input['folder_msd_data'], filename)
data = np.genfromtxt(path, delimiter='\t')
total_msd += data[:,1]
mean_msd[:,1] = total_msd / len(filelist)
savepath = '%s%smean.msd' % (Input['folder_processed_data'], Input['system'])
np.savetxt(savepath, mean_msd, delimiter='\t')
def get_msd_particles(Input, tau_inf=179):
filelist = bf.lookupfiles(path=Input['folder_msd_data'], filechr='INTER')
msd_particle = np.zeros([len(filelist), 2])
counter = 0
for particle in filelist:
filename = '%s%s' % (Input['folder_msd_data'], particle)
data = np.genfromtxt(filename, delimiter='\t')
msd_particle[counter,0] = int(particle.strip('.msd').strip('INTER'))
msd_particle[counter,1] = data[tau_inf,1]
counter += 1
msd_particle = msd_particle[msd_particle[:,0].argsort()]
savename = '%s%smsdparticles.val' % (Input['folder_processed_data'], Input['system'])
format_ = ['%6d','%.12e']
np.savetxt(savename, msd_particle, delimiter='\t', fmt=format_)
def average_values(Input):
"""get the average of the cell volume over the simulation"""
cell_volume_folder = '%svolumes_voronoi/' % Input['systempath']
filelist = bf.lookupfiles(path=cell_volume_folder, filechr='Wigner')
data = np.genfromtxt(cell_volume_folder + filelist[0], delimiter=' ')
average = np.zeros([len(data),2])
for file_ in filelist:
filename = '%s%s' % (cell_volume_folder,file_)
data = np.genfromtxt(filename, delimiter=' ')
data = data[data[:,0].argsort()]
average[:,1] += data[:,4]*(1.0/len(filelist))
average[:,0] = data[:,0]
savename = '%s%saveragevoronoi.vol' % (Input['folder_processed_data'], Input['system'])
format_ = ['%6d','%.12e']
np.savetxt(savename, average, delimiter='\t', fmt=format_)
def get_msd_particles(Input, tau_inf=179):
filelist = bf.lookupfiles(path=Input['folder_msd_data'], filechr='INTER')
msd_particle = np.zeros([len(filelist), 2])
counter = 0
for particle in filelist:
filename = '%s%s' % (Input['folder_msd_data'], particle)
data = np.genfromtxt(filename, delimiter='\t')
msd_particle[counter, 0] = int(particle.strip('.msd').strip('INTER'))
msd_particle[counter, 1] = data[tau_inf, 1]
counter += 1
msd_particle = msd_particle[msd_particle[:, 0].argsort()]
savename = '%s%smsdparticles.val' % (Input['folder_processed_data'],
Input['system'])
format_ = ['%6d', '%.12e']
np.savetxt(savename, msd_particle, delimiter='\t', fmt=format_)
def meanmsd_wholefolder(Input):
filelist = bf.lookupfiles(path=Input['folder_msd_data'], filechr='INTER')
path = '%s%s' % (Input['folder_msd_data'], filelist[0])
data = np.genfromtxt(path, delimiter='\t')
total_msd = np.zeros(len(data))
mean_msd = np.zeros((len(data), 2))
mean_msd[:, 0] = data[:, 0]
for filename in filelist:
path = '%s%s' % (Input['folder_msd_data'], filename)
data = np.genfromtxt(path, delimiter='\t')
total_msd += data[:, 1]
mean_msd[:, 1] = total_msd / len(filelist)
savepath = '%s%smean.msd' % (Input['folder_processed_data'],
Input['system'])
np.savetxt(savepath, mean_msd, delimiter='\t')
def prepareposfiles(Input, base):
bp_folder = '%spositions_bp/' % Input['systempath']
os.mkdir(bp_folder)
filelist = bf.lookupfiles(path=Input['folder_snapshot_data'], filechr='Wigner')
for file_ in filelist:
filename = '%s%s' % (Input['folder_snapshot_data'], file_)
f = open(filename, 'r')
xml.ReadHeader(f)
if base is True:
positions = xml.ReadCoordinates(f, Input['BASEparticles'])
else:
positions = xml.ReadCoordinates(f, Input['ALLparticles'])
f.close()
bp_positions = positions + 0.5*Input['BOXsize']
savename = '%s%s.dat' % (bp_folder, file_.strip('.xml'))
saveasdatfile(Input, bp_positions, savename, base)
def average_values(Input):
"""get the average of the cell volume over the simulation"""
cell_volume_folder = '%svolumes_voronoi/' % Input['systempath']
filelist = bf.lookupfiles(path=cell_volume_folder, filechr='Wigner')
data = np.genfromtxt(cell_volume_folder + filelist[0], delimiter=' ')
average = np.zeros([len(data), 2])
for file_ in filelist:
filename = '%s%s' % (cell_volume_folder, file_)
data = np.genfromtxt(filename, delimiter=' ')
data = data[data[:, 0].argsort()]
average[:, 1] += data[:, 4] * (1.0 / len(filelist))
average[:, 0] = data[:, 0]
savename = '%s%saveragevoronoi.vol' % (Input['folder_processed_data'],
Input['system'])
format_ = ['%6d', '%.12e']
np.savetxt(savename, average, delimiter='\t', fmt=format_)
def prepareposfiles(Input, base):
bp_folder = '%spositions_bp/' % Input['systempath']
os.mkdir(bp_folder)
filelist = bf.lookupfiles(path=Input['folder_snapshot_data'],
filechr='Wigner')
for file_ in filelist:
filename = '%s%s' % (Input['folder_snapshot_data'], file_)
f = open(filename, 'r')
xml.ReadHeader(f)
if base is True:
positions = xml.ReadCoordinates(f, Input['BASEparticles'])
else:
positions = xml.ReadCoordinates(f, Input['ALLparticles'])
f.close()
bp_positions = positions + 0.5 * Input['BOXsize']
savename = '%s%s.dat' % (bp_folder, file_.strip('.xml'))
saveasdatfile(Input, bp_positions, savename, base)
def prepareforvoro(Input):
voronoi_folder = '%spositions_voronoi/' % Input['systempath']
os.mkdir(voronoi_folder)
filelist = bf.lookupfiles(path=Input['folder_snapshot_data'],
filechr='Wigner')
for file_ in filelist:
filename = '%s%s' % (Input['folder_snapshot_data'], file_)
f = open(filename, 'r')
xml.ReadHeader(f)
Positions = xml.ReadCoordinates(f, Input['ALLparticles'])
f.close()
voronoiformat = np.concatenate(
(np.reshape(np.arange(1, Input['ALLparticles'] + 1),
(-1, 1)), Positions),
axis=1)
savename = '%s%s' % (voronoi_folder, file_.strip('.xml'))
format_ = ['%6d', '%.18e', '%.18e', '%.18e']
np.savetxt(savename, voronoiformat, fmt=format_, delimiter='\t')
def calculate_deviations(Input):
"""Calculates the deviations from the average particle site"""
filelist = bf.lookupfiles(path=Input['folder_snapshot_data'],
filechr='Wigner')
i = 0
for filename in filelist:
if int(filename.strip('Wigner').strip('.xml')) > 10000000:
filepath = '%s%s' % (Input['folder_snapshot_data'], filename)
f = open(filepath, 'r')
xml.ReadHeader(f)
positions = xml.ReadCoordinates(f, Input['ALLparticles'])
f.close()
if i == 0:
all_positions = np.reshape(positions, (1, len(positions), 3))
else:
positions = unfold_boundary_crossings(Input, all_positions,
positions, i)
positions = np.reshape(positions, (1, len(positions), 3))
all_positions = np.append(all_positions, positions, axis=0)
i += 1
average_positions = np.average(all_positions, axis=0)
deviations = np.std(np.sqrt(
np.sum(np.square(all_positions - average_positions), axis=2)),
axis=0)
filepath = '%sWigner.0099000000.xml' % (Input['folder_snapshot_data'])
f = open(filepath, 'r')
xml.ReadHeader(f)
positions = xml.ReadCoordinates(f, Input['ALLparticles'])
f.close()
positions = unfold_boundary_crossings(Input, all_positions, positions, i)
snapdeviations = np.sqrt(
np.sum(np.square(average_positions - positions), axis=1))
posanddev = np.zeros((Input['ALLparticles'], 8))
posanddev[:, :3] = average_positions
posanddev[:, 3] = deviations / Input['a']
posanddev[:, 4:7] = positions
posanddev[:, 7] = snapdeviations / Input['a']
head = '%-18s\t%-18s\t%-18s\t%-18s\t%-18s\t%-18s\t%-18s\t%-18s' % (
'x[sigma]', 'y[sigma]', 'z[sigma]', 'lindemann', 'snapx[sigma]',
'snapy[sigma]', 'snapz[sigma]', 'snapdev[sigma]')
savename = '%s%s.deviations' % (Input['folder_processed_data'],
Input['system'])
np.savetxt(savename, posanddev, fmt='%18.11e', header=head, delimiter='\t')
return positions, deviations
def averagebondparameter(Input, base=False):
dir_bp = '%sbondparameters/' % Input['systempath']
filelist = bf.lookupfiles(path=dir_bp, filechr='Wigner')
if base is True:
average_data = np.zeros((Input['BASEparticles'], 6))
else:
average_data = np.zeros((Input['ALLparticles'], 6))
columns = np.array([0,4,5,6,7,8])
count = 0
for file_ in filelist:
number = int(file_.strip('Wigner.').strip('.dat'))
if number > Input['analysisstart']:
filename = '%s%s' % (dir_bp, file_)
data = np.genfromtxt(filename, delimiter=' ')
average_data += data[:,columns]
count +=1
average_data = average_data/count
savename = '%s%saveragebp.txt' % (Input['folder_processed_data'], Input['system'])
np.savetxt(savename, average_data, delimiter='\t')
def folder_unfolder(Input, nprocs=15):
"""Unfoldes tracks in folder if barrier is crossed"""
def worker(Input, filelist, out_q):
outdict = {}
for filename in filelist:
outdict[i] = track_unfolder(Input, filename)
out_q.put(outdict)
os.mkdir(Input['folder_backup'])
filelist = bf.lookupfiles(path=Input['folder_interstitial_track'], filechr='INTER')
out_q = multiprocessing.Queue()
chunksize = int(np.ceil(len(filelist) / float(nprocs)))
procs = []
for i in range(nprocs):
p = multiprocessing.Process(target=worker, args=(Input, filelist[chunksize*i:chunksize*(i + 1)], out_q))
procs.append(p)
p.start()
for p in procs:
p.join()
logging.info('finished calculating all harrays.')
def msd_wholefolder(Input, nprocs=25):
"""Calculates the msd profiles of a folder with tracks"""
def worker(Input, filelist, out_q):
outdict = {}
for filename in filelist:
outdict[i] = calculate_msd(Input, filename)
out_q.put(outdict)
os.mkdir(Input['folder_msd_data'])
filelist = bf.lookupfiles(path=Input['folder_interstitial_track'], filechr='INTER')
out_q = multiprocessing.Queue()
chunksize = int(np.ceil(len(filelist) / float(nprocs)))
procs = []
for i in range(nprocs):
p = multiprocessing.Process(target=worker, args=(Input,filelist[chunksize*i:chunksize*(i + 1)], out_q))
procs.append(p)
p.start()
for p in procs:
p.join()
logging.info('finished calculating all independent msd profiles.')
def averagebondparameter(Input, base=False):
dir_bp = '%sbondparameters/' % Input['systempath']
filelist = bf.lookupfiles(path=dir_bp, filechr='Wigner')
if base is True:
average_data = np.zeros((Input['BASEparticles'], 6))
else:
average_data = np.zeros((Input['ALLparticles'], 6))
columns = np.array([0, 4, 5, 6, 7, 8])
count = 0
for file_ in filelist:
number = int(file_.strip('Wigner.').strip('.dat'))
if number > Input['analysisstart']:
filename = '%s%s' % (dir_bp, file_)
data = np.genfromtxt(filename, delimiter=' ')
average_data += data[:, columns]
count += 1
average_data = average_data / count
savename = '%s%saveragebp.txt' % (Input['folder_processed_data'],
Input['system'])
np.savetxt(savename, average_data, delimiter='\t')
def calculate_variation(Input, resolution):
"""Calculates the variation in the activation Energy"""
filelist = bf.lookupfiles(path=Input['folder_snapshot_data'],
filechr='Wigner')
filenumbers = np.zeros((len(filelist)))
i = 0
for filename in filelist:
filepath = '%s%s' % (Input['folder_snapshot_data'], filename)
f = open(filepath, 'r')
xml.ReadHeader(f)
positions = xml.ReadCoordinates(f, Input['BASEparticles'])
f.close()
coordinates, potential = potential_slice(Input, positions, resolution)
if i == 0:
all_potential = potential.reshape(
(1, len(potential), len(potential)))
else:
all_potential = np.append(all_potential,
potential.reshape(
(1, len(potential), len(potential))),
axis=0)
filenumber = int(filename.strip('Wigner.').strip('.xml'))
filenumbers[i] = filenumber
if i == 0:
save_potential_field(Input, coordinates, potential, 'testje')
i += 1
sorted_filenumbers = np.argsort(filenumbers, axis=0)
sorted_potentials = all_potential[sorted_filenumbers]
average_potential = np.average(sorted_potentials, axis=0)
variance_potential = np.var(sorted_potentials, axis=0)
coordx, coordy = np.nonzero(np.less(variance_potential, 100))
print len(coordx)
if len(coordx) > 0:
time = np.sort(sorted_filenumbers) * Input['dt'] * Input['BASEtau']
#eplt.plotsliceheatmap(Input, coordinates, potential, positions, resolution, plotpart=False)
eplt.plot_energy_track(
time, sorted_potentials[:, coordx[0], coordy[0]] -
np.min(sorted_potentials[:, coordx[0], coordy[0]]))
def msd_wholefolder(Input, nprocs=25):
"""Calculates the msd profiles of a folder with tracks"""
def worker(Input, filelist, out_q):
outdict = {}
for filename in filelist:
outdict[i] = calculate_msd(Input, filename)
out_q.put(outdict)
os.mkdir(Input['folder_msd_data'])
filelist = bf.lookupfiles(path=Input['folder_interstitial_track'],
filechr='INTER')
out_q = multiprocessing.Queue()
chunksize = int(np.ceil(len(filelist) / float(nprocs)))
procs = []
for i in range(nprocs):
p = multiprocessing.Process(
target=worker,
args=(Input, filelist[chunksize * i:chunksize * (i + 1)], out_q))
procs.append(p)
p.start()
for p in procs:
p.join()
logging.info('finished calculating all independent msd profiles.')
def folder_unfolder(Input, nprocs=15):
"""Unfoldes tracks in folder if barrier is crossed"""
def worker(Input, filelist, out_q):
outdict = {}
for filename in filelist:
outdict[i] = track_unfolder(Input, filename)
out_q.put(outdict)
os.mkdir(Input['folder_backup'])
filelist = bf.lookupfiles(path=Input['folder_interstitial_track'],
filechr='INTER')
out_q = multiprocessing.Queue()
chunksize = int(np.ceil(len(filelist) / float(nprocs)))
procs = []
for i in range(nprocs):
p = multiprocessing.Process(
target=worker,
args=(Input, filelist[chunksize * i:chunksize * (i + 1)], out_q))
procs.append(p)
p.start()
for p in procs:
p.join()
logging.info('finished calculating all harrays.')
def calculate_deviations(Input):
"""Calculates the deviations from the average particle site"""
filelist = bf.lookupfiles(path=Input['folder_snapshot_data'],
filechr='Wigner')
i = 0
for filename in filelist:
if int(filename.strip('Wigner').strip('.xml')) > 10000000:
filepath = '%s%s' % (Input['folder_snapshot_data'], filename)
f = open(filepath, 'r')
xml.ReadHeader(f)
positions = xml.ReadCoordinates(f, Input['ALLparticles'])
f.close()
if i == 0:
all_positions = np.reshape(positions, (1, len(positions), 3))
else:
positions = unfold_boundary_crossings(Input, all_positions, positions, i)
positions = np.reshape(positions, (1, len(positions), 3))
all_positions = np.append(all_positions, positions, axis=0)
i += 1
average_positions = np.average(all_positions, axis=0)
deviations = np.std(np.sqrt(np.sum(np.square(all_positions - average_positions), axis=2)),axis=0)
filepath = '%sWigner.0099000000.xml' % (Input['folder_snapshot_data'])
f = open(filepath, 'r')
xml.ReadHeader(f)
positions = xml.ReadCoordinates(f, Input['ALLparticles'])
f.close()
positions = unfold_boundary_crossings(Input, all_positions, positions, i)
snapdeviations = np.sqrt(np.sum(np.square(average_positions - positions), axis=1))
posanddev = np.zeros((Input['ALLparticles'],8))
posanddev[:,:3] = average_positions
posanddev[:,3] = deviations / Input['a']
posanddev[:,4:7] = positions
posanddev[:,7] = snapdeviations / Input['a']
head = '%-18s\t%-18s\t%-18s\t%-18s\t%-18s\t%-18s\t%-18s\t%-18s' % ('x[sigma]', 'y[sigma]', 'z[sigma]', 'lindemann','snapx[sigma]', 'snapy[sigma]', 'snapz[sigma]', 'snapdev[sigma]')
savename = '%s%s.deviations' % (Input['folder_processed_data'], Input['system'])
np.savetxt(savename, posanddev, fmt='%18.11e', header=head, delimiter='\t')
return positions, deviations