def logCRtotal(filename, ndim=3, dt=0.002, outputfile=''):
"""calcualte rotational dynamics over all particles
only the initial configuration as reference
"""
print('-------calculate LOG overall rotational dynamics-------')
d = readangular(filename, ndim)
d.read_onefile()
#-----get the unit vector-----
velocity = [
u / np.linalg.norm(u, axis=1)[:, np.newaxis] for u in d.velocity
]
results = np.zeros((d.SnapshotNumber - 1, 2))
names = 't CRt'
results[:, 0] = (np.array(d.TimeStep[1:]) - d.TimeStep[0]) * dt
CII = velocity[1:] * velocity[0]
results[:, 1] = (CII.sum(axis=2)).mean(axis=1)
if outputfile:
np.savetxt(outputfile, results, fmt='%.6f', header=names, comments='')
print('-------calculate LOG overall rotational dynamics over-------')
return results, names
def RorderIJ(filename,
ndim=3,
UIJ=0.9,
neighborfile='',
outputfile='',
outputfileij=''):
"""rotational order parameter to characterize the structure
alignment of center against its neighbors by orientation
"""
print('-------calculate orientational alignment-------')
from ParticleNeighbors import Voropp
d = readangular(filename, ndim)
d.read_onefile()
#-----get the unit vector-----
velocity = [
u / np.linalg.norm(u, axis=1)[:, np.newaxis] for u in d.velocity
]
fneighbor = open(neighborfile, 'r')
results = np.zeros((d.ParticleNumber[0], d.SnapshotNumber))
if outputfileij: fij = open(outputfileij, 'w')
for n in range(d.SnapshotNumber):
Neighborlist = Voropp(
fneighbor, d.ParticleNumber[n]) ##neighbor list [number, list....]
if outputfileij: fij.write('id cn UIJ_list\n')
for i in range(d.ParticleNumber[n]):
CII = velocity[n][i] * velocity[n][Neighborlist[
i, 1:1 + Neighborlist[i, 0]]]
#psi = np.linalg.norm(CII, axis = 1)
psi = np.abs(CII.sum(axis=1))
results[i, n] = (psi > UIJ).sum()
if outputfileij:
fij.write('%d %d ' % (i + 1, Neighborlist[i, 0]))
for j in range(Neighborlist[i, 0]):
fij.write('%.6f ' % psi[j])
fij.write('\n')
fneighbor.close()
results = np.column_stack((np.arange(d.ParticleNumber[0]) + 1, results))
if outputfile:
names = 'id UIJ'
np.savetxt(outputfile, results, fmt='%d', header=names, comments='')
if outputfileij: fij.close()
print('-------calculate orientational alignment over-------')
return results
def CRtotal(filename, ndim=3, dt=0.002, phi=0.2, outputfile=''):
"""calcualte rotational dynamics over all particles by moving average
the time interval should be the same for average with the same deltat
"""
print('-------calculate overall rotational dynamics-------')
d = readangular(filename, ndim)
d.read_onefile()
#-----get the unit vector-----
velocity = [
u / np.linalg.norm(u, axis=1)[:, np.newaxis] for u in d.velocity
]
TimeStep = d.TimeStep[1] - d.TimeStep[0]
if TimeStep != d.TimeStep[-1] - d.TimeStep[-2]:
print('Warning: ********time interval changes********')
ParticleNumber = d.ParticleNumber[0]
if ParticleNumber != d.ParticleNumber[-1]:
print('Warning: ********particle number changes**********')
results = np.zeros((d.SnapshotNumber - 1, 5))
names = 't CRt X4 Qt QtX4'
cal_CRt = pd.DataFrame(np.zeros(d.SnapshotNumber - 1)[np.newaxis, :])
cal_Qt = pd.DataFrame(np.zeros(d.SnapshotNumber - 1)[np.newaxis, :])
deltat = np.zeros((d.SnapshotNumber - 1, 2), dtype=np.int)
for n in range(d.SnapshotNumber - 1): #time interval
CII = (velocity[n + 1:] * velocity[n]).sum(axis=2)
CII_CRt = CII.sum(axis=1)
cal_CRt = pd.concat([cal_CRt, pd.DataFrame(CII_CRt[np.newaxis, :])])
CII_Qt = (CII >= phi).sum(axis=1)
cal_Qt = pd.concat([cal_Qt, pd.DataFrame(CII_Qt[np.newaxis, :])])
cal_CRt = cal_CRt.iloc[1:]
cal_Qt = cal_Qt.iloc[1:]
deltat[:, 0] = np.array(cal_CRt.columns) + 1 #time interval
deltat[:, 1] = np.array(cal_CRt.count()) #time interval frequency
results[:, 0] = deltat[:, 0] * TimeStep * dt
results[:, 1] = cal_CRt.mean() / ParticleNumber
results[:,
2] = ((cal_CRt**2).mean() - (cal_CRt.mean())**2) / ParticleNumber
results[:, 3] = cal_Qt.mean() / ParticleNumber
results[:, 4] = ((cal_Qt**2).mean() - (cal_Qt.mean())**2) / ParticleNumber
if outputfile:
np.savetxt(outputfile, results, fmt='%.6f', header=names, comments='')
print('-------calculate overall rotational dynamics over-------')
return results, names
def Rorder(filename, ndim=3, neighborfile='', outputfile='', use_abs=True):
"""rotational order parameter to characterize the structure
local rotational symmetry over the nearest neighbors
"""
print('-------calculate local rotational ordering-------')
from ParticleNeighbors import Voropp
d = readangular(filename, ndim)
d.read_onefile()
#-----get the unit vector-----
velocity = [
u / np.linalg.norm(u, axis=1)[:, np.newaxis] for u in d.velocity
]
fneighbor = open(neighborfile, 'r')
results = np.zeros((d.ParticleNumber[0], d.SnapshotNumber))
for n in range(d.SnapshotNumber):
Neighborlist = Voropp(
fneighbor, d.ParticleNumber[n]) ##neighbor list [number, list....]
for i in range(d.ParticleNumber[n]):
CII = velocity[n][i] * velocity[n][Neighborlist[
i, 1:1 + Neighborlist[i, 0]]]
if use_abs:
results[i, n] = np.abs(CII.sum(axis=1)).mean()
else:
results[i, n] = (CII.sum(axis=1)).mean()
fneighbor.close()
results = np.column_stack((np.arange(d.ParticleNumber[0]) + 1, results))
if outputfile:
names = 'id Psi'
numformat = '%d ' + '%.6f ' * (results.shape[1] - 1)
np.savetxt(outputfile,
results,
fmt=numformat,
header=names,
comments='')
print('-------calculate local rotational ordering over-------')
return results
def S4(file_positions,
file_orientations,
ndim,
X4time,
filetype='lammps',
moltypes='',
dt=0.002,
phi=0.2,
qrange=10,
outputfile=''):
""" Compute four-point dynamic structure factor at peak timescale of dynamic susceptibility
Based on dynamics overlap function CRtotal and its corresponding dynamic susceptibility X4
file_positions: atomic positions
file_orientations: atomic orientations
phi is the cutoff for the dynamics overlap function
X4time is the peaktime scale of X4
dt is the timestep in MD simulations
Dynamics should be calculated before computing S4
Only considered the particles which are rotationally slow
"""
print('-----Compute dynamic S4(q) of rotational slow particles-----')
from WaveVector import choosewavevector
from dump import readdump
from math import pi
#read positions
d1 = readdump(file_positions, ndim, filetype, moltypes)
d1.read_onefile()
#read orientations
d2 = readangular(file_orientations, ndim)
d2.read_onefile()
#get unit vector
velocity = [
u / np.linalg.norm(u, axis=1)[:, np.newaxis] for u in d2.velocity
]
#check files
if d1.SnapshotNumber != d2.SnapshotNumber:
print('warning: ******check configurations*****')
if d1.ParticleNumber[0] != d2.ParticleNumber[0]:
print('warning: ******check configurations*****')
if d1.TimeStep[0] != d2.TimeStep[0]:
print('warning: ******check configurations*****')
TimeStep = d1.TimeStep[1] - d1.TimeStep[0]
if TimeStep != d1.TimeStep[-1] - d1.TimeStep[-2]:
print('Warning: *****time interval changes*****')
ParticleNumber = d1.ParticleNumber[0]
if ParticleNumber != d1.ParticleNumber[-1]:
print('Warning: *****particle number changes*****')
#calculate dynamics and structure factor
X4time = int(X4time / dt / TimeStep)
twopidl = 2 * pi / d1.Boxlength[0] #list over dimensions
Numofq = int(qrange / twopidl.max())
wavevector = choosewavevector(
Numofq, ndim)[:, 1:] #Only S4(q) at low wavenumber range is interested
wavevector = wavevector.astype(
np.float64) * twopidl[np.newaxis, :] #considering non-cubic box
wavenumber = np.linalg.norm(wavevector, axis=1)
sqresults = np.zeros((wavevector.shape[0], 2))
sqresults[:, 0] = wavenumber
for n in range(d1.SnapshotNumber - X4time):
RII = (velocity[n + X4time] * velocity[n]).sum(axis=1)
RII = np.where(RII >= phi, 1, 0)
sqtotal = np.zeros_like(sqresults)
for i in range(ParticleNumber):
if RII[i]:
thetas = (d1.Positions[n][i][np.newaxis, :] *
wavevector).sum(axis=1)
sqtotal[:, 0] += np.sin(thetas)
sqtotal[:, 1] += np.cos(thetas)
sqresults[:, 1] += np.square(sqtotal).sum(axis=1) / ParticleNumber
sqresults[:, 1] /= (d1.SnapshotNumber - X4time)
sqresults = pd.DataFrame(sqresults).round(6)
results = sqresults.groupby(sqresults[0]).mean().reset_index().values
names = 'q S4'
if outputfile:
np.savetxt(outputfile, results, fmt='%.6f', header=names, comments='')
print('--------- Compute S4(q) of slow particles over ------')
return results, names