def main():
# Read in the users data file as two columns
# timestep, dr (ang)
try:
fin = sys.argv[1]
step = float(sys.argv[2])
f = open(fin, 'r')
except IndexError:
print '\nusage ' + sys.argv[0] + ' f_bond.dat timestep_in_A.U.\n'
sys.exit(0)
lines = f.readlines()
f.close()
x, y = [], []
for line in lines:
row = line.split()
x.append(float(row[0]))
y.append(float(row[1]))
# Calculate the PSD and save a figure
psd_obj = PSD.psd(x, y)
f, psd = psd_obj.getPSD()
# Write PSD data to file
out = open('PSD.dat', 'w')
out.write('#f psd\n')
# Neglect the zeroth value
for i in range(len(f) / 2 - 1):
out.write(str(f[i + 1]) + ' ' + str(psd[i + 1]) + '\n')
# Domiqnant frequency
psd = list(psd)[1:len(psd) / 2]
f = list(f)[1:len(f) / 2]
max_f = f[psd.index(max(psd))]
# Corresponding period
T = 1.0 / max_f
# Convert period from number of timesteps to femtoseconds
T = T * (step * 2.41880e-02)
out.close()
print 'The period is ' + str(T) + ' femtoseconds'
def main():
# Read in the users data file as two columns
# timestep, dr (ang)
try:
fin = sys.argv[1]
step = float(sys.argv[2])
f = open(fin,'r')
except IndexError:
print '\nusage '+sys.argv[0]+' f_bond.dat timestep_in_A.U.\n'
sys.exit(0)
lines = f.readlines()
f.close()
x, y = [], []
for line in lines:
row = line.split()
x.append(float(row[0]))
y.append(float(row[1]))
# Calculate the PSD and save a figure
psd_obj = PSD.psd(x,y)
f, psd = psd_obj.getPSD()
# Write PSD data to file
out = open('PSD.dat','w')
out.write('#f psd\n')
# Neglect the zeroth value
for i in range(len(f)/2-1):
out.write(str(f[i+1])+' '+str(psd[i+1])+'\n')
# Domiqnant frequency
psd = list(psd)[1:len(psd)/2]
f = list(f)[1:len(f)/2]
max_f = f[ psd.index(max(psd)) ]
# Corresponding period
T = 1.0 / max_f
# Convert period from number of timesteps to femtoseconds
T = T * (step * 2.41880e-02)
out.close()
print 'The period is '+str(T)+' femtoseconds'
def main():
# Check for required user input
try:
fname = sys.argv[1]
step = int(sys.argv[2])
except IndexError:
print '\nusage: '+sys.argv[0]+' TRAJEC.cbn step_in_au\n'
sys.exit(0)
# Get the neighbor list from cbn file
nn = nearest_neighbor(fname)
# Open and read header from cbn file
f = open(fname,'r')
for i in range(8):
if i == 3:
alat = float(f.readline().split()[-1])*0.5291772 # Convert to angstroms
natom = int(f.readline().split()[-1])
f.readline()
lines = f.readlines()
f.close()
# Calculate all the bond lengths over time
dr = [[] for i in range(len(nn))]
Nconfigs = len(lines)/len(nn)
for j in range(len(lines)/(len(nn))):
# Report progress to user
if j % 200 == 0 and j != 0: print int(round((float(j)/Nconfigs)*100)), '% ...'
for i in range(natom):
# Current atom's coords and its partner's
typat1,x1,y1,z1,dummy1 = lines[i].split()
typat2,x2,y2,z2,dummy2 = lines[nn[i]].split()
# Caclulate bond length using the minimum image convention
dx = (float(x1) - float(x2)) * 0.5291772
dy = (float(y1) - float(y2)) * 0.5291772
dz = (float(z1) - float(z2)) * 0.5291772
dx -= alat*pbc_round(dx/alat)
dy -= alat*pbc_round(dy/alat)
dz -= alat*pbc_round(dz/alat)
# Calculate bond length in angstroms
r = ( dx**2 + dy**2 + dz**2 )**0.5
dr[i].append(abs(r))
for k in range(natom): lines.pop(0)
frequencies, periods, bond_lengths = [], [], []
for dr_array in dr:
# Calculate the PSD
psd_obj = PSD.psd([i for i in range(len(dr_array))],dr_array)
f, psd = psd_obj.getPSD()
f, psd = list(f), list(psd)
# Dominant frequency (in region of interest)
max_f = 0
while max_f == 0:
max_f = f.pop( psd[1:len(psd)/2].index(max(psd[1:len(psd)/2])) )
# Corresponding period in femtoseconds
T = (1.0 / max_f) * (step * 2.4188000000000003e-02)
frequencies.append(max_f)
periods.append(T)
bond_lengths += dr_array
"""
# Write PSD data to file
out = open('PSD.dat','w')
out.write('#f psd\n')
# Neglect the zeroth value and the second half
for i in range(len(f)/2-1):
out.write(str(f[i+1])+' '+str(psd[i+1])+'\n')
out.close()
"""
# Histograms of values
pylab.figure(num=1,figsize=(5,3),facecolor='w',edgecolor='k')
pylab.hist(frequencies, normed=1, label='_nolegend_')
pylab.savefig('bond_frequencies_hist.png')
pylab.clf()
pylab.hist(periods, normed=1, label='_nolegend_')
pylab.savefig('bond_periods_hist.png')
pylab.clf()
pylab.hist(bond_lengths, bins=250, normed=1, label='_nolegend_')
pylab.savefig('bond_length_hist.png')
pylab.clf()
