fig_x, fig_y = JupyterPlots()
prefix = 'data/'
roundoff = 5e-6
fp = int(sys.argv[1])
fdump = prefix + f'dump_{fp}.lammpstrj'
if os.path.isfile(fdump + '.pkl'):
dumpdata = load_obj(fdump, pstatus=True)
else:
duf = DumpFile(fdump, voronoi_flag=False, cg_flag=False)
dumpdata = duf.read_dump()
save_obj(dumpdata, fdump, pstatus=True)
fig, axarr = plt.subplots(6, sharex=True, figsize=[fig_x, fig_y * 6])
N = len(dumpdata)
ncount = N
atom_num = 2
fxs = np.empty([ncount], float)
fys = np.empty([ncount], float)
xs = np.empty([ncount], float)
ys = np.empty([ncount], float)
muxs = np.empty([ncount], float)
muys = np.empty([ncount], float)
for i, data in enumerate(dumpdata[:ncount + 1]):
else:
Ds = np.empty([len(rhos)], float)
Derrs = np.empty([len(rhos)], float)
for j, rho in enumerate(rhos):
flog = prefix + f'log_{fp}_{rho}.lammps.log'
ll = LogLoader(flog, remove_chunk=0, merge_data=True)
ts = ll.data['Step']
MSD = ll.data['c_mymsdd[4]']
Ds[j], Derrs[j], yint = thermo.compute_Deff(ts, MSD)
save_obj([Ds, Derrs], pkl_name, pstatus=True)
print(Ds, Derrs)
print(thermo.ideal_Deff(fp))
ax.errorbar(rhos,
Ds / thermo.ideal_Deff(fp),
yerr=Derrs / thermo.ideal_Deff(fp),
fmt='-o',
color=colors[i],
label=rf'$f^P={fp}$')
ax.set_ylabel(r'$D_{\mathrm{eff}}$')
ax.set_xlabel(r'$\rho$')
ax.set_yscale('log')
#ax.set_xscale('log')
ax.legend()
Ps = np.empty([len(rhos)], float)
Perrs = np.empty([len(rhos)], float)
for j, rho in enumerate(rhos):
flog = prefix + f'log_{fp}_{rho}.lammps.log'
ll = LogLoader(flog, remove_chunk=0, merge_data=True)
ts = ll.data['Step']
Press = ll.data['c_press']
P_cuts = Press[ts > tcut]
Ps[j] = np.mean(P_cuts)
Perrs[j] = np.std(P_cuts) / np.sqrt(len(P_cuts))
save_obj([Ps, Perrs], pkl_name, pstatus=True)
ax.errorbar(rhos,
Ps,
yerr=Perrs,
fmt='-o',
color=colors[i],
label=rf'$f^P={fp}$')
ax.set_ylabel(r'$P$')
ax.set_xlabel(r'$\rho$')
#ax.set_yscale('log')
#ax.set_xscale('log')
ax.legend()
fig.subplots_adjust(left=0.25)
rho = sys.argv[1]
fps = np.array([0, 1, 5, 10, 20, 40, 60, 80, 100])
load_prefix = 'raw_data/'
save_prefix = 'pickled_data/'
for fp in fps:
fname = f'dump_{fp}_{rho}.lammpstrj'
dfile = DumpFile(load_prefix + fname)
data = dfile.read_dump(pstatus=True, min_step=1000000)
save_obj(data, save_prefix + fname)
ret_o = {}
calculate_items(ret_o,
data,
min_neigh=4,
cutoff=1.5,
MAXnb=100,
nbins=2000,
nbinsq=50,
Pe=10,
rho_0=0.60)
dc_name = save_prefix + f'ret_o_{fp}_{rho}'
save_obj(ret_o, dc_name)