header += " ] [ <F_k(Dt)> ] [ stdDev(F_k(Dt)) ] [ Delta_Steps ] "
np.savetxt(flow, tobesaved, fmt="%f %f %f %d", header=header)
#########
tobesaved = np.loadtxt(outName)
plt.figure(31, [10, 6])
plt.semilogx(tobesaved[:, 0], tobesaved[:, 1])
## TODO: add the error bars here, using stdDev to have their size.
plt.title(r'Self-intermediate scattering'
) #(self-spatial, 2-point temporal correlation function)
plt.xlabel('$\Delta t$')
plt.ylabel(r'$F_k(\Delta t)$')
plt.ylim([0, 1])
plt.xlim([0.01, 1e9])
module_importPlotParams.savefig_perso(
outName + module_importPlotParams.fsaveFigFormat())
plt.close(31)
## saving the RMSD file: ##
if compute_RMSD == True:
tobesaved = np.zeros((len(Delta_t_range), 4))
tobesaved[:,
0] = Delta_t_range * tau_KA_per_frame * every_forMemory
tobesaved[:, 1] = RMSD_t
tobesaved[:, 2] = RMSD_t_std
tobesaved[:, 3] = Delta_t_range
nonzero_indices = RMSD_t != 0
tobesaved = tobesaved[nonzero_indices]
outName = rootname_OBS + "RMSD_prt=" + kind + windowTag
with open(outName, 'a') as flow:
plt.figure(10,[10,6])
every_plot=1
atom = 44
# ts = np.arange( 0, unique_times_number ,dtype=int)
# mask = phops[:,atom]>0
mask_nonzero = (phops[:,atom]>p_threshold_low)
hoptoplot = np.log(10.0*phops[mask_nonzero,atom])
plt.plot((unique_times[mask_nonzero])[::every_plot], hoptoplot[::every_plot], ls='', marker='o', lw=2.0)
unrolled_traj = module_phop.unrolled_traj_func(trajectory, Lx, atom)
unrolled_traj -= np.mean(unrolled_traj,0)
plt.plot(unique_times[::every_plot],unrolled_traj[::every_plot]*3.0)
plt.title(r'atom $'+str(atom)+'$')
plt.xlabel(r'$t$')
plt.ylabel(r'$3x,3y,3z, \log(p_{hop})$')
outName=rootname_PHOP + "single-atom-traj-"+str(atom)
module_importPlotParams.savefig_perso(outName+module_importPlotParams.fsaveFigFormat())
plt.ylim([-4,2])
outName=rootname_PHOP + "several-atoms-phop-cut"
module_importPlotParams.savefig_perso(outName+module_importPlotParams.fsaveFigFormat())
#plt.plot(activityState[:,0], activityState[:,1]) ## XXX
# ### phops of all atoms over time: ####
# ### (scatterplot of all activity)
# ## selecting only the big phop events
# ## we flatten the array at this point (Natoms+trajDuration)
# plt.figure(13,[10,6])
# print('we found ', len(decent_phop), ' decent_phop with p >', p_threshold_low)
### scatterplot: decent_phop(t) for the decent_phop, all atoms.
# plt.semilogy(tstoplot, decent_phop, ls='', marker='x')
# plt.xlabel(r'$t_{event}$')