def plot_gamma_slip(a_fcc=3.840):
ms = np.array([0, 0]) # mean and std
# from 1b to 7b # debug
for i in np.arange(7):
m1, s1 = calc_gamma_all(a_fcc=a_fcc, b_slip=i+1)
temp = np.array([ m1, s1 ])
ms = np.vstack([ms, temp])
filename = 'SRO_gamma_slip.txt'
np.savetxt(filename, ms)
# plot ms
fig_wh = [3.15, 2.8]
fig_subp = [1, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
ax1.set_position([0.2, 0.15, 0.75, 0.8])
xi = np.arange( ms.shape[0] )
ax1.errorbar(xi, ms[:,0], yerr=ms[:,1], \
fmt='-o', capsize=1)
ax1.set_xlabel('Slip (b)')
ax1.set_ylabel('Anti-phase boundary $\\gamma_\\mathrm{APB}$ (mJ/m$^2$)')
ax1.set_xticks(xi)
filename= 'SRO_gamma_slip.pdf'
plt.savefig(filename)
plt.close('all')
def plot_E_in_d0(sys_name, d0):
nd = len(d0)
if np.mod(nd, 2) == 1:
iref = int((nd - 1) / 2)
else:
iref = int(nd / 2)
fig_wh = [3.15, 2.5]
fig_subp = [1, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
fig_pos = np.array([0.26, 0.19, 0.71, 0.73])
ax1.set_position(fig_pos)
xi = np.arange(len(d0)) + 1
ax1.plot(xi, d0 / d0[iref] - 1, '-o')
ax1.set_xlabel('Index $i$ of interlayer distance $d_i$')
str1 = '$d_i / d_{ %d } -1$' % (iref + 1)
ax1.set_ylabel(str1)
str1 = '%s' % (sys_name)
xlim = ax1.get_xlim()
ylim = ax1.get_ylim()
ax1.text( xlim[0]+(xlim[1]-xlim[0])*0.5, ylim[0]+(ylim[1]-ylim[0])*0.8, \
str1, horizontalalignment = 'center' )
plt.savefig('y_post_E_in.d0.pdf')
plt.close('all')
def plot_eos(V, Etot, fitres, p_dft, V1, ca, magtot):
qe = vf.phy_const('qe')
E0 = fitres[0]
V0 = fitres[1]
B0 = fitres[2] *qe*1e21
B1 = fitres[3]
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig_wh = [3.15, 9]
fig_subp = [4, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
fig_pos = np.array([0.23, 0.77, 0.70, 0.205])
fig_dpos = np.array([0, -0.24, 0, 0])
for i in np.arange(4):
ax1[i].set_position(fig_pos + i* fig_dpos)
xi=np.arange(V.min()/V0, V.max()/V0, 1e-4)
ax1[0].plot(V/V0, (Etot-E0), 'o')
yi1 = myeqn(fitres[:-1], xi*V0) -E0
ax1[0].plot(xi, yi1, '-')
ax1[1].plot(V/V0, p_dft, 'o')
yi2 = myeosp(fitres[:-1], xi*V0) *qe*1e21 #[GPa]
ax1[1].plot(xi, yi2, '-')
ax1[2].plot(V/V0, ca, 'o')
ax1[3].plot(V/V0, magtot, 'o')
ax1[3].set_ylim([-0.1, np.ceil(magtot.max()+1e-6)])
plt.setp(ax1[-1], xlabel='Volume $V/V_0$')
plt.setp(ax1[0], ylabel='Energy $E-E_0$ (eV/atom)')
plt.setp(ax1[1], ylabel='Pressure $p$ (GPa)')
plt.setp(ax1[2], ylabel='$c/a$')
plt.setp(ax1[3], ylabel='Net magnetic moment ($\\mu_B$/atom)')
ax1[0].text(xi[0]+(xi[-1]-xi[0])*0.2, yi1.max()*0.7, \
'$E_0$ = %.4f eV/atom \n$V_0$ = %.4f $\mathrm{\AA}^3$/atom \n$B_0$ = %.2f GPa \n'
%(E0, V0, B0) )
ax1[1].text(xi[0]+(xi[-1]-xi[0])*0.4, yi2.max()*0.7, \
'$p_\mathrm{Pulay} = p_\mathrm{DFT} - p_\mathrm{true}$\n$\\approx$ %.2f GPa'
%( (V1/V0-1)*B0 ) )
plt.savefig('y_post_eos.pdf')
def plot_output(jobn, latoms, dpos_all, gamma, Asf, ibulk):
from ase.formula import Formula
njobs = len(jobn)
print('njobs:', njobs)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig_wh = [3.15, 3]
fig_subp = [1, 1]
xi = latoms[ibulk].positions[:, 2].copy()
for i in np.arange(njobs):
if np.linalg.norm( dpos_all[i, :] ) > 1e-10:
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
temp = np.hstack([ dpos_all[i, :], xi[:, np.newaxis] ])
ind = np.argsort(temp[:, -1])
temp = temp[ind, :]
ax1.plot(temp[:, -1], temp[:, 0], '-s', label='$u_1$' )
ax1.plot(temp[:, -1], temp[:, 1], '-o', label='$u_2$' )
ax1.plot(temp[:, -1], temp[:, 2], '-^', label='$u_3$' )
ax1.legend(loc='lower center', ncol=3, framealpha=0.4)
ax1.set_xlabel('Atom positions in $x_3$ ($\\mathrm{\\AA}$)')
ax1.set_ylabel('Displacements $u_i$ ($\\mathrm{\\AA}$)')
ax1.set_position([0.25, 0.16, 0.7, 0.76])
if jobn[i] == 'ssf':
str1 = '$\\gamma_\\mathrm{ssf} =$ %.0f mJ/m$^2$' %(gamma[i])
elif jobn[i] == 'usf':
str1 = '$\\gamma_\\mathrm{usf} =$ %.0f mJ/m$^2$' %(gamma[i])
elif jobn[i] == 'surf':
str1 = '$\\gamma_\\mathrm{surf} =$ %.0f mJ/m$^2$' %(gamma[i]/2)
else:
str1 = '$\\Delta E / A =$ %.0f mJ/m$^2$' %(gamma[i])
str2 = latoms[ibulk].get_chemical_formula()
str2 = Formula(str2).format('latex')
str_all = '%s\n$A =$%.4f $\\mathrm{\\AA}^2$\n%s' \
%(str2, Asf, str1)
ax1.text( xi.max()*0.2, dpos_all[i, :].max()*0.6, str_all )
filename = 'y_post_planar_relaxed.%s.pdf' %(jobn[i])
plt.savefig(filename)
def calc_yield_strength_et_T(self):
T_list = np.array([77, 300, 500])
et_list = np.arange(-10, 3, 1)
sigmay_all = np.zeros( [len(T_list), len(et_list)] )
for i in np.arange(len(T_list)):
for j in np.arange(len(et_list)):
sigmay_all[i, j] = self.calc_yield_strength(
param = { 'T': T_list[i], 'et': 10.0**(et_list[j]) } )
print('==> sigmay:', sigmay_all)
from myvasp import vasp_func as vf
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig_wh = [3.15, 3]
fig_subp = [1, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
fig_pos = np.array([0.20, 0.16, 0.75, 0.8])
ax1.set_position(fig_pos)
xi = 10.0**et_list
ax1.set_xscale('log')
for i in np.arange( len(T_list) ):
str1 = '%d K' %(T_list[i])
ax1.plot(xi, sigmay_all[i,:], '-o', label = str1)
ax1.legend(loc='best')
[ymin, ymax] = ax1.get_ylim()
ax1.set_ylim([0, ymax])
ax1.set_xlabel('Loading strain rate $\\dot{\\epsilon}$ (s$^{-1}$)')
ax1.set_ylabel('Yield strength $\\sigma_y$ (MPa)')
plt.savefig('fig_sigmay_et_T.pdf')
plt.close('all')
def plot_convergence(ljobn, lEtot, lEent):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig_wh = [7, 7]
fig_subp = [3, 2]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp, fig_sharex=False)
fig_pos = np.array([0.12, 0.73, 0.35, 0.25])
fig_dpos = np.array([0, -1 / 3, 0, 0])
fig_dpos2 = np.array([0.5, 0, 0, 0])
for i in np.arange(len(ljobn)):
ax1[i, 0].set_position(fig_pos + i * fig_dpos)
ax1[i, 1].set_position(fig_pos + i * fig_dpos + fig_dpos2)
for i in np.arange(2):
ax1[i, 0].plot(ljobn[i], lEtot[i], '-o')
ax1[i, 1].plot(ljobn[i][0:-1], np.diff(lEtot[i]) * 1e3, '-o')
ax1[2, 0].plot(ljobn[2], lEent[2], '-s')
ax1[2, 1].plot(ljobn[2][0:-1], np.diff(lEent[2]) * 1e3, '-s')
for j in np.arange(2):
ax1[0, j].set_xlabel('ENCUT')
ax1[1, j].set_xlabel('KP')
ax1[2, j].set_xlabel('SIGMA')
ax1[0, j].set_xlim([0, 1000])
ax1[1, j].set_xlim([0, 150])
ax1[2, j].set_xlim([0, 0.5])
for i in np.arange(3):
ax1[i, 1].set_ylim([-1, 1])
ax1[0, 0].set_ylabel('Energy (eV/atom)')
ax1[1, 0].set_ylabel('Energy (eV/atom)')
ax1[2, 0].set_ylabel('EENTRO (eV/atom)')
ax1[0, 1].set_ylabel('$\\Delta_{(i+1)-(i)}$ (meV/atom)')
ax1[1, 1].set_ylabel('$\\Delta_{(i+1)-(i)}$ (meV/atom)')
ax1[2, 1].set_ylabel('$\\Delta_{(i+1)-(i)}$ (meV/atom)')
plt.savefig('y_post_convergence.pdf')
plt.close('all')
def plot_output(gamma, da33, str_all):
njobs = len(gamma)
print(njobs)
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig_wh = [3.15, 5]
fig_subp = [2, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
disp = da33.copy()
xi = da33.copy()
ax1[0].plot(xi, gamma, '-o')
tau = np.diff(gamma) / np.diff(disp) *1e-2 #[GPa]
x_tau = xi[0:-1].copy() + np.diff(xi)/2
ax1[1].plot(x_tau, tau, '-o')
fig_pos = np.array([0.23, 0.57, 0.70, 0.40])
fig_dpos = np.array([0, -0.46, 0, 0])
ax1[0].set_position(fig_pos)
ax1[1].set_position(fig_pos + fig_dpos )
ax1[-1].set_xlabel('Vacuum layer thickness ($\\mathrm{\\AA}$)')
ax1[0].set_ylabel('Decohesion energy (mJ/m$^2$)')
ax1[1].set_ylabel('Tensile stress $\\sigma$ (GPa)')
ax1[0].text(4, gamma.max()/2, \
'$\\gamma_\\mathrm{surf}^\\mathrm{unrelaxed}$ \n= %.0f mJ/m$^2$' %( gamma[-1]/2 ))
ax1[1].text(4, tau.max()/2, \
'$\\sigma_\\mathrm{max}$ \n= %.1f GPa' %( tau.max() ))
ax1[0].text( 2, gamma.max()*0.2, str_all )
plt.savefig('y_post_decohesion.pdf')
def plot_hist(b_slip):
filename='SRO_gamma_all_%d.txt' %(b_slip)
gamma_all = np.loadtxt( filename )
miu = np.mean(gamma_all)
sigma = np.std(gamma_all)
fig_wh = [3.15, 2.8]
fig_subp = [1, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
ax1.set_position([0.2, 0.15, 0.75, 0.8])
ax1.hist(x=gamma_all, bins=20, density=True)
ax1.set_xlabel('$\\gamma_\\mathrm{APB}$ (mJ/m$^2$)')
ax1.set_ylabel('Probability density')
xmin, xmax = ax1.get_xlim()
ax1.set_xlim([xmin, xmax] )
ymin, ymax = ax1.get_ylim()
xi = np.linspace( xmin, xmax )
yi = my_gaussian(miu, sigma, xi)
ax1.plot(xi, yi, '--k')
str1 = 'mean = %.3f\nstd = %.3f' %(miu, sigma )
ax1.text( xmin+(xmax-xmin)*0.95, ymin+(ymax-ymin)*0.8, \
str1 , horizontalalignment='right' )
filename = 'SRO_gamma_hist_%d.pdf' %(b_slip)
plt.savefig(filename)
plt.close('all')
def plot_MC(EPI_beta, Ef_all, T):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
Ef_rand = EPI_beta[0]
fig_wh = [3.15, 2.7]
fig_subp = [1, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
ax1.set_position([0.23, 0.16, 0.72, 0.78])
xi = np.arange(len(Ef_all)) / 1e3
ax1.plot(xi, (Ef_all-Ef_rand)*1e3, '-' , \
label='$T_a$ = %d K' %(T))
ax1.set_xlabel('MC step ($\\times 10^3$)')
ax1.set_ylabel('$E_{f,\\mathrm{Pred}} - E^\\mathrm{rand}_{f}$ (meV/atom)')
ax1.legend(framealpha=0.5, loc="best")
filename = 'fig_MC_T_%04.0f_nstep_%d.pdf' % (T, len(Ef_all) - 1)
plt.savefig(filename)
plt.close('all')
def plot_dp_shell(atoms_in,
EPI_beta=np.array([]),
dp_shell=np.array([]),
dp_type='dp'):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
from myvasp import vasp_EPI_dp_shell as vf2
atoms = copy.deepcopy(atoms_in)
nelem = atoms.get_nelem()
shellmax = (len(EPI_beta) - 1) / (nelem * (nelem - 1) / 2)
vf.confirm_int(shellmax)
shellmax = int(shellmax)
if len(dp_shell) < 0.1:
dp_shell = vf2.calc_pairs_per_shell(atoms, \
shellmax=shellmax, write_dp=True)
else:
print('==> Use input dp_shell.')
temp = int(len(dp_shell) / shellmax)
dp_shell_2 = dp_shell.reshape(shellmax, temp)
rcry, ncry = vf2.crystal_shell('fcc')
xi = rcry[0:shellmax].copy()
fig_xlim = np.array([xi[0] - 0.15, xi[-1] + 0.15])
# fig_ylim = np.array([-0.5, 0.5])
elem_sym = pd.unique(atoms.get_chemical_symbols())
print('elem_sym:', elem_sym)
nelem = elem_sym.shape[0]
fig_wh = [2.7, 2.5]
fig_subp = [1, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
fig_pos = np.array([0.27, 0.17, 0.70, 0.78])
ax1.set_position(fig_pos)
ax1.plot(fig_xlim, [0, 0], ':k')
k = -1
for i in np.arange(nelem):
for j in np.arange(i, nelem):
if i != j:
k = k + 1
elems_name = '%s%s' % (elem_sym[i], elem_sym[j])
# str1 = '$\\Delta \\eta_{\\mathrm{%s}, d}$' %(elems_name)
str1 = '%s' % (elems_name)
mycolor, mymarker = mycolors(elems_name)
ax1.plot(xi, dp_shell_2[:, k], '-', \
color = mycolor, marker = mymarker, \
label = str1)
vf.confirm_0(dp_shell_2.shape[1] - (k + 1))
ax1.legend(loc='best', ncol=2, framealpha=0.4, \
fontsize=7)
ax1.set_xlabel('Pair distance $d/a$')
ax1.set_xlim(fig_xlim)
# ax1.set_ylim( fig_ylim )
fig_ylim = ax1.get_ylim()
if dp_type == 'dp':
ax1.set_ylabel('$\\Delta \\eta_{nm, d}$')
if len(EPI_beta) > 0.1:
Ef = -0.5 * np.dot(dp_shell, EPI_beta[1:])
str1 = '$E_{f, \\mathrm{Pred}} - {E}^\\mathrm{rand}_{f}$\n= %.3f meV/atom' % (
Ef * 1e3)
ax1.text(
fig_xlim[0]+(fig_xlim[1]-fig_xlim[0])*0.95, \
fig_ylim[0]+(fig_ylim[1]-fig_ylim[0])*0.06, str1, \
horizontalalignment='right' )
filename = 'y_post_dp_shell.pdf'
elif dp_type == 'SRO':
ax1.set_ylabel('Warren-Cowley SRO $\\alpha_{nm, d}$')
filename = 'y_post_WC_SRO_shell.pdf'
plt.savefig(filename)
plt.close('all')
return dp_shell
def plot_cij_energy(ldata):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
xi = np.linspace(-0.003, 0.003, 1000)
fig_wh = [7, 10]
fig_subp = [5, 2]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
fig_pos = np.array([0.13, 0.8, 0.35, 0.16])
fig_dpos = np.array([0, -0.19, 0, 0])
fig_dpos2 = np.array([0.5, 0, 0, 0])
for i in np.arange(5):
ax1[i, 0].set_position(fig_pos + i * fig_dpos)
ax1[i, 1].set_position(fig_pos + i * fig_dpos + fig_dpos2)
# plot energy-strain
p0_tot = np.array([]) # for Cij
e0_tot = np.array([]) # to check shift
for i in np.arange(len(ldata)):
e = ldata[i]['e']
ed = ldata[i]['ed']
s = ldata[i]['s']
ax1[i, 0].plot(e, ed, 'o')
param = np.polyfit(e, ed, 2)
fun = np.poly1d(param)
yi = fun(xi)
p0_tot = np.append(p0_tot, param[0])
e0 = -param[1] / (2 * param[0])
e0_tot = np.append(e0_tot, e0)
ax1[i, 0].plot(xi, yi, '-C1')
str1 = '$\\epsilon_0$ = %.6f' % (e0)
ymin, ymax = ax1[i, 0].get_ylim()
ax1[i, 0].text(0, ymax * 0.85, str1)
C11 = p0_tot[0] * 2
C12 = p0_tot[1] - C11
C33 = p0_tot[3] * 2
C13 = p0_tot[2] - (C11 + C33) / 2
C44 = p0_tot[4] * 2
# plot stress-strain
lcolor = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5']
llegend = [
'$\\sigma_{xx}$', '$\\sigma_{yy}$', '$\\sigma_{zz}$', '$\\sigma_{yz}$',
'$\\sigma_{xz}$', '$\\sigma_{xy}$'
]
lslope = np.array([
[C11, C12, C13, 0, 0, 0], # c11
[C11 + C12, C12 + C11, C13 * 2, 0, 0, 0], # c12
[C11 + C13, C12 + C13, C13 + C33, 0, 0, 0], # c13
[C13, C13, C33, 0, 0, 0], # c33
[0, 0, 0, C44, 0, 0], # c44
])
for i in np.arange(len(ldata)):
e = ldata[i]['e']
ed = ldata[i]['ed']
s = ldata[i]['s']
iref = ldata[i]['iref']
for j in np.arange(6):
ax1[i, 1].plot(e, s[:, j], 'o', color=lcolor[j], label=llegend[j])
ax1[i, 1].plot(xi,
xi * lslope[i, j] + s[iref, j],
'-',
color=lcolor[j])
ax1[0, 1].legend(loc='upper left', ncol=2, framealpha=0.4)
for i in np.arange(len(ldata)):
for j in np.arange(2):
ax1[i, j].set_xticks(np.arange(-0.002, 0.004, 0.002))
ax1[4, j].set_xlabel('Strain')
ax1[i, 0].set_ylabel('Elastic energy density (GPa)')
ax1[i, 1].set_ylabel('DFT stress (GPa)')
str1 = '$C_{11}$, $C_{12}$, $C_{13}$, $C_{33}$, $C_{44}$ (GPa): \n%.2f, %.2f, %.2f, %.2f, %.2f ' \
%( C11, C12, C13, C33, C44 )
ymin, ymax = ax1[0, 0].get_ylim()
ax1[0, 0].text(-0.003, ymax * 1.05, str1)
plt.savefig('y_post_cij_energy.pdf')
plt.close('all')
write_cij_energy(np.array([C11, C12, C13, C33, C44]))
def plot_E_in(natoms, sys_name, Etot, V0, a, a0, t, t0, deform_type):
e, ed, param, R2, E0 = myfit(Etot, a, a0, V0)
xi = np.linspace(e[0], e[-1], 1000)
fun = np.poly1d(param)
yi = fun(xi)
fig_wh = [3.15, 5]
fig_subp = [2, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
fig_pos = np.array([0.25, 0.55, 0.70, 0.4])
fig_dpos = np.array([0, -0.45, 0, 0])
for i in np.arange(2):
ax1[i].set_position(fig_pos + i * fig_dpos)
ax1[0].plot(e, ed - param[-1], 'o')
ax1[0].plot(xi, yi - param[-1], '-')
xlim = ax1[0].get_xlim()
ylim = ax1[0].get_ylim()
ax1[0].plot(np.array([0, 0]), ylim, '--k', alpha=0.2)
ax1[0].set_ylim(ylim)
ax1[0].set_ylabel('Elastic energy density (GPa)')
#==========================
et = t / t0 - 1
param2 = np.polyfit(e, et, 1)
vf.confirm_0(param2[-1])
fun2 = np.poly1d(param2)
yi2 = fun2(xi)
ax1[1].plot(e, et, 'o')
ax1[1].plot(xi, yi2, '-')
ylim2 = ax1[1].get_ylim()
ax1[1].plot(np.array([0, 0]), ylim2, '--k', alpha=0.2)
ax1[1].set_ylim(ylim2)
ax1[1].set_ylabel('Out-of-plane strain $\\epsilon_{zz}$')
if deform_type == 'E_in_2':
ax1[1].set_xlabel(
'Biaxial in-plane strain $\\epsilon_{xx}$ and $\\epsilon_{yy}$')
str0 = '%s\n$a_0 = %.4f~\\mathrm{\AA}$\n\n$\\frac{ E_{x} }{ 1-\\nu_{xy} } = %.2f$ GPa\n$E_0 = %.4f$ eV/atom' \
%(sys_name, a0, param[0], E0/natoms)
str1 = '$t_0 = %.4f~\\mathrm{\AA}$\n\n$\\frac{ \\nu_{xz} }{ 1-\\nu_{xy} } = %.4f $' \
%(t0, param2[0]/(-2))
elif deform_type == 'E_in_1':
ax1[1].set_xlabel(
'Uniaxial in-plane strain $\\epsilon_{xx}$ ($\\epsilon_{yy} = 0$)')
str0 = '%s\n$a_0 = %.4f~\\mathrm{\AA}$\n\n$\\frac{ E_{x} }{ 1-\\nu_{xy}^2 } = %.2f$ GPa\n$E_0 = %.4f$ eV/atom' \
%(sys_name, a0, param[0]*2, E0/natoms)
str1 = '$t_0 = %.4f~\\mathrm{\AA}$\n\n$\\frac{ \\nu_{xz} }{ 1-\\nu_{xy} } = %.4f $' \
%(t0, param2[0]/(-1))
ax1[0].text( xlim[0]+(xlim[1]-xlim[0])*0.25, ylim[0]+(ylim[1]-ylim[0])*0.55, \
str0, horizontalalignment = 'left' )
ax1[1].text( xlim[0]+(xlim[1]-xlim[0])*0.55, ylim2[0]+(ylim2[1]-ylim2[0])*0.7, \
str1, horizontalalignment = 'left' )
plt.savefig('y_post_E_in.pdf')
plt.close('all')
def plot_output(E0, Eatom, Ecoh, V0, k, Etot, Vp, p, VB, B, p0, B0, magtot):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig_wh = [3.15, 9]
fig_subp = [4, 1]
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
fig_pos = np.array([0.20, 0.77, 0.75, 0.205])
fig_dpos = np.array([0, -0.24, 0, 0])
for i in np.arange(4):
ax1[i].set_position(fig_pos + fig_dpos * i)
Elimd = math.floor(Etot.min() - 0.5)
Elimu = -2 * Elimd
plim = -math.floor(p.min() / 10) * 10
Blimu = math.ceil(B0 * 1.2 / 50) * 50
Blimd = math.floor(B.min() / 50) * 50
maglimu = math.ceil(magtot.max() + 1e-6)
maglimd = -0.1
ax1[0].plot([1, 1], [Elimd, Elimu], '--k')
ax1[0].plot(k, Etot, '-o')
ax1[1].plot([0, 4], [0, 0], '--k')
ax1[1].plot([1, 1], [-plim, plim], '--k')
ax1[1].plot((Vp / V0)**(1 / 3), p, '-o')
ax1[1].plot(1, p0, 's')
ax1[2].plot([0, 4], [0, 0], '--k')
ax1[2].plot([1, 1], [Blimd, Blimu], '--k')
ax1[2].plot((VB / V0)**(1 / 3), B, '-o')
ax1[2].plot(1, B0, 's')
ax1[3].plot([1, 1], [maglimd, maglimu], '--k')
ax1[3].plot(k, magtot, '-o')
ax1[0].set_ylim([Elimd, Elimu])
ax1[1].set_ylim([-plim, plim])
ax1[2].set_ylim([Blimd, Blimu])
ax1[3].set_ylim([maglimd, maglimu])
plt.setp(ax1[-1], xlabel='$a/a_0$')
plt.setp(ax1[0], ylabel='DFT energy (eV/atom)')
plt.setp(ax1[1], ylabel='Pressure (GPa)')
plt.setp(ax1[2], ylabel='Bulk modulus (GPa)')
plt.setp(ax1[3], ylabel='Net magnetic moment ($\\mu_B$/atom)')
ax1[0].text(1.5, Elimd+(Elimu-Elimd)*0.6, \
'$E_0$ = %.4f eV \n$E_\mathrm{atom}$ = %.4f eV \n$E_\mathrm{coh}$ = %.4f eV \n\n$V_0$ = %.4f $\mathrm{\AA}^3$' \
%(E0, Eatom, Ecoh, V0) )
ax1[1].text(1.5, plim*0.4, \
'from diff: \n$p_0$ = %.1f GPa ' %(p0) )
ax1[2].text(1.5, Blimd+(Blimu-Blimd)*0.6, \
'from diff: \n$B_0$ = %.1f GPa ' %(B0) )
plt.savefig('y_post_cohesive.pdf')
def plot_output(latoms2, jobn):
try:
shutil.rmtree('./y_post_mag/')
except:
print('==> no folder')
os.mkdir('./y_post_mag/')
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
fig_wh = [3.15, 3]
fig_subp = [1, 1]
mag0_all = np.array([])
for i in np.arange( len(jobn) ):
magmom = latoms2[i].get_magnetic_moments()
mag0 = latoms2[i].get_magnetic_moment() \
/ len(latoms2[i].get_positions())
mag0_all = np.append(mag0_all, mag0)
chem_sym = latoms2[i].get_chemical_symbols()
elem_sym = pd.unique( chem_sym )
atom_num = latoms2[i].get_atomic_numbers()
elem_num = pd.unique( atom_num )
nelem = len(elem_num)
print('elem_sym, elem_num, nelem:', \
elem_sym, elem_num, nelem)
ymax = np.ceil( max([1.5, abs(max(magmom, key=abs)) ]) )
fig1, ax1 = vf.my_plot(fig_wh, fig_subp)
for j in np.arange(nelem):
mask = (atom_num == elem_num[j])
yi = magmom[mask]
temp = np.linspace(-0.2, 0.2, len(yi)+2 ) + j
xi = temp[1:-1]
ax1.plot( xi , yi, 'o' )
ax1.text(j, ymax/4*5.0, '%.2f' %( yi.mean() ), \
horizontalalignment='center' )
ax1.text(j, ymax/4*4.5, '%.2f' %( yi.std() ), \
horizontalalignment='center' )
ax1.text(-0.5, ymax/4*5.0, 'mean:', \
horizontalalignment='right' )
ax1.text(-0.5, ymax/4*4.5, 'std:', \
horizontalalignment='right' )
ax1.text(-0.5, ymax/4*(-5.3), 'Supercell mean= %.2f' %(mag0), \
horizontalalignment='left' )
ax1.plot( [-0.5, nelem-0.5], [0, 0], '--k' )
ax1.set_position([0.23, 0.15, 0.72, 0.70])
ax1.set_xlim([-0.5, nelem-0.5])
ax1.set_ylim([-ymax, ymax])
ax1.set_xticks(np.arange(nelem))
ax1.set_xticklabels(elem_sym)
ax1.set_ylabel('Atomic magnetic moment ($\\mu_B$)')
filename = './y_post_mag/y_post_mag_%s.pdf' %(jobn[i])
plt.savefig(filename)
plt.close('all')
mag0_all = np.abs( mag0_all )
f = open('./y_post_mag/y_post_mag_note.txt', 'w+')
f.write('# VASP magnetic moment , supercell mean (mu_B/atom): \n' )
f.write('\n%8s %8s %8s \n' \
%('mean', 'max', 'min' ) )
f.write('%8.2f %8.2f %8.2f \n\n' \
%(mag0_all.mean(), mag0_all.max(), mag0_all.min() ) )
for i in np.arange( len(jobn) ):
f.write('%20s %8.2f \n' \
%(jobn[i], mag0_all[i] ) )
f.close()
