eos_pars = {'.*': {'linear': True}}
matid = 83719
matf = eos.EosMaterial(matid, tables=['Piz_DT', 'Uiz_DT', 'Pe_DT', 'Ue_DT', 'Zfc_DT'],
options=eos_pars,
units='eospac', backend='eospac')
state0 = {"temp": 298, "rho": 2.7}
state1 = {"pres": np.logspace(-0.2, 2.2, 100)*100} # GPa
rh = RankineHugoniot.solve(state0, state1, backend='eospac',
eos_pars=eos_pars, material=matid,
root_opts={'method': 'lm'})
fig = plt.figure(figsize=get_figsize(wf=1.1, hf=0.6))
plt.subplots_adjust(wspace=0.3, hspace=0.3)
ax = [plt.subplot(221), plt.subplot(222), plt.subplot(223), plt.subplot(224)]
axins2 = inset_axes(ax[2], width="15%", height='12%', loc=4,
bbox_to_anchor=(-0.553, 0.13, 1, 1), bbox_transform=ax[2].figure.transFigure) # no zoom
axins1 = inset_axes(ax[1], width="15%", height='12%', loc=2,
bbox_to_anchor=(0.623, -0.12, 1, 1), bbox_transform=ax[1].figure.transFigure) # no zoom
for key, idf in df.iteritems():
ax[0].plot(idf.temp_source, idf.pres_0/100,'--', **plot_pars[key])
ax[1].plot(idf.pres_0/100, idf.u_s, '--', **plot_pars[key])
axins1.plot(idf.pres_0/100, idf.u_s, '--', **plot_pars[key])
ax[2].plot(idf.pres_0/100, idf.tion_0, '--', **plot_pars[key])
axins2.plot(idf.pres_0/100, idf.tion_0, '--', **plot_pars[key])
ax[3].plot(idf.velx_0, idf.u_s, '--', label='{0}'.format(key), **plot_pars[key])
ax[1].plot(rh.pres1/100, rh.u_s, **plot_pars['ref'])
state1['t'] = t
state0['x'] = x_s
state1['x'] = x_s
sto.result = (state0, state1)#t, get_shock_position(sl), get_state(r0, 10e-4))
#t,x = np.array(storage.values()).T
state0 = []
state1 = []
for val0, val1 in storage.values():
state0.append(val0)
state1.append(val1)
df0 = pd.DataFrame(state0)
df1 = pd.DataFrame(state1)
fig = plt.figure(figsize=get_figsize(wf=0.5, hf=0.5))
ax = [plt.subplot(111)]
for dfi in [df0,df1]:
dfi['u_s'] = savgol(np.array(dfi['t']), np.array(dfi['x']), window_size=5, order=2, deriv=1)*1e4
ax[0].plot(df0['t'],df0['x']*1e4, 'k')
ax[0].set_xlabel('Time (t) [ns]')
ax[0].set_ylabel('Shock position (x) [$\mathrm{\mu m}$]')
ax[0].xaxis.set_ticks([0, 1, 2, 3])
ax[0].yaxis.set_ticks([0, 30, 60, 90])
ax[0].set_xlim(0, 3)
ax[0].set_ylim(0, 90)
ax[0].grid(which='both')
fig.savefig('hugoniot_{0}_{1}.pdf'.format(EOS_TABLE, RUN_NAME), bbox_inches='tight')
tables=['Piz_DT', 'Uiz_DT', 'Pe_DT', 'Ue_DT', 'Zfc_DT'],
options=eos_pars,
units='eospac',
backend='eospac')
state0 = {"temp": 298, "rho": 2.7}
state1 = {"pres": np.logspace(-0.2, 2.2, 100) * 100} # GPa
rh = RankineHugoniot.solve(state0,
state1,
backend='eospac',
eos_pars=eos_pars,
material=matid,
root_opts={'method': 'lm'})
fig = plt.figure(figsize=get_figsize(wf=1.1, hf=0.6))
plt.subplots_adjust(wspace=0.3, hspace=0.3)
ax = [plt.subplot(221), plt.subplot(222), plt.subplot(223), plt.subplot(224)]
axins2 = inset_axes(ax[2],
width="15%",
height='12%',
loc=4,
bbox_to_anchor=(-0.553, 0.13, 1, 1),
bbox_transform=ax[2].figure.transFigure) # no zoom
axins1 = inset_axes(ax[1],
width="15%",
height='12%',
loc=2,
bbox_to_anchor=(0.623, -0.12, 1, 1),
bbox_transform=ax[1].figure.transFigure) # no zoom
state1['t'] = t
state0['x'] = x_s
state1['x'] = x_s
sto.result = (state0, state1
) #t, get_shock_position(sl), get_state(r0, 10e-4))
#t,x = np.array(storage.values()).T
state0 = []
state1 = []
for val0, val1 in storage.values():
state0.append(val0)
state1.append(val1)
df0 = pd.DataFrame(state0)
df1 = pd.DataFrame(state1)
fig = plt.figure(figsize=get_figsize(wf=0.5, hf=0.5))
ax = [plt.subplot(111)]
for dfi in [df0, df1]:
dfi['u_s'] = savgol(np.array(dfi['t']),
np.array(dfi['x']),
window_size=5,
order=2,
deriv=1) * 1e4
ax[0].plot(df0['t'], df0['x'] * 1e4, 'k')
ax[0].set_xlabel('Time (t) [ns]')
ax[0].set_ylabel('Shock position (x) [$\mathrm{\mu m}$]')
ax[0].xaxis.set_ticks([0, 1, 2, 3])
ax[0].yaxis.set_ticks([0, 30, 60, 90])
ax[0].set_xlim(0, 3)
