def plot_2d_density():
modes = 128
L = 80.
gamma = 0.1
N = L ** 2 / gamma
samples = 4
results = get('2d',
initial='opposite',
gamma=gamma, L_trap=L, t=20., nu=0.1,
steps=5000, modes=modes, ensembles=1, samples=samples)
n = numpy.array(results['density']).transpose(1, 0, 2, 3)
print("* 2d_density")
print(results['errors']['density'])
times = numpy.linspace(0, results['t'], results['samples'] + 1)
levels = numpy.linspace(-0.5, 0.5, 11)
fig = mpl.figure(width=2, aspect=0.35)
grid2 = ImageGrid(fig, [0.1, 0.0, 0.8, 1.0],
nrows_ncols = (1, 3),
direction="row",
axes_pad = 0.2,
add_all=True,
label_mode = "1",
share_all = True,
cbar_location="right",
cbar_mode="single",
cbar_size="5%",
cbar_pad=0.2,
)
grid2[0].set_xlabel('$\\mathit{y}$')
grid2[0].set_ylabel('$\\mathit{x}$')
for j, nt in enumerate([1, 2, 4]):
#s = fig.add_subplot([311, 321, 331][j], xlabel='$\\mathit{y}$', ylabel='$\\mathit{x}$')
s = grid2[j]
data = (n[0][nt] - n[1][nt]).T / (4 * N / (L**2))
data = numpy.clip(data, -0.5, 0.5)
#data = scipy.ndimage.zoom(data, 0.25)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data,
extent=(-L/2, L/2, -L/2, L/2),
vmin=-0.5, vmax=0.5,
cmap=cmap,
interpolation='none',
aspect=1)
fig.text(0.18 + 0.26 * j, 0.9, '$\\tau=' + str(int(times[nt])) + '$')
#fig.colorbar(im,orientation='vertical', shrink=0.6, ticks=levels).set_label('$\\mathit{j}$')
#fig.tight_layout()
grid2[-1].cax.colorbar(im, ticks=levels).set_label_text('$\\mathit{j}$')
grid2[-1].cax.toggle_label(True)
fig.savefig('2d_density' + suffix)
def plot_1d_no_coupling_varying_L():
gamma = 0.1
L_base = 10.
modes_base = 32
t = 25.
fig = mpl.figure()
colors = [mpl.color.f[c].main for c in ('blue', 'red', 'green', 'yellow')]
linestyles = ['-', '--', '-.', ':']
print("* 1d_no_coupling_varying_L")
s = fig.add_subplot(111,
xlabel='$\\mathit{\\tau}$',
ylabel='$\\mathit{J}$')
s.plot([0, t], [0, 0], color='grey', linestyle='--', linewidth=0.5)
for i, j in enumerate([0, 1, 2, 3]):
L = L_base * 2**j
modes = modes_base * 2**j
results = get('1d',
initial='opposite',
gamma=gamma,
L_trap=L,
t=t,
nu=0,
steps=10000,
modes=modes,
ensembles=1000)
N = L / gamma
times = numpy.linspace(0, results['t'], results['samples'] + 1)
n0m = numpy.array(results['Nminus_mean']) / N
n0e = numpy.array(results['Nminus_std']) / numpy.sqrt(
results['ensembles']) / N
n1m = numpy.array(results['Nplus_mean']) / N
n1e = numpy.array(results['Nplus_std']) / numpy.sqrt(
results['ensembles']) / N
print(L, results['errors']['Nminus_mean'])
ax = s.plot(times, (n1m - n0m) / 4,
label="$L={L},\\,M={M}$".format(L=L, M=modes),
color=colors[i],
linestyle=linestyles[i],
dashes=mpl.dash[linestyles[i]])
#s.plot(times, n0m + n0e, color=ax[0].get_color(), linestyle='--')
#s.plot(times, n0m - n0e, color=ax[0].get_color(), linestyle='--')
s.set_ylim(-0.5, 0.5)
s.set_yticks([-0.5, -0.25, 0, 0.25, 0.5])
#s.legend()
fig.tight_layout()
fig.savefig('1d_no_coupling_varying_L' + suffix)
def plot_1d_varying_g12():
gamma = 0.1
L = 80.
modes = 256
t = 25.
colors = [mpl.color.f[c].main for c in ('blue', 'red', 'green', 'yellow')]
linestyles = ['-', '--', '-.', ':']
print("* 1d_varying_g12")
fig = mpl.figure()
s = fig.add_subplot(111,
xlabel='$\\mathit{\\tau}$',
ylabel='$\\mathit{J}$')
s.plot([0, t], [0, 0], color='grey', linestyle='--', linewidth=0.5)
for i, U12 in enumerate([0, 0.3, 0.5, 0.7]):
results = get('1d',
initial='opposite',
gamma=gamma,
L_trap=L,
t=t,
nu=0.1,
steps=10000,
modes=modes,
ensembles=1000,
U12=U12)
N = L / gamma
times = numpy.linspace(0, results['t'], results['samples'] + 1)
n0m = numpy.array(results['Nminus_mean']) / N
n0e = numpy.array(results['Nminus_std']) / numpy.sqrt(
results['ensembles']) / N
n1m = numpy.array(results['Nplus_mean']) / N
n1e = numpy.array(results['Nplus_std']) / numpy.sqrt(
results['ensembles']) / N
print(U12, results['errors']['Nminus_mean'])
#ax = s.plot(times, n0m, label="$\\nu = {nu}$".format(nu=nu),
# color=colors[i], linestyle=linestyles[i], dashes=mpl.dash[linestyles[i]])
ax = s.plot(times, (n1m - n0m) / 4,
label="$\\g_{{12}} = {U12} g_{{11}}$".format(U12=U12),
color=colors[i],
linestyle=linestyles[i],
dashes=mpl.dash[linestyles[i]])
#s.plot(times, n0m + n0e, ax[0].get_color() + '--')
#s.plot(times, n0m - n0e, ax[0].get_color() + '--')
s.set_ylim(-0.5, 0.5)
s.set_yticks([-0.5, -0.25, 0, 0.25, 0.5])
#s.legend()
fig.tight_layout()
fig.savefig('1d_varying_g12' + suffix)
def plot_2d_varying_coupling():
gamma = 0.1
L = 80.
modes = 256
t = 25.
colors = [mpl.color.f[c].main for c in ('blue', 'red', 'green', 'yellow')]
linestyles = ['-', '--', '-.', ':']
print("* 2d_varying_coupling")
fig = mpl.figure()
s = fig.add_subplot(111,
xlabel='$\\mathit{\\tau}$',
ylabel='$\\mathit{J}$')
s.plot([0, t], [0, 0], color='grey', linestyle='--', linewidth=0.5)
for i, nu in enumerate([0.001, 0.01, 0.1, 1]):
results = get('2d',
initial='opposite',
gamma=gamma,
L_trap=L,
t=t,
nu=nu,
steps=4000,
modes=modes,
ensembles=200)
N = L**2 / gamma
times = numpy.linspace(0, results['t'], results['samples'] + 1)
n0m = numpy.array(results['Nminus_mean']) / N
n0e = numpy.array(results['Nminus_std']) / numpy.sqrt(
results['ensembles']) / N
n1m = numpy.array(results['Nplus_mean']) / N
n1e = numpy.array(results['Nplus_std']) / numpy.sqrt(
results['ensembles']) / N
print(nu, results['errors']['Nminus_mean'])
ax = s.plot(times, (n1m - n0m) / 4,
label="$\\nu = {nu}$".format(nu=nu),
color=colors[i],
linestyle=linestyles[i],
dashes=mpl.dash[linestyles[i]])
#s.plot(times, ((n1m - n0m) / 4 + n0e + n1e), color=ax[0].get_color(), linestyle='--')
#s.plot(times, ((n1m - n0m) / 4 - n0e - n1e), color=ax[0].get_color(), linestyle='--')
s.set_ylim(-0.5, 0.5)
s.set_yticks([-0.5, -0.25, 0, 0.25, 0.5])
fig.tight_layout()
fig.savefig('2d_varying_coupling' + suffix)
def plot_1d_density():
gamma = 0.1
L = 80.
modes = 256
N = L / gamma
results = get('1d',
initial='opposite',
gamma=gamma,
L_trap=L,
t=75.,
nu=0.1,
steps=50000,
modes=modes,
ensembles=1,
samples=250)
n = numpy.array(results['density']).transpose(1, 0, 2)
print("* 1d_density")
print(results['errors']['density'])
times = numpy.linspace(0, results['t'], results['samples'] + 1)
fig = mpl.figure()
s = fig.add_subplot(111,
xlabel='$\\mathit{\\tau}$',
ylabel='$\\mathit{z}$')
data = (n[0] - n[1]).T / (4 * N / L)
data = numpy.clip(data, -0.5, 0.5)
levels = numpy.linspace(-0.5, 0.5, 11)
#data = scipy.ndimage.zoom(data, 0.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data,
extent=(0, times[-1], -L / 2, L / 2),
vmin=-0.5,
vmax=0.5,
cmap=cmap,
interpolation='none',
aspect=(results['t']) / L / 1.6)
fig.colorbar(im, orientation='vertical', shrink=0.8,
ticks=levels).set_label('$\\mathit{j}$')
fig.tight_layout()
fig.savefig('1d_density' + suffix)
def plot_contents_pic():
modes = 128
L = 80.
gamma = 0.1
N = L**2 / gamma
samples = 4
results = get('2d',
initial='opposite',
gamma=gamma,
L_trap=L,
t=20.,
nu=0.1,
steps=5000,
modes=modes,
ensembles=1,
samples=samples)
n = numpy.array(results['density']).transpose(1, 0, 2, 3)
print("* contents (2d density)")
print(results['errors']['density'])
times = numpy.linspace(0, results['t'], results['samples'] + 1)
levels = numpy.linspace(-0.5, 0.5, 11)
nt = 2
fig = mpl.figure(width=2. / 3, aspect=0.9)
s = fig.add_subplot(111)
s.set_xticks([])
s.set_yticks([])
data = (n[0][nt] - n[1][nt]).T / (4 * N / (L**2))
data = numpy.clip(data, -0.5, 0.5)
#data = scipy.ndimage.zoom(data, 0.25)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data,
extent=(-L / 2, L / 2, -L / 2, L / 2),
vmin=-0.5,
vmax=0.5,
cmap=cmap,
interpolation='none',
aspect=1)
fig.tight_layout()
fig.savefig('contents' + suffix)
def plot_1d_no_coupling_varying_L():
gamma = 0.1
L_base = 10.
modes_base = 32
t = 25.
fig = mpl.figure()
colors = [mpl.color.f[c].main for c in ('blue', 'red', 'green', 'yellow')]
linestyles = ['-', '--', '-.', ':']
print("* 1d_no_coupling_varying_L")
s = fig.add_subplot(111, xlabel='$\\mathit{\\tau}$', ylabel='$\\mathit{J}$')
s.plot([0, t], [0, 0], color='grey', linestyle='--', linewidth=0.5)
for i, j in enumerate([0, 1, 2, 3]):
L = L_base * 2 ** j
modes = modes_base * 2 ** j
results = get('1d',
initial='opposite',
gamma=gamma, L_trap=L, t=t, nu=0,
steps=10000, modes=modes, ensembles=1000)
N = L / gamma
times = numpy.linspace(0, results['t'], results['samples'] + 1)
n0m = numpy.array(results['Nminus_mean']) / N
n0e = numpy.array(results['Nminus_std']) / numpy.sqrt(results['ensembles']) / N
n1m = numpy.array(results['Nplus_mean']) / N
n1e = numpy.array(results['Nplus_std']) / numpy.sqrt(results['ensembles']) / N
print(L, results['errors']['Nminus_mean'])
ax = s.plot(times, (n1m - n0m) / 4, label="$L={L},\\,M={M}$".format(L=L, M=modes),
color=colors[i], linestyle=linestyles[i], dashes=mpl.dash[linestyles[i]])
#s.plot(times, n0m + n0e, color=ax[0].get_color(), linestyle='--')
#s.plot(times, n0m - n0e, color=ax[0].get_color(), linestyle='--')
s.set_ylim(-0.5, 0.5)
s.set_yticks([-0.5, -0.25, 0, 0.25, 0.5])
#s.legend()
fig.tight_layout()
fig.savefig('1d_no_coupling_varying_L' + suffix)
def plot_1d_varying_coupling():
gamma = 0.1
L = 80.
modes = 256
t = 25.
colors = [mpl.color.f[c].main for c in ('blue', 'red', 'green', 'yellow')]
linestyles = ['-', '--', '-.', ':']
print("* 1d_varying_coupling")
fig = mpl.figure()
s = fig.add_subplot(111, xlabel='$\\mathit{\\tau}$', ylabel='$\\mathit{J}$')
s.plot([0, t], [0, 0], color='grey', linestyle='--', linewidth=0.5)
for i, nu in enumerate([0.0, 0.01, 0.1, 1]):
results = get('1d',
initial='opposite',
gamma=gamma, L_trap=L, t=t, nu=nu,
steps=10000, modes=modes, ensembles=1000)
N = L / gamma
times = numpy.linspace(0, results['t'], results['samples'] + 1)
n0m = numpy.array(results['Nminus_mean']) / N
n0e = numpy.array(results['Nminus_std']) / numpy.sqrt(results['ensembles']) / N
n1m = numpy.array(results['Nplus_mean']) / N
n1e = numpy.array(results['Nplus_std']) / numpy.sqrt(results['ensembles']) / N
print(nu, results['errors']['Nminus_mean'])
#ax = s.plot(times, n0m, label="$\\nu = {nu}$".format(nu=nu),
# color=colors[i], linestyle=linestyles[i], dashes=mpl.dash[linestyles[i]])
ax = s.plot(times, (n1m - n0m) / 4, label="$\\nu = {nu}$".format(nu=nu),
color=colors[i], linestyle=linestyles[i], dashes=mpl.dash[linestyles[i]])
#s.plot(times, n0m + n0e, ax[0].get_color() + '--')
#s.plot(times, n0m - n0e, ax[0].get_color() + '--')
s.set_ylim(-0.5, 0.5)
s.set_yticks([-0.5, -0.25, 0, 0.25, 0.5])
#s.legend()
fig.tight_layout()
fig.savefig('1d_varying_coupling' + suffix)
def plot_contents_pic():
modes = 128
L = 80.
gamma = 0.1
N = L ** 2 / gamma
samples = 4
results = get('2d',
initial='opposite',
gamma=gamma, L_trap=L, t=20., nu=0.1,
steps=5000, modes=modes, ensembles=1, samples=samples)
n = numpy.array(results['density']).transpose(1, 0, 2, 3)
print("* contents (2d density)")
print(results['errors']['density'])
times = numpy.linspace(0, results['t'], results['samples'] + 1)
levels = numpy.linspace(-0.5, 0.5, 11)
nt = 2
fig = mpl.figure(width=2./3, aspect=0.9)
s = fig.add_subplot(111)
s.set_xticks([])
s.set_yticks([])
data = (n[0][nt] - n[1][nt]).T / (4 * N / (L**2))
data = numpy.clip(data, -0.5, 0.5)
#data = scipy.ndimage.zoom(data, 0.25)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data,
extent=(-L/2, L/2, -L/2, L/2),
vmin=-0.5, vmax=0.5,
cmap=cmap,
interpolation='none',
aspect=1)
fig.tight_layout()
fig.savefig('contents' + suffix)
def plot_1d_density():
gamma = 0.1
L = 80.
modes = 256
N = L / gamma
results = get('1d',
initial='opposite',
gamma=gamma, L_trap=L, t=75., nu=0.1,
steps=50000, modes=modes, ensembles=1, samples=250)
n = numpy.array(results['density']).transpose(1, 0, 2)
print("* 1d_density")
print(results['errors']['density'])
times = numpy.linspace(0, results['t'], results['samples'] + 1)
fig = mpl.figure()
s = fig.add_subplot(111, xlabel='$\\mathit{\\tau}$', ylabel='$\\mathit{z}$')
data = (n[0] - n[1]).T / (4 * N / L)
data = numpy.clip(data, -0.5, 0.5)
levels = numpy.linspace(-0.5, 0.5, 11)
#data = scipy.ndimage.zoom(data, 0.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data,
extent=(0, times[-1], -L/2, L/2),
vmin=-0.5, vmax=0.5,
cmap=cmap,
interpolation='none',
aspect=(results['t']) / L / 1.6)
fig.colorbar(im,orientation='vertical', shrink=0.8, ticks=levels).set_label('$\\mathit{j}$')
fig.tight_layout()
fig.savefig('1d_density' + suffix)
def plot_2d_density_movie():
modes = 128
L = 80.
gamma = 0.01
nu = 0.01
t = 300.
N = L**2 / gamma
samples = 500
results = get('2d',
initial='opposite',
gamma=gamma,
L_trap=L,
t=t,
nu=nu,
steps=40000,
modes=modes,
ensembles=1,
samples=samples)
n = numpy.array(results['density']).transpose(1, 0, 2, 3)
n_nobs = numpy.array(results['density_nobs']).transpose(1, 0, 2, 3)
print("* 2d_density")
print(results['errors']['density'])
times = numpy.linspace(0, results['t'], results['samples'] + 1)
levels = numpy.linspace(-0.5, 0.5, 11)
comp = 0
for nt in range(samples + 1):
"""
fig = mpl.figure(aspect=0.9)
s = fig.add_subplot(111, xlabel='$\\mathit{y}$', ylabel='$\\mathit{x}$')
data = (n[0][nt] - n[1][nt]).T / (4 * N / (L**2))
data = numpy.clip(data, -0.5, 0.5)
levels = numpy.linspace(-0.5, 0.5, 11)
#data = scipy.ndimage.zoom(data, 0.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.contourf(
data,
extent=(-L/2, L/2, -L/2, L/2),
cmap=cmap,
extend='both',
antialiased=False,
aspect=1,
levels=levels)
t = add_inner_title(s, "$\\tau = %d$" % int(times[nt]), loc=3)
t.patch.set_alpha(0.5)
fig.tight_layout()
fig.savefig('temp/2d_density0_%03d.png' % nt)
"""
kwds = dict(extent=(-L / 2, L / 2, -L / 2, L / 2),
cmap=cmap,
interpolation='none',
aspect=1)
fig = mpl.figure(width=2, aspect=0.35)
grid2 = ImageGrid(
fig,
[0.1, 0.0, 0.8, 1.0],
nrows_ncols=(1, 3),
direction="row",
axes_pad=0.25,
add_all=True,
label_mode="1",
share_all=True,
cbar_location="top",
cbar_mode="each",
cbar_size="7%",
cbar_pad="1%",
)
grid2[0].set_xlabel('$\\mathit{y}$')
grid2[0].set_ylabel('$\\mathit{x}$')
s = grid2[0]
data = (n[0][nt] - n[1][nt]).T / (4 * N / (L**2))
data = numpy.clip(data, -0.5, 0.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data, vmin=-0.5, vmax=0.5, **kwds)
t = add_inner_title(s, "$j$, $\\tau = %d$" % int(times[nt]), loc=3)
t.patch.set_alpha(0.5)
grid2[0].cax.colorbar(im)
s = grid2[1]
data = (n_nobs[0][nt]).T / (N / (L**2))
data = numpy.clip(data, 0, 1.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data, vmin=0, vmax=1.5, **kwds)
t = add_inner_title(s, "$|\\psi_0|^2$", loc=3)
t.patch.set_alpha(0.5)
grid2[1].cax.colorbar(im)
s = grid2[2]
data = (n_nobs[1][nt]).T / (N / (L**2))
data = numpy.clip(data, 0, 1.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data, vmin=0, vmax=1.5, **kwds)
t = add_inner_title(s, "$|\\psi_1|^2$", loc=3)
t.patch.set_alpha(0.5)
grid2[2].cax.colorbar(im)
fig.savefig('temp/2d_density0_%03d.png' % nt, dpi=200)
os.system("./mencoder.sh 'mf://temp/2d_density0_*.png' 2d_density.avi")
def plot_3d_density():
modes = 128
L = 80.
gamma = 0.1
N = L**3 / gamma
samples = 4
results = get('3d',
initial='opposite',
gamma=gamma,
L_trap=L,
t=20.,
nu=0.1,
steps=5000,
modes=modes,
ensembles=1,
samples=samples)
n = numpy.array(results['slice']).transpose(1, 0, 2, 3)
print("* 3d_density")
print(results['errors']['slice'])
times = numpy.linspace(0, results['t'], results['samples'] + 1)
levels = numpy.linspace(-0.5, 0.5, 11)
fig = mpl.figure(width=2, aspect=0.35)
grid2 = ImageGrid(
fig,
[0.1, 0.0, 0.8, 1.0],
nrows_ncols=(1, 3),
direction="row",
axes_pad=0.2,
add_all=True,
label_mode="1",
share_all=True,
cbar_location="right",
cbar_mode="single",
cbar_size="5%",
cbar_pad=0.2,
)
grid2[0].set_xlabel('$\\mathit{y}$')
grid2[0].set_ylabel('$\\mathit{x}$')
for j, nt in enumerate([1, 2, 4]):
#s = fig.add_subplot([311, 321, 331][j], xlabel='$\\mathit{y}$', ylabel='$\\mathit{x}$')
s = grid2[j]
data = (n[0][nt] - n[1][nt]).T / (4 * N / (L**3))
data = numpy.clip(data, -0.5, 0.5)
#data = scipy.ndimage.zoom(data, 0.25)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data,
extent=(-L / 2, L / 2, -L / 2, L / 2),
vmin=-0.5,
vmax=0.5,
cmap=cmap,
interpolation='none',
aspect=1)
label = ['$\\tau=5$', '$\\tau=10$', '$\\tau=20$']
fig.text(0.18 + 0.26 * j, 0.9, label[j])
#fig.colorbar(im,orientation='vertical', shrink=0.6, ticks=levels).set_label('$\\mathit{j}$')
#fig.tight_layout()
grid2[-1].cax.colorbar(im, ticks=levels).set_label_text('$\\mathit{j}$')
grid2[-1].cax.toggle_label(True)
fig.savefig('3d_density' + suffix)
"""
def plot_2d_density_movie():
modes = 128
L = 80.
gamma = 0.01
nu = 0.01
t = 300.
N = L ** 2 / gamma
samples = 500
results = get('2d',
initial='opposite',
gamma=gamma, L_trap=L, t=t, nu=nu,
steps=40000, modes=modes, ensembles=1, samples=samples)
n = numpy.array(results['density']).transpose(1, 0, 2, 3)
n_nobs = numpy.array(results['density_nobs']).transpose(1, 0, 2, 3)
print("* 2d_density")
print(results['errors']['density'])
times = numpy.linspace(0, results['t'], results['samples'] + 1)
levels = numpy.linspace(-0.5, 0.5, 11)
comp = 0
for nt in range(samples+1):
"""
fig = mpl.figure(aspect=0.9)
s = fig.add_subplot(111, xlabel='$\\mathit{y}$', ylabel='$\\mathit{x}$')
data = (n[0][nt] - n[1][nt]).T / (4 * N / (L**2))
data = numpy.clip(data, -0.5, 0.5)
levels = numpy.linspace(-0.5, 0.5, 11)
#data = scipy.ndimage.zoom(data, 0.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.contourf(
data,
extent=(-L/2, L/2, -L/2, L/2),
cmap=cmap,
extend='both',
antialiased=False,
aspect=1,
levels=levels)
t = add_inner_title(s, "$\\tau = %d$" % int(times[nt]), loc=3)
t.patch.set_alpha(0.5)
fig.tight_layout()
fig.savefig('temp/2d_density0_%03d.png' % nt)
"""
kwds = dict(
extent=(-L/2, L/2, -L/2, L/2),
cmap=cmap,
interpolation='none',
aspect=1)
fig = mpl.figure(width=2, aspect=0.35)
grid2 = ImageGrid(fig, [0.1, 0.0, 0.8, 1.0],
nrows_ncols = (1, 3),
direction="row",
axes_pad = 0.25,
add_all=True,
label_mode = "1",
share_all = True,
cbar_location="top",
cbar_mode="each",
cbar_size="7%",
cbar_pad="1%",
)
grid2[0].set_xlabel('$\\mathit{y}$')
grid2[0].set_ylabel('$\\mathit{x}$')
s = grid2[0]
data = (n[0][nt] - n[1][nt]).T / (4 * N / (L**2))
data = numpy.clip(data, -0.5, 0.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data, vmin=-0.5, vmax=0.5, **kwds)
t = add_inner_title(s, "$j$, $\\tau = %d$" % int(times[nt]), loc=3)
t.patch.set_alpha(0.5)
grid2[0].cax.colorbar(im)
s = grid2[1]
data = (n_nobs[0][nt]).T / (N / (L**2))
data = numpy.clip(data, 0, 1.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data, vmin=0, vmax=1.5, **kwds)
t = add_inner_title(s, "$|\\psi_0|^2$", loc=3)
t.patch.set_alpha(0.5)
grid2[1].cax.colorbar(im)
s = grid2[2]
data = (n_nobs[1][nt]).T / (N / (L**2))
data = numpy.clip(data, 0, 1.5)
data = scipy.ndimage.gaussian_filter(data, sigma=0.5, order=0)
im = s.imshow(data, vmin=0, vmax=1.5, **kwds)
t = add_inner_title(s, "$|\\psi_1|^2$", loc=3)
t.patch.set_alpha(0.5)
grid2[2].cax.colorbar(im)
fig.savefig('temp/2d_density0_%03d.png' % nt, dpi=200)
os.system("./mencoder.sh 'mf://temp/2d_density0_*.png' 2d_density.avi")
