def spec_anim(flname, outfl, vmin=1e-8, vmax=1e-2, ntav=2):
global fl, Nt
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pylab as plt
import matplotlib.animation as anim
if (':' in flname):
flname = sync_rf(flname)
En, Fn, kn = spec(flname, ntav)
w, h = plt.figaspect(0.8)
fig, ax = plt.subplots(1, 1, sharey=True, figsize=(w, h))
qd = ax.loglog(kn[En > eps], En[En > eps], 'x-', kn[Fn > eps],
Fn[Fn > eps], '+-')
kr = np.arange(3, 50)
ax.loglog(kn[kr], 1e-4 * kn[kr]**(-3), 'k--')
ax.loglog(kn[kr], 1e-3 * kn[kr]**(-1), 'k--')
ax.legend(['$E(k)$', '$F(k)$'], fontsize=14)
ax.text(kn[kr[-10]], 3e-4 * kn[kr[-10]]**(-3), '$k^{-3}$', fontsize=14)
ax.text(kn[kr[-10]], 3e-3 * kn[kr[-10]]**(-1), '$k^{-1}$', fontsize=14)
ax.axis([kn[1] - eps, kn[-1], vmin, vmax])
ani = anim.FuncAnimation(fig,
update_spec_anim,
interval=0,
frames=Nt - ntav,
blit=True,
fargs=(qd, phires, nres, ntav))
ani.save(outfl, dpi=200, fps=25)
fl.close()
sbp.call(['vlc', outfl])
def plot_Surface_yx_3subplots(X,Y,T1,T2,T3,Titles):
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
fig = plt.figure(figsize=plt.figaspect(0.3)) #3 times as wide as high
ax = fig.add_subplot(1,3,1,projection='3d')
surf = ax.plot_surface(X, Y, T1, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('T [$^o$C]')
ax.set_title(Titles[0])
ax = fig.add_subplot(1,3,2,projection='3d')
surf = ax.plot_surface(X, Y, T2, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('T [$^o$C]')
ax.set_title(Titles[1])
ax = fig.add_subplot(1,3,3,projection='3d')
surf = ax.plot_surface(X, Y, T3, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('T [$^o$C]')
ax.set_title(Titles[2])
return fig, ax
def plot_sphere(grid):
fig = plt.figure(figsize=plt.figaspect(1.))
ax = fig.add_subplot(111, projection='3d')
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_zlabel("Z")
ax.set_aspect('equal')
ax.plot_surface(grid[0], grid[1], grid[2])
#ax.scatter(grid[0],grid[1],grid[2])
#ax.set_axis_off()
plt.savefig('sphere2.png')
plt.show()
def plot_Surface_yx_3subplots(X, Y, T1, T2, T3, Titles):
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
fig = plt.figure(figsize=plt.figaspect(0.3)) #3 times as wide as high
ax = fig.add_subplot(1, 3, 1, projection='3d')
surf = ax.plot_surface(X,
Y,
T1,
rstride=1,
cstride=1,
cmap=cm.coolwarm,
linewidth=0,
antialiased=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('T [$^o$C]')
ax.set_title(Titles[0])
ax = fig.add_subplot(1, 3, 2, projection='3d')
surf = ax.plot_surface(X,
Y,
T2,
rstride=1,
cstride=1,
cmap=cm.coolwarm,
linewidth=0,
antialiased=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('T [$^o$C]')
ax.set_title(Titles[1])
ax = fig.add_subplot(1, 3, 3, projection='3d')
surf = ax.plot_surface(X,
Y,
T3,
rstride=1,
cstride=1,
cmap=cm.coolwarm,
linewidth=0,
antialiased=False)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('T [$^o$C]')
ax.set_title(Titles[2])
return fig, ax
def plotP2_3d():
'''A function that plots the normalized scattering phase function for
diffuse scattering. For comparison see Myneni 1991 Fig 3.
'''
N = 16
g_wt = np.array(gauss_wt[str(N)])
mu_l = np.array(gauss_mu[str(N)])
azi = np.linspace(0., 2. * np.pi, N)
zen = np.linspace(0., np.pi, N)
sun = (110. / 180. * np.pi, 180. / 180. * np.pi) # sun zenith, azimuth
arch = 'e'
refl = 0.07
trans = 0.03
p = np.zeros((N, N))
for i, a in enumerate(azi):
for j, z in enumerate(zen):
p[j, i] = P2((z, a), sun, arch, refl, trans)
fig = plt.figure(figsize=plt.figaspect(0.5))
ax = fig.add_subplot(1, 1, 1, projection='3d')
a, z = np.meshgrid(azi * 180. / np.pi, zen * 180. / np.pi)
surf = ax.plot_surface(a,
z,
p,
rstride=1,
cstride=1,
cmap=cm.coolwarm,
linewidth=0,
antialiased=False)
#ax.set_zlim3d(0.62, 1.94)
fig.colorbar(surf, shrink=0.5, aspect=10)
s = '''sun zenith:%.2f, sun azimuth:%.2f, arch:%s,
refl:%.2f, trans:%.2f''' % \
(sun[0]*180/np.pi,sun[1]*180/np.pi, arch,\
refl, trans)
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
'''plt.text(.5,.5, s, bbox = props, transform=ax.transAxes,\
horizontalalignment='center', verticalalignment='center')'''
ax.set_xlabel("Azimuth angle")
ax.set_ylabel("Zenith angle")
ax.set_zlabel("P")
plt.title("P (Normalized Scattering Phase Function)")
print 'min: %.3f, max: %.3f' % (np.min(p), np.max(p))
plt.show()
def show_image(img, messages=None, fig=None, ax=None, cmap='Gray'):
if not ax or not fig:
w, h = plt.figaspect(img.shape[0] / img.shape[1])
fig = plt.figure(figsize=(w, h))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
bbox_opts = dict(ec='k', fc='k')
ax.clear()
img_axes = ax.imshow(img, cmap=cmaps[cmap])
ax.axis('off')
ax.text(0.03, 0.97, messages['filename'], fontsize=8, color='w', ha='left', va='top', transform=ax.transAxes,
bbox=bbox_opts)
ax.text(0.03, 0.03, messages['text_bottom'], fontsize=8, color='w', ha='left', va='bottom',
transform=ax.transAxes, bbox=bbox_opts)
fig.canvas.draw()
plt.autoscale(tight=True)
fig.show()
return fig, ax, img_axes
def my_contourf(x, y, z, levels=None, cmap=None, **kwargs):
dx = x.max() - x.min()
dy = y.max() - y.min()
w, h = plt.figaspect(float(dy/dx)) # float is must
# No frame, white background, w/h aspect ratio figure
fig = plt.figure(figsize=(w/2,h/2), frameon=False, dpi=150, facecolor='w')
# full figure subplot, no boarder, no axes
ax = fig.add_axes([0,0,1,1], frameon=False, axisbg='w')
# no ticks
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# Default: there are 256 contour levels
if levels is None:
step = (z.max() - z.min()) / 32
levels = np.arange(z.min(), z.max()+step, step)
# Default: colormap is Spectral from matplotlib.cm
if cmap is None:
cmap = plt.cm.Spectral
# actual plot
ax.contourf(x, y, z, levels=levels, cmap=cmap,
antialiased=False, **kwargs)
def anim(flname, outfl, vm=1.0, vmn=1.0, ntav=2):
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pylab as plt
import matplotlib.animation as anim
if (':' in flname):
flname = sync_rf(flname)
fl = h5.File(flname, "r")
phi = fl['fields/phi']
n = fl['fields/n']
w, h = plt.figaspect(0.5)
fig, ax = plt.subplots(1, 2, sharey=True, figsize=(w, h))
qd0 = ax[0].pcolormesh(np.real(phi[0, ].T),
shading='flat',
vmin=-vm,
vmax=vm,
cmap='seismic',
rasterized=True)
qd1 = ax[1].pcolormesh(np.real(n[0, ].T),
shading='flat',
vmin=-vmn,
vmax=vmn,
cmap='seismic',
rasterized=True)
fig.tight_layout()
ax[0].axis('square')
ax[1].axis('square')
Nt = phi.shape[0]
ani = anim.FuncAnimation(fig,
update_anim,
interval=0,
frames=Nt,
blit=True,
fargs=((qd0, qd1), phi, n))
ani.save(outfl, dpi=200, fps=25)
sbp.call(['vlc', outfl])
fl.close()
def plotP2_3d():
'''A function that plots the normalized scattering phase function for
diffuse scattering. For comparison see Myneni 1991 Fig 3.
'''
N = 16
g_wt = np.array(gauss_wt[str(N)])
mu_l = np.array(gauss_mu[str(N)])
azi = np.linspace(0., 2.*np.pi, N)
zen = np.linspace(0., np.pi, N)
sun = (110./180.*np.pi,180./180.*np.pi) # sun zenith, azimuth
arch = 'e'
refl = 0.07
trans = 0.03
p = np.zeros((N,N))
for i, a in enumerate(azi):
for j, z in enumerate(zen):
p[j, i] = P2((z, a), sun, arch, refl, trans)
fig = plt.figure(figsize=plt.figaspect(0.5))
ax = fig.add_subplot(1, 1, 1, projection='3d')
a, z = np.meshgrid(azi*180./np.pi, zen*180./np.pi)
surf = ax.plot_surface(a, z, p, rstride=1, cstride=1, cmap=cm.coolwarm,
linewidth=0, antialiased=False)
#ax.set_zlim3d(0.62, 1.94)
fig.colorbar(surf, shrink=0.5, aspect=10)
s = '''sun zenith:%.2f, sun azimuth:%.2f, arch:%s,
refl:%.2f, trans:%.2f''' % \
(sun[0]*180/np.pi,sun[1]*180/np.pi, arch,\
refl, trans)
props = dict(boxstyle='round',facecolor='wheat',alpha=0.5)
'''plt.text(.5,.5, s, bbox = props, transform=ax.transAxes,\
horizontalalignment='center', verticalalignment='center')'''
ax.set_xlabel("Azimuth angle")
ax.set_ylabel("Zenith angle")
ax.set_zlabel("P")
plt.title("P (Normalized Scattering Phase Function)")
print 'min: %.3f, max: %.3f' %(np.min(p), np.max(p))
plt.show()
'''
ax.plot(seq[:-1,0], seq[:-1,1], color=color,
linewidth=linewidth, linestyle=linestyle)
ax.arrow(seq[-2,0], seq[-2,1], seq[-1,0]-seq[-2,0], seq[-1,1]-seq[-2,1],
head_width=2.5, head_length=2, fc=color, ec=color, linewidth=1)
# In[4]:
logger.info("Initializing train dataset")
train_set, train_loader = data_loader(args, 'train', 'Biker')
# In[77]:
width, height = plt.figaspect(1) # 1.68
fig = plt.figure(figsize=(width,height), dpi=300)
ax = fig.add_subplot(1, 1, 1)
colors = iter(cm.rainbow(np.linspace(0, 1, args.batch_size)))
img = np.array(Image.open('reference.jpg'))
batch = next(iter(train_loader))
obs_seq_list, pred_seq_list = batch[0], batch[1]
ax.imshow(img)
for seq, pred in zip(obs_seq_list, pred_seq_list):
color = next(colors)
plot_seq(ax, img, seq.numpy(), color=color)
plot_seq(ax, img, pred.numpy(), color=color, linestyle=':', linewidth=0.5)
plt.pcolor(theta, phi, scores.reshape((21, 21)))
plt.colorbar()
plt.axis("image")
plt.show()
# %%%%%%%%%%%%%%%%%%%%
#%% Visualize tuning on a sphere!
# %%%%%%%%%%%%%%%%%%%%
from matplotlib import cm, colors
from mpl_toolkits.mplot3d import Axes3D
phi = np.linspace(-np.pi / 2, np.pi / 2, 11)
theta = np.linspace(-np.pi / 2, np.pi / 2, 11)
phi, theta = np.meshgrid(phi, theta)
# The Cartesian coordinates of the unit sphere
x = np.cos(phi) * np.cos(theta)
y = np.cos(phi) * np.sin(theta)
z = np.sin(phi)
#%%
# Calculate the spherical harmonic Y(l,m) and normalize to [0,1]
fcolors = scores.reshape(x.shape)
fmax, fmin = fcolors.max(), fcolors.min()
fcolors = (fcolors - fmin) / (fmax - fmin)
# Set the aspect ratio to 1 so our sphere looks spherical
fig3d = plt.figure(figsize=plt.figaspect(1.))
ax = fig3d.add_subplot(111, projection='3d')
ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=cm.seismic(fcolors))
# Turn off the axis planes
ax.set_axis_off()
plt.show()