def plot_images(dImages, title=None):
""" Based on dictionary dVectors, multiple vectors are
plotted next to each other. """
numbImages = len(dImages)
# init figure
fig = plt.figure(figsize=(15, 4))
grid = axes_grid1.AxesGrid(fig,
111,
nrows_ncols=(1, numbImages),
axes_pad=0.55,
# share_all=True,
cbar_location="right",
cbar_mode="each",
cbar_size="5%",
cbar_pad="2%",)
if title is not None:
fig.suptitle(title)
# plot vectors from dVectors
for idx, (key, relVal) in enumerate(dImages.items()):
im = grid[idx].imshow(relVal, cmap='jet')
grid[idx].set_title(key)
grid[idx].axis('off')
grid.cbar_axes[idx].colorbar(im)
# finalize figure
if title is None:
fig.tight_layout()
else:
fig.tight_layout(rect=[0, 0, 0.95, .95])
def show_and_save_power(people, heatmap, name='power.png'):
fig_power = plt.figure()
# fig_power.suptitle('Moc', fontsize=12)
grid = axes_grid1.AxesGrid(
fig_power,
111,
nrows_ncols=(1, 2),
axes_pad=1,
cbar_location="right",
cbar_mode="each",
cbar_size="5%",
cbar_pad="5%",
)
im1 = grid[0].imshow(people, cmap='plasma', interpolation='nearest')
grid.cbar_axes[0].colorbar(im1)
im2 = grid[1].imshow(heatmap,
cmap='plasma',
interpolation='nearest',
vmax=np.max(people))
grid.cbar_axes[1].colorbar(im2)
plt.savefig(name, bbox_inches='tight', pad_inches=0.0, dpi=500)
plt.show()
return fig_power
W1 = model.layers[0].weights[0].numpy()
B = model.layers[0].weights[1].numpy()
dim = int(W1.shape[1])
W11 = W1[:, 0]
W12 = W1[:, 1]
W11Im = render.vec2im(W11)
W12Im = render.vec2im(W12)
# init figure
fig = plt.figure()
grid = axes_grid1.AxesGrid(
fig,
111,
nrows_ncols=(1, dim),
axes_pad=0.55,
share_all=True,
cbar_location="right",
cbar_mode="each",
cbar_size="5%",
cbar_pad="2%",
)
fig.suptitle('Neural network weights images')
# plot weights
for i in range(dim):
im = grid[i].imshow(render.vec2im(W1[:, i]), cmap='jet')
grid[i].set_title('W1{i}'.format(i=i + 1) +
' (bias = {})'.format(round(B[i], 2)))
grid[i].axis('off')
grid.cbar_axes[i].colorbar(im)
def make_imageGrid(fig, rect=111, **grid_kw):
"""Create a grid using axesgrid1.AxesGrid.
Returns grid
"""
return axes_grid1.AxesGrid(fig, rect=111, **grid_kw)
#A_list.append([(x[1]+0.0001)/sum([i[1] for i in pb]) for x in pb])
# flipped_A_list=zip(*A_list)
# print flipped_A_list
A = np.array([pp for pp in A_list]) #the matrix
p = PrettyTable() #prettyprint matrix
for roar in A:
p.add_row(roar)
print p.get_string(header=False, border=True)
fig = plt.figure()
grid = axes_grid1.AxesGrid(
fig,
111,
nrows_ncols=(1, 2),
axes_pad=0.5,
cbar_location="right",
cbar_mode="each",
cbar_size="15%",
cbar_pad="5%",
)
im0 = grid[0].imshow(A, cmap='gist_yarg', interpolation='nearest')
grid.cbar_axes[0].colorbar(im0)
plt.show()
vals, vecs = eigen(A)
vals[0]
np.set_printoptions(suppress=True) # supress scientific notation on screen
print "Stationary distribution: ", norm(vecs[:, 0])
bigprob = []
def plotFigure(f, opt, canvas):
if opt['axesgrid'] is not None:
fig = plt.figure(figsize=opt['figSize'])
grid = axesgrid.AxesGrid(fig, 111, **opt['axesgrid'])
axs = []
for r in range(opt['nrows']):
for c in range(opt['ncols']):
index = r * opt['ncols'] + c
axs.append(grid[index])
axis = canvas[index]['axis']
if 'projection' in axis and axis['projection'] is not None:
apy.shell.exit(
'Projection needs to be implemented for the axesgrid (plot.py)'
)
elif opt['imagegrid'] is not None:
fig = plt.figure(figsize=opt['figSize'])
if 'nrows_ncols' not in opt['imagegrid']:
opt['imagegrid']['nrows_ncols'] = (opt['nrows'], opt['ncols'])
grid = axesgrid.ImageGrid(fig, 111, **opt['imagegrid'])
axs = []
for r in range(opt['nrows']):
for c in range(opt['ncols']):
index = r * opt['ncols'] + c
axs.append(grid[index])
axis = canvas[index]['axis']
if 'projection' in axis and axis['projection'] is not None:
apy.shell.exit(
'Projection needs to be implemented for the axesgrid (plot.py)'
)
elif opt['gridspec'] is not None:
# Create a figure using gridspec to get a tight layout
fig = plt.figure(figsize=opt['figSize'])
gs = mpl.gridspec.GridSpec(opt['nrows'], opt['ncols'],
**opt['gridspec'])
#if isinstance(opt['gridspec'],dict):
# gs.update(**opt['gridspec'])
axs = []
for r in range(opt['nrows']):
for c in range(opt['ncols']):
index = r * opt['ncols'] + c
if opt['merge'] and opt['merge'][0] == index:
ax = fig.add_subplot(gs[r, :])
axs.append(ax)
elif opt['merge'] and opt['merge'][1] == index:
continue
else:
axs.append(plt.subplot(gs[index]))
axis = canvas[index]['axis']
if 'projection' in axis and axis['projection'] is not None:
apy.shell.exit(
'Projection needs to be implemented for the gridspec (plot.py)'
)
else:
# Create a figure and plot all subplots
fig = plt.figure(figsize=opt['figSize'])
axs = []
for r in range(opt['nrows']):
for c in range(opt['ncols']):
index = r * opt['ncols'] + c
projection = '3d' if canvas[index]['subplot'][3] else None
figure = fig.add_subplot(opt['nrows'],
opt['ncols'],
index + 1,
projection=projection)
axs.append(figure)
for canv in canvas:
spIndex, spRows, spCols, spXYZ = canv['subplot']
plotSubplot(axs[spIndex], opt, canv, f)
if opt['gridspec'] is None and opt['axesgrid'] is None:
plt.tight_layout()
# Create a new directory and save the figure
apy.shell.mkdir(opt['dirResults'], 'u')
plt.savefig(opt['fileName'] % f, bbox_inches='tight')
# Close figure
plt.close(fig)
