def olr_r():
n_colors = 36
olr_map = [cm.gist_yarg(cc) for cc in range(5 * n_colors, 0, -5)]
olr_coltbl = ListedColormap(olr_map, name='orl_coltbl', N=n_colors)
#olr_coltbl.set_bad(color='gray')
olr_coltbl.set_bad(color=olr_coltbl.colors[0])
return olr_coltbl
def olr_r(): n_colors=36 olr_map =[cm.gist_yarg(cc) for cc in range(5*n_colors,0,-5)] olr_coltbl = ListedColormap(olr_map,name = 'orl_coltbl',N=n_colors) #olr_coltbl.set_bad(color='gray') olr_coltbl.set_bad(color= olr_coltbl.colors[0]) return olr_coltbl
def plotPerception(self):
# Let plt do dynamic plots
plt.ion()
# Clear current frame
plt.clf()
# Put the title
plt.title('Title: {}'.format(self.planner.mode))
plt.ylabel('height (in m)')
plt.xlabel('distance (in m)')
# Set the axes limits
axes = plt.gca()
axes.set_xlim([-0.4, 4.0])
axes.set_ylim([-0.1, 4.2])
# For each obstacle, plot a gray scale of the voteArray.
# The gap corresponds to the white, and the obstacle to the black
for obstacle in [self.firstObstacle, self.secondObstacle]:
xObstacle = obstacle.x.estimate * \
np.ones(self.discretizationFactor)
yObstacle = [(i + 0.5) * CEILING / self.discretizationFactor
for i in range(self.discretizationFactor)]
mini, maxi = np.min(obstacle.gapHeightFilter.voteArray), np.max(
obstacle.gapHeightFilter.voteArray)
diff = maxi - mini
new_array = obstacle.gapHeightFilter.voteArray - mini
if diff != 0:
new_array /= float(diff)
plt.scatter(xObstacle,
yObstacle,
c=cm.gist_yarg(new_array),
edgecolor='none',
label="obstacle")
# Plot in green the current best estimation of the gaps positions
xGaps = [self.firstObstacle.x.estimate, self.secondObstacle.x.estimate]
yGaps = [
self.firstObstacle.gapHeightFilter.estimate,
self.secondObstacle.gapHeightFilter.estimate
]
plt.plot(xGaps, yGaps, "go", label="Gaps")
# Plot the position of Flappy in blue
xFlappy = [0.0]
yFlappy = [self.state.y]
plt.plot(xFlappy, yFlappy, "bo", label="Flappy")
plt.show(block=False)
plt.pause(0.01)
def plot_shaded(arr_shaded,
angle=320,
exploded=True,
lims=(0, 60),
save_only=False,
save_name=None):
"""
arr_shaded : np.array
3xN array
angle : int
view_init angle to plot at
exploded : bool
whether or not to explode the points to double their original bounds
i.e. 30 cube -> 60 cube with spaces between everything
save_only : bool
whether to skip showing the output and just save
save_name : str
what name to save the plot as
"""
facecolors = cm.gist_yarg(arr_shaded)
facecolors[:, :, :, -1] = arr_shaded
facecolors = explode(facecolors)
filled = facecolors[:, :, :, -1] != 0
if exploded:
x, y, z = np.indices(np.array(filled.shape) + 1)
else:
x, y, z = expand_coordinates(np.indices(np.array(filled.shape) + 1))
fig = plt.figure(figsize=(5, 5))
ax = fig.gca(projection='3d')
ax.view_init(30, angle)
ax.set_xlim(lims)
ax.set_ylim(lims)
ax.set_zlim(lims)
ax.voxels(x, y, z, filled, facecolors=facecolors)
if save_name:
plt.savefig(save_name)
if not save_only:
plt.show()
plt.close()
def plot_dots(arr_shaded,
angle=320,
lims=(0, 30),
save_only=False,
save_name=None,
dotsize_scale=1,
dotsize_offset=0,
figsize=(4, 4)):
coords = binvox.dense_to_sparse(arr_shaded)
colors = cm.gist_yarg(arr_shaded.ravel())
dot_sizes = arr_shaded.ravel() * dotsize_scale - dotsize_offset
fig = plt.figure(figsize=figsize)
ax = fig.gca(projection='3d')
ax.view_init(30, 30)
ax.scatter3D(coords[0],
coords[1],
coords[2],
c=colors,
s=dot_sizes,
depthshade=False,
marker='.')
plt.show()
def olr(): olr_map =[cm.gist_yarg(cc) for cc in range(0,255,5)] olr_coltbl = ListedColormap(olr_map,name = 'orl_coltbl',N=36) olr_coltbl.set_bad(color= olr_coltbl.colors[-1]) return olr_coltbl
def olr():
olr_map = [cm.gist_yarg(cc) for cc in range(0, 255, 5)]
olr_coltbl = ListedColormap(olr_map, name='orl_coltbl', N=36)
olr_coltbl.set_bad(color=olr_coltbl.colors[-1])
return olr_coltbl
