def plot_graph_3d(G):
from mayavi import mlab
for (node0, node1) in G.edges():
(x0, y0, z0) = G.node[node0]["xyz"]
(x1, y1, z1) = G.node[node1]["xyz"]
mlab.plot3d([x0, x1], [y0, y1], [z0, z1], color=(1, 0, 0), tube_radius=0.0025)
def show3d(): # cube a = list(itertools.product(([0.5,1]),repeat=3)) controls = [np.array(list(b)) for b in a] # random controls = np.random.uniform(0.0,1.0,(5,3)) norms = np.apply_along_axis(np.linalg.norm, 1, controls) controls = np.transpose(controls.T/norms) cone = get_cone(controls,verbose=1) print "Controls:",controls print "Cone:",cone for c in controls: plan3d(c,np.array([0]*3)) mlab.points3d(controls[:,0],controls[:,1],controls[:,2]) for c in cone: l = np.array([c*0.0] + [c*1.0]) x = l[:,0] y = l[:,1] z = l[:,2] mlab.plot3d(x,y,z,line_width=1.0) mlab.show()
def test_stericValue_1(self):
print("m0, m1, m2", self.cg1.steric_value(["m0", "m1", "m2"]))
from_ = np.amin(self.cg1.coords._coordinates)
to_ = np.amax(self.cg1.coords._coordinates)
x, y, z = np.mgrid[from_:to_:4, from_:to_:4, from_:to_:4]
from mayavi import mlab
s = np.zeros_like(x)
for i, j, k in np.ndindex(x.shape):
s[i, j, k] = self.cg1.steric_value(
np.array([x[i, j, k], y[i, j, k], z[i, j, k]]), "r**-3")
#mlab.contour3d(x,y,z,s, contours= [0.5, 1, 2, 5], opacity=0.3)
src = mlab.pipeline.scalar_field(x, y, z, s)
mlab.pipeline.volume(src)
#mlab.pipeline.iso_surface(src, contours=[0.1, ], opacity=0.3)
#mlab.pipeline.iso_surface(src, contours=[0.5, ], opacity=0.7)
#mlab.pipeline.iso_surface(src, contours=[1, ])
colors = {"s": (0, 1, 0), "h": (0, 0, 1), "m": (1, 0, 0), "i": (
1, 1, 0), "f": (0.5, 0.5, 0.5), "t": (0.5, 0.5, 0.5)}
for d in self.cg1.defines:
x = self.cg1.coords[d][0][0], self.cg1.coords[d][1][0]
y = self.cg1.coords[d][0][1], self.cg1.coords[d][1][1]
z = self.cg1.coords[d][0][2], self.cg1.coords[d][1][2]
mlab.plot3d(x, y, z, tube_radius=2, color=colors[d[0]])
mlab.show()
assert False
def plot_matrix(connectmat_file, centers_file, threshold_pct=5, weight_edges=False,
node_scale_factor=2, edge_radius=.5, resolution=8, name_scale_factor=1,
names_file=None, node_indiv_colors=[], highlight_nodes=[], fliplr=False):
"""
Given a connectivity matrix and a (x,y,z) centers file for each region, plot the 3D network
"""
matrix = core.file_reader(connectmat_file)
nodes = core.file_reader(centers_file)
if names_file:
names = core.file_reader(names_file,1)
num_nodes = len(nodes)
edge_thresh_pct = threshold_pct / 100.0
matrix_flat = np.array(matrix).flatten()
edge_thresh = np.sort(matrix_flat)[len(matrix_flat)-int(len(matrix_flat)*edge_thresh_pct)]
matrix = core.file_reader(connectmat_file)
ma = np.array(matrix)
thresh = scipy.stats.scoreatpercentile(ma.ravel(),100-threshold_pct)
ma_thresh = ma*(ma > thresh)
if highlight_nodes:
nr = ma.shape[0]
subset_mat = np.zeros((nr, nr))
for i in highlight_nodes:
subset_mat[i,:] = 1
subset_mat[:,i] = 1
ma_thresh = ma_thresh * subset_mat
if fliplr:
new_nodes = []
for node in nodes:
new_nodes.append([45-node[0],node[1],node[2]]) # HACK
nodes = new_nodes
mlab.figure(bgcolor=(1, 1, 1), size=(400, 400))
for count,(x,y,z) in enumerate(nodes):
if node_indiv_colors:
mlab.points3d(x,y,z, color=colors[node_indiv_colors[count]], scale_factor=node_scale_factor, resolution=resolution)
else:
mlab.points3d(x,y,z, color=(0,1,0), scale_factor=node_scale_factor, resolution=resolution)
if names_file:
width = .025*name_scale_factor*len(names[count])
print width
print names[count]
mlab.text(x, y,names[count], z=z,width=.025*len(names[count]),color=(0,0,0))
for i in range(num_nodes-1):
x0,y0,z0 = nodes[i]
for j in range(i+1, num_nodes):
#if matrix[i][j] > edge_thresh:
if ma_thresh[i][j] > edge_thresh:
x1,y1,z1 = nodes[j]
if weight_edges:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=matrix[i][j]/matrix_flat.max(),
color=(1,1,1))
else:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=edge_radius,
color=(1,1,1))
def draw_pontoons(self, fig, truss, R, freeboard):
from mayavi import mlab
nE = truss.shape[0]
c = (0.5,0,0)
for k in xrange(nE):
if np.any(truss[k,2,:] > freeboard): continue
mlab.plot3d(truss[k,0,:], truss[k,1,:], truss[k,2,:], color=c, tube_radius=R, figure=fig)
def drawArrows(file1, file2, figure, bbox, index, descendant):
"""
Draw an 'arrow' from the cell 'index' to 'descendant'
'descendant' is assumed to be a 1D np.array with size 0, 1 or 2.
"""
#load the center of mass position
if descendant.size > 0 and descendant[0] != 0:
com1 = file1["features"][str(index[0])]["com"][:]
com2 = file2["features"][str(descendant[0])]["com"][:]
#write the cell label as text
mlab.text3d(com1[2]-bbox[2]+1,com1[1]-bbox[1]+1,com1[0]-bbox[0]+1, str(index), color=(1,1,1), figure=figure)
#plot a point where the current cell is
mlab.points3d([com1[2]-bbox[2]+1],[com1[1]-bbox[1]+1],[com1[0]-bbox[0]+1],color=(0,0,1), figure=figure)
#plot a line to the descendant's center
mlab.plot3d([com1[2]-bbox[2]+1,com2[2]-bbox[2]+1],
[com1[1]-bbox[1]+1,com2[1]-bbox[1]+1],
[com1[0]-bbox[0]+1,com2[0]-bbox[0]+1],
tube_radius=0.2, color=(1,0,0), figure=figure)
#plot a second line, if there is a split
if descendant.size == 2:
com3 = file2["features"][str(descendant[1])]["com"][:]
mlab.plot3d([com1[2]-bbox[2]+1,com3[2]-bbox[2]+1],
[com1[1]-bbox[1]+1,com3[1]-bbox[1]+1],
[com1[0]-bbox[0]+1,com3[0]-bbox[0]+1],
tube_radius=0.2, color=(1,0,0), figure=figure)
def draw_column(self, fig, centerline, freeboard, h_section, r_nodes, spacingVec=None, ckIn=None):
from mayavi import mlab
npts = 20
nsection = h_section.size
z_nodes = np.flipud( freeboard - np.r_[0.0, np.cumsum(np.flipud(h_section))] )
th = np.linspace(0, 2*np.pi, npts)
for k in xrange(nsection):
rk = np.linspace(r_nodes[k], r_nodes[k+1], npts)
z = np.linspace(z_nodes[k], z_nodes[k+1], npts)
R, TH = np.meshgrid(rk, th)
Z, _ = np.meshgrid(z, th)
X = R*np.cos(TH) + centerline[0]
Y = R*np.sin(TH) + centerline[1]
# Draw parameters
if ckIn is None:
ck = (0.6,)*3 if np.mod(k,2) == 0 else (0.4,)*3
else:
ck = ckIn
#ax.plot_surface(X, Y, Z, alpha=0.5, color=ck)
mlab.mesh(X, Y, Z, opacity=0.7, color=ck, figure=fig)
if spacingVec is None: continue
z = z_nodes[k] + spacingVec[k]
while z < z_nodes[k+1]:
rk = np.interp(z, z_nodes[k:], r_nodes[k:])
#ax.plot(rk*np.cos(th), rk*np.sin(th), z*np.ones(th.shape), 'r', lw=0.25)
mlab.plot3d(rk*np.cos(th) + centerline[0], rk*np.sin(th) + centerline[1], z*np.ones(th.shape), color=(0.5,0,0), figure=fig)
z += spacingVec[k]
'''
def lorenz_plot(name, N=10, res=2000, step=2, t=10, seed_=120):
# Select initial conditions
seed(seed_)
x0 = -15 + 30 * np.random.rand(N, 3)
# Solve for the trajectories
t = np.linspace(0, t, res)
pts = np.empty((N, res, 3))
for i, x in enumerate(x0):
pts[i] = odeint(lorenz_ode, x, t)
# Select the colors for the different curves.
colors = np.zeros((N, 3))
colors[:,1] = np.linspace(0, 1, N)
colors = map(tuple, colors.tolist())
# Plot the different trajectories.
for x, color in zip(pts, colors):
mlab.plot3d(x[:,0], x[:,1], x[:,2], tube_radius=.2, color=color)
# Position the camera.
mlab.gcf().scene.camera.position = np.array([165.40890060328016, -140.77357847515529, 8.2574865327247622]) / 1.55
mlab.gcf().scene.camera.focal_point = [-1.7792501449584961, -3.6287221908569336, 23.397351264953613]
mlab.gcf().scene.camera.view_up = [-0.078467260964232038, -0.20339450183237351, 0.97594752194015633]
mlab.gcf().scene.camera.clipping_range = [128.64624663718814, 328.22549479639167]
# Save the plot.
mlab.savefig(name)
mlab.clf()
def plotOrs(*ptss):
mlab.figure(34); mlab.clf()
r = 1
phi, theta = mgrid[0:pi:101j, 0:2*pi:101j]
x = r*sin(phi)*cos(theta)
y = r*sin(phi)*sin(theta)
z = r*cos(phi)
mlab.mesh(x,y,z, colormap='gray',opacity=.2)
colors = [(1,0,0),(0,1,0),(0,0,1)]
print len(colors)
print len(ptss)
for (pts,col) in izip(ptss,colors):
print col
ors = normr(pts)
# Create a sphere
x,y,z = ors.T
mlab.points3d(x,y,z,color=col)
mlab.plot3d(x,y,z,color=col,tube_radius=.025)
def save_spline(f, end_label, line_label, size, pnts, slabels):
llist = [] # get list of line indicies
for t in np.where(pnts['label']==end_label)[0]: # find the end points
ijk = pnts['ijk'][t].tolist() # get end point indices
slabels['hit'][tuple(ijk)] = 0 # clear end point hits
next_point(slabels, ijk, end_label, line_label, llist) # walk from the last end point
points = np.zeros((len(llist[0]), len(llist))) # indicies -> point coordinates
for i in range(len(llist)):
#points[:,i] = i2xyz(llist[i], size)
points[0,i] = 8 * 0.069 * (llist[i][0] + 0.5)
points[1,i] = 8 * 0.069 * (llist[i][1] + 0.5)
points[2,i] = 0.798 * (llist[i][2] + 0.5)
s = 15.0 # smoothness parameter
k = 3 # b-spline order
nest = -1 # estimate of number of knots needed (-1 = maximal)
tckp,u = splprep(points, s=s, k=k, nest=-1) # find b-spline knot points
xnew,ynew,znew = splev(np.linspace(0, 1, 30), tckp) # interpolate smooth points
f.write(str(len(xnew))+" ")
for i in range(len(xnew)):
f.write("%2.4f %2.4f %2.4f "% (xnew[i], ynew[i], znew[i]))
f.write('\n')
import mayavi.mlab as mylab
mylab.plot3d(points[0], points[1], points[2], color=(1,0,0))
mylab.plot3d(xnew, ynew, znew, color=(0,1,0))
mylab.show()
return
def show_lidar_with_boxes(pc_velo, objects, calib,
img_fov=False, img_width=None, img_height=None):
''' Show all LiDAR points.
Draw 3d box in LiDAR point cloud (in velo coord system) '''
if 'mlab' not in sys.modules: import mayavi.mlab as mlab
from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
print(('All point num: ', pc_velo.shape[0]))
fig = mlab.figure(figure=None, bgcolor=(0,0,0),
fgcolor=None, engine=None, size=(1000, 500))
if img_fov:
pc_velo = get_lidar_in_image_fov(pc_velo, calib, 0, 0,
img_width, img_height)
print(('FOV point num: ', pc_velo.shape[0]))
draw_lidar(pc_velo, fig=fig)
for obj in objects:
if obj.type=='DontCare':continue
# Draw 3d bounding box
box3d_pts_2d, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
# Draw heading arrow
ori3d_pts_2d, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
x1,y1,z1 = ori3d_pts_3d_velo[0,:]
x2,y2,z2 = ori3d_pts_3d_velo[1,:]
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig)
mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=(0.5,0.5,0.5),
tube_radius=None, line_width=1, figure=fig)
mlab.show(1)
def plot_tree(rrt):
from mayavi import mlab
tree = rrt.tree
leafs = [i for i in tree.nodes() if len(tree.successors(i)) == 0]
accounted = set()
paths = []
for leaf in leafs:
this_node = leaf
this_path = []
paths.append(this_path)
while True:
this_path.append(this_node)
accounted.add(this_node)
p = tree.predecessors(this_node)
if len(p) == 0:
break
assert len(p) == 1
this_node = p[0]
if this_node in accounted:
this_path.append(this_node) #repeat branching node
break
for p in paths:
s = np.array([rrt.tree.node[i]['state'] for i in p])
c = np.array([rrt.tree.node[i]['cost'] for i in p])
mlab.plot3d(s[:,0],s[:,1],s[:,2]/50,c,tube_radius=0.002,colormap='Spectral')
def plotDensity(mToB,coeffs,n=100,plotVals="real",plotBnd=True):
"""Plot boundary density with Mayavi
INPUT:
mToB - Mesh-to-Basis map
coeffs - Coefficients of boundary density
n - Discretisation points per element (default 100)
plotVals - "real", "imag" or "abs" for real/imaginary part or absolute values
(default "real")
plotBnd - Also plot boundary of shape (default True)
"""
from mayavi import mlab
transforms={"real":numpy.real,"imag":numpy.imag,"abs":numpy.abs}
x,f=evalDensity(mToB,coeffs,n)
f=transforms[plotVals](f)
xmin=min(x[0])
xmax=max(x[0])
ymin=min(x[1])
ymax=max(x[1])
zmin=max([-1,min(f)])
zmax=min([1,max(f)])
extent=[xmin,xmax,ymin,ymax,zmin,zmax]
ranges=[xmin,xmax,ymin,ymax,min(f),max(f)]
fig=mlab.figure(bgcolor=(1,1,1))
mlab.plot3d(x[0],x[1],f,extent=extent,color=(0,0,0),tube_radius=None,figure=fig)
if plotBnd:
mlab.plot3d(x[0],x[1],numpy.zeros(x[0].shape),extent=[xmin,xmax,ymin,ymax,0,0],tube_radius=None,figure=fig)
mlab.axes(extent=extent,ranges=ranges,line_width=3.0,figure=fig)
mlab.show()
def show_matches(pc1, pc2, samples1, samples2, matches, tube_radius=0.0003, N=50):
axis = 0
diff = 1
bbx = utils.compute_bounding_box_std(pc1)
pc1[:, axis] -= (bbx[axis][1] - bbx[axis][0])*1.5
samples1[:, axis] -= (bbx[axis][1] - bbx[axis][0])*1.5
pc2[:, axis] += (bbx[axis][1] - bbx[axis][0])*1.5
samples2[:, axis] += (bbx[axis][1] - bbx[axis][0])*1.5
show_pc(pc1, pc2)
assert(samples1.shape[0] == samples2.shape[0] == matches.shape[0])
if matches.shape[0] > N:
samples11 = samples1[matches[0:N,0].astype(dtype=np.int16)]
samples22 = samples2[matches[0:N,1].astype(dtype=np.int16)]
else:
samples11 = samples1[matches[:,0].astype(dtype=np.int16)]
samples22 = samples2[matches[:,1].astype(dtype=np.int16)]
assert (samples11.shape == samples22.shape)
line = np.zeros((2, 3))
i = 0
correct = 0
wrong = 0
for sample1, sample2 in zip(samples11, samples22):
line[0] = sample1
line[1] = sample2
if matches[i,0] == matches[i, 1]:
mlab.plot3d(line[:, 0], line[:, 1], line[:, 2], color=(0, 1, 0), tube_radius=tube_radius)
correct += 1
else:
mlab.plot3d(line[:, 0], line[:, 1], line[:, 2], color=(1, 0, 0), tube_radius=tube_radius)
wrong += 1
i += 1
print 'correct: ', float(correct) / (correct + wrong), ' worng: ', float(wrong) / (correct + wrong)
mlab.show()
return -1
def plotLines(self, X, color=None, size=None, scalars=None):
if self.autoRemove: self.removeLines()
if isinstance(X, list):
Ndim = 3
else:
Ndim = len(X.shape)
if color==None: color=(1,0,0)
#~ data.scene.disable_render = True
#~ view = data.scene.mlab.view()
#~ roll = data.scene.mlab.roll()
#~ move = data.scene.mlab.move()
if Ndim==2:
if scalars==None:
self.lines.append(mlab.plot3d(X[:,0], X[:,1], X[:,2], color=color, tube_radius=size))
else:
self.lines.append(mlab.plot3d(X[:,0], X[:,1], X[:,2], scalars, tube_radius=size))
elif Ndim==3:
for x in X:
if scalars==None:
self.lines.append(mlab.plot3d(x[:,0], x[:,1], x[:,2], color=color, tube_radius=size))
else:
self.lines.append(mlab.plot3d(x[:,0], x[:,1], x[:,2], scalars, tube_radius=size))
def save_poly(fname, lines):
fname += "_poly.txt"
f = open(fname, 'w')
print ' ', fname
for line in lines:
points = calc_points(line)
#spoints = bspline(points, n=points.shape[0], degree=20)
##m = len(points)
m = len(points)/2
if m<4: continue
kx = 3
##if(m>3): kx = 3
##else: kx = m-1
wx = np.ones(len(points))
wx[0] = wx[-1] = 100
tck,u=si.splprep(np.transpose(points),w=wx,k=kx,s=10)
##m /= 2
##if(m<4) : m=4
spoints = np.transpose([si.splev(np.linspace(0,1,m),tck)])
f.write("%2d " % m)
for spoint in spoints:
for vert in spoint:
f.write("%0.2f " % vert)
f.write('\n')
mylab.plot3d(points[:,0], points[:,1], points[:,2], color=(1,0,0))
mylab.plot3d(spoints[:,0], spoints[:,1], spoints[:,2], color=(0,1,0))
f.close()
mylab.show()
return
def plot_eclipse(a,b,c,color=(1,1,1)):
zs=np.linspace(-c*0.95,c*0.95,10)
for z0 in zs:
theta=np.linspace(0,pi*2,num=30)
r=(1-z0**2/c**2)/(np.cos(theta)**2/a**2+np.sin(theta)**2/b**2)
r=r**0.5
mlab.plot3d(r*np.cos(theta),r*np.sin(theta),np.ones_like(r)*z0,color=color,tube_radius=None)
def plot_matrix_metric(connectmat_file,centers_file,threshold_pct,grp_metrics=None,node_metric='bc',
weight_edges=0,node_scale_factor=2,edge_radius=.5,resolution=8,name_scale_factor=1,names_file=0,
red_nodes=None):
"""
Given a connectivity matrix and a (x,y,z) centers file for each region, plot the 3D network
"""
matrix=core.file_reader(connectmat_file)
nodes=core.file_reader(centers_file)
if names_file:
names = core.file_reader(names_file,1)
num_nodes = len(nodes)
edge_thresh_pct = threshold_pct / 100.0
matrix_flat=np.array(matrix).flatten()
edge_thresh=np.sort(matrix_flat)[len(matrix_flat)-int(len(matrix_flat)*edge_thresh_pct)]
if grp_metrics: # regional metrics caclulated elsewhere, loaded in
node_colors = {} # define colors for each metric
node_colors['s'] = (1, 0.733, 0)
node_colors['cc'] = (0.53, 0.81, 0.98)
node_colors['bc'] = (0.5, 1, 0)
node_colors['eloc'] = (1, 0 , 1)
node_colors['ereg'] = (1, 1, 0)
node_metrics={}
metrics = np.array(core.file_reader(grp_metrics))
cols = np.shape(metrics)[1]
for i in range(cols):
colmean = np.mean(metrics[:,i])
colscale = 2 / colmean
metrics[:,i] = metrics[:,i] * colscale # make node mean size 2
node_metrics['s'] = metrics[:,0] # strength
node_metrics['cc'] = metrics[:,1] # clustering coefficient
node_metrics['bc'] = metrics[:,2] # betweenness centrality
node_metrics['eloc'] = metrics[:,3] # local efficiency
node_metrics['ereg'] = metrics[:,4] # regional efficiency
mlab.figure(bgcolor=(1, 1, 1), size=(800, 800))
for count,(x,y,z) in enumerate(nodes):
if grp_metrics:
mlab.points3d(x,y,z, color=node_colors[node_metric],
scale_factor=node_metrics[node_metric][count], resolution=resolution)
else:
mlab.points3d(x,y,z, color=(0,1,0), scale_factor=node_scale_factor, resolution=resolution)
if names_file:
mlab.text(x, y,names[count], z=z,width=.02*name_scale_factor*len(names[count]),color=(0,0,0))
for i in range(num_nodes-1):
x0,y0,z0=nodes[i]
for j in range(i+1, num_nodes):
if matrix[i][j] > edge_thresh:
x1,y1,z1=nodes[j]
if weight_edges:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=matrix[i][j]/matrix_flat.max(),
color=(1,1,1))
else:
mlab.plot3d([x0,x1], [y0,y1], [z0,z1],
tube_radius=edge_radius,
color=(1,1,1))
def plot_3D_spectrum(self, xmin=None, xmax=None, xN=None, ymin=None,
ymax=None, yN=None, trajectory=False, tube_radius=1e-2,
part='imag'):
"""Plot the Riemann sheet structure around the EP.
Parameters:
-----------
xmin, xmax: float
Dimensions in x-direction.
ymin, ymax: float
Dimensions in y-direction.
xN, yN: int
Number of sampling points in x and y direction.
trajectory: bool
Whether to include a projected trajectory of the eigenbasis
coefficients.
part: str
Which function to apply to the eigenvalues before plotting.
tube_radius: float
Trajectory tube thickness.
"""
from mayavi import mlab
X, Y, Z = self.sample_H(xmin, xmax, xN, ymin, ymax, yN)
Z0, Z1 = [Z[..., n] for n in (0, 1)]
def get_min_and_max(*args):
data = np.concatenate(*args)
return data.min(), data.max()
surf_kwargs = dict(colormap='Spectral', mask=np.diff(Z0.real) > 0.015)
mlab.figure(0)
Z_min, Z_max = get_min_and_max([Z0.real, Z1.real])
mlab.surf(X.real, Y.real, Z0.real, vmin=Z_min, vmax=Z_max, **surf_kwargs)
mlab.surf(X.real, Y.real, Z1.real, vmin=Z_min, vmax=Z_max, **surf_kwargs)
mlab.axes(zlabel="Re(E)")
mlab.figure(1)
Z_min, Z_max = get_min_and_max([Z0.imag, Z1.imag])
mlab.mesh(X.real, Y.real, Z0.imag, vmin=Z_min, vmax=Z_max, **surf_kwargs)
mlab.mesh(X.real, Y.real, Z1.imag, vmin=Z_min, vmax=Z_max, **surf_kwargs)
mlab.axes(zlabel="Im(E)")
if trajectory:
x, y = self.get_cycle_parameters(self.t)
_, c1, c2 = self.solve_ODE()
for i, part in enumerate([np.real, np.imag]):
e1, e2 = [part(self.eVals[:, n]) for n in (0, 1)]
z = map_trajectory(c1, c2, e1, e2)
mlab.figure(i)
mlab.plot3d(x, y, z, tube_radius=tube_radius)
mlab.points3d(x[0], y[0], z[0],
# color=line_color,
scale_factor=1e-1,
mode='sphere')
mlab.show()
def draw_line(pt_1, pt_2, color=(1.0, 0.0, 0.0)):
"""Draw a line between two points"""
mlab.plot3d(
np.hstack([pt_1[0], pt_2[0]]),
np.hstack([pt_1[1], pt_2[1]]),
np.hstack([pt_1[2], pt_2[2]]),
color=color
)
def line(self,n,p,c=None,l=1,o=1,w=0.1):
s = np.linspace(0,l)
l = [p+e*n for e in s]
xs = [e[0] for e in l]
ys = [e[1] for e in l]
zs = [e[2] for e in l]
mlab.plot3d(xs,ys,zs,figure=self.fig,color=c,opacity=o)
def find_path_through_point_cloud(xyzs, plotting=False):
xyzs = np.asarray(xyzs).reshape(-1, 3)
S = skeletonize_point_cloud(xyzs)
segs = get_segments(S)
if plotting:
from mayavi import mlab
mlab.figure(1)
mlab.clf()
plot_graph_3d(S)
S, segs = prune_skeleton(S, segs)
if plotting:
from mayavi import mlab
mlab.figure(3)
mlab.clf()
plot_graph_3d(S)
segs3d = [np.array([S.node[i]["xyz"] for i in seg]) for seg in segs]
if plotting:
plot_paths_2d(segs3d)
C = make_cost_matrix(segs3d)
PG = make_path_graph(C, [len(path) for path in segs3d])
(score, nodes) = longest_path_through_segment_graph(PG)
print nodes
total_path = []
for node in nodes[::2]:
if node % 2 == 0:
total_path.extend(segs[node // 2])
else:
total_path.extend(segs[node // 2][::-1])
total_path_3d = remove_duplicate_rows(np.array([S.node[i]["xyz"] for i in total_path]))
total_path_3d = unif_resample(total_path_3d, seg_len=0.02, tol=0.003) # tolerance of 1mm
total_path_3d = unif_resample(total_path_3d, seg_len=0.02, tol=0.003) # tolerance of 1mm
# total_path_3d = unif_resample(total_path_3d, seg_len=.02, tol=.003) # tolerance of 1mm
if plotting:
mlab.figure(2)
mlab.clf()
for seg in segs3d:
x, y, z = np.array(seg).T
mlab.plot3d(x, y, z, color=(0, 1, 0), tube_radius=0.001)
x, y, z = np.array(total_path_3d).T
mlab.plot3d(x, y, z, color=(1, 0, 0), tube_radius=0.01, opacity=0.2)
x, y, z = np.array(xyzs).reshape(-1, 3).T
mlab.points3d(x, y, z, scale_factor=0.01, opacity=0.1, color=(0, 0, 1))
return total_path_3d
def _show3(self, color_range=True):
X = self[["x", "y", "z"]].values
if color_range:
t = np.linspace(0, 100, len(X))
mlab.plot3d(X[:, 0], X[:, 1], X[:, 2], -t, colormap="gist_gray")
else:
mlab.plot3d(X[:, 0], X[:, 1], X[:, 2], color=(0, 0, 0))
def _show3(self,color_range=True,linewidth=0.01):
X=self[['x','y','z']].values
if color_range:
t = np.linspace(0, 100, len(X))
mlab.plot3d(X[:,0],X[:,1],X[:,2],-t,colormap='gist_gray',tube_radius=linewidth)
else:
mlab.plot3d(X[:,0],X[:,1],X[:,2],color=(0,0,0),tube_radius=linewidth)
def _show3(self,color_range=True):
X=self[['x','y','z']].values
if color_range:
t = np.linspace(0, 100, len(X))
mlab.plot3d(X[:,0],X[:,1],X[:,2],-t,colormap='gist_gray')
else:
mlab.plot3d(X[:,0],X[:,1],X[:,2],color=(0,0,0))
def plot3d():
num = np.pi / 1000
pts = np.arange(0, 2 * np.pi + num, num)
x = np.cos(pts) * (1 + np.cos(pts * 6))
y = np.sin(pts) * (1 + np.cos(pts * 6))
z = np.sin(pts * 6 / 11)
mlab.plot3d(x, y, z)
mlab.savefig("plot3d.png", size=png_size)
mlab.clf()
def plot_tracks(trk_file):
"""
Plot streamlines from a DSI Studio .txt tracks file
"""
trks = core.file_reader(trk_file)
ta = np.array(trks)
for t in ta:
tl = len(t)
trs = np.reshape(t, (tl/3,3))
mlab.plot3d(trs[:,0], trs[:,1], trs[:,2])
def plotvMF(mu,tau,pi,figm,color=(1.,0.0,0.0)): X,Y,Z = S2.mesh(1.) q = np.c_[X.ravel(),Y.ravel(),Z.ravel()].T pdf = pi*np.exp(mu.T.dot(q)*tau)*tau/(4*np.pi*np.sinh(tau)) +1. X*=np.reshape(pdf,X.shape) Y*=np.reshape(pdf,X.shape) Z*=np.reshape(pdf,X.shape) mlab.mesh(X,Y,Z,color=color,opacity=0.3,figure=figm,mode='2dtriangle') mlab.plot3d([0,mu[0]],[0,mu[1]],[0,mu[2]],color=color,opacity=0.9,figure=figm)
def view_bonds(mol,doshow=True):
from mayavi import mlab
for i,j in mol.bonds():
mlab.plot3d([mol.atoms[i].r[0], mol.atoms[j].r[0]],
[mol.atoms[i].r[1], mol.atoms[j].r[1]],
[mol.atoms[i].r[2], mol.atoms[j].r[2]],
tube_radius=0.2,
tube_sides=20)
if doshow: mlab.show()
return
def plot_trajectory(td, trial_index, state_indices):
prob = integrator_chains.Problem.loadJSONdict(td['trials'][trial_index]['problem_instance'])
state_dim = prob.output_dim*prob.number_integrators
try:
traj = np.array(td['trials'][trial_index]['trajectory'])
except KeyError:
print('Trial '+str(trial_index)+' does not have an associated trajectory.')
sys.exit(-1)
if state_indices.strip() == '':
state_indices = []
else:
state_index_bounds = (2+prob.output_dim, 2+prob.output_dim+state_dim-1)
state_indices = _get_index_list(state_indices, state_index_bounds)
if state_indices is None:
print('One of the requested state indices is out of bounds, '
'[0, '+str(state_index_bounds[1]-state_index_bounds[0])+']')
sys.exit(-1)
if len(state_indices) == 0:
print('Try `--state` option to select indices for plotting.')
sys.exit(0)
x = traj[:,state_indices]
if len(state_indices) == 2:
import matplotlib.pyplot as plt
plt.plot(x.T[0], x.T[1], 'r.-')
plt.plot(x.T[0][0], x.T[1][0], 'r*')
plt.show()
elif len(state_indices) == 3:
from mayavi import mlab
# mlab.plot3d() of Mayavi fails if there are repetitions of rows,
# so we first manually delete any.
y = np.zeros((x.shape[0], 3))
y[0] = x[0]
i = 1
for j in range(1, x.shape[0]):
if (x[j] != x[j-1]).any():
y[i] = x[j]
i += 1
y = y[:(i+1),:]
mlab.plot3d(y.T[0], y.T[1], y.T[2],
color=(1.0, 0.0, 0.0))
mlab.points3d(y.T[0][0], y.T[1][0], y.T[2][0],
color=(1.0, 0.0, 0.0), mode='sphere', opacity=0.5, scale_factor=0.2)
mlab.show()
else:
print('Plotting of trajectories requires selection of 2 or 3 indices.'
'\nTry `--state` option to select indices for plotting.')
sys.exit(0)
def show_mpl_heatmap(self, **kwargs): # pylint: disable=invalid-name,too-many-arguments,too-many-locals,too-many-statements,too-many-branches
"""
Show a heatmap of the trajectory with matplotlib.
"""
import numpy as np
from scipy import stats
try:
from mayavi import mlab
except ImportError:
raise ImportError(
'Unable to import the mayavi package, that is required to'
'use the plotting feature you requested. '
'Please install it first and then call this command again '
'(note that the installation of mayavi is quite complicated '
'and requires that you already installed the python numpy '
'package, as well as the vtk package'
)
from ase.data.colors import jmol_colors
from ase.data import atomic_numbers
# pylint: disable=invalid-name
def collapse_into_unit_cell(point, cell):
"""
Applies linear transformation to coordinate system based on crystal
lattice, vectors. The inverse of that inverse transformation matrix with the
point given results in the point being given as a multiples of lattice vectors
Than take the integer of the rows to find how many times you have to shift
the point back"""
invcell = np.matrix(cell).T.I
# point in crystal coordinates
points_in_crystal = np.dot(invcell, point).tolist()[0]
#point collapsed into unit cell
points_in_unit_cell = [i % 1 for i in points_in_crystal]
return np.dot(cell.T, points_in_unit_cell).tolist()
elements = kwargs.pop('elements', None)
mintime = kwargs.pop('mintime', None)
maxtime = kwargs.pop('maxtime', None)
stepsize = kwargs.pop('stepsize', None) or 1
contours = np.array(kwargs.pop('contours', None) or (0.1, 0.5))
sampling_stepsize = int(kwargs.pop('sampling_stepsize', None) or 0)
times = self.get_times()
if mintime is None:
minindex = 0
else:
minindex = np.argmax(times > mintime)
if maxtime is None:
maxindex = len(times)
else:
maxindex = np.argmin(times < maxtime)
positions = self.get_positions()[minindex:maxindex:stepsize]
try:
if self.get_attribute('units|positions') in ('bohr', 'atomic'):
bohr_to_ang = 0.52917720859
positions *= bohr_to_ang
except KeyError:
pass
symbols = self.symbols
if elements is None:
elements = set(symbols)
cells = self.get_cells()
if cells is None:
raise ValueError('No cell parameters have been supplied for TrajectoryData')
else:
cell = np.array(cells[0])
storage_dict = {s: {} for s in elements}
for ele in elements:
storage_dict[ele] = [np.array([]), np.array([]), np.array([])]
for iat, ele in enumerate(symbols):
if ele in elements:
for idim in range(3):
storage_dict[ele][idim] = np.concatenate(
(storage_dict[ele][idim], positions[:, iat, idim].flatten())
)
for ele in elements:
storage_dict[ele] = np.array(storage_dict[ele]).T
storage_dict[ele] = np.array([collapse_into_unit_cell(pos, cell) for pos in storage_dict[ele]]).T
white = (1, 1, 1)
mlab.figure(bgcolor=white, size=(1080, 720))
for i1, a in enumerate(cell):
i2 = (i1 + 1) % 3
i3 = (i1 + 2) % 3
for b in [np.zeros(3), cell[i2]]:
for c in [np.zeros(3), cell[i3]]:
p1 = b + c
p2 = p1 + a
mlab.plot3d([p1[0], p2[0]], [p1[1], p2[1]], [p1[2], p2[2]], tube_radius=0.1)
for ele, data in storage_dict.items():
kde = stats.gaussian_kde(data, bw_method=0.15)
_x = data[0, :]
_y = data[1, :]
_z = data[2, :]
xmin, ymin, zmin = _x.min(), _y.min(), _z.min()
xmax, ymax, zmax = _x.max(), _y.max(), _z.max()
_xi, _yi, _zi = np.mgrid[xmin:xmax:60j, ymin:ymax:30j, zmin:zmax:30j] # pylint: disable=invalid-slice-index
coords = np.vstack([item.ravel() for item in [_xi, _yi, _zi]])
density = kde(coords).reshape(_xi.shape)
# Plot scatter with mayavi
#~ figure = mlab.figure('DensityPlot')
grid = mlab.pipeline.scalar_field(_xi, _yi, _zi, density)
#~ min = density.min()
maxdens = density.max()
#~ mlab.pipeline.volume(grid, vmin=min, vmax=min + .5*(max-min))
surf = mlab.pipeline.iso_surface(grid, opacity=0.5, colormap='cool', contours=(maxdens * contours).tolist())
lut = surf.module_manager.scalar_lut_manager.lut.table.to_array()
# The lut is a 255x4 array, with the columns representing RGBA
# (red, green, blue, alpha) coded with integers going from 0 to 255.
# We modify the alpha channel to add a transparency gradient
lut[:, -1] = np.linspace(100, 255, 256)
lut[:, 0:3] = 255 * jmol_colors[atomic_numbers[ele]]
# and finally we put this LUT back in the surface object. We could have
# added any 255*4 array rather than modifying an existing LUT.
surf.module_manager.scalar_lut_manager.lut.table = lut
if sampling_stepsize > 0:
mlab.points3d(
_x[::sampling_stepsize],
_y[::sampling_stepsize],
_z[::sampling_stepsize],
color=tuple(jmol_colors[atomic_numbers[ele]].tolist()),
scale_mode='none',
scale_factor=0.3,
opacity=0.3
)
mlab.view(azimuth=155, elevation=70, distance='auto')
mlab.show()
def draw_gt_boxes3d(gt_boxes3d,
fig,
color=(1, 1, 1),
line_width=1,
draw_text=True,
text_scale=(1, 1, 1),
color_list=None):
# gt_boxes3d=np.array([[4.8119, -1.9323, 39.7667],
# [3.0897, -1.9323, 39.6927],
# [3.0897, -0.1503, 39.6927],
# [4.8119, -0.1503, 39.7667],
# [4.6423, -1.9323, 43.7183],
# [2.9200, -1.9323, 43.6443],
# [2.9200, -0.1503, 43.6443],
# [4.6423, -0.1503, 43.7183]])
''' Draw 3D bounding boxes
Args:
gt_boxes3d: numpy array (n,8,3) for XYZs of the box corners
fig: mayavi figure handler
color: RGB value tuple in range (0,1), box line color
line_width: box line width
draw_text: boolean, if true, write box indices beside boxes
text_scale: three number tuple
color_list: a list of RGB tuple, if not None, overwrite color.
Returns:
fig: updated fig
'''
# gt_boxes3d=np.expand_dims(gt_boxes3d, 0)
num = len(gt_boxes3d)
#####33
# gt_boxes3d=np.swapaxes(gt_boxes3d, 0, 1)
# temp = np.copy(gt_boxes3d[:, 0])
# gt_boxes3d[:, 0] = gt_boxes3d[:, 1]
# gt_boxes3d[:, 1] = temp
#################################333
for ar in gt_boxes3d:
oldar = ar
temp = np.copy(ar[:, 0])
ar[:, 0] = ar[:, 2]
ar[:, 2] = temp
# ar[:, 1] = temp
# temp = np.copy(ar[:, 2])
# ar[:, 2] = ar[:, 1]
# ar[:, 1] = temp
############3
for n in range(num):
b = gt_boxes3d[n]
if color_list is not None:
color = color_list[n]
if draw_text:
mlab.text3d(b[4, 0],
b[4, 1],
b[4, 2],
'%d' % n,
scale=text_scale,
color=color,
figure=fig)
for k in range(0, 4):
# http://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html
i, j = k, (k + 1) % 4
mlab.plot3d([b[i, 0], b[j, 0]], [b[i, 1], b[j, 1]],
[b[i, 2], b[j, 2]],
color=color,
tube_radius=None,
line_width=line_width,
figure=fig)
i, j = k + 4, (k + 1) % 4 + 4
mlab.plot3d([b[i, 0], b[j, 0]], [b[i, 1], b[j, 1]],
[b[i, 2], b[j, 2]],
color=color,
tube_radius=None,
line_width=line_width,
figure=fig)
i, j = k, k + 4
mlab.plot3d([b[i, 0], b[j, 0]], [b[i, 1], b[j, 1]],
[b[i, 2], b[j, 2]],
color=color,
tube_radius=None,
line_width=line_width,
figure=fig)
# mlab.show()
# mlab.view(azimuth=180, elevation=70, focalpoint=[ 12.0909996 , -1.04700089, -2.03249991], distance=62.0, figure=fig)
return fig
color=None,
mode='point',
colormap='gnuplot',
scale_factor=1,
figure=fig)
# draw origin
mlab.points3d(0, 0, 0, color=(1, 1, 1), mode='sphere', scale_factor=0.2)
# draw axis
axes = np.array([
[2., 0., 0., 0.],
[0., 2., 0., 0.],
[0., 0., 2., 0.],
],
dtype=np.float64)
mlab.plot3d([0, axes[0, 0]], [0, axes[0, 1]], [0, axes[0, 2]],
color=(1, 0, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0, axes[1, 0]], [0, axes[1, 1]], [0, axes[1, 2]],
color=(0, 1, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0, axes[2, 0]], [0, axes[2, 1]], [0, axes[2, 2]],
color=(0, 0, 1),
tube_radius=None,
figure=fig)
mlab.view(azimuth=180,
elevation=70,
focalpoint=[12.0909996, -1.04700089, -2.03249991],
distance=62.0,
figure=fig)
##########################################################################################################
def show_lidar_with_depth(
pc_velo,
objects,
calib,
fig,
img_fov=False,
img_width=None,
img_height=None,
objects_pred=None,
depth=None,
cam_img=None,
constraint_box=False,
pc_label=False,
save=False,
):
""" Show all LiDAR points.
Draw 3d box in LiDAR point cloud (in velo coord system) """
if "mlab" not in sys.modules:
import mayavi.mlab as mlab
from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
print(("All point num: ", pc_velo.shape[0]))
if img_fov:
pc_velo_index = get_lidar_index_in_image_fov(pc_velo[:, :3], calib, 0,
0, img_width, img_height)
pc_velo = pc_velo[pc_velo_index, :]
print(("FOV point num: ", pc_velo.shape))
print("pc_velo", pc_velo.shape)
draw_lidar(pc_velo, fig=fig, pc_label=pc_label)
# Draw depth
if depth is not None:
depth_pc_velo = calib.project_depth_to_velo(depth, constraint_box)
indensity = np.ones((depth_pc_velo.shape[0], 1)) * 0.5
depth_pc_velo = np.hstack((depth_pc_velo, indensity))
print("depth_pc_velo:", depth_pc_velo.shape)
print("depth_pc_velo:", type(depth_pc_velo))
print(depth_pc_velo[:5])
draw_lidar(depth_pc_velo, fig=fig, pts_color=(1, 1, 1))
if save:
data_idx = 0
vely_dir = "data/object/training/depth_pc"
save_filename = os.path.join(vely_dir, "%06d.bin" % (data_idx))
print(save_filename)
# np.save(save_filename+".npy", np.array(depth_pc_velo))
depth_pc_velo = depth_pc_velo.astype(np.float32)
depth_pc_velo.tofile(save_filename)
color = (0, 1, 0)
for obj in objects:
if obj.type == "DontCare":
continue
# Draw 3d bounding box
_, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
print("box3d_pts_3d_velo:")
print(box3d_pts_3d_velo)
draw_gt_boxes3d([box3d_pts_3d_velo],
fig=fig,
color=color,
label=obj.type)
if objects_pred is not None:
color = (1, 0, 0)
for obj in objects_pred:
if obj.type == "DontCare":
continue
# Draw 3d bounding box
_, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
print("box3d_pts_3d_velo:")
print(box3d_pts_3d_velo)
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
# Draw heading arrow
_, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
x1, y1, z1 = ori3d_pts_3d_velo[0, :]
x2, y2, z2 = ori3d_pts_3d_velo[1, :]
mlab.plot3d(
[x1, x2],
[y1, y2],
[z1, z2],
color=color,
tube_radius=None,
line_width=1,
figure=fig,
)
mlab.show(1)
def plot3d(data,
xvar,
yvar,
zvar,
fvar,
ival='Dive',
day=None,
filament=None,
cmap='RdYlBu',
rev_cmap=True,
alpha=1,
cmin=None,
cmax=None,
title='',
temp=None,
chl=None,
intdat=None):
"""
Create 3D curtain plot
Parameters
----------
data : dataframe
Pandas dataframe containing the needed data.
xvar : character
Name of the x axis variable, in the data dataframe (normally lon - or however longitude is name in the dataframe) .
yvar : character
Name of the y axis variable, in the data dataframe (normally lat - or however latitude is name in the dataframe) .
zvar : character
Name of the z axis variable, in the data dataframe (normally a rounded Depth or Pressure value).
fvar : character
Name of the variable used for the filling (for example temperature, salinity, Sv1000 or whtaever you want it to be).
ival : character, optional
This is a grouping value, if not using 'Dive', make sure to define it. The default is 'Dive'.
day : dataframe, optional
Reference to a dataframe containing day/night information, if defined, this will put a bar on top of the plot. The default is None.
filament : dataframe, optional
This will put a bar below the plot, for example to delimit certain regions. The default is None.
cmap : colormap, optional
Character describing a valid colormap. The default is 'RdYlBu'.
rev_cmap : boolean, optional
Define if the colormap should be reversed. The default is True.
alpha : float, optional
Sets the transparenct. The default is 1.
cmin : float, optional
Define the color scale minimum. The default is None.
cmax : float, optional
Define the color scale maximum. The default is None.
title : character, optional
Add a title to the plot. The default is ''.
temp : TYPE, optional
Add temperature background map. The default is None.
chl : TYPE, optional
Add chlorophyll background map. The default is None.
intdat : TYPE, optional
DESCRIPTION. The default is None.
Returns
-------
None.
"""
ne = 0
#bring x,y,z data into shape, create 3 pivot tables
x = pd.pivot(data=data.reset_index(),
index=ival,
columns=zvar,
values=xvar)
y = pd.pivot(data=data.reset_index(),
index=ival,
columns=zvar,
values=yvar)
z = -pd.pivot(
data=data.reset_index(), index=ival, columns=zvar, values=zvar)
#create pivot table with fill value, where ival become the index
fill = pd.pivot(data=data.reset_index(),
index=ival,
columns=zvar,
values=fvar)
#define cmax and cmin if they are not given
if cmax is None:
cmax = np.ceil(np.nanmax(fill))
if cmin is None:
cmin = np.floor(np.nanmin(fill))
#create a mayavi figure
fig = mlab.figure(size = (2048,1526),\
bgcolor = (1,1,1), fgcolor = (0.5, 0.5, 0.5), figure=title)
#create a mesh from the x,y,z and fill info
svp = mlab.mesh(x,
y,
z,
scalars=fill,
colormap=cmap,
opacity=alpha,
extent=[0.2, 1, 0.1, 1, 0.22, .69],
figure=fig,
representation='surface')
#reverse color map if needed
if rev_cmap == True:
svp.module_manager.scalar_lut_manager.reverse_lut = True
#set color range
svp.module_manager.scalar_lut_manager.data_range = (cmin, cmax)
#svp.module_manager.scalar_lut_manager.use_below_range_color = True
#svp.module_manager.scalar_lut_manager.below_range_color = (0,0,0,0)
#svp.module_manager.scalar_lut_manager.lut.number_of_colors = 64
#add colorbar
cb = mlab.colorbar(object=svp, title=title, orientation='vertical')
cb.scalar_bar.unconstrained_font_size = True
cb.label_text_property.font_size = 20
cb.title_text_property.font_size = 46
#make plot window
mlab.gcf()
#set plot limits
xmin = x.values[np.isfinite(x.values)].min()
xmax = x.values[np.isfinite(x.values)].max()
ymin = y.values[np.isfinite(y.values)].min()
ymax = y.values[np.isfinite(y.values)].max()
zmin = z.values[np.isfinite(z.values)].min()
zmax = z.values[np.isfinite(z.values)].max()
#change the aesthetics
ax = mlab.axes(nb_labels=5,
xlabel='Longitude',
ylabel='Latitude',
zlabel='Depth [m]',
ranges=[xmin, xmax, ymin, ymax, zmin, zmax])
ax.axes.font_factor = 0.5
ax.axes.label_format = ' %6.2f'
#add day night at top
if day is not None:
lut = np.zeros((2, 4))
lut[1, :] = [0, 0, 0, 100]
lut[0, :] = [100, 100, 0, 100]
p3d = mlab.plot3d(day.lon.values,
day.lat.values,
day['depth'].values,
day['numSun'].values,
tube_radius=0.001,
figure=fig,
opacity=1,
extent=[0.2, 1, 0.1, 1, 0.695, 0.71])
p3d.module_manager.scalar_lut_manager.lut.number_of_colors = 2
p3d.module_manager.scalar_lut_manager.lut.table = lut
#add line at bottom of plot
if filament is not None:
mlab.plot3d(filament.lon.values,
filament.lat.values,
filament['fildep'].values,
filament['numfil'].values,
tube_radius=0.01,
figure=fig,
opacity=1,
extent=[0.2, 1, 0.1, 1, 0.19, 0.21],
colormap='Set1')
#add chl background map
if chl is not None:
if ne == 0:
zl = 0.2
else:
zl = 0.72
ne += 1
x1 = chl.longitude.values.min()
x2 = chl.longitude.values.max()
y1 = chl.latitude.values.min()
y2 = chl.latitude.values.max()
dx = xmax - xmin
dy = ymax - ymin
xl1 = -(xmin - x1) / dx
xl2 = -(xmax - x2) / dx
yl1 = -(ymin - y1) / dy
yl2 = -(ymax - y2) / dy
zs = pd.pivot_table(data=chl,
index='latitude',
columns='longitude',
values='chlorophyll').transpose()
#cmap2 = matplotlib.cm.get_cmap()
#cmap2.set_bad('white',1.)
sat1 = mlab.imshow(
zs,
figure=fig,
extent=[0.2 + xl1, 1 + xl2, 0.1 + yl1, 1 + yl2, zl, zl],
opacity=0.8,
colormap=cmap,
interpolate=False)
sat1.module_manager.scalar_lut_manager.reverse_lut = True
sat1.module_manager.scalar_lut_manager.data_range = (0, 10)
sat1.module_manager.scalar_lut_manager.lut.nan_color = (0, 0, 0, 0)
cb2 = mlab.colorbar(object=sat1,
title='CHla',
orientation='horizontal')
cb2.scalar_bar.unconstrained_font_size = True
cb2.label_text_property.font_size = 20
cb2.title_text_property.font_size = 46
#add temp backgorund map
if temp is not None:
if ne == 0:
zl = 0.2
else:
zl = 0.72
ne += 1
x1 = temp.longitude.values.min()
x2 = temp.longitude.values.max()
y1 = temp.latitude.values.min()
y2 = temp.latitude.values.max()
dx = xmax - xmin
dy = ymax - ymin
xl1 = -(xmin - x1) / dx
xl2 = -(xmax - x2) / dx
yl1 = -(ymin - y1) / dy
yl2 = -(ymax - y2) / dy
zs2 = pd.pivot_table(data=temp,
index='latitude',
columns='longitude',
values='sst').transpose()
sat2 = mlab.imshow(
zs2,
figure=fig,
extent=[0.2 + xl1, 1 + xl2, 0.1 + yl1, 1 + yl2, zl, zl],
colormap=cmap,
opacity=0.8,
interpolate=False)
sat2.module_manager.scalar_lut_manager.reverse_lut = True
sat2.module_manager.scalar_lut_manager.lut.nan_color = (0, 0, 0, 0)
#sat2.module_manager.scalar_lut_manager.data_range = (10, 25)
#ignore this, as this was specific for the 38 kHz map for calibration
if intdat is not None:
x1 = intdat.Lon_S.values.min()
x2 = intdat.Lon_S.values.max()
y1 = intdat.Lat_S.values.min()
y2 = intdat.Lat_S.values.max()
dx = xmax - xmin
dy = ymax - ymin
xi1 = -(xmin - x1) / dx
xi2 = -(xmin - x2) / dx
yi1 = -(ymin - y1) / dy
yi2 = -(ymin - y2) / dy
xi = pd.pivot(data=intdat,
index='Ping_S',
columns='Depth_mean',
values='Lon_S')
yi = pd.pivot(data=intdat,
index='Ping_S',
columns='Depth_mean',
values='Lat_S')
zi = pd.pivot(data=intdat,
index='Ping_S',
columns='Depth_mean',
values='Depth_mean')
filli = pd.pivot(data=intdat,
index='Ping_S',
columns='Depth_mean',
values='Sv38')
ivp = mlab.mesh(xi,
yi,
zi,
scalars=filli,
colormap=cmap,
opacity=alpha,
extent=[xi1, xi2, yi1, yi2, 0.2, .7],
figure=fig,
representation='surface')
if rev_cmap == True:
ivp.module_manager.scalar_lut_manager.reverse_lut = True
ivp.module_manager.scalar_lut_manager.data_range = (cmin, cmax)
#set initial view
mlab.view(90, 110, -2)
BEND = pi/6
TWIST = 2*pi
thread = Thread()
xyz0 = thread.getXYZ()
#mlab.plot3d(x,y,z,tube_radius=.4,color=(1,1,1))
mlab.figure(1); mlab.clf()
T = 16
for (t,cons) in enumerate(twists.jumprope2(thread,pi)):
#twists.twist_ctrl(thread,2*np.pi/T,0)
thread.setConstraints(cons)
x,y,z = thread.getXYZ()
mlab.plot3d(x,y,z,tube_radius=.1,color=(1-t/T,0,t/T))
xyz = np.array([x,y,z])
print twists.opt_rot(xyz,xyz0)
#x,y,z = tips
#mlab.points3d(x,y,z,color=(1,1,1))
for (t,cons) in enumerate(twists.jumprope2(thread,pi)):
#twists.twist_ctrl(thread,2*np.pi/T,0)
thread.setConstraints(cons)
x,y,z = thread.getXYZ()
mlab.plot3d(x,y,z,tube_radius=.1,color=(0,t/T,1-t/T))
import numpy as np
from mayavi import mlab
def lorenz_ode(state, t):
x, y, z = state
return np.array(lorenz(x, y, z))
t = np.linspace(0., 50., 2000)
r = odeint(lorenz_ode, (10., 50., 50.), t)
#x, y, z = r[:,0], r[:,1], r[:,2]
x, y, z = r.T
mlab.plot3d(x, y, z, t, tube_radius=None)
def draw(p1, p2, front=1):
mlab.plot3d([p1[0], p2[0]], [p1[1], p2[1]], [p1[2], p2[2]],
color=in_color[names.index(lab) * 2 + front],
tube_radius=None,
line_width=2,
figure=fig)
def add_arrow(self,
coords,
map_surface=None,
tube_radius=3.0,
color="white",
alpha=1,
name=None,
hemi=None):
"""
Add an arrow across the brain between two co-ordinates
:param coords: list of co-ordinates or (n, 3) numpy array. Co-ordinate
space must match that of the underlying MRI image
:param tube_radius: float
controls the size of the arrow
:param color: matplotlib color code
HTML name, RGB tuple or hex code
:param alpha: float in [0, 1]
opacity of coordinate spheres
:param name: str
internal name to use
:param hemi: str | None
If None, assumed to belong to the hemisphere being shown.
If two hemispheresa are being shown, an error will be thrown
"""
hemi = self._check_hemi(hemi)
if map_surface is None:
foci_vtxs = surfer.utils.find_closest_vertices(
self.geo[hemi].coords, coords)
foci_coords = self.geo[hemi].coords[foci_vtxs]
else:
foci_surf = surfer.utils.Surface(self.subject_id,
hemi,
map_surface,
subjects_dir=self.subjects_dir)
foci_surf.load_geometry()
foci_vtxs = surfer.utils.find_closest_vertices(
foci_surf.coords, coords)
foci_coords = self.geo[hemi].coords[foci_vtxs]
# foci_vtxs = surfer.utils.find_closest_vertices(self.geo[hemi].coords, coords)
# foci_coords = self.geo[hemi].coords[foci_vtxs]
# Convert the color code
if not isinstance(color, tuple):
color = colorConverter.to_rgb(color)
if name is None:
name = "arrow_%s" % (len(self.arrows_dict) + 1)
nsegs = 100
x = np.linspace(foci_coords[0, 0], foci_coords[1, 0], nsegs)
y = np.linspace(foci_coords[0, 1], foci_coords[1, 1], nsegs)
z = np.linspace(foci_coords[0, 2], foci_coords[1, 2], nsegs)
line_coords = np.vstack((x, y, z)).transpose()
step = 5
idx_a = range(0, nsegs + 1, step)
idx_b = range(10, nsegs + 1, step)
views = self._toggle_render(False)
al = []
for brain in self._brain_list:
if brain['hemi'] == hemi:
for start, end in zip(idx_a, idx_b):
seg_width = tube_radius - (start *
(tube_radius - .5) / 100.)
al.append(
mlab.plot3d(line_coords[start:end, 0],
line_coords[start:end, 1],
line_coords[start:end, 2],
np.ones_like(line_coords[start:end, 0]),
color=color,
opacity=alpha,
tube_radius=seg_width,
name=name,
figure=brain['brain']._f))
self.arrows_dict[name] = al
self._toggle_render(True, views)
def draw_lidar(pc,
color=None,
fig=None,
bgcolor=(0, 0, 0),
pts_scale=1,
pts_mode='point',
pts_color=None):
"""
Draw lidar points
:param pc: numpy array (n,3) of XYZ
:param color: numpy array (n) of intensity or whatever
:param fig: mayavi figure handler, if None create new one otherwise will use it
:param bgcolor:
:param pts_scale:
:param pts_mode:
:param pts_color:
:return: created or used fig
"""
if "mlab" not in sys.modules:
try:
import mayavi.mlab as mlab
except BaseException:
print("mlab module should be installed")
return
if fig is None:
fig = mlab.figure(figure=None,
bgcolor=bgcolor,
fgcolor=None,
engine=None,
size=(1600, 1000))
if color is None:
color = pc[:, 2]
mlab.points3d(pc[:, 0],
pc[:, 1],
pc[:, 2],
color,
color=pts_color,
mode=pts_mode,
colormap='gnuplot',
scale_factor=pts_scale,
figure=fig)
# draw origin
mlab.points3d(0, 0, 0, color=(1, 1, 1), mode='sphere', scale_factor=0.2)
# draw axis
axes = np.array([
[2., 0., 0., 0.],
[0., 2., 0., 0.],
[0., 0., 2., 0.],
],
dtype=np.float64)
mlab.plot3d([0, axes[0, 0]], [0, axes[0, 1]], [0, axes[0, 2]],
color=(1, 0, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0, axes[1, 0]], [0, axes[1, 1]], [0, axes[1, 2]],
color=(0, 1, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0, axes[2, 0]], [0, axes[2, 1]], [0, axes[2, 2]],
color=(0, 0, 1),
tube_radius=None,
figure=fig)
# draw fov Todo: update to real sensor spec.
fov = np.array(
[ # 45 degree
[20., 20., 0., 0.],
[20., -20., 0., 0.],
],
dtype=np.float64)
mlab.plot3d([0, fov[0, 0]], [0, fov[0, 1]], [0, fov[0, 2]],
color=(1, 1, 1),
tube_radius=None,
line_width=1,
figure=fig)
mlab.plot3d([0, fov[1, 0]], [0, fov[1, 1]], [0, fov[1, 2]],
color=(1, 1, 1),
tube_radius=None,
line_width=1,
figure=fig)
# draw square region
TOP_Y_MIN = -20
TOP_Y_MAX = 20
TOP_X_MIN = 0
TOP_X_MAX = 40
TOP_Z_MIN = -2.0
TOP_Z_MAX = 0.4
x1 = TOP_X_MIN
x2 = TOP_X_MAX
y1 = TOP_Y_MIN
y2 = TOP_Y_MAX
mlab.plot3d([x1, x1], [y1, y2], [0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig)
mlab.plot3d([x2, x2], [y1, y2], [0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig)
mlab.plot3d([x1, x2], [y1, y1], [0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig)
mlab.plot3d([x1, x2], [y2, y2], [0, 0],
color=(0.5, 0.5, 0.5),
tube_radius=0.1,
line_width=1,
figure=fig)
# mlab.orientation_axes()
mlab.view(azimuth=180,
elevation=70,
focalpoint=[12.0909996, -1.04700089, -2.03249991],
distance=62.0,
figure=fig)
return fig
df = np.loadtxt(f)
r = np.ones(len(df)) * 0.03
color = (0.2, 0.4, 0.5)
mlab.points3d(df[:, 0],
df[:, 2],
df[:, 1],
r,
color=color,
colormap='gnuplot',
scale_factor=1,
figure=fig)
mlab.plot3d([0.0, 0.0], [0.0, 0.0], [0.0, CUBE_MAX],
color=(0, 0, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0.0, CUBE_MAX], [0.0, 0.0], [0.0, 0.0],
color=(1, 0, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0.0, 0.0], [0.0, CUBE_MAX / 2], [0.0, 0.0],
color=(0, 1, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0.0, 0.0], [0.0, CUBE_MAX / 2], [CUBE_MAX, CUBE_MAX],
color=(0, 0, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0.0, CUBE_MAX], [0.0, 0.0], [CUBE_MAX, CUBE_MAX],
color=(0, 0, 0),
NR.extend( np.flatnonzero(particles[:,:,:,i] == 27).tolist() )
NCX.extend( np.flatnonzero(particles[:,:,:,i] == 25).tolist() )
Ca = np.unravel_index(Ca, particles[:,:,:,0].shape )
NR = np.unravel_index(NR, particles[:,:,:,0].shape )
PMCA = np.unravel_index(PMCA, particles[:,:,:,0].shape )
NCX = np.unravel_index(NCX, particles[:,:,:,0].shape )
print('Num Ca : ', Ca[0].shape[0])
print('Num PMCA : ', PMCA[0].shape[0])
print('Num NMDAR : ', NR[0].shape[0])
# plot_points1 = mlab.points3d(Ca[0], Ca[1], Ca[2], color=(0,0,1), scale_factor=2.0,line_width=0.1)
plot_points2 = mlab.points3d(NR[0], NR[1], NR[2], color=(1,0,0), scale_factor=2.0,line_width=0.1)
# plot_points3 = mlab.points3d(PMCA[0], PMCA[1], PMCA[2], color=(0,1,0), scale_factor=2.0,line_width=0.1)
#
xvnum,yvnum,zvnum = particles[:,:,:,0].shape
Zoff = 0
xvnum = xvnum / 2
voxel_length = 50
length = voxel_length*pitch
text = "{0:.1f} um".format(length)
mlab.plot3d( [xvnum,xvnum],[0,voxel_length],[Zoff,Zoff],color=(0.7,0.7,0.7),tube_radius=2.5)
mlab.text3d( xvnum, voxel_length, Zoff-30, text, scale=10,color=(0.2,0.2,0.2))
# 0.02 um x 100
mlab.savefig(output_file)
mlab.show()
def lineplot(x, y, z, colormap='Spectral', r=0.1, color=(0, 0, 0)):
mlab.plot3d(x, y, z, tube_radius=r, color=color, colormap=colormap)
def plotMF(fig, R, col=None):
mfColor = []
mfColor.append((232 / 255.0, 65 / 255.0, 32 / 255.0)) # red
mfColor.append((32 / 255.0, 232 / 255.0, 59 / 255.0)) # green
mfColor.append((32 / 255.0, 182 / 255.0, 232 / 255.0)) # tuerkis
pts = np.zeros((3, 6))
for i in range(0, 3):
pts[:, i * 2] = -R[:, i]
pts[:, i * 2 + 1] = R[:, i]
if col is None:
mlab.plot3d(pts[0, 0:2],
pts[1, 0:2],
pts[2, 0:2],
figure=fig,
color=mfColor[0])
mlab.plot3d(pts[0, 2:4],
pts[1, 2:4],
pts[2, 2:4],
figure=fig,
color=mfColor[1])
mlab.plot3d(pts[0, 4:6],
pts[1, 4:6],
pts[2, 4:6],
figure=fig,
color=mfColor[2])
else:
mlab.plot3d(pts[0, 0:2],
pts[1, 0:2],
pts[2, 0:2],
figure=fig,
color=col)
mlab.plot3d(pts[0, 2:4],
pts[1, 2:4],
pts[2, 2:4],
figure=fig,
color=col)
mlab.plot3d(pts[0, 4:6],
pts[1, 4:6],
pts[2, 4:6],
figure=fig,
color=col)
dataPrev = dataCurr
else:
print dataPrev, dataCurr
count = count + 1
print "-------", i
dataNew = np.array(dataNew)
print type(dataTraj), type(dataNew), count
#print dataNew
x = dataNew[:, 1]
y = dataNew[:, 2]
z = dataNew[:, 3]
#print dataNew
#mlab.points3d(x, y, z) # s, colormap="copper", scale_factor=.25, mode='cube')
mlab.plot3d(x, y, z, tube_radius=0.25,
color=(1, 0, 0)) #, representation='points')
dataGeom = np.loadtxt("rw3D_Geometry__Hrad_" + strHrad + "__Brad_" + strBrad +
"__HARDWALL.csv",
dtype=int)
XX = dataGeom[:, 0]
YY = dataGeom[:, 1]
ZZ = dataGeom[:, 2]
mlab.points3d(XX,
YY,
ZZ,
mode='cube',
color=(210. / 256., 180. / 256., 140. / 256.),
scale_factor=1.0)
def make_sphere(self):
"""
Plots Bloch sphere and data sets.
"""
# setup plot
# Figure instance for Bloch sphere plot
from mayavi import mlab
import matplotlib.colors as colors
if self.user_fig:
self.fig = self.user_fig
else:
self.fig = mlab.figure(
1,
size=self.size,
bgcolor=colors.colorConverter.to_rgb(self.bgcolor),
fgcolor=colors.colorConverter.to_rgb(self.fgcolor))
sphere = mlab.points3d(0,
0,
0,
figure=self.fig,
scale_mode='none',
scale_factor=2,
color=colors.colorConverter.to_rgb(
self.sphere_color),
resolution=100,
opacity=self.sphere_alpha,
name='bloch_sphere')
# Thse commands make the sphere look better
sphere.actor.property.specular = 0.45
sphere.actor.property.specular_power = 5
sphere.actor.property.backface_culling = True
# make frame for sphere surface
if self.frame:
theta = np.linspace(0, 2 * np.pi, 100)
for angle in np.linspace(-np.pi, np.pi, self.frame_num):
xlat = np.cos(theta) * np.cos(angle)
ylat = np.sin(theta) * np.cos(angle)
zlat = np.ones_like(theta) * np.sin(angle)
xlon = np.sin(angle) * np.sin(theta)
ylon = np.cos(angle) * np.sin(theta)
zlon = np.cos(theta)
mlab.plot3d(xlat,
ylat,
zlat,
color=colors.colorConverter.to_rgb(
self.frame_color),
opacity=self.frame_alpha,
tube_radius=self.frame_radius)
mlab.plot3d(xlon,
ylon,
zlon,
color=colors.colorConverter.to_rgb(
self.frame_color),
opacity=self.frame_alpha,
tube_radius=self.frame_radius)
# add axes
axis = np.linspace(-1.0, 1.0, 10)
other = np.zeros_like(axis)
mlab.plot3d(axis,
other,
other,
color=colors.colorConverter.to_rgb(self.axes_color),
tube_radius=self.axes_radius,
opacity=self.axes_alpha)
mlab.plot3d(other,
axis,
other,
color=colors.colorConverter.to_rgb(self.axes_color),
tube_radius=self.axes_radius,
opacity=self.axes_alpha)
mlab.plot3d(other,
other,
axis,
color=colors.colorConverter.to_rgb(self.axes_color),
tube_radius=self.axes_radius,
opacity=self.axes_alpha)
# add data to sphere
self.plot_points()
self.plot_vectors()
# #add labels
mlab.text3d(0,
0,
self.zlpos[0],
self.zlabel[0],
color=colors.colorConverter.to_rgb(self.font_color),
scale=self.font_scale)
mlab.text3d(0,
0,
self.zlpos[1],
self.zlabel[1],
color=colors.colorConverter.to_rgb(self.font_color),
scale=self.font_scale)
mlab.text3d(self.xlpos[0],
0,
0,
self.xlabel[0],
color=colors.colorConverter.to_rgb(self.font_color),
scale=self.font_scale)
mlab.text3d(self.xlpos[1],
0,
0,
self.xlabel[1],
color=colors.colorConverter.to_rgb(self.font_color),
scale=self.font_scale)
mlab.text3d(0,
self.ylpos[0],
0,
self.ylabel[0],
color=colors.colorConverter.to_rgb(self.font_color),
scale=self.font_scale)
mlab.text3d(0,
self.ylpos[1],
0,
self.ylabel[1],
color=colors.colorConverter.to_rgb(self.font_color),
scale=self.font_scale)
color=(1, 1, 1),
scale_mode='none')
H2 = mlab.points3d(atoms_x[-1:],
atoms_y[-1:],
atoms_z[-1:],
scale_factor=2,
resolution=20,
color=(1, 1, 1),
scale_mode='none')
# The bounds between the atoms, we use the scalar information to give
# color
mlab.plot3d(atoms_x,
atoms_y,
atoms_z, [1, 2, 1],
tube_radius=0.4,
colormap='Reds')
# Display the electron localization function ###################################
# Load the data, we need to remove the first 8 lines and the '\n'
str = ' '.join(file('h2o-elf.cube').readlines()[9:])
data = np.fromstring(str, sep=' ')
data.shape = (40, 40, 40)
source = mlab.pipeline.scalar_field(data)
min = data.min()
max = data.max()
vol = mlab.pipeline.volume(source,
vmin=min + 0.65 * (max - min),
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=(6, 6), dpi=300)
ax = plt.subplot(111, projection='3d')
for xx in e:
ax.plot(xx[:, 0], xx[:, 1], xx[:, 2], color='k', lw=1.0)
ax.set_xlim(-b1, b1)
ax.set_ylim(-b2, b2)
ax.set_zlim(-b3, b3)
plt.show()
from mayavi import mlab
fig = mlab.figure(bgcolor=(1, 1, 1), size=(800, 800))
bz_line_width = b1 / 200
for xx in e:
mlab.plot3d(xx[:, 0],
xx[:, 1],
xx[:, 2],
tube_radius=bz_line_width,
color=(0, 0, 0))
mlab.orientation_axes()
mlab.show()
def dataset_viz(show_frustum=False):
sunrgbd = sunrgbd_object(data_dir)
idxs = np.array(range(1,len(sunrgbd)+1))
np.random.shuffle(idxs)
for idx in range(len(sunrgbd)):
data_idx = idxs[idx]
print('--------------------', data_idx)
pc = sunrgbd.get_depth(data_idx)
print(pc.shape)
# Project points to image
calib = sunrgbd.get_calibration(data_idx)
uv,d = calib.project_upright_depth_to_image(pc[:,0:3])
print(uv)
print(d)
raw_input()
import matplotlib.pyplot as plt
cmap = plt.cm.get_cmap('hsv', 256)
cmap = np.array([cmap(i) for i in range(256)])[:,:3]*255
img = sunrgbd.get_image(data_idx)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i in range(uv.shape[0]):
depth = d[i]
color = cmap[int(120.0/depth),:]
cv2.circle(img, (int(np.round(uv[i,0])), int(np.round(uv[i,1]))), 2, color=tuple(color), thickness=-1)
Image.fromarray(img).show()
raw_input()
# Load box labels
objects = sunrgbd.get_label_objects(data_idx)
print(objects)
raw_input()
# Draw 2D boxes on image
img = sunrgbd.get_image(data_idx)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i,obj in enumerate(objects):
cv2.rectangle(img, (int(obj.xmin),int(obj.ymin)), (int(obj.xmax),int(obj.ymax)), (0,255,0), 2)
cv2.putText(img, '%d %s'%(i,obj.classname), (max(int(obj.xmin),15), max(int(obj.ymin),15)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,0,0), 2)
Image.fromarray(img).show()
raw_input()
# Draw 3D boxes on depth points
box3d = []
ori3d = []
for obj in objects:
corners_3d_image, corners_3d = utils.compute_box_3d(obj, calib)
ori_3d_image, ori_3d = utils.compute_orientation_3d(obj, calib)
print('Corners 3D: ', corners_3d)
box3d.append(corners_3d)
ori3d.append(ori_3d)
raw_input()
bgcolor=(0,0,0)
fig = mlab.figure(figure=None, bgcolor=bgcolor, fgcolor=None, engine=None, size=(1600, 1000))
mlab.points3d(pc[:,0], pc[:,1], pc[:,2], pc[:,2], mode='point', colormap='gnuplot', figure=fig)
mlab.points3d(0, 0, 0, color=(1,1,1), mode='sphere', scale_factor=0.2)
draw_gt_boxes3d(box3d, fig=fig)
for i in range(len(ori3d)):
ori_3d = ori3d[i]
x1,y1,z1 = ori_3d[0,:]
x2,y2,z2 = ori_3d[1,:]
mlab.plot3d([x1, x2], [y1, y2], [z1,z2], color=(0.5,0.5,0.5), tube_radius=None, line_width=1, figure=fig)
mlab.orientation_axes()
for i,obj in enumerate(objects):
print('Orientation: ', i, np.arctan2(obj.orientation[1], obj.orientation[0]))
print('Dimension: ', i, obj.l, obj.w, obj.h)
raw_input()
if show_frustum:
img = sunrgbd.get_image(data_idx)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
for i,obj in enumerate(objects):
box2d_fov_inds = (uv[:,0]<obj.xmax) & (uv[:,0]>=obj.xmin) & (uv[:,1]<obj.ymax) & (uv[:,1]>=obj.ymin)
box2d_fov_pc = pc[box2d_fov_inds, :]
img2 = np.copy(img)
cv2.rectangle(img2, (int(obj.xmin),int(obj.ymin)), (int(obj.xmax),int(obj.ymax)), (0,255,0), 2)
cv2.putText(img2, '%d %s'%(i,obj.classname), (max(int(obj.xmin),15), max(int(obj.ymin),15)), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,0,0), 2)
Image.fromarray(img2).show()
fig = mlab.figure(figure=None, bgcolor=bgcolor, fgcolor=None, engine=None, size=(1000, 1000))
mlab.points3d(box2d_fov_pc[:,0], box2d_fov_pc[:,1], box2d_fov_pc[:,2], box2d_fov_pc[:,2], mode='point', colormap='gnuplot', figure=fig)
raw_input()
coil_R * sin(theta) + ypos,
]).T
#right coil
right_coil = array([
-z_pitch * theta / (2. * pi) - .1049,
coil_R * cos(theta) + dx,
-coil_R * sin(theta) + ypos,
]).T
if __name__ == "__main__":
##lower electrode +
b1 = electrode2(phi0)
path = get_stuff_coil(dx=b1[0], dy=b1[1], r=.02, d=.08, nturns=12)
mlab.plot3d(path[:, 0],
path[:, 1],
path[:, 2],
tube_radius=.002,
color=copper)
radius = 1.4 * .0254
d = .04
dz, dtheta = (d) / 5.0, pi / 50.0
[z, theta] = mgrid[-d:0 + dz:dz, 0:2 * pi + dtheta * .5:dtheta]
x = radius * cos(theta) + b1[0]
y = radius * sin(theta) + b1[1]
mlab.mesh(x, y, z, color=copper)
radius = .003
d = 4.15 * .0254
dz, dtheta = (d) / 5.0, pi / 50.0
[z, theta] = mgrid[-d:0 + dz:dz, 0:2 * pi + dtheta * .5:dtheta]
x = radius * cos(theta) + b1[0]
def show_lidar_with_boxes(
pc_velo,
objects,
calib,
img_fov=False,
img_width=None,
img_height=None,
objects_pred=None,
depth=None,
cam_img=None,
):
""" Show all LiDAR points.
Draw 3d box in LiDAR point cloud (in velo coord system) """
if "mlab" not in sys.modules:
import mayavi.mlab as mlab
from viz_util import draw_lidar_simple, draw_lidar, draw_gt_boxes3d
print(("All point num: ", pc_velo.shape[0]))
fig = mlab.figure(figure=None,
bgcolor=(0, 0, 0),
fgcolor=None,
engine=None,
size=(1000, 500))
if img_fov:
pc_velo = get_lidar_in_image_fov(pc_velo[:, 0:3], calib, 0, 0,
img_width, img_height)
print(("FOV point num: ", pc_velo.shape[0]))
print("pc_velo", pc_velo.shape)
draw_lidar(pc_velo, fig=fig)
# pc_velo=pc_velo[:,0:3]
color = (0, 1, 0)
for obj in objects:
if obj.type == "DontCare":
continue
# Draw 3d bounding box
_, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
print("box3d_pts_3d_velo:")
print(box3d_pts_3d_velo)
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
# Draw depth
if depth is not None:
# import pdb; pdb.set_trace()
depth_pt3d = depth_region_pt3d(depth, obj)
depth_UVDepth = np.zeros_like(depth_pt3d)
depth_UVDepth[:, 0] = depth_pt3d[:, 1]
depth_UVDepth[:, 1] = depth_pt3d[:, 0]
depth_UVDepth[:, 2] = depth_pt3d[:, 2]
print("depth_pt3d:", depth_UVDepth)
dep_pc_velo = calib.project_image_to_velo(depth_UVDepth)
print("dep_pc_velo:", dep_pc_velo)
draw_lidar(dep_pc_velo, fig=fig, pts_color=(1, 1, 1))
# Draw heading arrow
_, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
x1, y1, z1 = ori3d_pts_3d_velo[0, :]
x2, y2, z2 = ori3d_pts_3d_velo[1, :]
mlab.plot3d(
[x1, x2],
[y1, y2],
[z1, z2],
color=color,
tube_radius=None,
line_width=1,
figure=fig,
)
if objects_pred is not None:
color = (1, 0, 0)
for obj in objects_pred:
if obj.type == "DontCare":
continue
# Draw 3d bounding box
_, box3d_pts_3d = utils.compute_box_3d(obj, calib.P)
box3d_pts_3d_velo = calib.project_rect_to_velo(box3d_pts_3d)
print("box3d_pts_3d_velo:")
print(box3d_pts_3d_velo)
draw_gt_boxes3d([box3d_pts_3d_velo], fig=fig, color=color)
# Draw heading arrow
_, ori3d_pts_3d = utils.compute_orientation_3d(obj, calib.P)
ori3d_pts_3d_velo = calib.project_rect_to_velo(ori3d_pts_3d)
x1, y1, z1 = ori3d_pts_3d_velo[0, :]
x2, y2, z2 = ori3d_pts_3d_velo[1, :]
mlab.plot3d(
[x1, x2],
[y1, y2],
[z1, z2],
color=color,
tube_radius=None,
line_width=1,
figure=fig,
)
mlab.show(1)
import mayavi.mlab as mlab
mlab.figure(1)
mlab.clf()
print "nodes"
for node in xrange(0, tracker_arr.shape[1],
max(1, tracker_arr.shape[1] // 30)):
xyz = tracker_arr[:, node, :]
good_inds = np.flatnonzero(~np.isnan(xyz[:, 0]))
if len(good_inds) > 0:
xyz_good = xyz[good_inds]
x, y, z = xyz_good.T
print xyz_good.min(axis=0), xyz_good.max(axis=0)
mlab.plot3d(x,
y,
z,
np.float32(good_inds) / good_inds.max(),
tube_radius=.001,
opacity=.3,
colormap='Spectral')
print "markers"
for node in used_leds:
xyz = owl_arr[:, node, :]
good_inds = np.flatnonzero(np.isfinite(xyz[:, 0]))
good_inds = good_inds[::100]
if len(good_inds) > 0:
xyz_good = xyz[good_inds]
print xyz_good.min(axis=0), xyz_good.max(axis=0)
x, y, z = xyz_good.T
mlab.points3d(x,
y,
def draw_lidar_simple(pc, location, color=None):
''' Draw lidar points. simplest set up. '''
##############################################33
# location=[13,-1,0]
# mlab.points3d(xmid, ymid, zmid, scale_factor=1.0)
################################################
fig = mlab.figure(figure=None,
bgcolor=(0, 0, 0),
fgcolor=None,
engine=None,
size=(1600, 1000))
# fig=draw_gt_boxes3d(1,fig)
if color is None: color = pc[:, 2]
# draw points
mlab.points3d(pc[:, 0],
pc[:, 1],
pc[:, 2],
color,
color=None,
mode='point',
colormap='gnuplot',
scale_factor=1,
figure=fig)
# draw origin
mlab.points3d(0, 0, 0, color=(1, 1, 1), mode='sphere', scale_factor=0.2)
# draw axis
axes = np.array([
[2., 0., 0., 0.],
[0., 2., 0., 0.],
[0., 0., 2., 0.],
],
dtype=np.float64)
mlab.plot3d([0, axes[0, 0]], [0, axes[0, 1]], [0, axes[0, 2]],
color=(1, 0, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0, axes[1, 0]], [0, axes[1, 1]], [0, axes[1, 2]],
color=(0, 1, 0),
tube_radius=None,
figure=fig)
mlab.plot3d([0, axes[2, 0]], [0, axes[2, 1]], [0, axes[2, 2]],
color=(0, 0, 1),
tube_radius=None,
figure=fig)
mlab.view(azimuth=180,
elevation=70,
focalpoint=[12.0909996, -1.04700089, -2.03249991],
distance=62.0,
figure=fig)
###################################################3
location = [24, 0, 1]
x_loc = location[0]
y_loc = location[1]
z_loc = location[2]
mlab.points3d(x_loc, y_loc, z_loc, scale_factor=1.0)
################################################
####################################################3
mlab.show()
return fig
#ki = Ry(np.deg2rad(mu)).dot(Rz(np.deg2rad(theta)).dot([k0, 0, 0]))
ki = np.dot(Rz(np.deg2rad(theta)), np.dot(Ry(np.deg2rad(mu)), [k0, 0, 0]))
#kf = np.dot(Rz(np.deg2rad(theta)), np.dot(Ry(np.deg2rad(mu)), np.dot(Rz(np.deg2rad(delta)), np.dot(Ry(np.deg2rad(gamma)),[k0, 0, 0]))))
kf = np.dot(
Rz(np.deg2rad(delta)),
np.dot(
Ry(np.deg2rad(gamma)),
np.dot(Rz(np.deg2rad(theta)), np.dot(Ry(np.deg2rad(mu)),
[k0, 0, 0]))))
#kf = Rz(np.deg2rad(theta-delta)).dot(Ry(np.deg2rad(mu-gamma)).dot([k0, 0, 0]))
#ki = rsp.RotationMatrix(0, np.deg2rad(mu), np.deg2rad(theta)).dot([k0,0,0])
#kf = rsp.RotationMatrix(0, np.deg2rad(-gamma), np.deg2rad(delta)).dot([k0,0,0])
mlab.plot3d([-ki[0], 0], [-ki[1], 0], [-ki[2], 0], color=(0, 0, 1))
mlab.plot3d([-ki[0], -ki[0] + kf[0]], [-ki[1], -ki[1] + kf[1]],
[-ki[2], -ki[2] + kf[2]],
color=(0, 0, 1))
sphere = mlab.points3d(-ki[0],
-ki[1],
-ki[2],
scale_mode='none',
scale_factor=2 * k0,
color=(0.67, 0.77, 0.93),
resolution=50,
opacity=0.7)
sphere.actor.property.specular = 0.45
sphere.actor.property.specular_power = 5
sphere.actor.property.backface_culling = True
def draw_grid(x1, y1, x2, y2, fig, tube_radius=None, color=(0.5, 0.5, 0.5)):
mlab.plot3d([x1, x1], [y1, y2], [0, 0], color=color, tube_radius=tube_radius, line_width=1, figure=fig)
mlab.plot3d([x2, x2], [y1, y2], [0, 0], color=color, tube_radius=tube_radius, line_width=1, figure=fig)
mlab.plot3d([x1, x2], [y1, y1], [0, 0], color=color, tube_radius=tube_radius, line_width=1, figure=fig)
mlab.plot3d([x1, x2], [y2, y2], [0, 0], color=color, tube_radius=tube_radius, line_width=1, figure=fig)
return fig
def render_platform(self, platform: _robot.Platform, *args, **kwargs):
# platform position and orientation
position = platform.pose.linear.position
dcm = platform.pose.angular.dcm
# temporary platform loop index
pidx = kwargs.pop('loop_index', -1)
# if the platform has a geometry assigned, we will plot the geometry
# and only add the anchor points to it
if platform.geometry is not None:
self.render(platform.geometry,
transformation=platform.pose.transformation,
name=f'platform {pidx}')
# otherwise, without a platform geometry, we will triangulate the
# anchor points and plot the platform shape via that
else:
# render bounding box of platform
if platform.is_cuboid:
# get original anchors as (K,M) matrix
anchors = self._extract_coordinates(platform.bi.swapaxes(0, 1))
# in 3D, we perform delaunay triangulation of the corners and
# retrieve the convex hull from there
try:
delau = _Delaunay(anchors.T)
except QhullError as e:
warnings.warn(RuntimeWarning(e))
return
edges = delau.convex_hull
vertices = delau.points
# ensure vertices are (N,3) arrays
vertices = _np.pad(vertices,
((0, 0), (0, 3 - vertices.shape[1])))
# also rotate and translate the platform anchors
vertices = (position[:, _np.newaxis] + dcm.dot(vertices.T)).T
# 3D plot
# first, plot the mesh of the platform i.e., its volume
_mlab.triangular_mesh(
*self._prepare_plot_coordinates(
self._extract_coordinates(vertices.T)), edges,
**update_recursive(
dict(
color=(0, 0, 0),
line_width=0.10,
), kwargs))
# close all edges by appending the first column
edges = _np.hstack((edges, edges[:, 0, _np.newaxis]))
# and loop over each edge to plot
for edge in edges:
_mlab.plot3d(
*self._prepare_plot_coordinates(vertices[edge, :].T),
**update_recursive(
dict(
color=self._RGB2rgb((13, 13, 13)),
line_width=0.10,
), kwargs))
# render all anchors
self._render_component_list(
platform,
'anchors',
transformation=platform.pose.transformation,
platform_index=pidx,
**kwargs)
# render reference coordinate system of platform
self.render_coordinate_system(position,
name=f'platform {pidx}: center')
# render rotated coordinate system of platform
if platform.can_rotate:
self.render_coordinate_system(position,
dcm,
name=f'platform {pidx}')
# -*- coding: utf-8 -*- """ Created on Wed Jul 5 09:43:18 2017 @author: Administrator """ import numpy as np from mayavi import mlab #建立数据 n_mer, n_long = 6, 11 dphi = np.pi / 1000.0 phi = np.arange(0.0, 2 * np.pi + 0.5 * dphi, dphi) mu = phi * n_mer x = np.cos(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5) y = np.sin(mu) * (1 + np.cos(n_long * mu / n_mer) * 0.5) z = np.sin(n_long * mu / n_mer) * 0.5 #对数据进行可视化 l = mlab.plot3d(x, y, z, np.sin(mu), tube_radius=0.025, colormap='Spectral') mlab.show()
# Integrate the Lorenz equations on the time grid t
#Implements a detailed use of memory space for optimised accuracy, not
#so much speed.
#@nb.jit('float64(float64,float64,float64,float64,float64,float64,float64,float64)',nopython=False)
def int_f(u0=u0, v0=v0, w0=w0, sigma=sigma, beta=beta, rho=rho, tma=tmax, n=n):
t = np.linspace(0, tmax, n)
f = odeint(deriv_lorenz, (u0, v0, w0), t, args=(sigma, beta, rho))
return f
x, y, z = int_f().T
Time = np.linspace(0, tmax, n)
mlab.plot3d(x, y, z, Time, colormap='Spectral', tube_radius=0.3)
mlab.outline(opacity=.4)
mlab.points3d(0, 0, 0, opacity=.5)
mlab.text3d(0, 0, 0, 'Origin')
mlab.points3d(u0, v0, w0, opacity=.5)
mlab.text3d(u0, v0, w0, 't_0')
mlab.points3d(np.sqrt(beta * (rho - 1)),
np.sqrt(beta * (rho - 1)),
rho - 1,
opacity=.5)
mlab.text3d(
np.sqrt(beta * (rho - 1)) * 1.2, np.sqrt(beta * (rho - 1)), rho - 1, 'C+')
mlab.points3d(-np.sqrt(beta * (rho - 1)),
-np.sqrt(beta * (rho - 1)),
rho - 1,
opacity=.5)
def do_landing_burn(self, goal_position, do_plot=True):
flight = self.vessel.flight()
orbit = self.vessel.orbit
# create a transform for the goal position as an x-up frame
# TODO: This is lazy and will fail if we are landing at the poles
x_ax = goal_position / np.linalg.norm(goal_position)
y_ax = np.cross(x_ax, np.array([0, 0, 1]))
z_ax = np.cross(x_ax, y_ax)
y_ax /= np.linalg.norm(y_ax)
z_ax /= np.linalg.norm(z_ax)
rot = np.column_stack([x_ax, y_ax, z_ax])
q = R.from_matrix(rot).as_quat()
ref_frame = self.conn.space_center.ReferenceFrame.create_relative(
self.body.reference_frame, position=goal_position, rotation=q)
# Solve the gfold problem based on current position and velocity
pos, vel = self.get_position_and_velocity(ref_frame)
start_time = self.conn.space_center.ut
config = self.create_gfold_config()
x, u, gam, z, dt = gfold_test.solve_gfold(config, pos, vel)
# plot the results
if do_plot:
f = mlab.figure(bgcolor=(0, 0, 0))
points3d([0], [0], [0],
scale_factor=200.0,
resolution=128,
color=(0, 0.5, 0.5))
s = plot3d(x[0, :],
x[1, :],
x[2, :],
tube_radius=5.0,
colormap='Spectral')
v = quiver3d(x[0, :], x[1, :], x[2, :], u[0, :], u[1, :], u[2, :])
mlab.axes()
mlab.show()
timesteps = np.linspace(0, x.shape[1] * dt, num=x.shape[1])
f = interp1d(timesteps, x[:], kind='cubic')
u = interp1d(timesteps, u, kind='cubic')
gam = interp1d(timesteps, gam, kind='cubic')
self.autopilot.reference_frame = ref_frame
self.autopilot.engage()
context = clive_log.Context("vpstream")
context.add_text_field("error")
context.add_text_field("distance")
while self.vessel.situation != self.conn.space_center.VesselSituation.landed:
cur_time = self.conn.space_center.ut - start_time
index_time = cur_time
try:
goal_position = f(index_time)
except ValueError:
goal_position = f(x.shape[1] * dt)
pos, vel = self.get_position_and_velocity(ref_frame)
err = goal_position - np.array([*pos, *vel])
thrust_vector = np.array([2., 0, 0])
thrust_vector[1:3] = err[1:3] * 0.015
thrust_vector[1:3] += err[4:6] * 0.36
self.autopilot.target_direction = tuple(thrust_vector)
thrust_vector[0] = 0.0
thrust_vector *= 0.05
thrust_vector[0] = err[0] * 2.0
thrust_vector[0] += err[3] * 4.3
thrust_vector[0] = np.max((thrust_vector[0], 0))
throttle = np.linalg.norm(thrust_vector) * 0.01 #0.25
self.vessel.control.throttle = throttle
distance = np.linalg.norm(pos)
context.write_text_field("error", f"Error: {err}")
context.write_text_field("distance", f"Distance: {distance}")
context.display()
self.vessel.control.throttle = 0
for (u, v) in G.edges_iter():
if edgeskip == 0 or i == edgeskip:
i = 0
if not show_between_states:
local_traj = np.array(
[G.node[u]['state'], G.node[v]['state']])
else:
rrg_data['V'][u]['successors'][v].insert(
0, G.node[u]['state'])
rrg_data['V'][u]['successors'][v].append(
G.node[v]['state'])
local_traj = np.array(rrg_data['V'][u]['successors'][v])
if is3D:
mlab.plot3d(local_traj.T[0],
local_traj.T[1],
local_traj.T[2],
color=(0.0, 0.0, 1.0),
line_width=0.5)
mlab.plot3d([G.node[u]['state'][0], G.node[v]['state'][0]],
[G.node[u]['state'][1], G.node[v]['state'][1]],
[G.node[u]['state'][2], G.node[v]['state'][2]],
color=(0.0, 0.0, 1.0),
line_width=0.5)
else:
plt.plot(local_traj.T[0], local_traj.T[1], 'b-')
plt.plot([G.node[u]['state'][0], G.node[v]['state'][0]],
[G.node[u]['state'][1], G.node[v]['state'][1]],
'b.')
if edgeskip > 0:
i += 1