def example():
# setup movie
m = movie(frame_rate=22.5)
# setup example
from mayavi import mlab
mlab.options.offscreen = True
mlab.test_contour3d()
f = mlab.gcf()
# save frames
for i in range(36*3):
f.scene.camera.azimuth(10) # rotate
f.scene.render()
mlab.savefig(m.next_frame(), figure=f)
# encode frames
encoded = m.encode()
# remove tempdir
del m
if not encoded:
# report error
exit(m.output)
def m2screenshot(mayavi_fig=None, mpl_axes=None, autocrop=True):
""" Capture a screeshot of the Mayavi figure and display it in the
matplotlib axes.
"""
import pylab as pl
# Late import to avoid triggering wx imports before needed.
try:
from mayavi import mlab
except ImportError:
# Try out old install of Mayavi, with namespace packages
from enthought.mayavi import mlab
if mayavi_fig is None:
mayavi_fig = mlab.gcf()
else:
mlab.figure(mayavi_fig)
if mpl_axes is not None:
pl.axes(mpl_axes)
filename = tempfile.mktemp('.png')
mlab.savefig(filename, figure=mayavi_fig)
image3d = pl.imread(filename)
if autocrop:
bg_color = mayavi_fig.scene.background
image3d = autocrop_img(image3d, bg_color)
pl.imshow(image3d)
pl.axis('off')
os.unlink(filename)
def mark_gt_box3d( lidar_dir, gt_boxes3d_dir, mark_dir):
os.makedirs(mark_dir, exist_ok=True)
fig = mlab.figure(figure=None, bgcolor=(0,0,0), fgcolor=None, engine=None, size=(500, 500))
dummy = np.zeros((10,10,3),dtype=np.uint8)
for file in sorted(glob.glob(lidar_dir + '/*.npy')):
name = os.path.basename(file).replace('.npy','')
lidar_file = lidar_dir +'/'+name+'.npy'
boxes3d_file = gt_boxes3d_dir+'/'+name+'.npy'
lidar = np.load(lidar_file)
boxes3d = np.load(boxes3d_file)
mlab.clf(fig)
draw_didi_lidar(fig, lidar, is_grid=1, is_axis=1)
if len(boxes3d)!=0:
draw_didi_boxes3d(fig, boxes3d)
azimuth,elevation,distance,focalpoint = MM_PER_VIEW1
mlab.view(azimuth,elevation,distance,focalpoint)
mlab.show(1)
imshow('dummy',dummy)
cv2.waitKey(1)
mlab.savefig(mark_dir+'/'+name+'.png',figure=fig)
def vis_breakups():
"""
This function allows to visualize break-ups as the highlighted branches in
the original skeleton.
"""
data_sk = glob.glob('/backup/yuliya/vsi05/skeletons_largdom/*.h5')
data_sk.sort()
data_br = glob.glob('/backup/yuliya/vsi05/breakups_con_correction/dict/*.npy')
data_br.sort()
for i,j in zip(data_sk[27:67][::-1], data_br[19:][::-1]):
d = tb.openFile(i, mode='r')
bran1 = np.copy(d.root.branches)
skel1 = np.copy(d.root.skel)
d.close()
br = np.load(j).item()
mlab.figure(1, bgcolor=(1,1,1), size=(1200,1200))
surf_skel = mlab.contour3d(skel1, colormap='hot')
surf_skel.actor.property.representation = 'points'
surf_skel.actor.property.line_width = 2.3
mask = np.zeros_like(skel1)
for k in br:
sl = br[k]
mask[sl] = (bran1[sl] == k)
surf_mask = mlab.contour3d(mask.astype('uint8'))
surf_mask.actor.property.representation = 'wireframe'
surf_mask.actor.property.line_width = 5
mlab.savefig('/backup/yuliya/vsi05/breakups_con_correction/video_hot/' + i[39:-3] + '.png')
mlab.close()
def run_plots(DataDirectory,Base_file):
root = DataDirectory+Base_file
filenames = get_filenames(root)
counter = 0
# create the plot for the initial raster
initial_file = filenames[0]
# read in the raster
raster = IO.ReadRasterArrayBlocks(initial_file)
f = mlab.figure(size=(1000,1000), bgcolor=(0.5,0.5,0.5))
s = mlab.surf(raster, warp_scale=0.4, colormap='gist_earth', vmax=100)
#mlab.outline(color=(0,0,0))
#mlab.axes(s, color=(1,1,1), z_axis_visibility=True, y_axis_visibility=False, xlabel='', ylabel='', zlabel='', ranges=[0,500,0,1000,0,0])
#@mlab.animate(delay=10)
#def anim():
# now loop through each file and update the z values
for fname in filenames:
this_rast = IO.ReadRasterArrayBlocks(fname)
s.mlab_source.scalars = this_rast
#f.scene.render()
#
mlab.savefig(fname[:-4]+'_3d.png')
def savefig(filename,mlabview=[],magnification = 3):
"""
Save mayavi figure
Parameters
----------
name : str
name of the figure
mlabview : [] | (x,y,z, np.array([ xroll,yroll,zroll]))
specifyy angle of camera view ( see mayavi.view )
magnification : int
resolution of the generated image ( see mayavi.savefig)
"""
import os
path = os.path.dirname(filename)
if not mlabview == []:
mlab.view(mlabview)
if os.path.exists(path):
mlab.savefig(filename+'.png',magnification=magnification )
else:
os.mkdir(path)
mlab.savefig(filename+'.png',magnification=magnification )
mlab.close()
def plot_mcontour(self, ndim0, ndim1, z, show_mode):
"use mayavi.mlab to plot contour."
if not mayavi_installed:
self.__logger.info("Mayavi is not installed on your device.")
return
#do 2d interpolation
#get slice object
s = np.s_[0:ndim0:1, 0:ndim1:1]
x, y = np.ogrid[s]
mx, my = np.mgrid[s]
#use cubic 2d interpolation
interpfunc = interp2d(x, y, z, kind='cubic')
newx = np.linspace(0, ndim0, 600)
newy = np.linspace(0, ndim1, 600)
newz = interpfunc(newx, newy)
#mlab
face = mlab.surf(newx, newy, newz, warp_scale=2)
mlab.axes(xlabel='x', ylabel='y', zlabel='z')
mlab.outline(face)
#save or show
if show_mode == 'show':
mlab.show()
elif show_mode == 'save':
mlab.savefig('mlab_contour3d.png')
else:
raise ValueError('Unrecognized show mode parameter : ' +
show_mode)
return
def plot_channel(self,fn_hist,fn_corrvec,fn_arrows):
list_channel = self.list_channel
import matplotlib.pyplot as plt
# plot vecs
plt.figure()
#vecs = np.array((3,len(list_channel)))
for i in range(3):
vecs = [chan.vec[i] for chan in list_channel]
plt.hist(vecs,bins=100)
plt.savefig(fn_hist)
# plot corr vecs
plt.figure()
v0 = [chan.vec[0] for chan in list_channel]
v1 = [chan.vec[1] for chan in list_channel]
v2 = [chan.vec[2] for chan in list_channel]
plt.plot(v0,v1,"o")
plt.plot(v0,v2,"o")
plt.plot(v1,v2,"o")
plt.savefig(fn_corrvec)
# cluster
import matplotlib.pyplot as plt
import mayavi.mlab as mlab
#from mlab import quiver3d
mlab.options.backend = 'envisage' # one way to save visualization
#f = mlab.figure()
mlab.figure()
x = [np.zeros(v.shape) for v in v0]
mlab.quiver3d(x,x,x,v0,v1,v2)
mlab.savefig(fn_arrows)
def action(u, x, xv, y, yv, t, n):
#print 'action, t=',t,'\nu=',u, '\Nx=',x, '\Ny=', y
if plot == 1:
mesh(xv, yv, u, title='t=%g' %t[n])
time.sleep(0.2) # pause between frames
elif plot == 2:
# mayavi plotting
mlab.clf()
extent1 = (0, 20, 0, 20,-2, 2)
s = mlab.surf(x , y, u, colormap='Blues', warp_scale=5,extent=extent1)
mlab.axes(s, color=(.7, .7, .7), extent=extent1,
ranges=(0, 10, 0, 10, -1, 1), xlabel='', ylabel='',
zlabel='',
x_axis_visibility=False, z_axis_visibility=False)
mlab.outline(s, color=(0.7, .7, .7), extent=extent1)
mlab.text(2, -2.5, '', z=-4, width=0.1)
mlab.colorbar(object=None, title=None, orientation='horizontal', nb_labels=None, nb_colors=None, label_fmt=None)
mlab.title('test 1D t=%g' % t[n])
mlab.view(142, -72, 50)
f = mlab.gcf()
camera = f.scene.camera
camera.yaw(0)
if plot > 0:
path = 'Figures_wave2D'
time.sleep(0) # pause between frames
if save_plot and plot != 2:
filename = '%s/%08d.png' % (path, n)
savefig(filename) # time consuming!
elif save_plot and plot == 2:
filename = '%s/%08d.png' % (path,n)
mlab.savefig(filename) # time consuming!
def short_branches():
"""
Visualization of short branches of the skeleton.
"""
data1_sk = glob.glob('/backup/yuliya/vsi05/skeletons_largdom/*.h5')
data1_sk.sort()
for i,j, k in zip(d[1][37:47], data1_sk[46:56], ell[1][37:47]):
g = nx.read_gpickle(i)
dat = tb.openFile(j)
skel = np.copy(dat.root.skel)
bra = np.copy(dat.root.branches)
mask = np.zeros_like(skel)
dat.close()
length = nx.get_edge_attributes(g, 'length')
number = nx.get_edge_attributes(g, 'number')
num_dict = {}
for m in number:
for v in number[m]:
num_dict.setdefault(v, []).append(m)
find_br = ndimage.find_objects(bra)
for l in list(length.keys()):
if length[l]<0.5*k: #Criteria
for b in number[l]:
mask[find_br[b-1]] = bra[find_br[b-1]]==b
mlab.figure(bgcolor=(1,1,1), size=(1200,1200))
mlab.contour3d(skel, colormap='hot')
mlab.contour3d(mask)
mlab.savefig('/backup/yuliya/vsi05/skeletons/short_bran/'+ i[42:-10] + '.png')
mlab.close()
def mayavi_scraper(block, block_vars, gallery_conf):
"""Scrape Mayavi images.
Parameters
----------
block : tuple
A tuple containing the (label, content, line_number) of the block.
block_vars : dict
Dict of block variables.
gallery_conf : dict
Contains the configuration of Sphinx-Gallery
Returns
-------
rst : str
The ReSTructuredText that will be rendered to HTML containing
the images. This is often produced by
:func:`sphinx_gallery.gen_rst.figure_rst`.
"""
from mayavi import mlab
image_path_iterator = block_vars['image_path_iterator']
image_paths = list()
e = mlab.get_engine()
for scene, image_path in zip(e.scenes, image_path_iterator):
mlab.savefig(image_path, figure=scene)
# make sure the image is not too large
scale_image(image_path, image_path, 850, 999)
image_paths.append(image_path)
mlab.close(all=True)
return figure_rst(image_paths, gallery_conf['src_dir'])
def make_fractal_movie(seed="X", iter=6):
"""
"""
f = hilbert.VoxelisedFractal.fromSeed(seed, iter)
f.center_fractal()
pos = np.array([vox.pos for vox in f.fractal])
max_pos = np.max(pos, axis=0)
mask = lambda x: np.sum((x[0:3]/max_pos[0:3])**2) <= 1
f = f.to_pretty_plot(refine=5, mayavi=True, mask=mask)
fig = mlab.gcf()
fig.scene.set_size([1024, 768])
fig.scene.render()
mlab.savefig("example/img/fractal_rotate0000.png")
step = 5
for ii in range(1, int(720/step)):
fig.scene.camera.azimuth(step)
fig.scene.render()
mlab.savefig("example/img/fractal_rotate{:04d}.png".format(ii))
mlab.savefig("example/img/fractal_zoom0000.png")
nsteps = 200
for ii in range(nsteps):
fig.scene.camera.zoom(1.02)
fig.scene.render()
mlab.savefig("example/img/fractal_zoom{:04d}.png".format(ii))
for ii in range(nsteps, nsteps + int(720/step)):
fig.scene.camera.azimuth(step)
fig.scene.render()
mlab.savefig("example/img/fractal_zoom{:04d}.png".format(ii))
return None
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 Main(outputFolder, isData, normalized = True):
assert isinstance(outputFolder, str)
min, max = 0., 1.
if os.path.splitext(args.file)[1] == '.arff':
datasets, targets, targetMap = loadFromArff(args.file)
elif os.path.splitext(args.file)[1] == '.npy':
datasets = numpy.load(args.file)
min = -1.
else:
assert False
datasets = (lambda x : histogramEqualization(x, min=min, max=max) if normalized else x)(datasets)
if normalized : assert (datasets.min(), datasets.max()) == (min, max)
if not os.path.isdir("%s/Pictures" % outputFolder):
os.makedirs("%s/Pictures" % outputFolder)
global listIndex
if listIndex is None or (len(listIndex) >= len(datasets)):
listIndex = xrange(len(datasets))
for index in listIndex:
assert 0 <= index < len(datasets)
mlab.figure("Index : %d" % index, bgcolor=(1,1,1))
showArray(datasets[index], isData)
mlab.savefig("%s/Pictures/Index_%d.png" % (outputFolder, index))
if isData:
saveData('%s/Pictures/%s_Index_%d.txt' % (outputFolder, targetMap.reverse_mapping[targets[index]], index), datasets[index])
else:
saveData('%s/Pictures/Index_%d.txt' % (outputFolder, index), datasets[index])
mlab.close()
def draw_volume(scalars):
# filts out low data
nmax = np.amax(scalars)
nmin = np.amin(scalars)
#nptp = nmax - nmin
#lowBound = nmin + nptp*0
#for i in np.nditer(scalars, op_flags=['readwrite']):
# if i<lowBound: i[...] = 0
# Get the shape of axes
shape = scalars.shape
# draw
fig = mlab.figure(bgcolor=(0,0,0), size=(800,800) )
src = mlab.pipeline.scalar_field(scalars)
src.update_image_data = True
#src.spacing = [1, 1, 1]
vol = mlab.pipeline.volume(src)
change_volume_property(vol)
#change_test(vol)
#vol = mlab.pipeline.image_plane_widget(src,plane_orientation='z_axes', slice_index=shape[2]/2)
ax = mlab.axes(nb_labels=5, ranges=(0,shape[0],0,shape[1],0,shape[2]))
mlab.savefig("result.jpg")
print "nmax =", nmax
print "nmin =", nmin
viewkeeper = mlab.view()
print "View", mlab.view()
draw_map(shape[0],shape[1])
mlab.view(viewkeeper[0],viewkeeper[1],viewkeeper[2],viewkeeper[3])
def plot_isosurface(crystal):
filename = './output/potentialfield.txt'
data = np.genfromtxt(filename, delimiter='\t')
size = np.round((len(data))**(1/3))
X = np.reshape(data[:,0], (size,size,size))
Y = np.reshape(data[:,1], (size,size,size))
Z = np.reshape(data[:,2], (size,size,size))
DeltaU = np.reshape(data[:,3], (size,size,size))
average = np.average(crystal.coordinates[:,0])
start = average - crystal.a
end = average + crystal.a
coords1 = np.array([[start, start, start]])
coords2 = np.array([[end, end, end]])
array1 = np.repeat(coords1,len(crystal.coordinates),axis=0)
array2 = np.repeat(coords2,len(crystal.coordinates),axis=0)
basefilter1 = np.greater(crystal.coordinates,array1)
basefilter2 = np.less(crystal.coordinates,array2)
basefilter = np.nonzero(np.all(basefilter1*basefilter2, axis=1))
base = crystal.coordinates[basefilter]
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(1, 1, 1), size=(2048,2048))
dataset = mlab.contour3d(X, Y, Z, DeltaU, contours=[3.50],color=(1,0.25,0))
scatter = mlab.points3d(base[:,0],
base[:,1],
base[:,2],
color=(0.255,0.647,0.88),
resolution=24,
scale_factor=1.0,
opacity=0.40)
mlab.view(azimuth=17, elevation=90, distance=10, focalpoint=[average,average-0.2,average])
mlab.draw()
savename = './output/3Dpotential.png'
mlab.savefig(savename, size=(2048,2048))
mlab.show()
def snapshot(cuds, filename):
""" Save a snapshot of the cuds object using the default visualisation.
Parameters
----------
cuds :
A top level cuds object (e.g. a mesh). The method will detect
the type of object and create the appropriate visualisation.
filename : string
The filename to use for the output file.
"""
# adapt to CUDSSource
if isinstance(cuds, (ABCMesh, ABCParticles, ABCLattice)):
source = CUDSSource(cuds=cuds)
else:
msg = 'Provided object {} is not of any known cuds type'
raise TypeError(msg.format(type(cuds)))
# set image size
size = 800, 600
with get_figure(size=size):
# add source
mlab.pipeline.add_dataset(source)
modules = default_module(source)
# add default modules
for module in modules:
source.add_module(module)
mlab.savefig(filename, size=size)
def setUp(self):
# set up source
sgrid = datasets.generateStructuredGrid()
source = VTKDataSource(data=sgrid)
self.engine = mlab.get_engine()
# set up scene, first scene is empty
# second scene has the settings we want to restore
for _ in range(2):
fig = mlab.figure()
fig.scene.off_screen_rendering = True
# add source
self.engine.add_source(source)
# add more modules
self.engine.add_module(IsoSurface())
self.engine.add_module(Text3D())
self.modules = source.children[0].children
# set camera
self.view = (25., 14., 20., [0., 0., 2.5])
mlab.view(*self.view)
# save the visualisation
self.temp_dir = tempfile.mkdtemp()
self.filename = os.path.join(self.temp_dir, "test_vis.mv2")
self.engine.save_visualization(self.filename)
# save the scene as an image for comparison later
self.ref_saved_filename = os.path.join(self.temp_dir, "ref_saved.png")
mlab.savefig(self.ref_saved_filename)
def test_restore_scene(self):
# create a new scene with new data source
fig = mlab.figure()
fig.scene.off_screen_rendering = True
sgrid_2 = datasets.generateStructuredGrid()
source = VTKDataSource(data=sgrid_2)
self.engine.add_source(source)
# when
restore_scene(self.filename, scene_index=1)
# then
modules = source.children[0].children
self.check_items_same_types(modules, self.modules)
self.check_items_not_same_object(modules, self.modules)
self.check_camera_view(mlab.view(), self.view)
# save the scene to a file
saved_filename = os.path.join(self.temp_dir, "test_restore.png")
mlab.savefig(saved_filename)
# compare the pixels to the desired one
self.check_images_almost_identical(saved_filename,
self.ref_saved_filename)
def plot_inverse_operator(subject, fnout_img=None, verbose=None):
""""Reads in and plots the inverse solution."""
# get name of inverse solution-file
subjects_dir = os.environ.get('SUBJECTS_DIR')
fname_dir_inv = os.path.join(subjects_dir,
subject)
for files in os.listdir(fname_dir_inv):
if files.endswith(",cleaned_epochs_avg-7-src-meg-inv.fif"):
fname_inv = os.path.join(fname_dir_inv,
files)
break
try:
fname_inv
except NameError:
print "ERROR: No forward solution found!"
sys.exit()
# read inverse solution
inv = min_norm.read_inverse_operator(fname_inv)
# print some information if desired
if verbose is not None:
print "Method: %s" % inv['methods']
print "fMRI prior: %s" % inv['fmri_prior']
print "Number of sources: %s" % inv['nsource']
print "Number of channels: %s" % inv['nchan']
# show 3D source space
lh_points = inv['src'][0]['rr']
lh_faces = inv['src'][0]['tris']
rh_points = inv['src'][1]['rr']
rh_faces = inv['src'][1]['tris']
# create figure and plot results
fig_inverse = mlab.figure(size=(1200, 1200),
bgcolor=(0, 0, 0))
mlab.triangular_mesh(lh_points[:, 0],
lh_points[:, 1],
lh_points[:, 2],
lh_faces)
mlab.triangular_mesh(rh_points[:, 0],
rh_points[:, 1],
rh_points[:, 2],
rh_faces)
# save result
if fnout_img is not None:
mlab.savefig(fnout_img,
figure=fig_inverse,
size=(1200, 1200))
mlab.close(all=True)
def helix(name, resolution=401):
z = np.linspace(0, 2, resolution)
x = np.cos(4 * np.pi * z)
y = np.sin(4 * np.pi * z)
c = mlab.plot3d(x, y, z, line_width=.2, color=(1, 0, 0))
# Save the plot.
mlab.savefig(name)
mlab.clf()
def anim():
f = mlab.gcf()
for count, i in enumerate(range(2,361,2)):
#while 1:
f.scene.camera.azimuth(degree_step)
f.scene.render()
mlab.savefig('/Users/jessebrown/Desktop/mayavi_figs/r_hipp_network%03d.png' %count)
yield
def beach(x,y):
q = x*(Lx - x)*y*(Ly - y)
q = 0.5*x**2 + 0.5*y
q += (q.min() + 0.1)
q /= (q.max() + 0.1)
s = mlab.mesh(x, y, q)
mlab.savefig("beach.png")
return q
def plot_u(u, x, xv, y, yv, t, n):
"""User action function for plotting."""
if t[n] == 0:
time.sleep(2)
if plot_method == 1:
# Works well with Gnuplot backend, not with Matplotlib
st.mesh(x, y, u, title='t=%g' % t[n], zlim=[-1,1],
caxis=[-1,1])
elif plot_method == 2:
# Works well with Gnuplot backend, not with Matplotlib
st.surfc(xv, yv, u, title='t=%g' % t[n], zlim=[-1, 1],
colorbar=True, colormap=st.hot(), caxis=[-1,1],
shading='flat')
elif plot_method == 3:
print 'Experimental 3D matplotlib...under development...'
# Probably too slow
#plt.clf()
ax = fig.add_subplot(111, projection='3d')
u_surf = ax.plot_surface(xv, yv, u, alpha=0.3)
#ax.contourf(xv, yv, u, zdir='z', offset=-100, cmap=cm.coolwarm)
#ax.set_zlim(-1, 1)
# Remove old surface before drawing
if u_surf is not None:
ax.collections.remove(u_surf)
plt.draw()
time.sleep(1)
elif plot_method == 4:
# Mayavi visualization
mlab.clf()
extent1 = (0, 20, 0, 20,-2, 2)
s = mlab.surf(x , y, u,
colormap='Blues',
warp_scale=5,extent=extent1)
mlab.axes(s, color=(.7, .7, .7), extent=extent1,
ranges=(0, 10, 0, 10, -1, 1),
xlabel='', ylabel='', zlabel='',
x_axis_visibility=False,
z_axis_visibility=False)
mlab.outline(s, color=(0.7, .7, .7), extent=extent1)
mlab.text(6, -2.5, '', z=-4, width=0.14)
mlab.colorbar(object=None, title=None,
orientation='horizontal',
nb_labels=None, nb_colors=None,
label_fmt=None)
mlab.title('Gaussian t=%g' % t[n])
mlab.view(142, -72, 50)
f = mlab.gcf()
camera = f.scene.camera
camera.yaw(0)
if plot_method > 0:
time.sleep(0) # pause between frames
if save_plot:
filename = 'tmp_%04d.png' % n
if plot_method == 4:
mlab.savefig(filename) # time consuming!
elif plot_method in (1,2):
st.savefig(filename) # time consuming!
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 points3d():
pts = np.linspace(0, 4 * np.pi, 30)
x = np.sin(2 * pts)
y = np.cos(pts)
z = np.cos(2 * pts)
s = 2 + np.sin(pts)
mlab.points3d(x, y, z, s, colormap="cool", scale_factor=0.15)
mlab.savefig("points3d.png", size=png_size)
mlab.clf()
def field(potential, outfile=None, title="BEM Calculation (field)", cmap="spectral"):
from mayavi import mlab
u,v,w = np.gradient(potential)
obj = mlab.quiver3d(u,v,w, colormap=cmap, vmax=10)
mlab.colorbar()
if outfile:
mlab.savefig(outfile)
return obj
def snapshot(self,params):
res=np.ceil(500*params.dpi/8000*111) #TODO this resolution is too hi
from mayavi import __version__ as mayavi_version
if float(mayavi_version[:3]) >= 4.3:
mlab.savefig(params.savefile,figure=self.scene.mayavi_scene,
size=(res,res))
else:
self.hack_mlabsavefig(params.savefile,size=(res,res))
def surfacePlot(
Hmap,
nrows,
ncols,
xyspacing,
zscale,
name,
hRange,
file_path,
lutfromfile,
lut,
lut_file_path,
colorbar_on,
save,
show,
):
# Create a grid of the x and y coordinates corresponding to each pixel in the height matrix
x, y = np.mgrid[0 : ncols * xyspacing : xyspacing, 0 : nrows * xyspacing : xyspacing]
# Create a new figure
mlab.figure(size=(1000, 1000))
# Set the background color if desired
# bgcolor=(0.16, 0.28, 0.46)
# Create the surface plot of the reconstructed data
plot = mlab.surf(x, y, Hmap, warp_scale=zscale, vmin=hRange[0], vmax=hRange[1], colormap=lut)
# Import the LUT from a file if necessary
if lutfromfile:
plot.module_manager.scalar_lut_manager.load_lut_from_file(lut_file_path)
# Draw the figure with the new LUT if necessary
mlab.draw()
# Zoom in to fill the entire window
f = mlab.gcf()
f.scene.camera.zoom(1.05)
# Change the view to a top-down perspective
mlab.view(270, 0)
# Add a colorbar if indicated by colorbar_on (=True)
if colorbar_on:
# mlab.colorbar(title='Height (nm)', orientation='vertical')
mlab.colorbar(orientation="vertical")
# Save the figure if indicated by save (=True)
if save:
mlab.savefig(file_path, size=(1000, 1000))
if show == False:
mlab.close()
# Keep the figure open if indicated by show (=True)
if show:
mlab.show()
def anim():
from mayavi import mlab as M
M.options.offscreen = True
M.test_mesh()
f = M.figure()
print f
for i in range(36):
f.scene.camera.azimuth(10)
M.savefig('img%02i.png'%i)
cen = np.vstack((cen, [0, 0, 0, 1]))
mlab.points3d(cen[:, 0],
cen[:, 1],
cen[:, 2],
color=(1, 0, 0),
scale_factor=0.5)
velo = filter_ground(velo, cen, th=1000)
# draw annotated objects
filled_idx = np.zeros((velo.shape[0], ), dtype=bool)
for j, box in enumerate(tracklet_rects[i]):
draw_class.draw_box(box, tracklet_ids[i][j])
idx = in_hull(velo[:, :3], box[:3, :].T)
draw_class.draw_cluster(velo[idx, :], tracklet_ids[i][j])
filled_idx |= idx
# print other points
draw_class.draw_cluster(velo[~filled_idx, :])
mlab.view(azimuth=180, elevation=0, distance=30,
focalpoint=[0., 0., 0.]) # tracking-view
# mlab.view(azimuth=180, elevation=0, distance=70, roll=90, focalpoint=[0., 0., 0.]) # over-view
plt_img = imayavi_return_inline(fig=fig)
plt.imshow(plt_img)
plt_fig.canvas.draw()
mlab.savefig('./output/%s_%s/test_%03d.png' % (date, drive, i))
print('./output/%s_%s/test_%03d.png' % (date, drive, i), plt_img.shape)
def plotIsosurfaces3D(x3D,
y3D,
z3D,
surface3Dlist,
contourList,
slice3Dlist=(None, ),
boundSurface=(None, ),
customColors=(None, ),
figDir='./',
name='UntitledIsosurface',
sliceOriantations=(None, ),
sliceOffsets=(None, ),
boundSlice=(None, ),
sliceValRange=(None, )):
from mayavi import mlab
from mayavi.modules.contour_grid_plane import ContourGridPlane
from mayavi.modules.outline import Outline
from mayavi.api import Engine
import numpy as np
from warnings import warn
x3D, y3D, z3D = np.array(x3D), np.array(y3D), np.array(z3D)
engine = Engine()
engine.start()
# if len(engine.scenes) == 0:
# engine.new_scene()
if customColors[0] is not None:
customColors = iter(customColors)
else:
customColors = iter(
('inferno', 'viridis', 'coolwarm', 'Reds', 'Blues') * 2)
contourList = iter(contourList)
if sliceOriantations[0] is not None:
sliceOriantations = iter(sliceOriantations)
else:
sliceOriantations = iter(('z_axes', ) * len(slice3Dlist))
if boundSurface[0] is None:
boundSurface = (x3D.min(), x3D.max(), y3D.min(), y3D.max(), z3D.min(),
z3D.max())
if boundSlice[0] is None:
boundSlice = (x3D.min(), x3D.max(), y3D.min(), y3D.max(), z3D.min(),
z3D.max())
if sliceOffsets[0] is None:
sliceOffsets = iter((0, ) * len(slice3Dlist))
else:
sliceOffsets = iter(sliceOffsets)
if sliceValRange[0] is None:
sliceValRange = iter((None, None) * len(slice3Dlist))
else:
sliceValRange = iter(sliceValRange)
mlab.figure(name,
engine=engine,
size=(1200, 900),
bgcolor=(1, 1, 1),
fgcolor=(0.5, 0.5, 0.5))
for surface3D in surface3Dlist:
color = next(customColors)
# If a colormap given
if isinstance(color, str):
mlab.contour3d(x3D,
y3D,
z3D,
surface3D,
contours=next(contourList),
colormap=color,
extent=boundSurface)
# If only one color of (0-1, 0-1, 0-1) given
else:
mlab.contour3d(x3D,
y3D,
z3D,
surface3D,
contours=next(contourList),
color=color,
extent=boundSurface)
# cgp = ContourGridPlane()
# engine.add_module(cgp)
# cgp.grid_plane.axis = 'y'
# cgp.grid_plane.position = x3D.shape[1] - 1
# cgp.contour.number_of_contours = 20
# # contour_grid_plane2.actor.mapper.scalar_range = array([298., 302.])
# # contour_grid_plane2.actor.mapper.progress = 1.0
# # contour_grid_plane2.actor.mapper.scalar_mode = 'use_cell_data'
# cgp.contour.filled_contours = True
# cgp.actor.property.lighting = False
for slice3D in slice3Dlist:
sliceOriantation = next(sliceOriantations)
sliceOffset = next(sliceOffsets)
if sliceOriantation == 'z_axes':
origin = np.array([boundSlice[0], boundSlice[2], sliceOffset])
point1 = np.array([boundSlice[1], boundSlice[2], sliceOffset])
point2 = np.array([boundSlice[0], boundSlice[3], sliceOffset])
elif sliceOriantation == 'x_axes':
origin = np.array([sliceOffset, boundSlice[2], boundSlice[4]])
point1 = np.array([sliceOffset, boundSlice[3], boundSlice[4]])
point2 = np.array([sliceOffset, boundSlice[2], boundSlice[5]])
else:
origin = np.array([boundSlice[0], sliceOffset, boundSlice[4]])
point1 = np.array([boundSlice[1], sliceOffset, boundSlice[4]])
point2 = np.array([boundSlice[0], sliceOffset, boundSlice[5]])
image_plane_widget = mlab.volume_slice(
x3D,
y3D,
z3D,
slice3D,
plane_orientation=sliceOriantation,
colormap=next(customColors),
vmin=next(sliceValRange),
vmax=next(sliceValRange))
image_plane_widget.ipw.reslice_interpolate = 'cubic'
image_plane_widget.ipw.origin = origin
image_plane_widget.ipw.point1 = point1
image_plane_widget.ipw.point2 = point2
# image_plane_widget.ipw.slice_index = 2
image_plane_widget.ipw.slice_position = sliceOffset
# Contour grid plane at last y if the last slice is in xy plane
if sliceOriantation == 'z_axes':
cgp2 = ContourGridPlane()
engine.add_module(cgp2)
cgp2.grid_plane.axis = 'y'
cgp2.grid_plane.position = x3D.shape[1] - 1
cgp2.contour.number_of_contours = 20
cgp2.contour.filled_contours = True
cgp2.actor.property.lighting = False
outline = Outline()
engine.add_module(outline)
outline.actor.property.color = (0.2, 0.2, 0.2)
outline.bounds = np.array(boundSurface)
outline.manual_bounds = True
mlab.view(azimuth=270, elevation=45)
mlab.move(-500, 0, 0)
mlab.savefig(figDir + '/' + name + '.png', magnification=3)
print('\nFigure ' + name + ' saved at ' + figDir)
mlab.show()
scene = engine.scenes[0]
scene.scene.background = (1.0, 1.0, 1.0) # white background
scene.scene.camera.position = [
-62.711753438810604, -53.97895685490163, 35.505462783605786
]
scene.scene.camera.focal_point = [
-85.78755652469613, -4.492737440904958, -3.103982951529997
]
scene.scene.camera.view_angle = 15.36
scene.scene.camera.view_up = [
-0.24399876718044772, 0.5232570448142633, 0.8164965809277254
]
scene.scene.camera.clipping_range = [39.987551578962204, 100.87872165042502]
scene.scene.camera.compute_view_plane_normal()
scene.scene.render()
col_options += ["elevation"]
for col_type in col_options:
meshes[col_type].visible = True
scene.scene.camera.compute_view_plane_normal()
scene.scene.render()
meshes[col_type].visible = True
if args.nolight:
meshes[col_type].actor.property.edge_visibility = True
meshes[col_type].actor.property.line_width = 1.5
filename = save_path / f"{col_type}.png"
mlab.savefig(str(filename), magnification=1)
meshes[col_type].visible = False
meshes[col_type].actor.property.edge_visibility = False
#scan_dir = f'data/kitti/object/training/velodyne_reduced/{file_id}.bin'
scan_dir = f'velodyne_reduced_reduced/{file_id}.bin'
if not os.path.exists(scan_dir):
print("skip %s for pc" % file_id)
continue
scan = np.fromfile(scan_dir, dtype=np.float32).reshape(-1, 4)
# load labels
#label_dir = f'data/kitti/object/training/label_2/{file_id}.txt'
label_dir = f'label_2_reduced/{file_id}.txt'
with open(label_dir, 'r') as f:
labels = f.readlines()
# draw point cloud
plot = mlab.points3d(scan[:, 0],
scan[:, 1],
scan[:, 2],
mode="point",
figure=fig,
color=(1, 0, 0))
for line in labels:
draw_box(line, colors)
mlab.view(azimuth=230, distance=100)
mlab.savefig(filename='reduced_output/%s_GT.png' % file_id)
print("Saved %s_GT.png" % file_id)
mlab.clf(fig)
mlab.close(all=True)
brain = stc.plot(
subject='fsaverage',
hemi='both',
background='white',
foreground='black',
time_label='',
initial_time=0,
smoothing_steps=5,
figure=fig,
)
brain.scale_data_colormap(0, 1, stc.data.max(), True)
brain.add_annotation('aparc', borders=2)
# Save some views
mlab.view(0, 90, 450, [0, 0, 0])
mlab.savefig('../paper/figures/degree_rh.png', magnification=4)
mlab.view(180, 90, 450, [0, 0, 0])
mlab.savefig('../paper/figures/degree_lh.png', magnification=4)
mlab.view(180, 0, 450, [0, 10, 0])
mlab.savefig('../paper/figures/degree_top.png', magnification=4)
mlab.view(180, 180, 480, [0, 10, 0])
mlab.savefig('../paper/figures/degree_bottom.png', magnification=4)
# Plot the connectivity diagram
fig, _ = con_parc.plot(title='Parcel-wise Connectivity',
facecolor='white',
textcolor='black',
node_edgecolor='white',
colormap='plasma_r',
vmin=0,
show=False)
def rotat_visu():
hours = np.tile(np.repeat(range(0, 24), 5), 3)
azimuths = np.arange(0, 360)
distances = np.append(np.linspace(20000, 6000, 120), np.repeat(6000, 240))
for azimuth, hour, distance in zip(azimuths[234:], hours[234:],
distances[234:]):
print(hour)
T = concat_dfs(inpath, column=hour, regex="TPC")
Q = concat_dfs(inpath, column=hour, regex="QPC")
U = concat_dfs(inpath, column=hour, regex="UPC")
V = concat_dfs(inpath, column=hour, regex="VPC")
# Sum the PCs
T = T.sum(axis=1)
Q = Q.sum(axis=1)
U = U.sum(axis=1)
V = V.sum(axis=1)
T = T.values.reshape(239, 244)
Q = Q.values.reshape(239, 244)
U = U.values.reshape(239, 244)
V = V.values.reshape(239, 244)
W = np.zeros(U.shape)
s = np.ones(U.shape)
mlab.figure(bgcolor=(0.7, 0.7, 0.7), fgcolor=(0., 0., 0.))
# # Surface plot
pts = mlab.quiver3d(x,
y,
z,
s,
s,
s,
scalars=T,
mode='cube',
scale_factor=0,
colormap='bwr')
pts.glyph.color_mode = "color_by_scalar"
pts.glyph.glyph_source.glyph_source.center = [0, 0, 0]
# pts.module_manager.scalar_lut_manager.lut.table = T
# draw()
mesh = mlab.pipeline.delaunay2d(pts) # Create and visualize the mesh
surf = mlab.pipeline.surface(mesh, colormap='bwr', vmin=12, vmax=26)
# surf.module_manager.scalar_lut_manager.reverse_lut = True # reverse colormap
sc = mlab.scalarbar(surf, title="Temperature (C)", label_fmt="%.0f")
sc.scalar_bar_representation.position = [0.1, 0.9]
sc.scalar_bar_representation.position2 = [0.8, 0.05]
# Quiver plot
mask_point = 4
quiver3d(x[::mask_point, ::mask_point],
y[::mask_point, ::mask_point],
z[::mask_point, ::mask_point] + 50,
U[::mask_point, ::mask_point],
V[::mask_point, ::mask_point],
W[::mask_point, ::mask_point],
opacity=0.8,
mode='2darrow',
color=(0.3, 0.3, 0.3),
scale_factor=150,
transparent=True)
# mlab.axes()
mlab.text(0.45,
0.97,
"Time: " + str(hour).zfill(3) + "H",
width=0.1,
line_width=0.01,
color=(0, 0, 0))
mlab.view(azimuth=azimuth,
elevation=60,
distance=distance,
focalpoint=np.array([3500, 2500, 0]))
mlab.savefig(outpath + str(azimuth).zfill(3) + ".png",
size=(1420, 1080),
magnification='auto')
mlab.close()
coil_data_fil, params_fil = get_all_coil_data("../../tests/w7x/w7x_fil.hdf5")
_, _, r_fil, _ = CoilSet.get_outputs(coil_data_fil, params_fil)
centroid_fil = CoilSet.get_r_centroid(coil_data_fil, params_fil)
coil_data_fb, params_fb = get_all_coil_data("../../tests/w7x/w7x_fb.hdf5")
_, _, r_fb, _ = CoilSet.get_outputs(coil_data_fb, params_fb)
centroid_fb = CoilSet.get_r_centroid(coil_data_fb, params_fb)
coil_data_rot, params_rot = get_all_coil_data("../../tests/w7x/w7x_rot.hdf5")
_, _, r_rot, _ = CoilSet.get_outputs(coil_data_rot, params_rot)
centroid_rot = CoilSet.get_r_centroid(coil_data_rot, params_rot)
r_surf, nn, sg, _ = read_w7x_data()
mlab.options.offscreen = True
mlab.figure(size=(2400, 2400), bgcolor=(1, 1, 1))
p = plot_surface(r_surf)
#r_c = compute_r_centroid(NS, fc_init)
#p = plot_coils(r_fil, color=(0.8,0.0,0.0))
#p = plot_coils(r_fb, color=(0.0,0.0,0.8))
p = plot_coils(r_rot, color=(0.0, 0.0, 0.8))
mlab.view(azimuth=0.0,
elevation=180,
distance=26.5,
focalpoint=(0.0, 0.0, 0.0),
roll=None,
reset_roll=True)
mlab.savefig('w7x.png', figure=mlab.gcf())
#mlab.savefig('w7x.pdf', figure=mlab.gcf())
#mlab.savefig('w7x.eps', figure=mlab.gcf())
mlab.clf()
#H=nx.krackhardt_kite_graph()
#H=nx.Graph();H.add_edge('a','b');H.add_edge('a','c');H.add_edge('a','d')
#H=nx.grid_2d_graph(4,5)
H=nx.cycle_graph(20)
# reorder nodes from 0,len(G)-1
G=nx.convert_node_labels_to_integers(H)
# 3d spring layout
pos=nx.spring_layout(G,dim=3)
# numpy array of x,y,z positions in sorted node order
xyz=np.array([pos[v] for v in sorted(G)])
# scalar colors
scalars=np.array(G.nodes())+5
mlab.figure(1, bgcolor=(0, 0, 0))
mlab.clf()
pts = mlab.points3d(xyz[:,0], xyz[:,1], xyz[:,2],
scalars,
scale_factor=0.1,
scale_mode='none',
colormap='Blues',
resolution=20)
pts.mlab_source.dataset.lines = np.array(G.edges())
tube = mlab.pipeline.tube(pts, tube_radius=0.01)
mlab.pipeline.surface(tube, color=(0.8, 0.8, 0.8))
mlab.savefig('mayavi2_spring.png')
mlab.show() # interactive window
def cross_section(self,
axis,
pct=0.5,
show_axis=True,
show=True,
save=None,
startup=True):
"""
Plot a cross-section of the model, with the Y-axis plotting depth
If axis == X, the slice is normal to the Y-axis
if axis == Y, the slice is normal to the X-axis
:type axis: str
:param axis: axis of axis to slice along, 'X', 'Y'
:type pct: float
:param pct: percentage to slice the model at based on the model range.
defaults to 50%, or the middle of the model
:type show_axis: bool
:param show_axis: show the axis of the plot
:type save: str
:param save: save the figure with a unique generic identifier
:type show: bool
:param show: show the figure after making it
:type startup: bool
:param startup: run the _startup() function which closes all instances
of mlab and creates a new mlab figure and engine. Normally the
necessary thing to do so defaults to True
"""
src_color = self.kwargs.get("src_color", "g")
src_marker = self.kwargs.get("src_marker", "sphere")
rcv_color = self.kwargs.get("rcv_color", "w")
rcv_marker = self.kwargs.get("rcv_marker", "2ddiamond")
if startup:
self._startup()
axis = axis.upper()
def slice_range(ranges_):
"""
Convenience function to calculate where to slice by checking the
min and max of the range and multiplying by a percentage
:type ranges_: list
:param ranges_: [xmin, xmax, ymin, ymax, zmin, zmax]
"""
if axis == "X":
return pct * (ranges_[1] - ranges_[0]) + ranges_[0]
elif axis == "Y":
return pct * (ranges_[3] - ranges_[2]) + ranges_[2]
# Determine what part of the axis we are slicing
slice_val = slice_range(self.ranges)
tag = slice_range(self.axes_ranges)
logger.info(
f"Cross section [{axis}] of '{self.fid}' at {slice_val*1E-3}")
if show_axis:
set_axes(xyz=[True, True, False],
ranges=self.axes_ranges,
**self.kwargs)
self._cut_plane(axis=axis, slice_at=slice_val)
# Differentiate which way were slicing to put the sources and receivers
slice_x, slice_y = None, None
if axis == "X":
slice_x = slice_val
elif axis == "Y":
slice_y = slice_val
# Plot the sources and receivers along a given slice
if self.rcvs is not None:
srcrcv(self.rcvs,
color=rcv_color,
x_value=slice_x,
y_value=slice_y,
marker=rcv_marker)
if self.srcs is not None:
# Sources default to spheres because I didn't want to figure out how
# to rotate 2d glyphs
srcrcv(self.srcs,
color=src_color,
x_value=slice_x,
y_value=slice_y,
marker=src_marker)
# Set the camera with a side on view. No preset so set to the preset and
# rotate to get to the proper frame of reference
scene = self.engine.scenes[0]
if axis == "X":
scene.scene.x_plus_view()
elif axis == "Y":
scene.scene.y_plus_view()
scene.scene.camera.roll(90)
# Plot extras
colorscale(orientation="horizontal", **self.kwargs)
# Annotation location changes based on which axis you slice
anno_tag = f"{axis.lower()}={pct*1E2}%={tag:.2f}km"
if axis == "X":
annotate(x=.2, y=.3, s=anno_tag, c="k", width=0.15)
elif axis == "Y":
annotate(x=.27, y=.3, s=anno_tag, c="k", width=0.15)
# Finalize
save_tag = None
if save:
save_tag = save.format(tag=f"{tag:.0f}km")
mlab.savefig(save_tag)
if show:
mlab.show()
else:
mlab.close(self.fig)
return save_tag
def depth_slice(self,
depth_km="surface",
save=None,
show=True,
startup=True,
anno_text=None):
"""
Plot a topdown view of the model, either with a surface projection
(map view) or at some depth slice determined by `depth_km`
:type depth_km: float or None
:param depth_km: depth to show the model at in units of km, by default
plots a map view of the 'surface'
:type save: str
:param save: save the figure with a unique generic identifier
:type show: bool
:param show: show the figure after making it
:type startup: bool
:param startup: run the _startup() function which closes all instances
of mlab and creates a new mlab figure and engine. Normally the
necessary thing to do so defaults to True
:type anno_text: str
:param anno_text: text to annotate into the corner of the figure,
defaults to annotating the depth value
"""
src_color = self.kwargs.get("src_color", "g")
src_marker = self.kwargs.get("src_marker", "2dcircle")
rcv_color = self.kwargs.get("rcv_color", "w")
rcv_marker = self.kwargs.get("rcv_marker", "2ddiamond")
if startup:
self._startup()
logger.info(f"Depth Slice (Z) of '{self.fid}' at {depth_km} [km]")
coastline_z = self.ranges[-1]
if depth_km != "surface":
depth_m = -1 * abs(depth_km) * 1E3
coastline_z = depth_m
# Put the coastline at some height above the topography, or at depth
if self.coast is not None:
coastline(self.coast, coastline_z)
# Plot the stations and receivers
if self.rcvs is not None:
srcrcv(self.rcvs,
color=rcv_color,
z_value=coastline_z,
marker=rcv_marker)
if self.srcs is not None:
srcrcv(self.srcs,
color=src_color,
z_value=coastline_z,
marker=src_marker)
# Plot the model at the given height
# Axes behave weirdly when cutting planes so turn off Y-axis, not sure
# why this works... sorry
if depth_km == "surface":
# Show a surface projection
self.engine.add_filter(Surface(), self.vtkfr)
tag = depth_km
set_axes(xyz=[True, True, False],
ranges=self.axes_ranges,
**self.kwargs)
else:
# Show a slice (plane) at a given depth
self._cut_plane(axis="Z", slice_at=depth_m)
tag = f"depth_{int(abs(depth_km))}km"
set_axes(xyz=[True, False, True],
ranges=self.axes_ranges,
**self.kwargs)
# Set the camera with top down view
scene = self.engine.scenes[0]
scene.scene.z_plus_view()
# Plot extras
colorscale(orientation="vertical", **self.kwargs)
if anno_text is None:
anno_text = tag.replace("_", " ")
annotate(s=anno_text, c="k", width=0.175) # was 0.2
# Finalize
save_tag = None
if save:
mlab.savefig(save.format(tag=tag))
save_tag = save.format(tag=tag)
if show:
mlab.show()
else:
mlab.close(self.fig)
return save_tag
all_boxes = np.concatenate(all_box)
draw_gt_boxes3d(all_boxes,
conf,
thres=thres,
color=(0, 1, 0),
fig=fig,
engine=e)
#mlab.view(azimuth=0, elevation=45, focalpoint='auto', roll=90, figure=fig)
#mlab.savefig("new6.png", figure=fig)
scene = e.scenes[-1]
cam = scene.scene.camera
mlab.savefig('/home/manojpc/PointRCNN/imagelist/{}_0.png'.format(PC_ID),
size=(1920, 1080),
magnification=3)
scene.scene.x_plus_view()
cam.zoom(2.8)
# scene.scene.save('/home/manojpc/snapshotx.png',size=(1600,1400))
mlab.savefig('/home/manojpc/PointRCNN/imagelist/{}_1.png'.format(PC_ID),
size=(1920, 1080),
magnification=3)
# mlab.savefig("myfigurex.jpg", size=(1600,1400),figure=fig)
scene.scene.y_plus_view()
cam.zoom(1.9)
mlab.savefig('/home/manojpc/PointRCNN/imagelist/{}_2.png'.format(PC_ID),
size=(1920, 1080),
magnification=3)
# mlab.savefig("myfigurey.jpg", size=(1600,1400),figure=fig)
import numpy as np
from particle.pipeline import Sand
from mayavi import mlab
# data = np.load("output/geometry/generatedParticles/vae.npy")
# initInd = 281
# for i, particle in enumerate(data[initInd:initInd+30]):
# Sand(particle).visualize()
# mlab.savefig(f"/home/chuan/obj/{i+1:02d}.obj")
# mlab.close(all=True)
data = np.load("data/liutao/v1/particles.npz")["trainSet"]
print(data.shape)
initInd = 114
for i, particle in enumerate(data[initInd:initInd+30]):
Sand(particle[0]).visualize()
mlab.savefig(f"/home/chuan/obj/{i+1:02d}.obj")
mlab.savefig(f"/home/chuan/obj/{i+1:02d}.png")
mlab.close(all=True)
foreground='black',
time_label='',
initial_time=0,
smoothing_steps=5,
figure=fig,
colormap="jet", # others "bwr"
subjects_dir=mri_dir,
)
brain.scale_data_colormap(fmin, fmid / 4, fmax, True,
center=0) # optimize these values !!
brain.add_annotation('HCPMMP1_combined', borders=1, alpha=0.9)
# Save some views
mlab.view(0, 90, 450, [0, 0, 0])
mlab.savefig('{d}{cont}_connectivity_{f}_hcp_sum_rh.png'.format(
d=save_dir, cont=contrast, f=freq),
magnification=4)
mlab.view(180, 90, 450, [0, 0, 0])
mlab.savefig('{d}{cont}_connectivity_{f}_hcp_sum_lh.png'.format(
d=save_dir, cont=contrast, f=freq),
magnification=4)
mlab.view(180, 0, 450, [0, 10, 0])
mlab.savefig('{d}{cont}_connectivity_{f}_hcp_sum_top.png'.format(
d=save_dir, cont=contrast, f=freq),
magnification=4)
mlab.view(180, 180, 480, [0, 10, 0])
mlab.savefig(
'{d}{cont}_connectivity_{f}_hcp_sum_bottom.png'.format(
d=save_dir, cont=contrast, f=freq),
magnification=4)
# mlab.close(fig)
def plot_potential(grid, potential, along_axes=False, interactive=False, size=(800,700)):
# The Grid
u, v = grid.get_nodes(split=True, flat=False)
u = real(u)
v = real(v)
# Create potential and evaluate eigenvalues
potew = potential.evaluate_eigenvalues_at(grid)
potew = [ level.reshape(grid.get_number_nodes(overall=False)) for level in potew ]
# Plot the energy surfaces of the potential
fig = mlab.figure(size=size)
for level in potew:
mlab.surf(u, v, real(level))
fig.scene.parallel_projection = True
fig.scene.isometric_view()
fig.scene.show_axes = True
mlab.savefig("potential_3D_view.png")
# Parallele views
if along_axes is True:
fig.scene.x_minus_view()
mlab.savefig("potential_xm_view.png")
fig.scene.x_plus_view()
mlab.savefig("potential_xp_view.png")
fig.scene.y_minus_view()
mlab.savefig("potential_ym_view.png")
fig.scene.y_plus_view()
mlab.savefig("potential_yp_view.png")
fig.scene.z_minus_view()
mlab.savefig("potential_zm_view.png")
fig.scene.z_plus_view()
mlab.savefig("potential_zp_view.png")
if interactive is True:
# Enable interactive plot
mlab.show()
else:
mlab.close(fig)
model = minkunet(args.model, pretrained=True)
elif 'SPVCNN' in args.model:
model = spvcnn(args.model, pretrained=True)
elif 'SPVNAS' in args.model:
model = spvnas_specialized(args.model, pretrained=True)
else:
raise NotImplementedError
model = model.to(device)
input_point_clouds = sorted(os.listdir(args.velodyne_dir))
for point_cloud_name in input_point_clouds:
if not point_cloud_name.endswith('.bin'):
continue
label_file_name = point_cloud_name.replace('.bin', '.label')
vis_file_name = point_cloud_name.replace('.bin', '.png')
gt_file_name = point_cloud_name.replace('.bin', '_GT.png')
pc = np.fromfile('%s/%s' % (args.velodyne_dir, point_cloud_name),
dtype=np.float32).reshape(-1, 4)
label = np.fromfile('%s/%s' % (args.velodyne_dir, label_file_name),
dtype=np.int32)
feed_dict = process_point_cloud(pc, label)
inputs = feed_dict['lidar'].to(device)
outputs = model(inputs)
predictions = outputs.argmax(1).cpu().numpy()
predictions = predictions[feed_dict['inverse_map']]
fig = draw_lidar(feed_dict['pc'], predictions.astype(np.int32))
mlab.savefig('%s/%s' % (output_dir, vis_file_name))
fig = draw_lidar(feed_dict['pc'], feed_dict['targets_mapped'])
mlab.savefig('%s/%s' % (output_dir, gt_file_name))
def show_selected_grasps_with_color(m, ply_name_, title, obj_):
m_good = m[m[:, 1] <= 0.4]
if len(m_good) > 25:
m_good = m_good[np.random.choice(len(m_good), size=25, replace=True)]
m_bad = m[m[:, 1] >= 1.8]
if len(m_bad) > 25:
m_bad = m_bad[np.random.choice(len(m_bad), size=25, replace=True)]
collision_grasp_num = 0
if save_fig or show_fig:
# fig 1: good grasps
mlab.figure(bgcolor=(1, 1, 1),
fgcolor=(0.7, 0.7, 0.7),
size=(1000, 1000))
mlab.pipeline.surface(mlab.pipeline.open(ply_name_))
for a in m_good:
# display_gripper_on_object(obj, a[0]) # real gripper
collision_free = display_grasps(a[0], obj_,
color='d') # simulated gripper
if not collision_free:
collision_grasp_num += 1
if save_fig:
mlab.savefig("good_" + title + ".png")
mlab.close()
elif show_fig:
mlab.title(title, size=0.5)
# fig 2: bad grasps
mlab.figure(bgcolor=(1, 1, 1),
fgcolor=(0.7, 0.7, 0.7),
size=(1000, 1000))
mlab.pipeline.surface(mlab.pipeline.open(ply_name_))
for a in m_bad:
# display_gripper_on_object(obj, a[0]) # real gripper
collision_free = display_grasps(a[0], obj_, color=(1, 0, 0))
if not collision_free:
collision_grasp_num += 1
if save_fig:
mlab.savefig("bad_" + title + ".png")
mlab.close()
elif show_fig:
mlab.title(title, size=0.5)
mlab.show()
elif generate_new_file:
# only to calculate collision:
collision_grasp_num = 0
ind_good_grasp_ = []
for i_ in range(len(m)):
collision_free = display_grasps(m[i_][0], obj_, color=(1, 0, 0))
if not collision_free:
collision_grasp_num += 1
else:
ind_good_grasp_.append(i_)
collision_grasp_num = str(collision_grasp_num)
collision_grasp_num = (
4 - len(collision_grasp_num)) * " " + collision_grasp_num
print("collision_grasp_num =", collision_grasp_num, "| object name:",
title)
return ind_good_grasp_
color=(1,1,0), scale_factor=10, opacity=1)
mlab.quiver3d(x_sats[co_orb], y_sats[co_orb], z_sats[co_orb],\
vx_sats[co_orb], vy_sats[co_orb], vz_sats[co_orb],\
color=(1,1,0), scale_factor=30, opacity=1)
mlab.points3d(x_sats[counter_orb], y_sats[counter_orb], z_sats[counter_orb],\
color=(1,0,1), scale_factor=10, opacity=1)
mlab.quiver3d(x_sats[counter_orb], y_sats[counter_orb], z_sats[counter_orb],\
vx_sats[counter_orb], vy_sats[counter_orb], vz_sats[counter_orb],\
color=(1,0,1), scale_factor=30, opacity=1)
mlab.points3d(0, 0, 0, 20, mode='2dcircle', color=(1,1,1),\
scale_factor=1.0, opacity=1, resolution=100)
mlab.savefig('mw_satellites_3d.png', size=(200, 200))
#mlab.close()
#Function that reads the N-body simulation orbit
"""
def reading_Nbody(snap_name):
data = np.loadtxt(snap_name)
#time = data[:,0]
#Rgal = data[:,1]
x_sat= data[:,6]
y_sat = data[:,7]
z_sat = data[:,8]
x_gal = data[:,0]
y_gal = data[:,1]
z_gal = data[:,2]
#Vgal = data[:,8]
def generate_file_rst(fname, target_dir, src_dir, plot_gallery):
""" Generate the rst file for a given example.
"""
image_name = fname[:-2] + 'png'
global rst_template, plot_rst_template
this_template = rst_template
last_dir = os.path.split(src_dir)[-1]
# to avoid leading . in file names
if last_dir == '.': last_dir = ''
else: last_dir += '_'
short_fname = last_dir + fname
src_file = os.path.join(src_dir, fname)
example_file = os.path.join(target_dir, fname)
shutil.copyfile(src_file, example_file)
if plot_gallery and fname.startswith('plot'):
# generate the plot as png image if file name
# starts with plot and if it is more recent than an
# existing image.
if not os.path.exists(os.path.join(target_dir, 'images')):
os.makedirs(os.path.join(target_dir, 'images'))
image_file = os.path.join(target_dir, 'images', image_name)
if (not os.path.exists(image_file)
or os.stat(image_file).st_mtime <= os.stat(src_file).st_mtime):
print 'plotting %s' % fname
import matplotlib.pyplot as plt
plt.close('all')
try:
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
mlab.close(all=True)
except:
pass
try:
execfile(example_file, {'pl': plt})
facecolor = plt.gcf().get_facecolor() # hack to keep black bg
if facecolor == (0.0, 0.0, 0.0, 1.0):
plt.savefig(image_file, facecolor='black')
else:
plt.savefig(image_file)
try:
try:
from mayavi import mlab
except ImportError:
from enthought.mayavi import mlab
e = mlab.get_engine()
if len(e.scenes) > 0:
mlab.savefig(image_file, size=(400, 400))
except:
pass
except:
print 80 * '_'
print '%s is not compiling:' % fname
traceback.print_exc()
print 80 * '_'
this_template = plot_rst_template
docstring, short_desc, end_row = extract_docstring(example_file)
f = open(os.path.join(target_dir, fname[:-2] + 'rst'), 'w')
f.write(this_template % locals())
f.flush()
def UV_PCs(nbpc=0, use_U=False, scale_factor=150):
# Get reconstructed variables field
inpath = "/home/thomas/phd/framework/model/out/local_downscaling/ribeirao_articleII/pres_obs_3d/"
if use_U:
outpath = "/home/thomas/phd/framework/visu/res/pres/U/" + "PC" + str(
nbpc + 1) + "/"
else:
outpath = "/home/thomas/phd/framework/visu/res/pres/V/" + "PC" + str(
nbpc + 1) + "/"
# 360 numbers between 0 and 24h
hours = range(0, 23)
for hour in hours:
print(hour)
# T = concat_dfs(inpath, column = hour, regex="TPC")
# Q = concat_dfs(inpath, column = hour, regex="QPC")
U = concat_dfs(inpath, column=hour, regex="UPC")
V = concat_dfs(inpath, column=hour, regex="VPC")
# Sum the PCs
# T = T.iloc[:,nbpc]
# Q = Q.sum(axis=1)
if use_U == False:
U = U.iloc[:, 0]
V = V.iloc[:, nbpc]
else:
U = U.iloc[:, nbpc]
V = V.iloc[:, 0]
# T = T.values.reshape(239,244)
# Q = Q.values.reshape(239,244)
U = U.values.reshape(239, 244)
V = V.values.reshape(239, 244)
if use_U == False:
U = np.zeros(U.shape)
else:
V = np.zeros(V.shape)
W = np.zeros(U.shape)
s = np.ones(U.shape)
T = np.ones(U.shape)
mlab.figure(bgcolor=(0.7, 0.7, 0.7), fgcolor=(0., 0., 0.))
# # # Surface plot
pts = mlab.quiver3d(x,
y,
z,
s,
s,
s,
scalars=T,
mode='cube',
scale_factor=0,
colormap='bwr')
pts.glyph.color_mode = "color_by_scalar"
pts.glyph.glyph_source.glyph_source.center = [0, 0, 0]
pts.module_manager.scalar_lut_manager.lut.table = T
draw()
mesh = mlab.pipeline.delaunay2d(pts) # Create and visualize the mesh
surf = mlab.pipeline.surface(mesh, colormap='Greys', vmin=-3, vmax=10)
# surf.module_manager.scalar_lut_manager.reverse_lut = True # reverse colormap
# sc = mlab.scalarbar(surf, title="Temperature (C)", label_fmt="%.0f")
#
# sc.scalar_bar_representation.position = [0.1, 0.9]
# sc.scalar_bar_representation.position2 = [0.8, 0.05]
#
#
# Quiver plot
mask_point = 4
quiver3d(x[::mask_point, ::mask_point],
y[::mask_point, ::mask_point],
z[::mask_point, ::mask_point] + 100,
U[::mask_point, ::mask_point],
V[::mask_point, ::mask_point],
W[::mask_point, ::mask_point],
opacity=0.8,
line_width=3,
mode='2darrow',
color=(0.1, 0.1, 0.1),
scale_factor=scale_factor,
transparent=True)
#
# mlab.axes()
#
# mlab.text(0.45, 0.97, "Time: "+str(hour).zfill(2) + "H", width=0.1, line_width=0.01, color=(0,0,0))
mlab.view(azimuth=255,
elevation=40,
distance=6500,
focalpoint=np.array([3500, 2600, 0]))
mlab.savefig(outpath + str(hour).zfill(2) + ".png",
size=(1420, 1080),
magnification='auto')
mlab.close()
mlab.text3d(x[0] - 0.075*nX, y[0] - 0.075*nY, ZOffset/2, "gE", orientation=[0, 0, 0],
orient_to_camera=True, color=(0, 0, 0), line_width=3, scale=textScale)
mlab.text3d(x[1] + 0.075*nX, y[1] + 0.075*nY, ZOffset/2, "gI", orientation=[0, 0, 0],
orient_to_camera=True, color=(0, 0, 0), line_width=3, scale=textScale)
# Neuron labels
startLabelOffset = 1
labelScale = 1.5
mlab.text3d(-startLabelOffset, -startLabelOffset, ZOffset - 1,
str(nX), orientation=[0, 0, 0], orient_to_camera=True, color=(0, 0, 0),
line_width=3, scale=labelScale)
mlab.text3d(nX, -startLabelOffset, ZOffset - 1,
"1", orientation=[0, 0, 0], orient_to_camera=True, color=(0, 0, 0),
line_width=3, scale=labelScale)
mlab.text3d(nX - 1, nY + 1, ZOffset + 0.5,
str(nY), orientation=[0, 0, 0], orient_to_camera=True, color=(0, 0, 0),
line_width=3, scale=labelScale)
roll = 177.9584710619396
view = (-20.96663248113742,
107.30927449790735,
94.066015884153884,
np.array([ 17.14404891, 16.30124532, 9.85753332]))
mlab.view(*view)
mlab.roll(roll)
fname = outputDir + "/network_layers.png"
mlab.savefig(fname)
def T_PCs(nbpc=0, vmin=None, vmax=None):
# Get reconstructed variables field
inpath = "/home/thomas/phd/framework/model/out/local_downscaling/ribeirao_articleII/pres_obs_3d/"
outpath = "/home/thomas/phd/framework/visu/res/pres/T/PC" + str(nbpc +
1) + "/"
# 360 numbers between 0 and 24h
hours = range(0, 23)
for hour in hours:
print(hour)
T = concat_dfs(inpath, column=hour, regex="TPC")
# Q = concat_dfs(inpath, column = hour, regex="QPC")
# U = concat_dfs(inpath, column = hour, regex="UPC")
# V = concat_dfs(inpath, column = hour, regex="VPC")
# Sum the PCs
T = T.iloc[:, nbpc]
# Q = Q.sum(axis=1)
# U = U.sum(axis=1)
# V = V.sum(axis=1)
T = T.values.reshape(239, 244)
# Q = Q.values.reshape(239,244)
# U = U.values.reshape(239,244)
# V = V.values.reshape(239,244)
# W = np.zeros(U.shape)
s = np.ones((239, 244))
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0., 0., 0.))
# # Surface plot
pts = mlab.quiver3d(x,
y,
z,
s,
s,
s,
scalars=T,
mode='cube',
scale_factor=0,
colormap='bwr')
pts.glyph.color_mode = "color_by_scalar"
pts.glyph.glyph_source.glyph_source.center = [0, 0, 0]
# pts.module_manager.scalar_lut_manager.lut.table = T
# draw()
mesh = mlab.pipeline.delaunay2d(pts) # Create and visualize the mesh
surf = mlab.pipeline.surface(mesh,
colormap='coolwarm',
vmin=vmin,
vmax=vmax)
# surf.module_manager.scalar_lut_manager.reverse_lut = True # reverse colormap
# sc = mlab.scalarbar(surf, title="Temperature (C)", label_fmt="%.1f")
# sc.scalar_bar_representation.position = [0.2, 0.85]
# sc.scalar_bar_representation.position2 = [0.6, 0.08]
# sc.scalar_bar_representation.position = [0.2, 0]
# sc.scalar_bar_representation.position2 = [0.6, 0.08]
#
#
# # Quiver plot
# mask_point=4
# quiver3d(x[::mask_point,::mask_point],y[::mask_point,::mask_point],z[::mask_point,::mask_point]+100,
# U[::mask_point,::mask_point], V[::mask_point,::mask_point], W[::mask_point,::mask_point],
# opacity=0.8,
# mode='2darrow', color=(0.3,0.3,0.3), scale_factor=150, transparent=True)
# mlab.axes()
# mlab.text(0.78, 0.13, "Time: "+str(hour).zfill(2) + "H", width=0.2, line_width=0.01, color=(0,0,0))
mlab.view(azimuth=255,
elevation=40,
distance=6500,
focalpoint=np.array([3500, 2600, 0]))
# mlab.view(azimuth=260, elevation=45, distance=7000, focalpoint=np.array([3250,2000,0]))
mlab.savefig(outpath + str(hour).zfill(2) + ".png",
size=(1420, 1080),
magnification='auto')
mlab.close()
})
vecomp.widget.widget.set_transform(vecomp._transform)
vecomp.widget.update_implicit_function()
vecomp.render()
vecomp.widget.widget.enabled = False
# vecomp.filter.inside_out = True
# Surface with z as colormap
surf = mlab.pipeline.surface(vecomp, vmax=1, vmin=-1, colormap='RdYlBu')
surf.actor.property.interpolation = 'flat'
mlab.view(elevation=0, azimuth=0)
# Set the view (coordinates from Mayavi GUI)
fig.scene.camera.position = [
225.79348206790564, -65.35463216386151, 152.22309264982135
]
fig.scene.camera.focal_point = [
89.481086187422022, 89.317925608749434, 9.359478555383852
]
fig.scene.camera.view_angle = 30.0
fig.scene.camera.view_up = [
-0.39936240943471379, 0.40687820822779919, 0.82155936462305368
]
fig.scene.camera.clipping_range = [21.185478265041723, 538.96253637184236]
fig.scene.camera.compute_view_plane_normal()
mlab.savefig('system_3d_cylinder.png')
mlab.show()
def creator(name, curDir):
saveName = name.split('.')[0]
data = np.load(curDir+'/'+name)
plotter = np.array([data[0]]).reshape(160,160)*255
mlab.barchart(plotter, auto_scale=False, reset_zoom=False)
mlab.savefig('../../mayaHist/'+saveName+'.png')
fig1 = mlab.figure(size=(300, 300))
fig2 = mlab.figure(size=(300, 300))
brain1 = stc_face.plot(
subject='sub002',
hemi='split',
views='med',
background='white',
foreground='black',
time_label='',
initial_time=1,
figure=[fig1, fig2],
)
brain1.scale_data_colormap(1e-23, 1.5e-23, 2.4e-23, True)
mlab.savefig('../paper/figures/power_face_lh.png',
figure=fig1,
magnification=4)
mlab.savefig('../paper/figures/power_face_rh.png',
figure=fig2,
magnification=4)
fig3 = mlab.figure(size=(300, 300))
fig4 = mlab.figure(size=(300, 300))
brain2 = stc_scrambled.plot(
subject='fsaverage',
hemi='split',
views='med',
background='white',
foreground='black',
time_label='',
initial_time=1,
def plot_surf_views(surfin, hemi, pngoutdir, cmapout, colorbarflag):
s1 = dfsio.readdfs(surfin)
pex = np.max(np.abs(s1.attributes))
cdict = cm.Colormap.create_bidirectional_logpvalues_cmap_dict(pex)
cdict_orig = deepcopy(cdict)
# Scale the attribute values in cdict from 0 to 1
for i in range(0, len(cdict['red'])):
cdict['red'][i] = ((cdict['red'][i][0] + pex) / (2 * pex),
cdict['red'][i][1], cdict['red'][i][2])
for i in range(0, len(cdict['green'])):
cdict['green'][i] = ((cdict['green'][i][0] + pex) / (2 * pex),
cdict['green'][i][1], cdict['green'][i][2])
for i in range(0, len(cdict['blue'])):
cdict['blue'][i] = ((cdict['blue'][i][0] + pex) / (2 * pex),
cdict['blue'][i][1], cdict['blue'][i][2])
my_cmap = mpl_colors.LinearSegmentedColormap('my_bi_cmap', cdict, 256)
my_cmap._init()
# my_cmap, cdict_orig = cm.create_bidirectional_log_colormap(pminneg,pmaxneg,pminpos,pmaxpos,pIDneg,pIDpos)
head, tail = os.path.split(surfin)
cmap_png_filename = pngoutdir + '/' + os.path.splitext(
tail)[0] + '_colormap' + '.png.eps'
try:
os.mkdir(pngoutdir)
except OSError:
pass
if cmapout is not None:
cmapfilename = os.path.join(pngoutdir, 'colormap')
cm.Colormap.exportParaviewCmap(cdict_orig, cmapfilename + '.xml')
# cm.Colormap.exportMayavi2LUT(-1*pex, 1*pex, cmapfilename)
if colorbarflag:
cm.Colormap.show_and_save_colorbar(s1.attributes, my_cmap, cdict_orig,
cmap_png_filename)
mlab.figure(size=(1200, 800), bgcolor=(1, 1, 1))
tmesh = mlab.triangular_mesh(s1.vertices[:, 0],
s1.vertices[:, 1],
s1.vertices[:, 2],
s1.faces,
colormap='hot',
scalars=s1.attributes,
vmin=cdict_orig['red'][0][0],
vmax=cdict_orig['red'][-1][0])
lut = tmesh.module_manager.scalar_lut_manager.lut.table.to_array()
tmesh.module_manager.scalar_lut_manager.lut.table = ((
my_cmap._lut[0:256, :] * 255).astype('uint32'))
# if colorbaronlyflag:
# mlab.colorbar(orientation='vertical',nb_labels=14,nb_colors=256)
# pngfilename = pngoutdir + '/' + os.path.splitext(tail)[0] +'_colobar' +'.png'
# mlab.savefig(pngfilename)
# img = imcrop.autoCrop(pngfilename)
# img = imcrop.imTrans(img)
# img.save(pngfilename,'PNG')
# return
if hemi == 'lh':
vs = np.array([[170, 65], [0, 90], [90, 100], [-90, 85], [-90, 180],
[-90, 0]])
rollvar = [75, -75, None, None, 0, 0]
else:
vs = np.array([[10, 75], [180, 90], [90, 100], [-90, 85], [-90, 180],
[-90, 0]])
rollvar = [-75, 75, None, None, 0, 0]
for i in np.arange(0, len(vs)):
mlab.view(vs[i][0], vs[i][1], roll=rollvar[i])
pngfilename = pngoutdir + '/' + os.path.splitext(
tail)[0] + '_view' + str(i + 1) + '.png'
mlab.savefig(pngfilename)
# img = imcrop.autoCrop(pngfilename)
# img = imcrop.imTrans(img)
# img.save(pngfilename,'PNG')
sys.stdout.write(str(i) + ' ')
sys.stdout.flush()
'''
num = len(gt_boxes3d)
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(1)
# mlab.view(azimuth=180, elevation=70, focalpoint=[ 12.0909996 , -1.04700089, -2.03249991], distance=62.0, figure=fig)
return fig
if __name__ == '__main__':
pc = np.loadtxt('mayavi/kitti_sample_scan.txt')
fig = draw_lidar(pc)
mlab.savefig('pc_view.jpg', figure=fig)
raw_input()
print "el volumen haciendo la suma de los voxels de la mask es:", nGFPfinal
intensidadmedia = float((fGmascarafinal).sum()) / nGFPfinal
print "la intensidad media con la suma de voxels de la mask es:", intensidadmedia
return mask3d
"""mostramos las imágenes 3d"""
vol = volumen_voxels(data_0)
vol = np.multiply(vol, data_0)
"""fig1 = mlab.figure(figure='Superficie',bgcolor=(0, 0, 0), size=(400, 500))
fig1.scene.disable_render = True
src1= mlab.pipeline.scalar_field(vol)
representacion_1=mlab.pipeline.iso_surface(src1,color=(1,0,0))
fig2 = mlab.figure(figure='Volumen',bgcolor=(0, 0, 0), size=(400, 500))
fig2.scene.disable_render = True
src2= mlab.pipeline.scalar_field(vol)
representacion_0=mlab.pipeline.volume(src2,color=(0,1,0))
fig3 = mlab.figure(figure='Superficie y Volumen',bgcolor=(0, 0, 0), size=(400, 500))
fig3.scene.disable_render = True
src3= mlab.pipeline.scalar_field(vol)
representacion_2=mlab.pipeline.iso_surface(src3,color=(1,0,0))
representacion_3=mlab.pipeline.volume(src3,color=(0,1,0))"""
mlab.options.offscreen = True
mlab.pipeline.iso_surface(mlab.pipeline.scalar_field(vol),
color=(1, 0, 0),
opacity=0.5)
mlab.savefig(sys.argv[1])
"""mlab.close(all=True)
mlab.show()"""
xp = 2 * linewidth * np.sin(phi) * np.cos(theta) + x[pos]
yp = 2 * linewidth * np.sin(phi) * np.sin(theta) + y[pos]
zp = 2 * linewidth * np.cos(phi) + z[pos]
mlab.mesh(xp + shift, yp, zp, opacity=1, color=(0.07, 0.68, 0.37))
if "Pv" in args.filename:
begin = end
continue
ind = int(cent.mean() / resolution / length * m)
xp = 2 * linewidth * np.sin(phi) * np.cos(theta) + x[ind]
yp = 2 * linewidth * np.sin(phi) * np.sin(theta) + y[ind]
zp = 2 * linewidth * np.cos(phi) + z[ind]
mlab.mesh(xp + shift, yp, zp, opacity=1, color=(0.67, 0.77, 0.93))
begin = end
if args.outfile is None:
outfile = args.filename.replace("results",
"images").replace(".txt", ".png")
else:
outfile = args.outfile
try:
os.makedirs(os.path.dirname(outfile))
except OSError:
pass
mlab.savefig(outfile, magnification=4)
if args.show:
mlab.show()
def saveFigure(self, name=None):
'''
Similar to plot, except that figures are saved to file
:param name: the file name of the plot image
'''
if self.k == 3:
import mayavi.mlab as mlab
mlab.options.offscreen = True
predictFig = mlab.figure(figure='predict')
mlab.clf(figure='predict')
errorFig = mlab.figure(figure='error')
mlab.clf(figure='error')
if self.testfunction:
truthFig = mlab.figure(figure='test')
mlab.clf(figure='test')
dx = 1
pts = 75j
X, Y, Z = np.mgrid[0:dx:pts, 0:dx:pts, 0:dx:pts]
scalars = np.zeros(X.shape)
errscalars = np.zeros(X.shape)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
for k1 in range(X.shape[2]):
errscalars[i][j][k1] = self.predicterr_normalized(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
scalars[i][j][k1] = self.predict_normalized(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
if self.testfunction:
tfscalars = np.zeros(X.shape)
for i in range(X.shape[0]):
for j in range(X.shape[1]):
for k1 in range(X.shape[2]):
tfscalars[i][j][k1] = self.testfunction(
[X[i][j][k1], Y[i][j][k1], Z[i][j][k1]])
mlab.contour3d(tfscalars,
contours=15,
transparent=True,
figure=truthFig,
compute_normals=False)
# obj = mlab.contour3d(scalars, contours=10, transparent=True)
pred = mlab.contour3d(scalars,
contours=15,
transparent=True,
figure=predictFig)
pred.compute_normals = False
errpred = mlab.contour3d(errscalars,
contours=15,
transparent=True,
figure=errorFig)
errpred.compute_normals = False
mlab.savefig('%s_prediction.wrl' % name, figure=predictFig)
mlab.savefig('%s_error.wrl' % name, figure=errorFig)
if self.testfunction:
mlab.savefig('%s_actual.wrl' % name, figure=truthFig)
mlab.close(all=True)
if self.k == 2:
samplePoints = zip(*self.X)
# Create a set of data to plot
plotgrid = 61
x = np.linspace(0, 1, num=plotgrid)
y = np.linspace(0, 1, num=plotgrid)
X, Y = np.meshgrid(x, y)
# Predict based on the optimized results
zs = np.array([
self.predict_normalized([x, y])
for x, y in zip(np.ravel(X), np.ravel(Y))
])
Z = zs.reshape(X.shape)
Z = (
Z *
(self.ynormRange[1] - self.ynormRange[0])) + self.ynormRange[0]
# Calculate errors
zse = np.array([
self.predicterr_normalized([x, y])
for x, y in zip(np.ravel(X), np.ravel(Y))
])
Ze = zse.reshape(X.shape)
if self.testfunction:
# Setup the truth function
zt = self.testfunction(
np.array(
zip(
np.ravel((X * (self.normRange[0][1] -
self.normRange[0][0])) +
self.normRange[0][0]),
np.ravel((Y * (self.normRange[1][1] -
self.normRange[1][0])) +
self.normRange[1][0]))))
ZT = zt.reshape(X.shape)
# Plot real world values
X = (X * (self.normRange[0][1] - self.normRange[0][0])
) + self.normRange[0][0]
Y = (Y * (self.normRange[1][1] - self.normRange[1][0])
) + self.normRange[1][0]
spx = (self.X[:, 0] * (self.normRange[0][1] - self.normRange[0][0])
) + self.normRange[0][0]
spy = (self.X[:, 1] * (self.normRange[1][1] - self.normRange[1][0])
) + self.normRange[1][0]
fig = plt.figure(figsize=(8, 6))
# contour_levels = np.linspace(min(zt), max(zt),50)
contour_levels = 15
plt.plot(spx, spy, 'ow')
cs = plt.colorbar()
if self.testfunction:
pass
plt.plot(spx, spy, 'ow')
cs = plt.colorbar()
plt.plot(spx, spy, 'ow')
ax = fig.add_subplot(212, projection='3d')
ax.plot_surface(X, Y, Z, rstride=3, cstride=3, alpha=0.4)
if self.testfunction:
ax.plot_wireframe(X, Y, ZT, rstride=3, cstride=3)
if name:
plt.savefig(name)
else:
plt.savefig('pyKrigingResult.png')
# data2[45,45:56,55]=m1;
# data2[55,45:56,45]=m1;
# data2[45,55,45:56]=m1;
# data2[55,45,45:56]=m1;
field=mlab.pipeline.scalar_field(x, y, z,(data))
# field2=mlab.pipeline.scalar_field(x, y, z,data2)
vol=mlab.pipeline.volume(field)#, contours=10, transparent=True)
vol._ctf = ctf
vol.update_ctf = True
# vol._otf = otf
# vol._volume_property.set_scalar_opacity(otf)
# vol2=mlab.pipeline.volume(field2, vmax=0)
# vol2._volume_property.set_color(ctf)
# # vol2._ctf = ctf
# # vol2.update_ctf = True
# vol2._otf = otf
# vol2._volume_property.set_scalar_opacity(otf)
mlab.view(azimuth=(-150+(i*5)), elevation=60, distance=500, focalpoint=(49,49,49))
# mlab.move(right=30)
mlab.savefig(filename='./anim/fig%03d.png'%i)
# mlab.show()
mlab.close()
# mlab.show()
#ffmpeg -r 10 -i fig%03d.png -c:v libx264 -vf fps=60 -pix_fmt yuv420p out.mp4
#ffmpeg -f concat -i list.txt -c copy output.mp4
