def plot_3D_data(x,y,z,fig_title):
# Visualize it with mlab.surf
from mayavi import mlab
mlab.figure(bgcolor=(1, 1, 1))
surf = mlab.surf(z, colormap='cool')
# Retrieve the LUT of the surf object.
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(0, 255, 256)
# 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
mlab.title(fig_title,color=(0,0,0))
#
mlab.axes(surf, color=(.7, .7, .7),
ranges=(0, 1, 0, 1, 0, 1), xlabel='', ylabel='',
zlabel='Probability',
x_axis_visibility=False, z_axis_visibility=False)
# We need to force update of the figure now that we have changed the LUT.
#mlab.axes()
mlab.orientation_axes()
#mlab.xlabel('X')
mlab.view(40, 85)
#mlab.title(fig_title)
mlab.draw()
mlab.show()
def surfcf(gridx, gridy, phase, modulus, colormap=None):
r"""Plot the modulus of a complex valued function :math:`f:R^2 -> C`
together with its phase in a color coded fashion.
:param gridx: The grid nodes along the :math:`x` axis of the real domain :math:`R^2`
:param gridy: The grid nodes along the :math:`y` axis of the real domain :math:`R^2`
:param phase: The phase of the complex domain result f(grid)
:param modulus: The modulus of the complex domain result f(grid)
:param colormap: The colormap to use, if none is given, compute the 'default' QM colormap.
"""
if colormap is None:
colormap = compute_color_map()
# The real(.) is necessary just to get an array with dtype real
mesh = mlab.mesh(gridx, gridy, real(modulus), scalars=phase)
# Set the custom color map
mesh.module_manager.scalar_lut_manager.use_default_range = False
mesh.module_manager.scalar_lut_manager.data_range = [-pi, pi]
lut = mesh.module_manager.scalar_lut_manager.lut.table.to_array()
lut[:,0:3] = colormap.copy()
mesh.module_manager.scalar_lut_manager.lut.table = lut
# Update the figure
mlab.draw()
return mesh
def zoncaview(m):
"""
m is a healpix sky map, such as provided by WMAP or Planck.
"""
nside = hp.npix2nside(len(m))
vmin = -1e3; vmax = 1e3
# Set up some grids:
xsize = ysize = 1000
theta = np.linspace(np.pi, 0, ysize)
phi = np.linspace(-np.pi, np.pi, xsize)
longitude = np.radians(np.linspace(-180, 180, xsize))
latitude = np.radians(np.linspace(-90, 90, ysize))
# Project the map to a rectangular matrix xsize x ysize:
PHI, THETA = np.meshgrid(phi, theta)
grid_pix = hp.ang2pix(nside, THETA, PHI)
grid_map = m[grid_pix]
# Create a sphere:
r = 0.3
x = r*np.sin(THETA)*np.cos(PHI)
y = r*np.sin(THETA)*np.sin(PHI)
z = r*np.cos(THETA)
# The figure:
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300))
mlab.clf()
mlab.mesh(x, y, z, scalars=grid_map, colormap="jet", vmin=vmin, vmax=vmax)
mlab.draw()
return
def visualize_different_color():
# Primitives
N = 200 # Number of points
ones = np.ones(N)
scalars = np.arange(N) # Key point: set an integer for each point
# scalars = np.random.randint(0, 5, N)
# Define color table (including alpha), which must be uint8 and [0,255]
colors = (np.random.random((N, 4)) * 255).astype(np.uint8)
colors[:, -1] = 255 # No transparency
# Define coordinates and points
x, y, z = colors[:,
0], colors[:,
1], colors[:,
2] # Assign x, y, z values to match color
pts = mlab.quiver3d(x,
y,
z,
ones,
ones,
ones,
scalars=scalars,
mode='sphere') # Create points
pts.glyph.color_mode = 'color_by_scalar' # Color by scalar
# Set look-up table and redraw
pts.module_manager.scalar_lut_manager.lut.table = colors
mlab.draw()
mlab.show()
return 0
def mlab_imshowColor(im, alpha=255, **kwargs):
"""
Plot a color image with mayavi.mlab.imshow.
im is a ndarray with dim (n, m, 3) and scale (0->255]
alpha is a single number or a ndarray with dim (n*m) and scale (0->255]
**kwargs is passed onto mayavi.mlab.imshow(..., **kwargs)
"""
try:
alpha[0]
except:
alpha = pl.ones(im.shape[0] * im.shape[1]) * alpha
if len(alpha.shape) != 1:
alpha = alpha.flatten()
# The lut is a Nx4 array, with the columns representing RGBA
# (red, green, blue, alpha) coded with integers going from 0 to 255,
# we create it by stacking all the pixles (r,g,b,alpha) as rows.
myLut = pl.c_[im.reshape(-1, 3), alpha]
myLutLookupArray = pl.arange(im.shape[0] * im.shape[1]).reshape(im.shape[0], im.shape[1])
#We can display an color image by using mlab.imshow, a lut color list and a lut lookup table.
theImshow = mlab.imshow(myLutLookupArray, colormap='binary', **kwargs) #temporary colormap
theImshow.module_manager.scalar_lut_manager.lut.table = myLut
mlab.draw()
return theImshow
def draw_scene():
s = mlab.pipeline.triangular_mesh_source(x, y, z, triIndices)
s.data.cell_data.scalars = np.cos(phaseAngle)
surf = mlab.pipeline.surface(s)
surf.contour.filled_contours = True
surf.contour.minimum_contour = 0.0
surf.contour.maximum_contour = 1.0
surf.module_manager.scalar_lut_manager.data_range = (0,1)
mlab.plot3d(xSun_plt, ySun_plt, zSun_plt, tube_radius=fStretch/1000, color=(1,1,0))
mlab.plot3d(xSC_plt, ySC_plt, zSC_plt, tube_radius=fStretch/1000, color=(0,0,1))
ball_x = []
ball_y = []
ball_z = []
for i in range(nPixelsX):
for j in range(nPixelsY):
p = np.dot(R, pVectors[:,i,j])
p_tan = np.dot(rCG, p) * p + rSC
xVIR_plt, yVIR_plt, zVIR_plt = plt_coords(rSC, 1.1*fStretch*p)
mlab.plot3d(xVIR_plt, yVIR_plt, zVIR_plt, tube_radius=fStretch/5000, color=(0,0,0))
ball_x.append(p_tan[0])
ball_y.append(p_tan[1])
ball_z.append(p_tan[2])
mlab.points3d(ball_x, ball_y, ball_z, np.ones(len(ball_x)), scale_factor=150,
color=(1,0.7,0.1))
mlab.draw()
def surfcf(gridx, gridy, phase, modulus, colormap=None):
r"""Plot the modulus of a complex valued function :math:`f:R^2 -> C`
together with its phase in a color coded fashion.
:param gridx: The grid nodes along the :math:`x` axis of the real domain :math:`R^2`
:param gridy: The grid nodes along the :math:`y` axis of the real domain :math:`R^2`
:param phase: The phase of the complex domain result f(grid)
:param modulus: The modulus of the complex domain result f(grid)
:param colormap: The colormap to use, if none is given, compute the 'default' QM colormap.
"""
if colormap is None:
colormap = compute_color_map()
# The real(.) is necessary just to get an array with dtype real
mesh = mlab.mesh(gridx, gridy, real(modulus), scalars=phase)
# Set the custom color map
mesh.module_manager.scalar_lut_manager.use_default_range = False
mesh.module_manager.scalar_lut_manager.data_range = [-pi, pi]
lut = mesh.module_manager.scalar_lut_manager.lut.table.to_array()
lut[:, 0:3] = colormap.copy()
mesh.module_manager.scalar_lut_manager.lut.table = lut
# Update the figure
mlab.draw()
return mesh
def draw_sats(self, figure):
# Draws all satellites as a single plot3D
if self.sat_points is None:
x, y, z = self.get_mayavi_xyz()
self.sat_points = mlab.points3d(x,
y,
z,
np.arange(len(self)),
figure=figure,
scale_mode='none',
scale_factor=SCALE_FACTOR,
color=tuple(self.colors[0, 0:3] /
255))
if len(self) > 1:
self.sat_points.module_manager.scalar_lut_manager.lut.number_of_colors = len(
self.colors)
self.sat_points.module_manager.scalar_lut_manager.lut.table = self.colors
mlab.draw()
else:
x, y, z = self.get_mayavi_xyz()
self.sat_points.mlab_source.trait_set(x=x, y=y, z=z)
def generate_plots_3d(self):
self.ax = mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(800, 600))
self.clf = mlab.clf()
minS, maxS = maxint, 0
contour_plots = []
for cond in self.conductors.itervalues():
minS, maxS, face_data = self.generate_plot_data_for_faces_3d(cond, minS, maxS)
for (x, y, z, s) in face_data:
if isinstance(cond, conductor_type_3d['Unstructured']):
pts = mlab.points3d(x, y, z, s, scale_mode='none', scale_factor=0.002)
mesh = mlab.pipeline.delaunay3d(pts)
contour_plots.append(mlab.pipeline.surface(mesh, colormap='viridis'))
else:
if np.min(s) < 0.0:
contour_plots.append(mlab.mesh(x, y, z, color=(0, 0, 0), colormap='viridis'))
else:
contour_plots.append(mlab.mesh(x, y, z, scalars=s, colormap='viridis'))
for cp in contour_plots:
cp.module_manager.scalar_lut_manager.trait_set(default_data_range=[minS * 0.95, maxS * 1.05])
mlab.draw()
mlab.colorbar(object=contour_plots[0], orientation='vertical')
mlab.show()
def e_field(q, pos, x_grid, y_grid, z_grid, x_field, y_field, z_field,
no_lines):
fig = mplt.figure()
X, Y, Z = np.meshgrid(x_grid, y_grid, z_grid, indexing='ij')
for charge, location in zip(q, pos):
# draw sphere for point charge
sphere(charge, location)
# draw electric field lines
ball = mplt.flow(X,
Y,
Z,
x_field,
y_field,
z_field,
figure=fig,
seedtype='sphere',
integration_direction='both')
ball.seed.widget.center = location
# number of field lines to integrate over
ball.seed.widget.theta_resolution = no_lines
ball.seed.widget.phi_resolution = no_lines
# number of integration steps
ball.stream_tracer.maximum_propagation = 200
ball.seed.widget.radius = 1
# dodgy hax... TL;DR widgets are dumb
ball.seed.widget.enabled = False
ball.seed.widget.enabled = True
mplt.axes()
# set view to x-axis coming out of screen
fig.scene.x_plus_view()
mplt.draw(figure=fig)
mplt.show()
def show_contrasts(subject, contrasts, side, threshold):
x, y, z, triangles = get_geometry(subject, side, "inflated") ## inflated or white
curv = get_curvature_sign(subject, side)
f = mlab.figure()
mlab.clf()
# anatomical mesh
mlab.triangular_mesh(x, y, z, triangles, transparent=False,
opacity=1., name=subject,
scalars=curv, colormap="bone", vmin=-1, vmax=2)
mlab.title(subject)
cmaps = [colormaps[c.split("-")[0]]['colormap'] for c in contrasts]
for contrast, colormap in zip(contrasts, cmaps):
# functional mesh
data = get_contrast(subject, contrast, side)
func_mesh = mlab.pipeline.triangular_mesh_source(x, y, z, triangles,
scalars=data)
# threshold
thresh = mlab.pipeline.threshold(func_mesh, low=threshold)
surf = mlab.pipeline.surface(thresh, colormap='hot', transparent=True,
opacity=.8) # diminuer pour avoir plus de transparence
lut = (np.array([colormap(v) for v in np.linspace(.25, 1., 256)]) * 255
).astype(int)
surf.module_manager.scalar_lut_manager.lut.table = lut
mlab.draw()
return f
def _set_3d_view(figure, azimuth, elevation, focalpoint, distance):
from mayavi import mlab
with warnings.catch_warnings(record=True): # traits
with SilenceStdout():
mlab.view(azimuth, elevation, distance,
focalpoint=focalpoint, figure=figure)
mlab.draw(figure)
def generate_plots_3d(self):
self.ax = mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(800, 600))
self.clf = mlab.clf()
minS, maxS = maxint, 0
contour_plots = []
for cond in self.conductors.itervalues():
minS, maxS, face_data = self.generate_plot_data_for_faces_3d(cond, minS, maxS)
for (x, y, z, s) in face_data:
if isinstance(cond, conductor_type_3d['Unstructured']):
pts = mlab.points3d(x, y, z, s, scale_mode='none', scale_factor=0.002)
mesh = mlab.pipeline.delaunay3d(pts)
contour_plots.append(mlab.pipeline.surface(mesh, colormap='viridis'))
else:
if np.min(s) < 0.0:
contour_plots.append(mlab.mesh(x, y, z, color=(0, 0, 0), colormap='viridis'))
else:
contour_plots.append(mlab.mesh(x, y, z, scalars=s, colormap='viridis'))
for cp in contour_plots:
cp.module_manager.scalar_lut_manager.trait_set(default_data_range=[minS * 0.95, maxS * 1.05])
mlab.draw()
mlab.colorbar(object=contour_plots[0], orientation='vertical')
mlab.show()
def draw_conns(self,new_edges=None):
try:
self.thres.set(lower_threshold=self.ds.thresval)
lo=self.thres.lower_threshold; hi=self.thres.upper_threshold
set_lut(self.vectors,self.ds.opts.activation_map)
if new_edges is not None:
new_starts=self.ds.lab_pos[new_edges[:,0]]
new_vecs=self.ds.lab_pos[new_edges[:,1]] - new_starts
self.vectors.mlab_source.reset(
x=new_starts[:,0],y=new_starts[:,1],z=new_starts[:,2],
u=new_vecs[:,0],v=new_vecs[:,1],w=new_vecs[:,2])
if self.ds.curr_node is not None:
self.vectors.actor.property.opacity=.75
self.txt.set(text=' %s'%self.ds.labnam[self.ds.curr_node])
else:
self.vectors.actor.property.opacity=(
.5 if self.ds.opts.tube_conns else .3)
self.txt.set(text='')
mlab.draw()
# In case the user changes the threshold while there are no connections
# present and so the VTK objects have not been created yet
except AttributeError:
pass
def view_patch(r, attrib=[], opacity=1, fig=0, show=1):
if fig == 0:
fig = mlab.figure()
else:
mlab.figure(fig)
if len(attrib) > 0:
mlab.triangular_mesh(r.vertices[:, 0],
r.vertices[:, 1],
r.vertices[:, 2],
r.faces,
representation='surface',
opacity=opacity,
scalars=attrib)
else:
mlab.triangular_mesh(r.vertices[:, 0],
r.vertices[:, 1],
r.vertices[:, 2],
r.faces,
representation='surface',
opacity=opacity)
mlab.gcf().scene.parallel_projection = True
mlab.view(azimuth=0, elevation=90)
mlab.colorbar(orientation='horizontal')
mlab.draw()
if show > 0:
mlab.show(stop=True)
return fig
def draw(self):
x = np.linspace(self._base_square_x[0],
self._base_square_x[1],self._Nl)
y = np.linspace(self._base_square_y[0],
self._base_square_y[1],self._Nw)
x,y = np.meshgrid(x,y)
z = 0.0*x
for trans in self._transforms:
p = np.concatenate((x[:,:,None],
y[:,:,None],
z[:,:,None]),
axis=-1)
p = trans(p)
pflat = np.reshape(p,(-1,3))
x,y,z = pflat[:,0],pflat[:,1],pflat[:,2]
value = self._f(pflat,*self._f_args,**self._f_kwargs)
u,v,w = value[:,0],value[:,1],value[:,2]
m = mlab.quiver3d(x,y,z,u,v,w,mode='arrow',color=(0.4,0.4,0.4))
mlab.draw()
if self._plots is None:
self._plots = (m,trans),
else:
self._plots += (m,trans),
def draw(self,**kwargs):
x = np.linspace(self._base_square_x[0] + 0.5*(self._base_square_x[1]-self._base_square_x[0])/self._Nl,
self._base_square_x[1] - 0.5*(self._base_square_x[1]-self._base_square_x[0])/self._Nl,
self._Nl)
y = np.linspace(self._base_square_y[0] + 0.5*(self._base_square_y[1]-self._base_square_y[0])/self._Nw,
self._base_square_y[1] - 0.5*(self._base_square_y[1]-self._base_square_y[0])/self._Nw,
self._Nw)
x,y = np.meshgrid(x,y)
z = 0.0*x
for trans in self._transforms:
p = np.concatenate((x[:,:,None],
y[:,:,None],
z[:,:,None]),
axis=-1)
p = trans(p)
pflat = np.reshape(p,(-1,3))
x,y,z = pflat[:,0],pflat[:,1],pflat[:,2]
value = self._f(pflat,*self._f_args,**self._f_kwargs)
u,v,w = value[:,0],value[:,1],value[:,2]
m = mlab.quiver3d(x,y,z,u,v,w,mode='arrow',color=(1.0,1.0,1.0),scale_factor=self.scale_units,
resolution=20,**kwargs)
mlab.draw()
if self._plots is None:
self._plots = (m,trans),
else:
self._plots += (m,trans),
return [i[0] for i in self._plots]
def __init__(self,start,end,maxd=5000.,n=100):
'''
Constructor
'''
self.start=start
self.end=end
self.lopath=numpy.linspace(start[0], end[0], n)
self.lapath=numpy.linspace(start[1], end[1], n)
self.set_proj()
self.tile=TiffReader(lon=self.lopath[0],lat=self.lapath[0])
self.tile.readit()
for i in range(n):
if not self.tile==TiffReader(lon=self.lopath[i],lat=self.lapath[i]):
self.tile=TiffReader(lon=self.lopath[i],lat=self.lapath[i])
self.tile.readit()
if not hasattr(self,'mesh'):
lo,la,z=self.tile.subset(rect=None, around=(self.lopath[i],self.lapath[i],maxd))
x,y=self.proj(lo,la)
x=x-x.mean()
y=y-y.mean()
self.mesh=mlab.mesh(x,y,z,scalars=z,vmax=1500.,vmin=0.)
mlab.view(180.,45.,maxd,numpy.array([x.max(),0,z.max()]))
else:
lo,la,self.mesh.mlab_source.z=self.tile.subset(rect=None, around=(self.lopath[i],self.lapath[i],maxd))
self.mesh.mlab_source.scalars=self.mesh.mlab_source.z
mlab.view(180.,45.,5*x.max(),numpy.array([x.max(),0,self.mesh.mlab_source.z.max()]))
mlab.draw()
def draw_conns(self, new_edges=None):
try:
self.thres.set(lower_threshold=self.ds.thresval)
lo = self.thres.lower_threshold
hi = self.thres.upper_threshold
set_lut(self.vectors, self.ds.opts.activation_map)
if new_edges is not None:
new_starts = self.ds.lab_pos[new_edges[:, 0]]
new_vecs = self.ds.lab_pos[new_edges[:, 1]] - new_starts
self.vectors.mlab_source.reset(x=new_starts[:, 0],
y=new_starts[:, 1],
z=new_starts[:, 2],
u=new_vecs[:, 0],
v=new_vecs[:, 1],
w=new_vecs[:, 2])
if self.ds.curr_node is not None:
self.vectors.actor.property.opacity = .75
self.txt.set(text=' %s' % self.ds.labnam[self.ds.curr_node])
else:
self.vectors.actor.property.opacity = (
.5 if self.ds.opts.tube_conns else .3)
self.txt.set(text='')
mlab.draw()
# In case the user changes the threshold while there are no connections
# present and so the VTK objects have not been created yet
except AttributeError:
pass
def draw(self,**kwargs):
x = np.linspace(self._base_square_x[0],
self._base_square_x[1],self._Nl)
y = np.linspace(self._base_square_y[0],
self._base_square_y[1],self._Nw)
x,y = np.meshgrid(x,y)
z = 0.0*x
for trans in self._transforms:
p = np.concatenate((x[:,:,None],
y[:,:,None],
z[:,:,None]),
axis=-1)
p = trans(p)
pflat = np.reshape(p,(-1,3))
c = self._f(pflat,*self._f_args,**self._f_kwargs)
c = np.reshape(c,np.shape(p)[:-1])
if self._clim is None:
self._clim = (np.min(c),np.max(c))
m = mlab.mesh(p[:,:,0],p[:,:,1],p[:,:,2],
scalars=c,
vmin=self._clim[0],
vmax=self._clim[1],**kwargs)
m.module_manager.scalar_lut_manager.lut.table = self._rgba
mlab.colorbar()
mlab.draw()
if self._plots is None:
self._plots = (m,trans),
else:
self._plots += (m,trans),
return [i[0] for i in self._plots]
def process_launch():
'''Procédure reliant une fenetre graphique et le coeur du programme'''
global nb_etapesIV
nb_etapes=nb_etapesIV.get()#On récupère le nombre d'étapes
fig=mlab.figure(1)
mlab.clf()#La fenêtre de dessin est initialisée
mlab.draw(terrain([(0,1,2),(2,3,4),(4,5,6)],[(Point(0,0,0),Point(1,0,0)),(Point(1,0,0),Point(1,1,0)),(Point(0,0,0),Point(1,1,0)),(Point(1,1,0),Point(0,1,0)),(Point(0,0,0),Point(0,1,0)),(Point(0,0,0),Point(-1,1,0)),(Point(-1,1,0),Point(0,1,0))],nb_etapes))#On affiche le dessin
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 plot_potential(grid,
potential,
sparsify=1,
along_axes=False,
view=None,
interactive=False,
path='.'):
"""Plot the potential
:param iom: An :py:class:`IOManager` instance providing the simulation data.
"""
# The Grid
u, v = grid.get_nodes(split=True, flat=False)
u = real(u[::sparsify, ::sparsify])
v = real(v[::sparsify, ::sparsify])
# Create potential and evaluate eigenvalues
potew = potential.evaluate_eigenvalues_at(grid)
potew = [
real(level).reshape(
grid.get_number_nodes(overall=False))[::sparsify, ::sparsify]
for level in potew
]
# Plot
if not interactive:
mlab.options.offscreen = True
fig = mlab.figure(size=(800, 700))
for level in potew:
# The energy surfaces of the potential
src = mlab.pipeline.grid_source(u, v, level)
# Clip to given view
if view is not None:
geometry_filter = mlab.pipeline.user_defined(
src, filter='GeometryFilter')
geometry_filter.filter.extent_clipping = True
geometry_filter.filter.extent = view
src = mlab.pipeline.user_defined(geometry_filter,
filter='CleanPolyData')
# Plot the surface
normals = mlab.pipeline.poly_data_normals(src)
mlab.pipeline.surface(normals)
mlab.axes()
fig.scene.parallel_projection = True
fig.scene.isometric_view()
# fig.scene.show_axes = True
mlab.draw()
if interactive:
mlab.show()
else:
mlab.savefig(os.path.join(path, "potential_3D_view.png"))
mlab.close(fig)
def _set_3d_view(figure, azimuth, elevation, focalpoint, distance, roll=None,
reset_camera=True, update=True):
from mayavi import mlab
with warnings.catch_warnings(record=True): # traits
with SilenceStdout():
mlab.view(azimuth, elevation, distance,
focalpoint=focalpoint, figure=figure, roll=roll)
if update:
mlab.draw(figure)
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 simulate(sim_time, dt, sat, ground_track, sat_marker, picture_marker,
picture_points):
elapsed_time = 0
start_time = time.time()
step_start_time = start_time
time_deviation = 0
for i in [0, 1, 2]:
sat.vel[i] = -sat.vel[i]
sat = stepTime(sat, int(sat.period()), 10)
for i in [0, 1, 2]:
sat.vel[i] = -sat.vel[i]
sat = stepTime(sat, int(sat.period()) * 2, 10)
while elapsed_time < sim_time:
##SIMULATION LOOP##
#step through time
sat = stepTime(sat, dt, dt)
## create 2d map with track
updateGroundMap(ground_track, sat_marker, sat, picture_marker,
picture_points)
plt.pause(0.05)
plt.draw()
## create 3d globe with track
mlab.clf()
scaling_factor = m.sqrt(400000**2 / 3)
line = mlab.quiver3d(sat.last_pos[int(sat.period() / dt), 1],
sat.last_pos[int(sat.period() / dt), 0],
sat.last_pos[int(sat.period() / dt), 2],
sat.spin_vector[0],
sat.spin_vector[1],
sat.spin_vector[2],
scale_factor=400000,
line_width=2,
figure=fig,
color=(1, 0, 0),
mode='2darrow')
mlab.points3d(picture_points[:, 0],
picture_points[:, 1],
picture_points[:, 2],
figure=fig,
scale_factor=200000,
color=(1, 0, 1))
mlab.draw()
#make sure step only last one second in real time
if abs(time.time() - step_start_time) <= 1:
if 1 - abs(time.time() - step_start_time) - time_deviation > 0:
time.sleep(1 - abs(time.time() - step_start_time) -
time_deviation)
else:
time.sleep(1 - abs(time.time() - step_start_time))
step_start_time = time.time()
elapsed_time = time.time() - start_time
print(elapsed_time)
time_deviation = abs(start_time - time.time()) % 1
def visualize_trajectory(
gt,
title_name='defualt',
id=0,
step=10
): #, file_name='3d_trajectory.csv', dir_name='/home/steve/MatlabWs/VisualizationToolbox/'):
trajectory = np.zeros([gt.shape[0], 12])
trajectory[:, 0:3] = gt[:, 0:3] * 1.0
x_vec = np.asarray((1.0, 0.0, 0.0))
y_vec = np.asarray((0.0, 1.0, 0.0))
z_vec = np.asarray((0.0, 0.0, 1.0))
for i in range(0, trajectory.shape[0], step):
# trajectory[:,3:6] = np.linalg.inv(q2dcm(dg.gt[i,4:8])).dot(x_vec)
# trajectory[:,3:6] = (q2dcm(dg.gt[i,4:8])).dot(x_vec)
R = np.linalg.inv(q2dcm(gt[i, 3:7]))
# trajectory[i, 0:3] = gt[i, 1:4]
trajectory[i, 3:6] = R.dot(x_vec)
trajectory[i, 6:9] = R.dot(y_vec)
trajectory[i, 9:12] = R.dot(z_vec)
# np.savetxt(dir_name + file_name, trajectory,delimiter=',')
from mayavi import mlab
# ax.plot(trajectory[:, 0], trajectory[:, 1], trajectory[:, 2],label='trajector')
if id > 0:
fig = mlab.figure(id)
else:
fig = mlab.figure()
# fig.title(title_name)
# fig.
mlab.plot3d(trajectory[:, 0],
trajectory[:, 1],
trajectory[:, 2],
tube_radius=0.01,
color=(1.0, 1.0, 1.0))
t_name = title_name
mlab.title(t_name)
for k in range(3):
c = np.zeros(3)
c[k] = 1.0
ct = (c[0], c[1], c[2])
quiver = mlab.quiver3d(trajectory[:, 0],
trajectory[:, 1],
trajectory[:, 2],
trajectory[:, 3 + k * 3],
trajectory[:, 4 + k * 3],
trajectory[:, 5 + k * 3],
color=ct)
# quiver.glyph.mask_input_points = True
# quiver.glyph.mask_points.on_ratio=20
# if id==2:
# mlab.sync_camera(mlab.figure(1),)
mlab.axes()
mlab.draw(fig)
def draw_nucleus(v):
mlab.clf()
s = mlab.pipeline.triangular_mesh_source(x, y, z, triIndices)
s.data.cell_data.scalars = np.cos(phaseAngle)
surf = mlab.pipeline.surface(s)
surf.contour.filled_contours = True
surf.contour.minimum_contour = 0.0
surf.contour.maximum_contour = 1.0
surf.module_manager.scalar_lut_manager.data_range = (0,1)
mlab.view(v)
mlab.draw()
def force_render( figure=None ):
from mayavi import mlab
figure.scene.render()
mlab.draw(figure=figure)
from pyface.api import GUI
_gui = GUI()
orig_val = _gui.busy
_gui.set_busy(busy=True)
_gui.process_events()
_gui.set_busy(busy=orig_val)
_gui.process_events()
def draw_nucleus(v):
mlab.clf()
s = mlab.pipeline.triangular_mesh_source(x, y, z, triIndices)
s.data.cell_data.scalars = np.cos(phaseAngle)
surf = mlab.pipeline.surface(s)
surf.contour.filled_contours = True
surf.contour.minimum_contour = 0.0
surf.contour.maximum_contour = 1.0
surf.module_manager.scalar_lut_manager.data_range = (0, 1)
mlab.view(v)
mlab.draw()
def parse_box(box_data,indx):
"""
from box_data produce a 3D image of surfaces and display it using an
animation of a slice moving back and forth through the box
NOT FUNCTIONAL AT PRESENT - NEED TO WORK OUT THE MAYAVI SYNTAX
-can't get different slices to display in a nice fashion
"""
s=box_data
sp=1-box_data
mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
plane_orientation='z_axes',
slice_index=0,colormap='black-white'
)
mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
plane_orientation='x_axes',
slice_index=0,colormap='black-white'
)
l=mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
plane_orientation='y_axes',
slice_index=0,colormap='black-white'
)
#s=sp
fig=mlab.gcf()
ms=l.mlab_source
print 'indx=',dir(ms)
for i in range(90):
#l=mlab.pipeline.image_plane_widget(mlab.pipeline.scalar_field(s),
# plane_orientation='y_axes',
# slice_index=i,colormap='black-white'
# )
if(i%2):
ms.reset(scalars=sp)
else:
ms.reset(scalars=s)
#print dir(ms)
#print ms
#camera=fig.scene.camera
#camera.yaw(i)
fig.scene.reset_zoom()
mlab.draw()
filename='volume_slice_frame'+str(i)+'.png'
print filename
#mlab.savefig(filename)
mlab.show()
return
def draw_nodes(self): nc=self.ds.scalar_display_settings.node_color ns=self.ds.scalar_display_settings.node_size lhn=self.nodes_lh; lhn_ix=self.ds.lhnodes rhn=self.nodes_rh; rhn_ix=self.ds.rhnodes for nodes,ixes in ((lhn,lhn_ix),(rhn,rhn_ix)): nr=len(ixes) #set node size if self.ds.display_mode=='scalar' and ns: nodes.glyph.scale_mode='scale_by_vector' nodes.glyph.glyph.scale_factor=8 nodes.mlab_source.dataset.point_data.vectors=( np.tile(self.ds.node_scalars[ns][ixes],(3,1)).T) else: nodes.glyph.scale_mode='data_scaling_off' nodes.glyph.glyph.scale_factor=3 #set node color -- we dont care about ds.node_colors for mayavi if (self.ds.display_mode=='normal' or (self.ds.display_mode=='scalar' and not nc)): set_lut(nodes,self.ds.opts.default_map) nodes.mlab_source.dataset.point_data.scalars=np.tile(.3,nr) elif self.ds.display_mode=='scalar': #and nc must be true set_lut(nodes,self.ds.opts.scalar_map) nodes.mlab_source.dataset.point_data.scalars=( self.ds.node_scalars[nc][ixes]) elif self.ds.display_mode=='module_single': set_lut(nodes,self.ds.opts.default_map) new_colors=np.tile(.3,self.ds.nr_labels) new_colors[self.ds.get_module()]=.8 nodes.mlab_source.dataset.point_data.scalars=new_colors[ixes] elif self.ds.display_mode=='module_multi': new_colors=np.array(self.ds.module_colors[:self.ds.nr_modules]) manager=nodes.module_manager.scalar_lut_manager #set the mayavi object to the dummy cmap that we hide from user #so that when changed notifications will work correctly manager.lut_mode='black-white' #now adjust its LUT manually manager.number_of_colors=self.ds.nr_modules manager.lut.table=new_colors #set the mayavi scalars to be fractions between 0 and 1 import bct nodes.mlab_source.dataset.point_data.scalars=(bct.ls2ci( self.ds.modules,zeroindexed=True)/self.ds.nr_modules)[ixes] mlab.draw()
def anim():
global vessels, colorGradient, timeText, showingNode
timeText = None
while True:
if timeText != None:
timeText.remove()
timeText = mlab.text(0.01, 0.01, 'Time: ' + str(g.globals.time), width=0.3)
for (top, bottom, host) in vessels:
lutT = top.module_manager.scalar_lut_manager.lut.table.to_array()
lutB = bottom.module_manager.scalar_lut_manager.lut.table.to_array()
count = host.getBacteriaCount()
colorNdx = int(float(count) / parameters.cell_count_color_mapping * (len(colorGradient) - 1))
if colorNdx >= len(colorGradient):
colorNdx = len(colorGradient) - 1
color = colorGradient[colorNdx]
assert len(color) == 6
ones = np.ones(np.shape(lutT[:, 0]))
R = int(color[0:2], 16)
G = int(color[2:4], 16)
B = int(color[4:6], 16)
#R
lutT[:,0] = ones * R
lutB[:,0] = ones * R
#G
lutT[:,1] = ones * G
lutB[:,1] = ones * G
#B
lutT[:,2] = ones * B
lutB[:,2] = ones * B
top.module_manager.scalar_lut_manager.lut.table = lutT
bottom.module_manager.scalar_lut_manager.lut.table = lutB
if showingNode is not None:
(ax1, ax2) = plots
ax1.cla()
history = copy.deepcopy(showingNode.getCellCountHistory())
x = np.linspace(0, len(history) * parameters.cell_count_history_interval, len(history))
y = history
ax1.plot(x, y, 'r')
ax1.set_title('Bacteria Count history')
ax2.cla()
history = copy.deepcopy(showingNode.getFlowHistory())
x = np.linspace(0, len(history) * parameters.cell_count_history_interval, len(history))
y = history
ax2.plot(x, y, 'b')
ax2.set_title('Blood flow history')
mlab.draw()
if parameters.verbose:
print("updating graph")
yield
def plot_shape(self, kind, spatialSize=None, renderSize=None,
features=None):
""" outline=True - box edges
title=True - print kind of picture
**kwargs # one of: vertA, faceA, features, x, y, z, spatialSize
"""
f = mlab.figure(bgcolor=(1, 1, 1))
if kind == 'solid':
vertA, faceA = self.vertices, self.faces
mlab.triangular_mesh(vertA[:, 0], vertA[:, 1], vertA[:, 2], faceA)
elif kind == 'transparent':
vertA, faceA = self.vertices, self.faces
mlab.triangular_mesh(vertA[:,0], vertA[:, 1], vertA[:, 2], faceA,
opacity=0.1)
elif kind == 'wireframe':
vertA, faceA = self.vertices, self.faces
mlab.triangular_mesh(vertA[:, 0], vertA[:, 1], vertA[:, 2], faceA,
representation='wireframe')
elif kind == "Bool":
x, y, z = map(np.array, self.voxelize(spatialSize, renderSize))
assert len(x) == len(y) == len(z)
N = len(x)
scalars = np.arange(N) # Key point: set an integer for each point
colors = np.zeros((N, 4), dtype=np.uint8)
colors[:, -1] = 255 # No transparency
if features is not None:
features = features.ravel()
colors[:, 0] = 255
colors[:, 1] = (
255 * (1 - features / np.max(features))).astype(np.uint8)
colors[:, 2] = (
255 * (1 - features / np.max(features))).astype(np.uint8)
else:
colors[:, 0] = 0
colors[:, 1] = 255
colors[:, 2] = 0
pts = mlab.quiver3d(
x-spatialSize/2,
y-spatialSize/2+0.5,
z-spatialSize/2+0.5,
np.ones(N), np.zeros(N), np.zeros(N),
scalars=scalars, mode='cube', scale_factor=0.7, line_width=10)
pts.glyph.color_mode = 'color_by_scalar'
try:
pts.module_manager.scalar_lut_manager.lut.table = colors
except:
pass
mlab.draw()
return f
def updateAnimation(xx, yy, zz, xT, yT, zT):
t = 0
while t < timesize:
mlab.draw(figure=None)
ball.mlab_source.set(x=xx[:, t], y=yy[:, t], z=zz[:, t])
#redefine the dragontail matrix
for j in range(0, numparticles):
oxR = range(0, 5)
for updateN in oxR[4:None:-1]:
if updateN > 0:
xT[j][updateN] = xT[j][updateN - 1]
yT[j][updateN] = yT[j][updateN - 1]
zT[j][updateN] = zT[j][updateN - 1]
else:
xT[j][updateN] = xx[j, t]
yT[j][updateN] = yy[j, t]
zT[j][updateN] = zz[j, t]
dragontail[j].mlab_source.reset(x=xT[j], y=yT[j], z=zT[j])
# for k in range(0,np.size(xx,axis=0)):
# text[k].mlab_source.reset(x = xx[k,t], y = yy[k,t], z = zz[k,t])
#for j in range(0,numparticles):
# for update in reversed(range(0,DragonFrame)):
# if update>0:
# DTx[update] = DTx[update-1]
# DTy[update] = DTy[update-1]
# DTz[update] = DTz[update-1]
# else:
# DTx[update] = xx[]
#run calculations for dragontail data
#if t>6:
# dtLB = t-DragonFrame
# dtUB = t+1
#
# for jj in range(0,numparticles):
# xpts = xx[jj,dtLB:dtUB]
# ypts = yy[jj,dtLB:dtUB]
# zpts = zz[jj,dtLB:dtUB]
# dragontail[jj].mlab_source.reset(x=xpts,y=ypts,z=zpts)
t += 1
if t == interpstep - 1:
xT = xds
yT = yds
zT = zds
t = 0
yield
def m_vector_wire(ori, loc, grid, x_grid, y_grid, z_grid, x_field, y_field,
z_field):
fig = mplt.figure()
# draw sphere for point charge
for orientation, location in zip(ori, loc):
wire(orientation, location, grid)
# draw vector field
X, Y, Z = np.meshgrid(x_grid, y_grid, z_grid, indexing='ij')
mplt.quiver3d(X, Y, Z, x_field, y_field, z_field)
mplt.axes()
# set view to x-axis coming out of screen
fig.scene.x_plus_view()
mplt.draw(figure=fig)
mplt.show()
def plot_torus(theta, gamma, Z, weight_deform=0., torus_radius=5., tube_radius=3.0, try_mayavi=True, draw_colorbar=True):
'''
Plot a torus, with the color set by Z.
Also possible to deform the sphere according to Z, by putting a nonzero weight_deform.
Need theta in [0, 2pi] and gamma in [0, pi]
'''
Z_norm = Z/Z.max()
X, Y = np.meshgrid(theta, gamma)
x = (torus_radius+ tube_radius*np.cos(X)*(1.+weight_deform*Z_norm))*np.cos(Y)
y = (torus_radius+ tube_radius*np.cos(X)*(1.+weight_deform*Z_norm))*np.sin(Y)
z = tube_radius*np.sin(X)*(1.+weight_deform*Z_norm)
use_mayavi = False
if try_mayavi:
try:
import mayavi.mlab as mplt
use_mayavi = True
except:
pass
if use_mayavi:
# mplt.figure(bgcolor=(0.7,0.7,0.7))
mplt.figure(bgcolor=(1.0, 1.0, 1.0))
mplt.mesh(x, y, z, scalars=Z_norm, vmin=0.0)
if draw_colorbar:
cb = mplt.colorbar(title='', orientation='vertical', label_fmt='%.2f', nb_labels=5)
mplt.outline(color=(0., 0., 0.))
mplt.draw()
else:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_surface(x, y, z, facecolors=cm.jet(Z_norm), rstride=1, cstride=1, linewidth=0, antialiased=True, shade=False)
# Colorbar
m = cm.ScalarMappable(cmap=cm.jet)
m.set_array(Z_norm)
if draw_colorbar:
plt.colorbar(m)
def process_launch():
'''Procédure reliant une fenetre graphique et le coeur du programme'''
global nb_etapesIV
nb_etapes = nb_etapesIV.get() #On récupère le nombre d'étapes
fig = mlab.figure(1)
mlab.clf() #La fenêtre de dessin est initialisée
mlab.draw(
terrain([(0, 1, 2), (2, 3, 4),
(4, 5, 6)], [(Point(0, 0, 0), Point(1, 0, 0)),
(Point(1, 0, 0), Point(1, 1, 0)),
(Point(0, 0, 0), Point(1, 1, 0)),
(Point(1, 1, 0), Point(0, 1, 0)),
(Point(0, 0, 0), Point(0, 1, 0)),
(Point(0, 0, 0), Point(-1, 1, 0)),
(Point(-1, 1, 0), Point(0, 1, 0))],
nb_etapes)) #On affiche le dessin
def plot_potential(grid, potential, sparsify=1, along_axes=False, view=None, interactive=False, path='.'):
"""Plot the potential
:param iom: An :py:class:`IOManager` instance providing the simulation data.
"""
# The Grid
u, v = grid.get_nodes(split=True, flat=False)
u = real(u[::sparsify, ::sparsify])
v = real(v[::sparsify, ::sparsify])
# Create potential and evaluate eigenvalues
potew = potential.evaluate_eigenvalues_at(grid)
potew = [real(level).reshape(grid.get_number_nodes(overall=False))[::sparsify, ::sparsify] for level in potew]
# Plot
if not interactive:
mlab.options.offscreen = True
fig = mlab.figure(size=(800, 700))
for level in potew:
# The energy surfaces of the potential
src = mlab.pipeline.grid_source(u, v, level)
# Clip to given view
if view is not None:
geometry_filter = mlab.pipeline.user_defined(src, filter='GeometryFilter')
geometry_filter.filter.extent_clipping = True
geometry_filter.filter.extent = view
src = mlab.pipeline.user_defined(geometry_filter, filter='CleanPolyData')
# Plot the surface
normals = mlab.pipeline.poly_data_normals(src)
mlab.pipeline.surface(normals)
mlab.axes()
fig.scene.parallel_projection = True
fig.scene.isometric_view()
# fig.scene.show_axes = True
mlab.draw()
if interactive:
mlab.show()
else:
mlab.savefig(os.path.join(path, "potential_3D_view.png"))
mlab.close(fig)
def fmlab(volume, slices, spacing=np.array([1.,1.,1.]), no_slices=5, filename=None, size=[1024,768]):
'''Creates a volume with slices of data from up to 2 scalar fields.
Volume and slices are scalar fields that are used to generate their respective
parts of the plot. They can be the same field, but must be 4- or 3-Dimensional.
If a field is 4-Dimensional, however, only the first point is used. It's best to
pass only 3D data to this.
Spacing is the 'streching factor' of the data in x,y,z format.
no_slices determines the number of slices that are generated. '''
# Only plot one time slice, so remove 4th dimension
if np.shape(volume) == 4:
volume = volume[0,:,:,:]
if np.shape(slices) == 4:
slices = slices[0,:,:,:]
# Creating the mlab scene which will contain all of the visualisations
plot = mlab.figure()
# Loads in the data as a scalar field, creates the VTK data source
# Can be changed for other types of data source (vector field, etc)
s_field1 = mlab.pipeline.scalar_field(volume)
s_field2 = mlab.pipeline.scalar_field(slices)
# Changes the spacing of the grid points to make the data look right
# fiddle with until it looks good.
s_field1.spacing = spacing
s_field2.spacing = spacing
#Creates the 'cloud' of the whole data.
mlab.pipeline.volume(s_field1, figure=plot)
# Adds in the 'slices' within the volume
for n in np.around(range(no_slices)):
mlab.pipeline.image_plane_widget(s_field2, figure=plot, plane_orientation = 'y_axes', slice_index=( np.int((n + 3./4.)*((np.shape(slices)[1]/no_slices)) ) ) )
if filename is not None:
mlab.options.offscreen = True
mlab.draw(figure = plot)
mlab.savefig(str(filename) + '.png', size=size, figure=plot, magnification=1.)
else:
mlab.draw(figure=plot)
mlab.show()
def mlab_imshow_color(img_array, **kwargs):
"""
Plot a color image with mayavi.mlab.imshow.
img_array is a ndarray with dim (n, m, 4) and scale (0->255]
**kwargs is passed onto mayavi.mlab.imshow(..., **kwargs)
"""
my_lut = pl.c_[img_array.reshape(-1, 4)]
my_lut_lookup_array = pl.arange(img_array.shape[0] *
img_array.shape[1]).reshape(
img_array.shape[0], img_array.shape[1])
the_imshow = mlab.imshow(my_lut_lookup_array, colormap='binary',
**kwargs) # temporary colormap
the_imshow.module_manager.scalar_lut_manager.lut.table = my_lut
mlab.draw()
return the_imshow
def draw_scene():
s = mlab.pipeline.triangular_mesh_source(x, y, z, triIndices)
s.data.cell_data.scalars = np.cos(phaseAngle)
surf = mlab.pipeline.surface(s)
surf.contour.filled_contours = True
surf.contour.minimum_contour = 0.0
surf.contour.maximum_contour = 1.0
surf.module_manager.scalar_lut_manager.data_range = (0, 1)
mlab.plot3d(xSun_plt,
ySun_plt,
zSun_plt,
tube_radius=fStretch / 1000,
color=(1, 1, 0))
mlab.plot3d(xSC_plt,
ySC_plt,
zSC_plt,
tube_radius=fStretch / 1000,
color=(0, 0, 1))
ball_x = []
ball_y = []
ball_z = []
for i in range(nPixelsX):
for j in range(nPixelsY):
p = np.dot(R, pVectors[:, i, j])
p_tan = np.dot(rCG, p) * p + rSC
xVIR_plt, yVIR_plt, zVIR_plt = plt_coords(rSC, 1.1 * fStretch * p)
mlab.plot3d(xVIR_plt,
yVIR_plt,
zVIR_plt,
tube_radius=fStretch / 5000,
color=(0, 0, 0))
ball_x.append(p_tan[0])
ball_y.append(p_tan[1])
ball_z.append(p_tan[2])
mlab.points3d(ball_x,
ball_y,
ball_z,
np.ones(len(ball_x)),
scale_factor=150,
color=(1, 0.7, 0.1))
mlab.draw()
def surface3D(x,y,z,idx,ux,uy,uz,dat,cm='seismic',quiv=True,fac=0.01,col=True):
from mayavi import mlab
lut=eval('plt.cm.'+cm+'(np.linspace(0,1,255))*255')
if col:
col = (0.43, 0.43, 0.43)
else:
col = None
mlab.figure(size=(800, 800))
mesh_handle = mlab.mesh(x[..., idx], y[..., idx], z[..., idx], scalars=dat[..., idx])
mesh_handle.module_manager.scalar_lut_manager.lut.table = lut
# mesh_handle.module_manager.scalar_lut_manager.reverse_lut = True
if quiv:
mlab.quiver3d(x, y, z, ux, uy, uz, color=col, scale_mode='vector', mode='arrow', mask_points=4, scale_factor=fac)
#mlab.show()
mlab.draw()
def spatial_map(vector,r_vertices,r_faces,msk_small_region,labs,val):
r_labels = np.zeros([r_vertices.shape[0]])
r_labels[~msk_small_region] = vector
r_labels[msk_small_region]=labs
mlab.figure(size=(1024, 768), \
bgcolor=(1, 1, 1),fgcolor=(0.5,0.5,0.5))
mlab.triangular_mesh(r_vertices[:, 0], r_vertices[:, 1], r_vertices[:,
2], r_faces, representation='surface',
opacity=1, scalars=np.float64(r_labels))
mlab.gcf().scene.parallel_projection = True
mlab.view(azimuth=0, elevation=270)
mlab.draw()
mlab.colorbar(orientation='vertical')
#mlab.show()
mlab.options.offscreen = True
mlab.savefig('precentral_back'+str(val)+'.png')
mlab.close()
return r_labels
def _run_calculation_changed(self, value):
# action = ThreadedAction(self.data, self.figure)
# action.start()
global frame
print("Update 3D plots calculation in Frame %d" % frame, end=' ')
truth_points, obs_points, pred_points, contour = self.data
if log_plot:
contour_s = np.log(gm_s_list[frame] + np.finfo(np.float).tiny)
else:
contour_s = gm_s_list[frame]
contour.mlab_source.scalars = contour_s
for i in range(frame, max(0, frame - 8), -1):
opacity = 1. - 0.1 * (frame - i)
truth_points[i].actor.property.opacity = opacity
obs_points[i].actor.property.opacity = opacity
pred_points[i].actor.property.opacity = opacity
print('done.')
mlab.draw()
frame += 1
def torus(size=1., levels=1, nphi=31, n=31, azimuth=90.):
'''
Construct a torus consisting of cubes with given size
'''
dphi = 2. * np.pi / nphi
if levels > 1:
size = 2.2 * 0.9**(1. / 3.) * 3**levels
radius = size / dphi
for i in range(n):
phi = i * dphi + np.pi / 2.
x = radius * np.cos(phi)
y = radius * (np.sin(phi) - 1.0)
z = 0.0
cube(x, y, z, size, phi)
ml.draw()
ml.view(focalpoint=[0., 0., 0.],
azimuth=azimuth,
elevation=75.,
distance=4 * radius)
def test_colormap(cmap):
from mayavi import mlab
x,y=meshgrid(linspace(0,100,101),linspace(0,100,101))
z=(x+y)*0.5
if True:
mesh_water=mlab.mesh(x.transpose(),y.transpose(),z.transpose(),colormap='gist_earth',vmin=0.,vmax=100.)
#Retrieve the LUT of the surf object.
lut = mesh_water.module_manager.scalar_lut_manager.lut.table.to_array()
lut[:, 0] = cmap[0]
lut[:, 1] = cmap[1]
lut[:, 2] = cmap[2]
mesh_water.module_manager.scalar_lut_manager.lut.table = lut
mlab.draw()
mlab.view(50.495536875966657,
26.902697031665959,
334.60652149512265,
array([ 50., 50., 50.]))
mlab.colorbar()
def mlab_imshowColor(im, alpha = 255, **kwargs):
"""
Plot a color image with mayavi.mlab.imshow.
im is a ndarray with dim (n, m, 3) and scale (0->255]
alpha is a single number or a ndarray with dim (n*m) and scale (0->255]
**kwargs is passed onto mayavi.mlab.imshow(..., **kwargs)
"""
from tvtk.api import tvtk
# Homogenous coordinate conversion
im = np.concatenate((im, alpha * np.ones((im.shape[0], im.shape[1], 1), dtype = np.uint8)), axis = -1)
colors = tvtk.UnsignedCharArray()
colors.from_array(im.reshape(-1, 4))
m_image = mlab.imshow(np.ones(im.shape[:2][::-1]))
m_image.actor.input.point_data.scalars = colors
mlab.draw()
mlab.show()
return
def surf_default(self, arrayindex):
surf = mlab.surf(
self.arrays[arrayindex, :,:,0],
# warp_scale=self.warp_scale
warp_scale=(1 / np.max(self.arrays[arrayindex])) * 40
)
# Retrieve the LUT of the surf object.
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(0, 255, 256)
# print lut.shape # (256, 4)
if arrayindex == 1:
lut[:, 0] = np.linspace(0, 255, 256) # red
lut[:, 1] = np.linspace(0, 0, 256) # green
lut[:, 2] = np.linspace(255, 0, 256) # blue
lut[:, 3] = np.linspace(255, 255, 256) # alpha
# lut[:, 3] = np.linspace(127, 127, 256) # alpha
else:
lut[:, 0] = np.linspace(0, 0, 256) # red
lut[:, 1] = np.linspace(0, 255, 256) # green
lut[:, 2] = np.linspace(255, 0, 256) # blue
lut[:, 3] = np.linspace(255, 255, 256) # alpha
# lut[:, 3] = np.linspace(127, 127, 256) # alpha
# 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
# We need to force update of the figure now that we have changed the LUT.
mlab.draw()
self.scene.scene_editor.isometric_view() #WARNING removing this line causes the surface not to display and the axes to rotate 90 degrees vertically!
self.scene.camera.azimuth(90)
return surf
def vis_colored_point_cloud(points, colors, **kwargs):
if colors.shape[-1] != 4:
raise ValueError('colors must be an (n, 4) array of rgba values')
n = len(points)
if len(colors) != n:
raise ValueError('colors must be the same length as points')
scalars = np.arange(n)
ones = np.ones((n, ))
x, y, z = points.T
pts = mlab.quiver3d(x,
y,
z,
ones,
ones,
ones,
scalars=scalars,
mode='sphere',
**kwargs)
pts.glyph.color_mode = 'color_by_scalar' # Color by scalar
pts.module_manager.scalar_lut_manager.lut.table = colors
mlab.draw()
def justPlotBoolArray(blob, figSize=(300, 300)):
'''Plots a 3D boolean array with points where array is True'''
Nx, Ny, Nz = blob.shape
indexX, indexY, indexZ = mgrid[0:Nx, 0:Ny, 0:Nz]
fig = mlab.figure(0, size=figSize)
mlab.clf(fig)
fig.scene.interactor.interactor_style = tvtk.InteractorStyleTerrain()
idx = blob
print idx.sum(), 'points'
if idx.sum() > 0:
idxFlat = idx.flatten()
pts = points3d(
indexX.flatten()[idxFlat] + .5,
indexY.flatten()[idxFlat] + .5,
indexZ.flatten()[idxFlat] + .5,
ones(sum(idxFlat)) * .9,
#((blob-mn) / (mx-mn) * .9)[idx],
color=(1, 1, 1),
mode='cube',
scale_factor=1.0)
lut = pts.module_manager.scalar_lut_manager.lut.table.to_array()
tt = linspace(0, 255, 256)
lut[:, 0] = tt * 0 + 255
lut[:, 1] = tt * 0 + 255
lut[:, 2] = tt * 0 + 255
lut[:, 3] = tt
pts.module_manager.scalar_lut_manager.lut.table = lut
#mlab.view(57.15, 75.55, 50.35, (7.5, 7.5, 7.5)) # nice view
#mlab.view(24, 74, 33, (5, 5, 5)) # Default older RBM
mlab.view(24, 88, 45, (5, 5, 10)) # Good for EF
mlab.draw()
def justPlotBoolArray(blob, figSize = (300,300)):
'''Plots a 3D boolean array with points where array is True'''
Nx,Ny,Nz = blob.shape
indexX, indexY, indexZ = mgrid[0:Nx,0:Ny,0:Nz]
fig = mlab.figure(0, size = figSize)
mlab.clf(fig)
fig.scene.interactor.interactor_style = tvtk.InteractorStyleTerrain()
idx = blob
print idx.sum(), 'points'
if idx.sum() > 0:
idxFlat = idx.flatten()
pts = points3d(indexX.flatten()[idxFlat] + .5,
indexY.flatten()[idxFlat] + .5,
indexZ.flatten()[idxFlat] + .5,
ones(sum(idxFlat)) * .9,
#((blob-mn) / (mx-mn) * .9)[idx],
color = (1,1,1),
mode = 'cube',
scale_factor = 1.0)
lut = pts.module_manager.scalar_lut_manager.lut.table.to_array()
tt = linspace(0, 255, 256)
lut[:, 0] = tt*0 + 255
lut[:, 1] = tt*0 + 255
lut[:, 2] = tt*0 + 255
lut[:, 3] = tt
pts.module_manager.scalar_lut_manager.lut.table = lut
#mlab.view(57.15, 75.55, 50.35, (7.5, 7.5, 7.5)) # nice view
#mlab.view(24, 74, 33, (5, 5, 5)) # Default older RBM
mlab.view(24, 88, 45, (5, 5, 10)) # Good for EF
mlab.draw()
def scatter3d_torus(theta, gamma, torus_radius=5., tube_radius=3.0, try_mayavi=True):
'''
Plot points on a torus.
Need theta \in [0, 2pi] and gamma \in [0, pi]
'''
x = (torus_radius+ tube_radius*np.cos(theta))*np.cos(gamma)
y = (torus_radius+ tube_radius*np.cos(theta))*np.sin(gamma)
z = tube_radius*np.sin(theta)
use_mayavi = False
if try_mayavi:
try:
import mayavi.mlab as mplt
use_mayavi = True
except:
pass
if use_mayavi:
# mplt.figure(bgcolor=(0.7,0.7,0.7))
mplt.figure(bgcolor=(1.0, 1.0, 1.0))
mplt.points3d(x, y, z, scale_factor=1, color=(0.0, 0.2, 0.9))
mplt.outline(color=(0., 0., 0.))
mplt.draw()
else:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z)
fig.canvas.draw()
return ax
def update_frame(self):
print('Frame %d' % self.current_frame)
if log_plot:
contour_s = np.log(
gm_s_list[self.current_frame] + np.finfo(np.float).tiny
)
else:
contour_s = gm_s_list[self.current_frame]
self.phd_contour.mlab_source.set(
scalars=contour_s
)
self.color_vector[:] = 0.
if observation_list is not None:
obs_array = observation_list[self.current_frame]
obs_index = [
np.where(
np.all(embeddings == sensor_vec.flatten(), axis=1)
)[0][0]
for sensor_vec in obs_array
]
self.color_vector[obs_index] = 1.
self.sensor_points.mlab_source.dataset.point_data.scalars = \
self.color_vector
mlab.draw()
def m_field_wire(ori, pos, grid, x_grid, y_grid, z_grid, x_field, y_field,
z_field, no_lines):
fig = mplt.figure()
X, Y, Z = np.meshgrid(x_grid, y_grid, z_grid, indexing='ij')
for orientation, location in zip(ori, pos):
# draw sphere for point charge
wire(orientation, location, grid)
# draw electric field lines
line = mplt.flow(X,
Y,
Z,
x_field,
y_field,
z_field,
figure=fig,
seedtype='line',
integration_direction='both')
# number of integration steps
line.stream_tracer.maximum_propagation = 200
mplt.axes()
# set view to x-axis coming out of screen
fig.scene.x_plus_view()
mplt.draw(figure=fig)
mplt.show()
def main():
parser = OptionParser(usage="Usage: %prog [options] <tract.vtp>")
parser.add_option("-s", "--scalar", dest="scalar",
default="FA", help="Scalar to measure")
parser.add_option("-n", "--num", dest="num", default=50,
type='int', help="Number of subdivisions along centroids")
parser.add_option("-l", "--local", dest="is_local", action="store_true", default=False,
help="Measure from Quickbundle assigned streamlines. Default is to measure from all streamlines")
parser.add_option("-d", "--dist", dest="dist", default=20,
type='float', help="Quickbundle distance threshold")
parser.add_option("--curvepoints", dest="curvepoints_file",
help="Define a curve to use as centroid. Control points are defined in a csv file in the same space as the tract points. The curve is the vtk cardinal spline implementation, which is a catmull-rom spline.")
parser.add_option('--yrange', dest='yrange')
parser.add_option('--xrange', dest='xrange')
parser.add_option('--reverse', dest='is_reverse', action='store_true',
default=False, help='Reverse the centroid measure stepping order')
parser.add_option('--pairplot', dest='pairplot',)
parser.add_option('--noviz', dest='is_viz',
action='store_false', default=True)
parser.add_option('--hide-centroid', dest='show_centroid',
action='store_false', default=True)
parser.add_option('--config', dest='config')
parser.add_option('--background', dest='bg_file',
help='Background NIFTI image')
parser.add_option('--annot', dest='annot')
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
sys.exit(2)
name_mapping = None
if options.config:
config = GtsConfig(options.config, configure=False)
name_mapping = config.group_labels
annotations = None
if options.annot:
with open(options.annot, 'r') as fp:
annotations = yaml.load(fp)
QB_DIST = options.dist
QB_NPOINTS = options.num
SCALAR_NAME = options.scalar
LOCAL_POINT_ASSIGN = options.is_local
filename = args[0]
filebase = path.basename(filename).split('.')[0]
reader = vtk.vtkXMLPolyDataReader()
reader.SetFileName(filename)
reader.Update()
polydata = reader.GetOutput()
tract_ids = []
for i in range(polydata.GetNumberOfCells()):
# get point ids in [[ids][ids...]...] format
pids = polydata.GetCell(i).GetPointIds()
ids = [pids.GetId(p) for p in range(pids.GetNumberOfIds())]
tract_ids.append(ids)
print 'tracks:', len(tract_ids)
verts = vtk_to_numpy(polydata.GetPoints().GetData())
print 'verts:', len(verts)
scalars = []
groups = []
subjects = []
pointdata = polydata.GetPointData()
for si in range(pointdata.GetNumberOfArrays()):
sname = pointdata.GetArrayName(si)
print sname
if sname == SCALAR_NAME:
scalars = vtk_to_numpy(pointdata.GetArray(si))
if sname == 'group':
groups = vtk_to_numpy(pointdata.GetArray(si))
groups = groups.astype(int)
if sname == 'tid':
subjects = vtk_to_numpy(pointdata.GetArray(si))
subjects = subjects.astype(int)
streamlines = []
stream_scalars = []
stream_groups = []
stream_pids = []
stream_sids = []
for i in tract_ids:
# index np.array by a list will get all the respective indices
streamlines.append(verts[i])
stream_scalars.append(scalars[i])
stream_pids.append(i)
stream_sids.append(subjects[i])
try:
stream_groups.append(groups[i])
except Exception:
# group might not exist
pass
streamlines = np.array(streamlines)
stream_scalars = np.array(stream_scalars)
stream_groups = np.array(stream_groups)
stream_pids = np.array(stream_pids)
stream_sids = np.array(stream_sids)
# get total average direction (where majority point towards)
avg_d = np.zeros(3)
# for line in streams:
# d = np.array(line[-1]) - np.array(line[0])
# d = d / la.norm(d)
# avg_d += d
# avg_d /= la.norm(avg_d)
avg_com = np.zeros(3)
avg_mid = np.zeros(3)
strl_len = [len(l) for l in streamlines]
stl_ori = np.array([np.abs(tm.mean_orientation(l)) for l in streamlines])
centroids = []
if options.curvepoints_file:
LOCAL_POINT_ASSIGN = False
cpoints = []
ctrlpoints = np.loadtxt(options.curvepoints_file, delimiter=',')
# have a separate vtkCardinalSpline interpreter for x,y,z
curve = [vtk.vtkCardinalSpline() for i in range(3)]
for c in curve:
c.ClosedOff()
for pi, point in enumerate(ctrlpoints):
for i, val in enumerate(point):
curve[i].AddPoint(pi, point[i])
param_range = [0.0, 0.0]
curve[0].GetParametricRange(param_range)
t = param_range[0]
step = (param_range[1] - param_range[0]) / (QB_NPOINTS - 1.0)
while t < param_range[1]:
cp = [c.Evaluate(t) for c in curve]
cpoints.append(cp)
t = t + step
centroids.append(cpoints)
centroids = np.array(centroids)
else:
"""
Use quickbundles to find centroids
"""
# streamlines = newlines
qb = QuickBundles(streamlines, dist_thr=QB_DIST, pts=QB_NPOINTS)
# bundle_distance_mam
centroids = qb.centroids
clusters = qb.clusters()
avg_d = np.zeros(3)
avg_com = np.zeros(3)
avg_mid = np.zeros(3)
#unify centroid list orders to point in the same general direction
for i, line in enumerate(centroids):
ori = np.array(tm.mean_orientation(line))
#d = np.array(line[-1]) - np.array(line[0])
#print line[-1],line[0],d
# get the unit vector of the mean orientation
if i == 0:
avg_d = ori
#d = d / la.norm(d)
dotprod = ori.dot(avg_d)
print 'dotprod', dotprod
if dotprod < 0:
print 'reverse', dotprod
centroids[i] = line[::-1]
line = centroids[i]
ori *= -1
avg_d += ori
if options.is_reverse:
for i, c in enumerate(centroids):
centroids[i] = c[::-1]
# prepare mayavi 3d viz
if options.is_viz:
bg_val = 0.
fig = mlab.figure(bgcolor=(bg_val, bg_val, bg_val))
scene = mlab.gcf().scene
fig.scene.render_window.aa_frames = 4
mlab.draw()
if options.bg_file:
mrsrc, bgdata = getNiftiAsScalarField(options.bg_file)
orie = 'z_axes'
opacity = 0.5
slice_index = 0
mlab.pipeline.image_plane_widget(mrsrc, opacity=opacity, plane_orientation=orie, slice_index=int(
slice_index), colormap='black-white', line_width=0, reset_zoom=False)
# prepare the plt plot
len_cent = len(centroids)
pal = sns.color_palette("bright", len_cent)
DATADF = None
"""
CENTROIDS
"""
for ci, cent in enumerate(centroids):
print '---- centroid:'
if LOCAL_POINT_ASSIGN:
"""
apply centroid to only their point assignments
through quickbundles
"""
ind = clusters[ci]['indices']
cent_streams = streamlines[ind]
cent_scalars = stream_scalars[ind]
cent_groups = stream_groups[ind]
cent_pids = stream_pids[ind]
cent_sids = stream_sids[ind]
else:
# apply each centriod to all the points
# instead of only their centroid assignments
cent_streams = streamlines
cent_scalars = stream_scalars
cent_groups = stream_groups
cent_pids = stream_pids
cent_sids = stream_sids
cent_verts = np.vstack(cent_streams)
cent_scalars = np.concatenate(cent_scalars)
cent_groups = np.concatenate(cent_groups)
cent_pids = np.concatenate(cent_pids)
cent_sids = np.concatenate(cent_sids)
cent_color = np.array(pal[ci])
c, labels = kmeans2(cent_verts, cent, iter=1)
cid = np.ones(len(labels))
d = {'value': cent_scalars, 'position': labels,
'group': cent_groups, 'pid': cent_pids, 'sid': cent_sids}
df = pd.DataFrame(data=d)
if DATADF is None:
DATADF = df
else:
pd.concat([DATADF, df])
UNIQ_GROUPS = df.group.unique()
UNIQ_GROUPS.sort()
# UNIQ_GROUPS = [0,1]
grppal = sns.color_palette("Set2", len(UNIQ_GROUPS))
print '# UNIQ GROUPS', UNIQ_GROUPS
# print df
# df = df[df['sid'] != 15]
# df = df[df['sid'] != 16]
# df = df[df['sid'] != 17]
# df = df[df['sid'] != 18]
"""
plot each group by their position
"""
fig = plt.figure(figsize=(14, 7))
ax1 = plt.subplot2grid((4, 3), (0, 0), colspan=3, rowspan=3)
ax2 = plt.subplot2grid((4, 3), (3, 0), colspan=3, sharex=ax1)
axes = [ax1, ax2]
plt.xlabel('Position Index')
if len(centroids) > 1:
cent_patch = mpatches.Patch(
color=cent_color, label='Centroid {}'.format(ci + 1))
cent_legend = axes[0].legend(handles=[cent_patch], loc=9)
axes[0].add_artist(cent_legend)
"""
Perform stats
"""
if len(UNIQ_GROUPS) > 1:
# df = resample_data(df, num_sample_per_pos=120)
# print df
pvalsDf = position_stats(df, name_mapping=name_mapping)
logpvals = np.log(pvalsDf) * -1
# print logpvals
pvals = logpvals.mask(pvalsDf >= 0.05)
import matplotlib.ticker as mticker
print pvals
cmap = mcmap.Reds
cmap.set_bad('w', 1.)
axes[1].pcolormesh(pvals.values.T, cmap=cmap,
vmin=0, vmax=10, edgecolors='face', alpha=0.8)
#axes[1].yaxis.set_major_locator(mticker.MultipleLocator(base=1.0))
axes[1].set_yticks(
np.arange(pvals.values.shape[1]) + 0.5, minor=False)
axes[1].set_yticklabels(
pvalsDf.columns.values.tolist(), minor=False)
legend_handles = []
for gi, GRP in enumerate(UNIQ_GROUPS):
print '-------------------- GROUP ', gi, '----------------------'
subgrp = df[df['group'] == GRP]
print len(subgrp)
if options.xrange:
x0, x1 = options.xrange.split(',')
x0 = int(x0)
x1 = int(x1)
subgrp = subgrp[(subgrp['position'] >= x0) &
(subgrp['position'] < x1)]
posGrp = subgrp.groupby('position', sort=True)
cent_stats = posGrp.apply(lambda x: stats_per_group(x))
if len(cent_stats) == 0:
continue
cent_stats = cent_stats.unstack()
cent_median_scalar = cent_stats['median'].tolist()
x = np.array([i for i in posGrp.groups])
# print x
# print cent_stats['median'].tolist()
mcolor = np.array(grppal[gi])
# if gi>0:
# mcolor*= 1./(1+gi)
cent_color = tuple(cent_color)
mcolor = tuple(mcolor)
if type(axes) is list:
cur_axe = axes[0]
else:
cur_axe = axes
cur_axe.set_ylabel(SCALAR_NAME)
# cur_axe.yaxis.label.set_color(cent_color)
# cur_axe.tick_params(axis='y', colors=cent_color)
#cur_axe.fill_between(x, [s[0] for s in cent_ci], [t[1] for t in cent_ci], alpha=0.3, color=mcolor)
# cur_axe.fill_between(x, [s[0] for s in cent_stats['whisk'].tolist()],
# [t[1] for t in cent_stats['whisk'].tolist()], alpha=0.1, color=mcolor)
qtile_top = np.array([s[0] for s in cent_stats['ci'].tolist()])
qtile_bottom = np.array([t[1] for t in cent_stats['ci'].tolist()])
x_new, qtop_sm = smooth(x, qtile_top)
x_new, qbottom_sm = smooth(x, qtile_bottom)
cur_axe.fill_between(x_new, qtop_sm, qbottom_sm,
alpha=0.25, color=mcolor)
# cur_axe.errorbar(x, cent_stats['median'].tolist(), yerr=[[s[0] for s in cent_stats['err'].tolist()],
# [t[1] for t in cent_stats['err'].tolist()]], color=mcolor, alpha=0.1)
x_new, median_sm = smooth(x, cent_stats['median'])
hnd, = cur_axe.plot(x_new, median_sm, c=mcolor)
legend_handles.append(hnd)
# cur_axe.scatter(x,cent_stats['median'].tolist(), c=mcolor)
if options.yrange:
plotrange = options.yrange.split(',')
cur_axe.set_ylim([float(plotrange[0]), float(plotrange[1])])
legend_labels = UNIQ_GROUPS
if name_mapping is not None:
legend_labels = [name_mapping[str(i)] for i in UNIQ_GROUPS]
cur_axe.legend(legend_handles, legend_labels)
if annotations:
for key, val in annotations.iteritems():
# print key
cur_axe.axvspan(val[0], val[1], fill=False, linestyle='dashed')
axis_to_data = cur_axe.transAxes + cur_axe.transData.inverted()
data_to_axis = axis_to_data.inverted()
axpoint = data_to_axis.transform((val[0], 0))
# print axpoint
cur_axe.text(axpoint[0], 1.02, key,
transform=cur_axe.transAxes)
"""
Plot 3D Viz
"""
if options.is_viz:
scene.disable_render = True
# scene.renderer.render_window.set(alpha_bit_planes=1,multi_samples=0)
# scene.renderer.set(use_depth_peeling=True,maximum_number_of_peels=4,occlusion_ratio=0.1)
# ran_colors = np.random.random_integers(255, size=(len(cent),4))
# ran_colors[:,-1] = 255
mypts = mlab.points3d(cent_verts[:, 0], cent_verts[:, 1], cent_verts[:, 2], labels,
opacity=0.3,
scale_mode='none',
scale_factor=2,
line_width=2,
colormap='blue-red',
mode='point')
# print mypts.module_manager.scalar_lut_manager.lut.table.to_array()
# mypts.module_manager.scalar_lut_manager.lut.table = ran_colors
# mypts.module_manager.scalar_lut_manager.lut.number_of_colors = len(ran_colors)
delta = len(cent) - len(cent_median_scalar)
if delta > 0:
cent_median_scalar = np.pad(
cent_median_scalar, (0, delta), mode='constant', constant_values=0)
# calculate the displacement vector for all pairs
uvw = cent - np.roll(cent, 1, axis=0)
uvw[0] *= 0
uvw = np.roll(uvw, -1, axis=0)
arrow_plot = mlab.quiver3d(
cent[:, 0], cent[:, 1], cent[:, 2],
uvw[:, 0], uvw[:, 1], uvw[:, 2],
scalars=cent_median_scalar,
scale_factor=1,
#color=mcolor,
mode='arrow')
gsource = arrow_plot.glyph.glyph_source.glyph_source
# for name, thing in inspect.getmembers(gsource):
# print name
arrow_plot.glyph.color_mode = 'color_by_scalar'
#arrow_plot.glyph.scale_mode = 'scale_by_scalar'
#arrow_plot.glyph.glyph.clamping = True
#arrow_plot.glyph.glyph.scale_factor = 5
#print arrow_plot.glyph.glyph.glyph_source
gsource.tip_length = 0.4
gsource.shaft_radius = 0.2
gsource.tip_radius = 0.3
if options.show_centroid:
tube_plot = mlab.plot3d(cent[:, 0], cent[:, 1], cent[
:, 2], cent_median_scalar, color=cent_color, tube_radius=0.2, opacity=0.25)
tube_filter = tube_plot.parent.parent.filter
tube_filter.vary_radius = 'vary_radius_by_scalar'
tube_filter.radius_factor = 10
# plot first and last
def plot_pos_index(p):
pos = cent[p]
mlab.text3d(pos[0], pos[1], pos[2], str(p), scale=0.8)
for p in xrange(0, len(cent - 1), 10):
plot_pos_index(p)
plot_pos_index(len(cent) - 1)
scene.disable_render = False
DATADF.to_csv(
'_'.join([filebase, SCALAR_NAME, 'rawdata.csv']), index=False)
outfile = '_'.join([filebase, SCALAR_NAME])
print 'save to {}'.format(outfile)
plt.savefig('{}.pdf'.format(outfile), dpi=300)
if options.is_viz:
plt.show(block=False)
mlab.show()
import numpy as np x, y = np.mgrid[-10:10:200j, -10:10:200j] z = 100 * np.sin(x * y) / (x * y) # Visualize it with mlab.surf from mayavi import mlab #mlab.options.backend = 'envisage' mlab.figure(bgcolor=(1, 1, 1)) surf = mlab.surf(z, colormap='cool') # Retrieve the LUT of the surf object. 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 print lut lut[:, -1] = np.linspace(0, 255, 256) print lut # 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 # We need to force update of the figure now that we have changed the LUT. mlab.draw() mlab.view(40, 85) mlab.show()
try:
if bg_color is None or sum(bg_color) < 2:
text_color = (1., 1., 1.)
else:
text_color = (0., 0., 0.)
cbar.label_text_property.color = text_color # set the labels(around the bar) color
except AttributeError:
pass
# _toggle_render(True, view)
state = True
if mlab.options.backend != "test":
figure.scene.disable_render = not state
if state is True and view is not None:
mlab.draw(figure=figure)
# mlab.view(*view, figure=figure) # The statement will raise some problem, so as the example!
if state is True:
# force render
figure.render()
mlab.draw(figure=figure)
# remove overlay
# --------------------------------------------------------------------------------
if pos_surf is not None:
pos_surf.remove()
pos_bar.visible = False
if neg_surf is not None:
neg_surf.remove()
neg_bar.visible = False