def plot_sln_mayavi(x, y, mesh, sln_values, colorbar=False):
"""
Plot a solution using mayavi.
Example:
>>> from numpy import array
>>> from femhub.plot import plot_sln_mayavi
>>> f = plot_sln_mayavi([0, 1, 1], [0, 0, 1], [1, 2, 3])
>>> f.savefig("a.png")
"""
from enthought.mayavi import mlab
#mlab.options.offscreen = True
mlab.clf()
#mlab.options.show_scalar_bar = False
z = [0] * len(x)
mlab.triangular_mesh(x, y, z, mesh, scalars=sln_values)
engine = mlab.get_engine()
image = engine.current_scene
image.scene.background = (1.0, 1.0, 1.0)
image.scene.foreground = (0.0, 0.0, 0.0)
if colorbar:
mlab.colorbar(orientation="vertical")
mlab.view(0, 0)
return mlab
def plotsln(mesh, z=None, sln=None, colorbar=False, view=(0,0),
filename="a.png"):
"""
Plot a solution for the mesh editor in the online lab.
"""
x = [n[0] for n in mesh.nodes]
y = [n[1] for n in mesh.nodes]
if z == None:
try:
z = [n[2] for n in mesh.nodes]
except IndexError:
z = [0]*len(y)
from enthought.mayavi import mlab
mlab.options.offscreen = True
mlab.clf()
#mlab.options.show_scalar_bar = False
mlab.triangular_mesh(x, y, z, mesh.elems, scalars=sln)
engine = mlab.get_engine()
image = engine.current_scene
image.scene.background = (1.0, 1.0, 1.0)
image.scene.foreground = (0.0, 0.0, 0.0)
if colorbar:
mlab.colorbar(orientation="vertical")
if view:
mlab.view(view[0], view[1])
mlab.savefig(filename)
def plot_surface(s):
x, y, z, t = gts.get_coords_and_face_indices(s, True)
mlab.triangular_mesh(x, y, z, t, color=(0.8, 0.8, 0.8))
mlab.triangular_mesh(x,
y,
z,
t,
color=(0, 0, 1),
representation='fancymesh',
tube_radius=.001,
scale_factor=0.001)
def plotbdy(self, fig, id, opac = 0.9):
idset = set(map(tuple, self.boundaries[id]))
bdyts = []
for t in self.triangles:
if idset.issuperset(set(t)): bdyts.append(t)
for q in self.quads:
if idset.issuperset(set(q)):
bdyts.append(q[[0,1,3]])
bdyts.append(q[[2,1,3]])
x,y,z = zip(*self.getPoints(np.array(bdyts).flatten()))
emm.triangular_mesh(x,y,z, np.arange(len(bdyts)*3).reshape(-1,3), representation = 'surface', figure = fig, color=(0.5,0.2,0.5), opacity = opac, line_width=0.5)
def plot_retino_image(mesh_path, name, tf=None, tex=None, curv=None, mask=None,
vmin=0, vmax=0):
"""Custom function to plot mesh in the case of retinotopic mapping
Parameters
----------
mesh_path: string, path of the mesh to plot
name: string, object identifier
tf: (4, 4) array
affine transformation that has to be applied to the mesh
tex: array of shape (mes.n_vertices),
texture to plot on the surface
curv: array of shape (mes.n_vertices),
curvature texture to plot on the surface
mask: array of shape (mes.n_vertices),
A mask for functional regions of interest
vmin, vmax: bounds for color clipping
"""
vertices, triangles = mesh_arrays(mesh_path)
af_coord = np.hstack((vertices, np.ones((vertices.shape[0], 1))))
if tf is not None:
af_coord = np.dot(af_coord, tf.T)
vertices = af_coord[:, :3]
x, y, z = vertices.T
# it is expected that the curvature takes values between 0 and 1
if curv is not None:
cmin = 2 * curv.min() - curv.max()
cmax = 2 * curv.max() - curv.min()
mlab.triangular_mesh(x, y, z, triangles, transparent=False, opacity=1.,
name=name, scalars=curv, colormap="bone",
vmin=cmin, vmax=cmax)
else:
mlab.triangular_mesh(x, y, z, triangles, transparent=False, opacity=1.,
name=name)
if tex is not None:
if mask is not None:
tex[mask == 0] = vmin - 1
func_mesh = mlab.pipeline.triangular_mesh_source(x, y, z,
triangles,
scalars=tex)
thresh = mlab.pipeline.threshold(func_mesh, low=vmin)
mlab.pipeline.surface(thresh, colormap="jet", vmin=vmin,
vmax=vmax, transparent=True,
opacity=.8)
mlab.scalarbar(thresh)
def CreateReducedModel(self,bandwidth=0.04,bandthresh=0.01,neighthresh=0.01,showdata=False,savefile=None):
self.measurements,indices = self.Prune(self.rawmeasurements,100,neighthresh**2,1)
uniformpoints,dists,pointscale = self.UniformlySampleSpace(bandwidth,bandthresh)
if showdata:
from enthought.mayavi import mlab
mlab.figure(1,fgcolor=(0,0,0), bgcolor=(1,1,1))
src = mlab.pipeline.scalar_field(dists)
mlab.pipeline.iso_surface(src,contours=[0.01],opacity=0.1)
mlab.pipeline.volume(mlab.pipeline.scalar_field(dists*500))
v = pointscale[0]*self.trimesh.vertices+pointscale[1]
mlab.triangular_mesh(v[:,0],v[:,1],v[:,2],self.trimesh.indices,color=(0,0,0.5))
if savefile is None:
savefile = self.getfilename()
print 'saving measurements to %s'%savefile
mkdir_recursive(os.path.split(savefile)[0])
savetxt(savefile,uniformpoints,'%f')
def plot_sln_mayavi(sln, notebook=False):
"""
Plots the Solution() instance sln using Linearizer() and matplotlib.
Currently only a very simple version is implemented, that takes the
vertices from linearizer and interpolates them. More sophisticated version
should take the triangles.
"""
lin = Linearizer()
lin.process_solution(sln)
vert = lin.get_vertices()
triangles = lin.get_triangles()
from numpy import zeros
from enthought.mayavi import mlab
x = vert[:, 0]
y = vert[:, 1]
z = zeros(len(y))
t = vert[:, 2]
if notebook:
# the off screen rendering properly works only with VTK-5.2 or above:
mlab.options.offscreen = True
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.view(0, 0)
# Below is a code that does exactly what the "View along the +Z axis"
# button does:
#scene = mlab.get_engine().current_scene.scene
#scene.camera.focal_point = [0, 0, 0]
#scene.camera.position = [0, 0, 1]
#scene.camera.view_up = [0, 1, 0]
#scene.renderer.reset_camera()
#scene.render()
# the above looks ok, but there is still quite a large margin, so we prefer
# to just call .view(0, 0), which seems to be working fine.
return s
def plot(self, **kwarg):
"""Plot mesh with Mayavi
"""
from enthought.mayavi import mlab
f = mlab.triangular_mesh(self.points[:, 0], self.points[:, 1], self.points[:, 2], self.faces, **kwarg)
return f
def show_blobs(blobs, v, faces,fa_slice=None,colormap='jet'):
"""Mayavi gets really slow when triangular_mesh is called too many times
so this function stacks blobs and calls triangular_mesh once
"""
print blobs.shape
xcen = blobs.shape[0]/2.
ycen = blobs.shape[1]/2.
zcen = blobs.shape[2]/2.
faces = np.asarray(faces, 'int')
xx = []
yy = []
zz = []
count = 0
ff = []
mm = []
for ii in xrange(blobs.shape[0]):
for jj in xrange(blobs.shape[1]):
for kk in xrange(blobs.shape[2]):
m = blobs[ii,jj,kk]
#m /= (2.2*m.max())
#m /= (2.2*abs(m).max())
#m /= (2.2*1.)
#m[m<.4*abs(m).max()]=0
x, y, z = v.T*m/2.2
x += ii - xcen
y += jj - ycen
z += kk - zcen
ff.append(count+faces)
count += len(x)
xx.append(x)
yy.append(y)
zz.append(z)
mm.append(m)
ff = np.concatenate(ff)
xx = np.concatenate(xx)
yy = np.concatenate(yy)
zz = np.concatenate(zz)
mm = np.concatenate(mm)
mlab.triangular_mesh(xx, yy, zz, ff, scalars=mm, colormap=colormap)
if fa_slice!=None:
mlab.imshow(fa_slice, colormap='gray', interpolate=False)
mlab.colorbar()
mlab.show()
def add_xy_plane():
z = [0, 0, 0, 0]
x = [-1, -1, 1, 1]
y = [-1, 1, 1, -1]
tris = [(0, 3, 2), (2, 1, 0)]
mesh = mlab.triangular_mesh(x, y, z,
tris,
color=(0, 0, 1),
opacity=g_plane_opacity)
def plot_mesh(mesh, name, tf=None, tex=None):
""" old version -- probably deprecated
"""
vertices, triangles = mesh_arrays(mesh)
af_coord = np.hstack((vertices, np.ones((vertices.shape[0], 1))))
if tf is not None:
af_coord = np.dot(af_coord, tf.T)
vertices = af_coord[:, :3]
x, y, z = vertices.T
# show with mayavi
if tex is None:
mlab.triangular_mesh(x, y, z, triangles, transparent=False, opacity=1.,
name=name, color=(0.5, 0.5, 0.))
else:
mlab.triangular_mesh(x, y, z, triangles, transparent=False, opacity=1.,
name=name, scalars=tex)
def color_patches(vertices, faces, heights, colors):
"""Mayavi gets really slow when triangular_mesh is called too many times
so this function stacks blobs and calls triangular_mesh once
"""
print colors.shape
xcen = colors.shape[0] / 2.0
ycen = colors.shape[1] / 2.0
zcen = colors.shape[2] / 2.0
faces = np.asarray(faces, "int")
xx = []
yy = []
zz = []
count = 0
ff = []
cc = []
for ii in xrange(colors.shape[0]):
for jj in xrange(colors.shape[1]):
for kk in xrange(colors.shape[2]):
c = colors[ii, jj, kk]
# m[m<.4*abs(m).max()]=0
m = heights[ii, jj, kk]
print "vertices", vertices.shape
print "m", m.shape
x, y, z = vertices.T * m
x += ii - xcen
y += jj - ycen
z += kk - zcen
ff.append(count + faces)
count += len(x)
xx.append(x)
yy.append(y)
zz.append(z)
cc.append(c)
ff = np.concatenate(ff)
xx = np.concatenate(xx)
yy = np.concatenate(yy)
zz = np.concatenate(zz)
cc = np.concatenate(cc)
mlab.triangular_mesh(xx, yy, zz, ff, scalars=cc)
mlab.show()
def add_xy_plane():
z = [0, 0, 0, 0]
x = [-1, -1, 1, 1]
y = [-1, 1, 1, -1]
tris = [(0, 3, 2), (2, 1, 0)]
mesh = mlab.triangular_mesh(x,
y,
z,
tris,
color=(0, 0, 1),
opacity=g_plane_opacity)
def show_blobs(blobs, v, faces):
"""Mayavi gets really slow when triangular_mesh is called too many times
sot this function stacks blobs and calls triangular_mesh once
"""
blobs
xcen = blobs.shape[0] / 2
ycen = blobs.shape[1] / 2
zcen = blobs.shape[2] / 2
faces = asarray(faces, 'int')
xx = []
yy = []
zz = []
count = 0
ff = []
mm = []
for ii in xrange(blobs.shape[0]):
for jj in xrange(blobs.shape[1]):
for kk in xrange(blobs.shape[2]):
m = blobs[ii, jj, kk]
m /= (2.2 * abs(m).max())
x, y, z = v.T * m
x += ii - xcen
y += jj - ycen
z += kk - zcen
ff.append(count + faces)
count += len(x)
xx.append(x)
yy.append(y)
zz.append(z)
mm.append(m)
ff = concatenate(ff)
xx = concatenate(xx)
yy = concatenate(yy)
zz = concatenate(zz)
mm = concatenate(mm)
mlab.triangular_mesh(xx, yy, zz, ff, scalars=mm)
def doit():
global s
global iter
if iter == 0:
print "plotting the triangular mesh..."
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
print " done."
print "adjusting view..."
mlab.view(0, 0)
print " done."
else:
print "changing the source..."
# This doesn't work due to a bug in mayavi/VTK:
# http://github.com/certik/mhd-hermes/issues#issue/1
#s.mlab_source.reset(x=x, y=y, z=z, triangles=triangles, scalars=t)
# so until this is fixed, let's call triangular_mesh and delete the
# old mesh (this is slow but works):
scene = mlab.gcf().scene
scene.disable_render = True
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.get_engine().scenes[0].children[:1] = []
scene.disable_render = False
print " done."
iter += 1
def doit():
global s
global iter
if iter == 0:
print "plotting the triangular mesh..."
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
print " done."
print "adjusting view..."
mlab.view(0, 0)
print " done."
else:
print "changing the source..."
# This doesn't work due to a bug in mayavi/VTK:
# http://github.com/certik/mhd-hermes/issues#issue/1
#s.mlab_source.reset(x=x, y=y, z=z, triangles=triangles, scalars=t)
# so until this is fixed, let's call triangular_mesh and delete the
# old mesh (this is slow but works):
scene = mlab.gcf().scene
scene.disable_render = True
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.get_engine().scenes[0].children[:1] = []
scene.disable_render = False
print " done."
iter += 1
def display(self):
""" Display Nucleus in Mayavi """
# Create and display a triangular mesh
from enthought.mayavi.mlab import triangular_mesh
f = mlab.figure(figure=mlab.gcf(),
bgcolor=(0,0,0),
size=(800,600))
self.mlab_mesh = triangular_mesh(self.pts[:, 0],
self.pts[:, 1],
self.pts[:, 2],
self.tris,
color=self.color,
name=self.name)
# Visual Tweaks
#self.mlab_mesh.actor.property.backface_culling = True
self.mlab_mesh.parent.parent.filter.splitting = False
def test_triangular_mesh_reset(self):
""" When reseting the triangular mesh (ie polydata), if we are
not careful, we can create a segfault by passing triangules
between points that do not exist.
"""
# We need to run this as a full test of mlab, rather than only a
# test of the source, as to get a segfault, we need a module
# opened on the source.
n = 100
triangles = np.c_[np.arange(n - 3),
np.arange(n - 3) + 1, n - 1 - np.arange(n - 3)]
x, y, z = np.random.random((3, n))
src = mlab.triangular_mesh(x, y, z, triangles)
# Now grow the mesh
n = 1000
triangles = np.c_[np.arange(n - 3),
np.arange(n - 3) + 1, n - 1 - np.arange(n - 3)]
x, y, z = np.random.random((3, n))
src.mlab_source.reset(x=x, y=y, z=z, triangles=triangles)
def plot_sln_mayavi(sln, offscreen=False, show_scale=True):
"""
Plots the Solution() instance sln using Linearizer() and matplotlib.
It takes the vertices from linearizer and interpolates them.
"""
lin = Linearizer()
lin.process_solution(sln)
vert = lin.get_vertices()
triangles = lin.get_triangles()
from numpy import zeros
from enthought.mayavi import mlab
x = vert[:, 0]
y = vert[:, 1]
z = zeros(len(y))
t = vert[:, 2]
if offscreen:
# the off screen rendering properly works only with VTK-5.2 or above:
mlab.options.offscreen = True
mlab.clf()
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.view(0, 0)
mlab.view(distance=4)
mlab.view(focalpoint=(.35, 0, 0))
mlab.colorbar(title="Solution", orientation="vertical")
#mlab.move(right=-1.0, up=-10.0)
# Below is a code that does exactly what the "View along the +Z axis"
# button does:
#scene = mlab.get_engine().current_scene.scene
#scene.camera.focal_point = [0, 0, 0]
#scene.camera.position = [0, 0, 1]
#scene.camera.view_up = [0, 1, 0]
#scene.renderer.reset_camera()
#scene.render()
# the above looks ok, but there is still quite a large margin, so we prefer
# to just call .view(0, 0), which seems to be working fine.
return mlab
def plot_sln_mayavi(sln, offscreen=False, show_scale=True):
"""
Plots the Solution() instance sln using Linearizer() and matplotlib.
It takes the vertices from linearizer and interpolates them.
"""
lin = Linearizer()
lin.process_solution(sln)
vert = lin.get_vertices()
triangles = lin.get_triangles()
from numpy import zeros
from enthought.mayavi import mlab
x = vert[:, 0]
y = vert[:, 1]
z = zeros(len(y))
t = vert[:, 2]
if offscreen:
# the off screen rendering properly works only with VTK-5.2 or above:
mlab.options.offscreen = True
mlab.clf()
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.view(0, 0)
mlab.view(distance=4)
mlab.view(focalpoint=(.35,0,0))
mlab.colorbar(title="Solution", orientation="vertical")
#mlab.move(right=-1.0, up=-10.0)
# Below is a code that does exactly what the "View along the +Z axis"
# button does:
#scene = mlab.get_engine().current_scene.scene
#scene.camera.focal_point = [0, 0, 0]
#scene.camera.position = [0, 0, 1]
#scene.camera.view_up = [0, 1, 0]
#scene.renderer.reset_camera()
#scene.render()
# the above looks ok, but there is still quite a large margin, so we prefer
# to just call .view(0, 0), which seems to be working fine.
return mlab
def test_triangular_mesh_reset(self):
""" When reseting the triangular mesh (ie polydata), if we are
not careful, we can create a segfault by passing triangules
between points that do not exist.
"""
# We need to run this as a full test of mlab, rather than only a
# test of the source, as to get a segfault, we need a module
# opened on the source.
n = 100
triangles = np.c_[np.arange(n-3),
np.arange(n-3)+1,
n-1-np.arange(n-3)]
x, y, z = np.random.random((3, n))
src = mlab.triangular_mesh(x, y, z, triangles)
# Now grow the mesh
n = 1000
triangles = np.c_[np.arange(n-3),
np.arange(n-3)+1,
n-1-np.arange(n-3)]
x, y, z = np.random.random((3, n))
src.mlab_source.reset(x=x, y=y, z=z, triangles=triangles)
def plot_sln_mayavi(sln, notebook=False):
"""
Plots the Solution() instance sln using Linearizer() and matplotlib.
Currently only a very simple version is implemented, that takes the
vertices from linearizer and interpolates them. More sophisticated version
should take the triangles.
"""
lin = Linearizer()
lin.process_solution(sln)
vert = lin.get_vertices()
triangles = lin.get_triangles()
from numpy import zeros
from enthought.mayavi import mlab
x = vert[:, 0]
y = vert[:, 1]
z = zeros(len(y))
t = vert[:, 2]
if notebook:
# the off screen rendering properly works only with VTK-5.2 or above:
mlab.options.offscreen = True
mlab.clf()
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.view(0, 0)
# Below is a code that does exactly what the "View along the +Z axis"
# button does:
#scene = mlab.get_engine().current_scene.scene
#scene.camera.focal_point = [0, 0, 0]
#scene.camera.position = [0, 0, 1]
#scene.camera.view_up = [0, 1, 0]
#scene.renderer.reset_camera()
#scene.render()
# the above looks ok, but there is still quite a large margin, so we prefer
# to just call .view(0, 0), which seems to be working fine.
return s
from pylab import *
import sys,triangleio
try:
name = sys.argv[1]
except:
raise Exception('Must enter filename to read')
nv,vertices,nf,facets = triangleio.read(name,faces=True)
from enthought.mayavi import mlab
mlab.triangular_mesh(vertices[:,0],vertices[:,1],vertices[:,2],facets)
mlab.show()
"""
print(__doc__)
# Authors: Denis Engemann <*****@*****.**>
# Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
import mne
from mne.surface import decimate_surface
path = mne.datasets.sample.data_path()
surf = mne.read_bem_surfaces(path + '/subjects/sample/bem/sample-head.fif')[0]
points, triangles = surf['rr'], surf['tris']
# reduce to 30000 meshes equaling ${SUBJECT}-head-medium.fif output from
# mne_make_scalp_surfaces.py and mne_make_scalp_surfaces
points_dec, triangles_dec = decimate_surface(points, triangles,
n_triangles=30000)
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
head_col = (0.95, 0.83, 0.83) # light pink
p, t = points_dec, triangles_dec
mlab.triangular_mesh(p[:, 0], p[:, 1], p[:, 2], t, color=head_col)
def draw_camera (R, t, scale = 0.1, opt = 'fancy', figure = None):
""" Draw a camera in world coords according to its pose
If R = id and t = [0 0 0]' show a camera in the world
center pointing towards the +Z axis.
Usage: draw_camera (R, t, scale, opt);
Input:
R - 3x3 Rotation matrix or 3x1 Rodrigues rotation vector (camera to
world rotation)
t - 3x1 Translation vector (camera to world translation)
scale - (optional) scaling parameter, in meters. Default: 0.1.
opt - Visualization option: (default: 'pyr')
'pyr' shows an inverted pyramid in the camera direction
'axis' shows the 3 axis (red for X, green for Y, blue for Z)
"""
# Generate figure if none given
if figure == None:
figure = mlab.figure()
# Enforce 2d column vector of translation
t = np.atleast_2d(t.ravel()).T
# Five points that define the pyramid
# Careful, because np.c_ somehow transposes this matrix, this is 3x6.
pyr_points = scale * np.c_[[ 0, 0, 0], \
[-0.4, -0.4, 1], \
[ 0.4, -0.4, 1], \
[ 0.4, 0.4, 1], \
[-0.4, 0.4, 1], \
[np.nan, np.nan, np.nan]]
pyr_tri_side = np.c_[[0, 1, 2], \
[0, 2, 3], \
[0, 3, 4], \
[0, 1, 4]].T
pyr_tri_front = np.c_[[1, 2, 3], \
[1, 3, 4]].T
# Four points that define the axis in 3-space
axis_points = scale * np.c_[[0, 0, 0], \
[1, 0, 0], \
[0, 1, 0], \
[0, 0, 1]]
# Order in which to draw the points, so that it looks like 3 axis
axis_idx_x = np.r_[1, 2] - 1
axis_idx_y = np.r_[1, 3] - 1
axis_idx_z = np.r_[1, 4] - 1
# Some color constants
RED = (1,0,0)
GREEN = (0,1,0)
BLUE = (0,0,1)
if opt == 'pyr':
# Rotate pyramid and plot it
tx_pyr = np.dot(R, pyr_points) + \
np.tile( t, (1, pyr_points.shape[1]) )
mlab.triangular_mesh(tx_pyr[0, :], \
tx_pyr[1, :], \
tx_pyr[2, :], \
pyr_tri_side, \
color = BLUE, \
figure = figure)
mlab.draw()
mlab.triangular_mesh(tx_pyr[0, :], \
tx_pyr[1, :], \
tx_pyr[2, :], \
pyr_tri_front, \
color = GREEN, \
figure = figure)
elif opt == 'axis':
# Rotate the 3 axis and plot them
tx_axis = np.dot(R, axis_points) + \
np.tile( t, (1, axis_points.shape[1]) )
mlab.plot3d(tx_axis[0, axis_idx_x], \
tx_axis[1, axis_idx_x], \
tx_axis[2, axis_idx_x], \
color = RED, \
tube_radius = .003, \
figure = figure)
mlab.plot3d(tx_axis[0, axis_idx_y], \
tx_axis[1, axis_idx_y], \
tx_axis[2, axis_idx_y], \
color = GREEN, \
tube_radius = .003, \
figure = figure)
mlab.plot3d(tx_axis[0, axis_idx_z], \
tx_axis[1, axis_idx_z], \
tx_axis[2, axis_idx_z], \
color = BLUE, \
tube_radius = .003, \
figure = figure)
elif opt == 'fancy':
# Rotate pyramid and plot it
tx_pyr = np.dot(R, pyr_points) + \
np.tile( t, (1, pyr_points.shape[1]) )
mlab.triangular_mesh(tx_pyr[0, :], \
tx_pyr[1, :], \
tx_pyr[2, :], \
pyr_tri_side, \
color = BLUE, \
figure = figure)
mlab.triangular_mesh(tx_pyr[0, :], \
tx_pyr[1, :], \
tx_pyr[2, :], \
pyr_tri_front, \
color = GREEN, \
figure = figure)
# Rotate the 3 axis and plot them
tx_axis = np.dot(R, axis_points) + \
np.tile( t, (1, axis_points.shape[1]) )
mlab.plot3d(tx_axis[0, axis_idx_x], \
tx_axis[1, axis_idx_x], \
tx_axis[2, axis_idx_x], \
color = RED, \
tube_radius = .003, \
figure = figure)
mlab.plot3d(tx_axis[0, axis_idx_y], \
tx_axis[1, axis_idx_y], \
tx_axis[2, axis_idx_y], \
color = GREEN, \
tube_radius = .003, \
figure = figure)
else:
print('Unrecognized option');
print(__doc__)
# Authors: Denis Engemann <*****@*****.**>
# Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
import mne
from mne.surface import decimate_surface
path = mne.datasets.sample.data_path()
surf = mne.read_bem_surfaces(path + '/subjects/sample/bem/sample-head.fif')[0]
points, triangles = surf['rr'], surf['tris']
# reduce to 30000 meshes equaling ${SUBJECT}-head-medium.fif output from
# mne_make_scalp_surfaces.py and mne_make_scalp_surfaces
points_dec, triangles_dec = decimate_surface(points,
triangles,
n_triangles=30000)
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
head_col = (0.95, 0.83, 0.83) # light pink
p, t = points_dec, triangles_dec
mlab.triangular_mesh(p[:, 0], p[:, 1], p[:, 2], t, color=head_col)
import mne
from mne.datasets import sample
data_path = sample.data_path()
fname = data_path + '/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif'
surfaces = mne.read_bem_surfaces(fname, add_geom=True)
print("Number of surfaces : %d" % len(surfaces))
###############################################################################
# Show result
head_col = (0.95, 0.83, 0.83) # light pink
skull_col = (0.91, 0.89, 0.67)
brain_col = (0.67, 0.89, 0.91) # light blue
colors = [head_col, skull_col, brain_col]
# 3D source space
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
mlab.figure(size=(600, 600), bgcolor=(0, 0, 0))
for c, surf in zip(colors, surfaces):
points = surf['rr']
faces = surf['tris']
mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2], faces,
color=c, opacity=0.3)
x = sin(phi)
y = cos(phi)
#normals = -vstack([n * x + b * y for n, b in zip(N, B)])
points = vstack([p + thickness * (n * x + b * y) for p, n, b in zip(path, N, B)])
return make_tube(points, sides)
if __name__ == '__main__':
import sys
m = Mesh(sys.argv[1])
x, y, z = m.points.T
print 'Points: %d' % len(m.points)
print 'Triangles: %d' % len(m.triangles)
#m.triangles[0] = m.triangles[0, ::-1] #Flip a triangle to test the closed shape detection
print 'Volume: %f (%s)' % (m.volume(), 'mesh appears closed and properly oriented' if m.is_closed(tol=1E-12) else 'MESH DOES NOT APPEAR CLOSED AND ORIENTED PROPERLY)\n (A translated copy had a different calculated volume at 1 part in 10^12;\n this could be a rounding error.')
print 'Print price: $%.2f (assuming units=mm and $0.30/cc)' % (m.volume() / 1000. * 0.30)
print 'X extents: (%f, %f)' % (min(x), max(x))
print 'Y extents: (%f, %f)' % (min(y), max(y))
print 'Z extents: (%f, %f)' % (min(z), max(z))
from enthought.mayavi import mlab
mlab.triangular_mesh(x, y, z, m.triangles)
mlab.show()
def dense_reconstruction(S, P1, P2, H1, H2, region=None, step=1, imbw1=None):
"""
Build and show a dense reconstruction.
Parameters
----------
S : ndarray
Matrix of disparities.
P1, P2 : ndarray
Projection matrices of the stereo pair.
H1, H2 : ndarray
Homographies which rectify the images of the stereo
pair. See projmat2rectify.
region : array-like, optional
Region of the matrix of disparities that will be used for
the dense reconstruction. It must be expressed
as [x_min, x_max, y_min, y_max].
If not given, the full matrix of disparities is considered.
step : integer, optional
Quality of the reconstruction. Only one of every 'step' pixels
inside the region of interest is taken for the reconstruction.
If not given, all pixels are used.
imbw1 : ndarray, optional
The image before rectification. This is used to assign a color
to the 3D vertices of the reconstruction. If not given, an arbitrary
color is assigned to vertices.
imbw1 should be a gray scale image.
"""
if region is None:
region = [0, S.shape[1], 0, S.shape[0]]
# The matrix of disparities is smoothed.
H = np.ones((3, 3)) / 9.0
S = ndimage.convolve(np.double(S), H, mode='constant')
# Point correspondences.
X, Y = np.mgrid[region[0]:region[1] + 1:step, region[2]:region[3] + 1:step]
num_points = np.prod(X.shape)
points1 = np.ones((3, num_points))
points1[0, :] = X.ravel()
points1[1, :] = Y.ravel()
X2 = X.ravel() + S[np.int32(points1[1, :]), np.int32(points1[0, :])]
points2 = np.copy(points1)
points2[0, :] = X2
# Colors
if imbw1 is not None:
points1back = hom2cart(np.dot(la.inv(H1), points1))
colors = ndimage.map_coordinates(imbw1,
[points1back[1], points1back[0]],
order=1)
else:
colors = None
# Polygons.
vlist = np.arange(0, num_points)
vlist = vlist.reshape(X.shape)
vlist1 = vlist[:-1, :-1]
vlist2 = vlist[:-1, 1:]
vlist3 = vlist[1:, 1:]
vlist4 = vlist[1:, :-1]
faces1 = np.array([vlist1.ravel(), vlist2.ravel(), vlist3.ravel()])
faces2 = np.array([vlist1.ravel(), vlist3.ravel(), vlist4.ravel()])
faces = matarray(faces1, faces2).T
# Reconstruction.
from stereo import reconstruct
M = reconstruct(points1, points2, np.dot(H1, P1), np.dot(H2, P2))
# Show the result.
try:
from enthought.mayavi import mlab
except ImportError:
from mayavi import mlab
mlab.clf()
mlab.triangular_mesh(M[0],
M[1],
M[2],
faces,
scalars=colors,
colormap='gray')
return M, faces, colors
vis, illum, outs = taco.calc_earth_vis(-p_earth_body,
att,
ngrid=30,
to_earth=True,
max_reflect=0)
mask_points = (len(outs) // 300) or None
rx = [out[0][0] for out in outs]
ry = [out[0][1] for out in outs]
rz = [out[0][2] for out in outs]
ru = [out[1][0] * (2 if out[3] else 1) for out in outs]
rv = [out[1][1] * (2 if out[3] else 1) for out in outs]
rw = [out[1][2] * (2 if out[3] else 1) for out in outs]
mlab.clf()
mlab.triangular_mesh(xs, ys, zs, triangles, color=(1, 1, 0), opacity=0.9)
mlab.triangular_mesh(xs,
ys,
zs,
triangles,
color=(1, .8, 0),
representation='wireframe',
line_width=4)
mlab.quiver3d(rx,
ry,
rz,
ru,
rv,
rw,
scale_factor=1.0,
mask_points=mask_points,
from enthought.mayavi import mlab
except:
from mayavi import mlab
lh_points = src[0]['rr']
lh_faces = src[0]['use_tris']
mlab.figure(size=(600, 600), bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
# show brain surface after proper coordinate system transformation
points = brain_surface['rr']
faces = brain_surface['tris']
coord_trans = fwd['mri_head_t']['trans']
points = np.dot(coord_trans[:3, :3], points.T).T + coord_trans[:3, -1]
mlab.triangular_mesh(points[:, 0],
points[:, 1],
points[:, 2],
faces,
color=(1, 1, 0),
opacity=0.3)
# show one cortical surface
mlab.triangular_mesh(lh_points[:, 0],
lh_points[:, 1],
lh_points[:, 2],
lh_faces,
color=(0.7, ) * 3)
# show dipole as small cones
dipoles = mlab.quiver3d(pos[:, 0],
pos[:, 1],
pos[:, 2],
ori[:, 0],
# v1 = np.array([0,0,0])
# v2 = np.array([100,0,0])
# mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)])
# v1 = np.array([0,0,0])
# v2 = np.array([0,100,0])
# mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)])
# v1 = np.array([0,0,0])
# v2 = np.array([0,0,100])
# mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)])
v1 = np.array([0, 0, 0])
v2 = np.array([80, 0, 0])
v2 = np.dot(R[:, 0:3], v2)
mlab.triangular_mesh([[v1[0] - 1, v1[0] + 1, v2[0] - 1, v2[0] + 1]],
[[v1[1] - 1, v1[1] + 1, v2[1] - 1, v2[1] + 1]],
[[v1[2] - 1, v1[2] + 1, v2[2] - 1, v2[2] + 1]],
[(0, 1, 2), (1, 2, 3)],
color=(1, 1, 1))
v1 = np.array([0, 0, 0])
v2 = np.array([0, 80, 0])
v2 = np.dot(R[:, 0:3], v2)
mlab.triangular_mesh([[v1[0] - 1, v1[0] + 1, v2[0] - 1, v2[0] + 1]],
[[v1[1] - 1, v1[1] + 1, v2[1] - 1, v2[1] + 1]],
[[v1[2] - 1, v1[2] + 1, v2[2] - 1, v2[2] + 1]],
[(0, 1, 2), (1, 2, 3)],
color=(1, 1, 1))
v1 = np.array([0, 0, 0])
v2 = np.array([0, 0, 80])
v2 = np.dot(R[:, 0:3], v2)
def show_odfs(odfs, vertices_faces, image=None, colormap='jet',
scale=2.2, norm=True, radial_scale=True):
"""
Display a grid of ODFs.
Parameters
----------
odfs : (X, Y, Z, M) ndarray
A 3-D arrangement of orientation distribution functions (ODFs). At
each ``(x, y, z)`` position, it contains the the values of the
corresponding ODF evaluated on the M vertices.
vertices_faces : str or tuple of (vertices, faces)
A named sphere from `dipy.data.get_sphere`, or a combination of
`(vertices, faces)`.
image : (X, Y) ndarray
Background image (e.g., fractional anisotropy) do display behind the
ODFs.
colormap : str
Color mapping.
scale : float
Increasing the scale spaces ODFs further apart.
norm : bool
Whether or not to normalize each individual ODF (divide by its maximum
absolute value).
radial_scale : bool
Whether or not to change the radial shape of the ODF according to its
scalar value. If set to False, the ODF is displayed as a sphere.
Notes
-----
Mayavi gets really slow when `triangular_mesh` is called too many times,
so this function stacks ODF data and calls `triangular_mesh` once.
Examples
--------
>>> from dipy.data import get_sphere
>>> verts, faces = get_sphere('symmetric724')
>>> angle = np.linspace(0, 2*np.pi, len(verts))
>>> odf1 = np.sin(angle)
>>> odf2 = np.cos(angle)
>>> odf3 = odf1**2 * odf2
>>> odf4 = odf1 + odf2**2
>>> odfs = [[[odf1, odf2],
... [odf3, odf4]]]
>>> show_odfs(odfs, (verts, faces), scale=5)
"""
vertices, faces = sphere_vf_from(vertices_faces)
odfs = np.asarray(odfs)
if odfs.ndim != 4:
raise ValueError("ODFs must by an (X,Y,Z,M) array. " +
"Has shape " + str(odfs.shape))
grid_shape = np.array(odfs.shape[:3])
faces = np.asarray(faces, dtype=int)
xx, yy, zz, ff, mm = [], [], [], [], []
count = 0
for ijk in np.ndindex(*grid_shape):
m = odfs[ijk]
if norm:
m /= abs(m).max()
if radial_scale:
xyz = vertices.T * m
else:
xyz = vertices.T.copy()
xyz += scale * (ijk - grid_shape / 2.)[:, None]
x, y, z = xyz
ff.append(count + faces)
xx.append(x)
yy.append(y)
zz.append(z)
mm.append(m)
count += len(x)
ff, xx, yy, zz, mm = (np.concatenate(arrs) for arrs in (ff, xx, yy, zz, mm))
mlab.triangular_mesh(xx, yy, zz, ff, scalars=mm, colormap=colormap)
if image is not None:
mlab.imshow(image, colormap='gray', interpolate=False)
mlab.colorbar()
mlab.show()
data_path = sample.data_path()
fname = data_path + '/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif'
surfaces = mne.read_bem_surfaces(fname, add_geom=True)
print "Number of surfaces : %d" % len(surfaces)
###############################################################################
# Show result
head_col = (0.95, 0.83, 0.83) # light pink
skull_col = (0.91, 0.89, 0.67)
brain_col = (0.67, 0.89, 0.91) # light blue
colors = [head_col, skull_col, brain_col]
# 3D source space
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
mlab.figure(size=(600, 600), bgcolor=(0, 0, 0))
for c, surf in zip(colors, surfaces):
points = surf['rr']
faces = surf['tris']
mlab.triangular_mesh(points[:, 0],
points[:, 1],
points[:, 2],
faces,
color=c,
opacity=0.3)
def main():
from optparse import OptionParser
parser = OptionParser(usage="Usage: %prog [options] <mesh.neu>")
parser.add_option("--cl", action="store_true", help="use OpenCL")
parser.add_option("-v", "--vis-every", type="int", metavar="S",
help="visualize on-line every S steps")
parser.add_option("--update-colors", action="store_true",
help="update colors in visualization (expensive)")
parser.add_option("--profile", type="int",
help="set the profiling level (CL only)")
parser.add_option("-i", "--ic", metavar="NAME",
help="use initial condition NAME (try 'help')",
default="gaussian")
parser.add_option("-t", "--final-time", metavar="T",
help="set final time", type="float",
default=5)
parser.add_option("-n", metavar="N", type="int", default=4,
help="use polynomial degree N")
options, args = parser.parse_args()
if not args:
parser.print_help()
return
ldis = LocalDiscretization2D(N=options.n)
print "loading mesh"
mesh = pydgeon.read_2d_gambit_mesh(args[0])
print "building discretization"
if options.cl:
from pydgeon.opencl import CLDiscretization2D
d = CLDiscretization2D(ldis, *mesh, **{"profile": options.profile})
else:
d = pydgeon.Discretization2D(ldis, *mesh)
print "%d elements" % d.K
# set initial conditions
if options.ic == "sine":
mmode = 3; nmode = 2
Hx = np.zeros((d.K, d.ldis.Np))
Hy = np.zeros((d.K, d.ldis.Np))
Ez = np.sin(mmode*np.pi*d.x)*np.sin(nmode*np.pi*d.y)
elif options.ic == "gaussian":
Hx = np.zeros((d.K, d.ldis.Np))
Hy = np.zeros((d.K, d.ldis.Np))
min_x = np.min(d.x)
max_x = np.max(d.x)
min_y = np.min(d.y)
max_y = np.max(d.y)
x0 = min_x + 0.85*(max_x-min_x)
y0 = min_y + 0.85*(max_y-min_y)
x = d.x-x0
y = d.y-y0
r = (max_x-min_x)*0.005
Ez = np.exp(-(x**2+y**2)/r**2)
else:
print "available ICs: sine, gaussian"
return
state = make_obj_array([Hx, Hy, Ez])
if options.cl:
state = make_obj_array([d.to_dev(x) for x in state])
# compute time step size
rLGL = JacobiGQ(0,0, d.ldis.N)[0]
rmin = abs(rLGL[0]-rLGL[1])
dt_scale = d.dt_scale()
dt = dt_scale.min()*rmin*2/3
# setup
if options.vis_every:
try:
import enthought.mayavi.mlab as mayavi
except ImportError:
options.vis_every = 0
if options.vis_every:
vis_mesh = mayavi.triangular_mesh(
d.x.ravel(), d.y.ravel(), Ez.ravel(),
d.gen_vis_triangles())
def vis_hook(step, t, state):
if options.vis_every and step % options.vis_every == 0:
if options.cl:
Hx, Hy, Ez = [d.from_dev(x) for x in state]
else:
Hx, Hy, Ez = state
vis_mesh.mlab_source.z = Ez.ravel()
# update colors, too (expensive)
if options.update_colors:
vis_mesh.mlab_source.scalars = Ez.ravel()
from time import time as wall_time
progress_every = 20
start_timing_at_step = progress_every
if step % 20 == 0:
if options.cl:
d.queue.finish()
if step == start_timing_at_step:
start_time[0] = wall_time()
elif step > start_timing_at_step:
elapsed = wall_time()-start_time[0]
timed_steps = step - start_timing_at_step
time_per_step = elapsed/timed_steps
line = ("step=%d, sim_time=%f, elapsed wall time=%.2f s,"
"time per step=%f s" % (
step, t, elapsed, time_per_step))
if options.cl:
flops = 5 * (inner_rhs.flops + 4*d.K*d.ldis.Np)
line += " %f gflops/s" % (flops/time_per_step/1e9)
print line
if options.cl:
from pydgeon.maxwell import CLMaxwellsRhs2D
inner_rhs = CLMaxwellsRhs2D(d)
def rhs(t, state):
return make_obj_array(inner_rhs(*state))
else:
from pydgeon.maxwell import MaxwellRHS2D
def rhs(t, state):
return make_obj_array(MaxwellRHS2D(d, *state))
# time loop
print "entering time loop"
start_time = [0]
time, final_state = integrate_in_time(state, rhs, dt,
final_time=options.final_time, vis_hook=vis_hook)
def show_odfs(odfs,
vertices_faces,
image=None,
colormap='jet',
scale=2.2,
norm=True,
radial_scale=True):
"""
Display a grid of ODFs.
Parameters
----------
odfs : (X, Y, Z, M) ndarray
A 3-D arrangement of orientation distribution functions (ODFs). At
each ``(x, y, z)`` position, it contains the the values of the
corresponding ODF evaluated on the M vertices.
vertices_faces : str or tuple of (vertices, faces)
A named sphere from `dipy.data.get_sphere`, or a combination of
`(vertices, faces)`.
image : (X, Y) ndarray
Background image (e.g., fractional anisotropy) do display behind the
ODFs.
colormap : str
Color mapping.
scale : float
Increasing the scale spaces ODFs further apart.
norm : bool
Whether or not to normalize each individual ODF (divide by its maximum
absolute value).
radial_scale : bool
Whether or not to change the radial shape of the ODF according to its
scalar value. If set to False, the ODF is displayed as a sphere.
Notes
-----
Mayavi gets really slow when `triangular_mesh` is called too many times,
so this function stacks ODF data and calls `triangular_mesh` once.
Examples
--------
>>> from dipy.data import get_sphere
>>> verts, faces = get_sphere('symmetric724')
>>> angle = np.linspace(0, 2*np.pi, len(verts))
>>> odf1 = np.sin(angle)
>>> odf2 = np.cos(angle)
>>> odf3 = odf1**2 * odf2
>>> odf4 = odf1 + odf2**2
>>> odfs = [[[odf1, odf2],
... [odf3, odf4]]]
>>> show_odfs(odfs, (verts, faces), scale=5)
"""
vertices, faces = sphere_vf_from(vertices_faces)
odfs = np.asarray(odfs)
if odfs.ndim != 4:
raise ValueError("ODFs must by an (X,Y,Z,M) array. " + "Has shape " +
str(odfs.shape))
grid_shape = np.array(odfs.shape[:3])
faces = np.asarray(faces, dtype=int)
xx, yy, zz, ff, mm = [], [], [], [], []
count = 0
for ijk in np.ndindex(*grid_shape):
m = odfs[ijk]
if norm:
m /= abs(m).max()
if radial_scale:
xyz = vertices.T * m
else:
xyz = vertices.T.copy()
xyz += scale * (ijk - grid_shape / 2.)[:, None]
x, y, z = xyz
ff.append(count + faces)
xx.append(x)
yy.append(y)
zz.append(z)
mm.append(m)
count += len(x)
ff, xx, yy, zz, mm = (np.concatenate(arrs)
for arrs in (ff, xx, yy, zz, mm))
mlab.triangular_mesh(xx, yy, zz, ff, scalars=mm, colormap=colormap)
if image is not None:
mlab.imshow(image, colormap='gray', interpolate=False)
mlab.colorbar()
mlab.show()
# Show result on 3D source space
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
lh_points = src[0]['rr']
lh_faces = src[0]['use_tris']
mlab.figure(size=(600, 600), bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
# show brain surface after proper coordinate system transformation
points = brain_surface['rr']
faces = brain_surface['tris']
coord_trans = fwd['mri_head_t']['trans']
points = np.dot(coord_trans[:3,:3], points.T).T + coord_trans[:3,-1]
mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2],
faces, color=(1, 1, 0), opacity=0.3)
# show one cortical surface
mlab.triangular_mesh(lh_points[:, 0], lh_points[:, 1], lh_points[:, 2],
lh_faces, color=(0.7, ) * 3)
# show dipole as small cones
dipoles = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],
ori[:,0], ori[:,1], ori[:,2],
opacity=1., scale_factor=4e-4, scalars=time,
mode='cone')
mlab.colorbar(dipoles, title='Dipole fit time (ms)')
# proper 3D orientation
mlab.get_engine().scenes[0].scene.x_plus_view()
def plot(self, fig):
x,y,z = zip(*self.getPoints(np.array(self.triangles).flatten()))
emm.triangular_mesh(x,y,z, np.arange(len(self.triangles)*3).reshape(-1,3), representation = 'wireframe', figure = fig, color=(0.4,0.4,0.4), opacity = 0.4, line_width=0.5)
def make_flash_bem(subject, subjects_dir, flash05, flash30, show=False):
"""Create 3-Layers BEM model from Flash MRI images
Parameters
----------
subject : string
Subject name
subjects_dir : string
Directory containing subjects data (Freesurfer SUBJECTS_DIR)
flash05 : string
Full path of the NIFTI file for the
FLASH sequence with a spin angle of 5 degrees
flash30 : string
Full path of the NIFTI file for the
FLASH sequence with a spin angle of 30 degrees
show : bool
Show surfaces in 3D to visually inspect all three BEM
surfaces (recommended)
Notes
-----
This program assumes that both Freesurfer/FSL, and MNE,
including MNE's Matlab Toolbox, are installed properly.
For reference please read the MNE manual and wiki, and Freesurfer's wiki:
http://www.nmr.mgh.harvard.edu/meg/manuals/
http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php
http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/MNE_register/index.php
http://surfer.nmr.mgh.harvard.edu/
http://surfer.nmr.mgh.harvard.edu/fswiki
References:
B. Fischl, D. H. Salat, A. J. van der Kouwe, N. Makris, F. Segonne,
B. T. Quinn, and A. M. Dale, "Sequence-independent segmentation of magnetic
resonance images," Neuroimage, vol. 23 Suppl 1, pp. S69-84, 2004.
J. Jovicich, S. Czanner, D. Greve, E. Haley, A. van der Kouwe, R. Gollub,
D. Kennedy, F. Schmitt, G. Brown, J. Macfall, B. Fischl, and A. Dale,
"Reliability in multi-site structural MRI studies: effects of gradient
non-linearity correction on phantom and human data," Neuroimage,
vol. 30, Epp. 436-43, 2006.
"""
os.environ['SUBJECT'] = subject
os.chdir(os.path.join(subjects_dir, subject, "mri"))
if not os.path.exists('flash'):
os.mkdir("flash")
os.chdir("flash")
# flash_dir = os.getcwd()
if not os.path.exists('parameter_maps'):
os.mkdir("parameter_maps")
print("--- Converting Flash 5")
os.system('mri_convert -flip_angle %s -tr 25 %s mef05.mgz' %
(5 * math.pi / 180, flash05))
print("--- Converting Flash 30")
os.system('mri_convert -flip_angle %s -tr 25 %s mef30.mgz' %
(30 * math.pi / 180, flash30))
print("--- Running mne_flash_bem")
os.system('mne_flash_bem --noconvert')
os.chdir(os.path.join(subjects_dir, subject, 'bem'))
if not os.path.exists('flash'):
os.mkdir("flash")
os.chdir("flash")
print("[done]")
if show:
fnames = ['outer_skin.surf', 'outer_skull.surf', 'inner_skull.surf']
head_col = (0.95, 0.83, 0.83) # light pink
skull_col = (0.91, 0.89, 0.67)
brain_col = (0.67, 0.89, 0.91) # light blue
colors = [head_col, skull_col, brain_col]
from enthought.mayavi import mlab
mlab.clf()
for fname, c in zip(fnames, colors):
points, faces = mne.read_surface(fname)
mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2], faces,
color=c, opacity=0.3)
mlab.show()
print(__doc__)
import os.path as op
import mne
from mne.datasets import sample
data_path = sample.data_path()
fname = op.join(data_path, 'subjects', 'sample', 'bem', 'sample-oct-6-src.fif')
add_geom = True # include high resolution source space
src = mne.read_source_spaces(fname, add_geom=add_geom)
# 3D source space (high sampling)
lh_points = src[0]['rr']
lh_faces = src[0]['tris']
rh_points = src[1]['rr']
rh_faces = src[1]['tris']
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
mlab.figure(size=(600, 600), bgcolor=(0, 0, 0),)
mesh = mlab.triangular_mesh(lh_points[:, 0], lh_points[:, 1], lh_points[:, 2],
lh_faces, colormap='RdBu')
mesh.module_manager.scalar_lut_manager.reverse_lut = True
mesh = mlab.triangular_mesh(rh_points[:, 0], rh_points[:, 1], rh_points[:, 2],
rh_faces, colormap='RdBu')
mesh.module_manager.scalar_lut_manager.reverse_lut = True
# v1 = np.array([0,0,0])
# v2 = np.array([100,0,0])
# mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)])
# v1 = np.array([0,0,0])
# v2 = np.array([0,100,0])
# mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)])
# v1 = np.array([0,0,0])
# v2 = np.array([0,0,100])
# mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)])
v1 = np.array([0,0,0])
v2 = np.array([80,0,0])
v2 = np.dot(R[:,0:3],v2)
mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)],color=(1,1,1))
v1 = np.array([0,0,0])
v2 = np.array([0,80,0])
v2 = np.dot(R[:,0:3],v2)
mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)],color=(1,1,1))
v1 = np.array([0,0,0])
v2 = np.array([0,0,80])
v2 = np.dot(R[:,0:3],v2)
mlab.triangular_mesh([[v1[0]-1,v1[0]+1,v2[0]-1,v2[0]+1]], [[v1[1]-1,v1[1]+1,v2[1]-1,v2[1]+1]], [[v1[2]-1,v1[2]+1,v2[2]-1,v2[2]+1]], [(0,1,2),(1,2,3)],color=(1,1,1))
print
#axis
mlab.triangular_mesh([[0,0,100]], [[-0.3,0.3,0]], [[0,0,0]], [(0,1,2)])
def plot3d(cf, vp1, vp2, precision, m1,m2,centerline,showbases,seq,mult,ghost): #DRAWS THE MOLECULE(S)
print "Loading mlab....."
from anim import stiffness, ACP
from draw import data3d
from enthought.mayavi import mlab
r, q, d, g, h, sequence = cf
nbbases = q.shape[0]
#mlab.options.backend = 'envisage' #to use the full Mayavi interface
fig = mlab.figure(1, bgcolor=(0, 0, 0), size=(1024,768)) #creates a new window
mlab.clf() #clears the window
if centerline >0 :
#DRAWS CENTRAL AXIS
#axis_nodes = mlab.points3d(q[:,0],q[:,1],q[:,2],scale_factor=0.4,color=(1,0,1))
ghost_axis = mlab.plot3d(q[:,0],q[:,1],q[:,2],tube_radius=0.05, color=(1,1,1)) #will never move
axis = mlab.plot3d(q[:,0],q[:,1],q[:,2],tube_radius=0.05, color=(1,0,1))
#DRAWS LAB FRAME e
ee = mlab.quiver3d([0,0,0],[0,0,0],[0,0,0],[1,0,0],[0,1,0],[0,0,1], \
mode='arrow',scale_factor=2, color=(1,1,1))
if showbases >0 :
#DRAWS BASES AND BASE FRAMES
points, triangles, centres, frames, i,j, repere, s = data3d(r,d,sequence) #see draw.py
if ghost >0 :
ghost = mlab.triangular_mesh(points[:,0],points[:,1],points[:,2],triangles,color=(1,1,1),opacity=0.4)
k = array(repere)[nbbases,0] #index i : points
l = array(repere)[nbbases,1] #index j : triangles
main_strand = mlab.triangular_mesh(points[:k,0],points[:k,1],points[:k,2],triangles[:l],\
scalars=s[:k], colormap="autumn") #main strand
comp_strand = mlab.triangular_mesh(points[k:i,0],points[k:i,1],points[k:i,2],triangles[l:j]-triangles[l,0],\
scalars=s[k:i], colormap="autumn") #complementary
baseframes = mlab.quiver3d(centres[:,0],centres[:,1],centres[:,2],frames[:,0],frames[:,1],frames[:,2], \
mode='arrow',scale_factor=2, colormap="Oranges")
#ANIMATION
if vp1 >0 :
if centerline >0 :
#axis_nodes_src = axis_nodes.mlab_source
axis_src = axis.mlab_source
if showbases >0 :
main_strand_src = main_strand.mlab_source
comp_strand_src = comp_strand.mlab_source
baseframes_src = baseframes.mlab_source
vecp = stiffness(m1,1,vp1-1) #chosen eigenvector 1
#mult = 2.0 #multiple of the added eigenvector
fig.scene.anti_aliasing_frames = 0 #antialiasing, default=8, off=0
for p in range(precision):
wmod = ACP(readParameters(),vecp,mult*(p+1)/precision) #modified configuration
cfmod = calculPoints(wmod) #modified coordinates
r_p, q_p, d_p, g_p, h_p, sequence = cfmod
fig.scene.disable_render = True #accelerates rendering
if showbases >0 :
points_p, triangles_p, centres_p, frames_p, i,j, repere, s = data3d(r_p,d_p,sequence) #modified graphics
main_strand_src.set(x=points_p[:k,0],y=points_p[:k,1],z=points_p[:k,2])
comp_strand_src.set(x=points_p[k:i,0],y=points_p[k:i,1],z=points_p[k:i,2])
baseframes_src.reset(x=centres_p[:,0],y=centres_p[:,1],z=centres_p[:,2],\
u=frames_p[:,0],v=frames_p[:,1],w=frames_p[:,2])
if centerline >0 :
#axis_nodes_src.set(x=q_p[:,0],y=q_p[:,1],z=q_p[:,2],scale_factor=0.7,color=(1,0,1))
axis_src.set(x=q_p[:,0],y=q_p[:,1],z=q_p[:,2],tube_radius=0.05, color=(1,0,1))
fig.scene.disable_render = False
if vp2 > 0:
if showbases >0 :
#new instance of the entire molecule
new_main_strand = mlab.triangular_mesh(points[:k,0],points[:k,1],points[:k,2],triangles[:l],\
scalars=s[:k], colormap="winter") #main strand
new_comp_strand = mlab.triangular_mesh(points[k:i,0],points[k:i,1],points[k:i,2],triangles[l:j]-triangles[l,0],\
scalars=s[k:i], colormap="winter") #complementary
new_baseframes = mlab.quiver3d(centres[:,0],centres[:,1],centres[:,2],frames[:,0],frames[:,1],frames[:,2], \
mode='arrow',scale_factor=2, colormap="Blues")
new_main_strand_src = new_main_strand.mlab_source
new_comp_strand_src = new_comp_strand.mlab_source
new_baseframes_src = new_baseframes.mlab_source
if centerline >0 :
new_axis = mlab.plot3d(q[:,0],q[:,1],q[:,2],tube_radius=0.05, color=(1,0,1))
new_axis_src = new_axis.mlab_source
vecp = stiffness(m2,1,vp2-1) #chosen eigenvector 2
#mult = 2.0 #multiple of the added eigenvector
for p in range(precision):
wmod = ACP(readParameters(),vecp,mult*(p+1)/precision) #modified configuration
cfmod = calculPoints(wmod) #modified coordinates
r_p, q_p, d_p, g_p, h_p, sequence = cfmod
if showbases >0 :
points_p, triangles_p, centres_p, frames_p, i,j, repere, s = data3d(r_p,d_p,sequence) #modified graphics
fig.scene.disable_render = True #accelerates rendering
new_main_strand_src.set(x=points_p[:k,0],y=points_p[:k,1],z=points_p[:k,2])
new_comp_strand_src.set(x=points_p[k:i,0],y=points_p[k:i,1],z=points_p[k:i,2])
new_baseframes_src.reset(x=centres_p[:,0],y=centres_p[:,1],z=centres_p[:,2],\
u=frames_p[:,0],v=frames_p[:,1],w=frames_p[:,2])
if centerline >0 :
#new_axis_nodes_src.set(x=q_p[:,0],y=q_p[:,1],z=q_p[:,2],scale_factor=0.7,color=(1,0,1))
new_axis_src.set(x=q_p[:,0],y=q_p[:,1],z=q_p[:,2],tube_radius=0.05, color=(1,0,1))
fig.scene.disable_render = False
mlab.orientation_axes()
mlab.show()
#
# License: BSD (3-clause)
print(__doc__)
import os.path as op
import mne
from mne.datasets import sample
data_path = sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-eeg-oct-6p-fwd.fif')
add_geom = True # include high resolution source space
src = mne.read_source_spaces(fname, add_geom=add_geom)
# 3D source space (high sampling)
lh_points = src[0]['rr']
lh_faces = src[0]['tris']
rh_points = src[1]['rr']
rh_faces = src[1]['tris']
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
mlab.figure(size=(600, 600), 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)
xs = []
ys = []
zs = []
triangles = []
for i, plane in enumerate(planes):
xs.extend([plane.p0[0], plane.p1[0], plane.p2[0]])
ys.extend([plane.p0[1], plane.p1[1], plane.p2[1]])
zs.extend([plane.p0[2], plane.p1[2], plane.p2[2]])
triangles.append((3*i, 3*i+1, 3*i+2))
p_earth_body = numpy.array([-20000e3, 0, 30000e3])
p_earth_body = numpy.array([-11913349.37481491, 1600513.79810546, 6787847.04879577])
att = [0, 0, 0]
vis, illum, outs = taco.calc_earth_vis(-p_earth_body, att, ngrid=30, to_earth=True, max_reflect=0)
mask_points = (len(outs) // 300) or None
rx = [out[0][0] for out in outs]
ry = [out[0][1] for out in outs]
rz = [out[0][2] for out in outs]
ru = [out[1][0] * (2 if out[3] else 1) for out in outs]
rv = [out[1][1] * (2 if out[3] else 1) for out in outs]
rw = [out[1][2] * (2 if out[3] else 1) for out in outs]
mlab.clf()
mlab.triangular_mesh(xs, ys, zs, triangles, color=(1, 1, 0), opacity=0.9)
mlab.triangular_mesh(xs, ys, zs, triangles, color=(1, .8, 0), representation='wireframe', line_width=4)
mlab.quiver3d(rx, ry, rz, ru, rv, rw, scale_factor=1.0, mask_points=mask_points, vmin=1, vmax=2)
mlab.show()