def newSurface(self):
processes = self.plotter.getProcesses()
if not self._cv_dlg:
self._cv_dlg = daeChooseVariable(daeChooseVariable.plot3D)
self._cv_dlg.updateProcessesList(processes)
self._cv_dlg.setWindowTitle('Choose variable for 3D plot')
if self._cv_dlg.exec_() != QtWidgets.QDialog.Accepted:
return False
variable, domainIndexes, domainPoints, xAxisLabel, yAxisLabel, zAxisLabel, xPoints, yPoints, zPoints, currentTime = self._cv_dlg.getPlot3DData(
)
xPoints = numpy.array(xPoints)
yPoints = numpy.array(yPoints)
xmax = numpy.max(xPoints)
ymax = numpy.max(yPoints)
zmax = numpy.max(zPoints)
xmin = numpy.min(xPoints)
ymin = numpy.min(yPoints)
zmin = numpy.min(zPoints)
warp = 'auto'
#if((xmax == xmin) or (ymax == ymin) or (zmax == zmin)):
# warp = 'auto'
#else:
# warp = math.sqrt( (xmax-xmin)*(ymax-ymin) ) / (zmax-zmin)
# colormap='gist_earth', 'RdBu'
stype = 'surface'
mlab.figure()
if (stype == 'surface'):
#print "warp=", warp
#print "[xmin, xmax, ymin, ymax, zmin, zmax]=", [xmin, xmax, ymin, ymax, zmin, zmax]
mlab.surf(xPoints,
yPoints,
zPoints,
warp_scale=warp,
representation='surface')
mlab.colorbar(orientation='vertical')
#mlab.title('polar mesh')
#mlab.outline()
mlab.axes(ranges=[xmin, xmax, ymin, ymax, zmin, zmax], nb_labels=3)
mlab.xlabel(xAxisLabel)
mlab.ylabel(yAxisLabel)
mlab.zlabel(zAxisLabel)
elif (stype == 'map'):
mlab.imshow(zPoints)
mlab.colorbar(orientation='vertical')
#mlab.title('polar mesh')
#mlab.outline()
mlab.axes(ranges=[xmin, xmax, ymin, ymax], nb_labels=3)
mlab.xlabel(xAxisLabel)
mlab.ylabel(yAxisLabel)
mlab.zlabel(zAxisLabel)
mlab.show()
def main():
#set some values to be used later
sh_order = 6
verts, edges, efaces = create_unit_sphere(4)
#read_data from disk
data, fa, bvec, bval, voxel_size = sample_hardi_data()
data_slice = data[32:76, 32:76, 26:27]
fa_slice = fa[32:76, 32:76, 26]
#normalize data by dividing by b0, this is needed so we can take log later
norm_data = normalize_data(data_slice, bval, min_signal=1)
#create an instance of the model
model_instance = MonoExpOpdfModel(sh_order, bval, bvec, .006)
model_instance.set_sampling_points(verts, edges)
#use the model it fit the data
opdfs_sampled_at_verts = model_instance.evaluate(norm_data)
opdfs_sph_harm_coef = model_instance.fit_data(norm_data)
#display the opdf blobs using mayavi
faces = edges[efaces, 0]
show_blobs(opdfs_sampled_at_verts, verts, faces)
mlab.imshow(fa_slice, colormap='gray', interpolate=False)
mlab.show()
def plotmask(self, region=None, resolution=20, slat=71, slon=-70, hemi="s"):
"""Plot the mask.
"""
try:
import enthought.mayavi.mlab as ml
mayavi = True
print "using mayavi"
except:
import pylab as pl
mayavi = False
print "using matplotlib"
if self.maskfile is None:
print "error: plotmask: get mask file first:"
print ">>> m = Mask()"
print ">>> m.getmask('fname')"
print "then you can do:"
print ">>> m.plotmask(region='left/right/bottom/top', resolution=20)"
sys.exit()
m_mask = self.m_mask
x_mask = self.x_mask
y_mask = self.y_mask
if region is not None:
left, right, bottom, top = str.split(region, "/")
left, bottom = self.mapll(left, bottom, slat=slat, slon=slon, hemi=hemi)
right, top = self.mapll(right, top, slat=slat, slon=slon, hemi=hemi)
jmin, = np.where(x_mask == np.rint(left))
jmax, = np.where(x_mask == np.rint(right))
imin, = np.where(y_mask == np.rint(bottom))
imax, = np.where(y_mask == np.rint(top))
# x_mask = x_mask[jmin:jmax+1:resolution]
# y_mask = y_mask[imin:imax+1:resolution]
m_mask = m_mask[imin : imax + 1 : resolution, jmin : jmax + 1 : resolution]
else:
# x_mask = x_mask[::resolution]
# y_mask = y_mask[::resolution]
m_mask = m_mask[::resolution, ::resolution]
print "plotting mask ..."
if mayavi:
ml.figure()
ml.imshow(m_mask)
ml.show()
else:
pl.figure()
pl.imshow(m_mask, origin="lower", interpolation="nearest")
pl.show()
print "done!"
def plotmask(self, region=None, resolution=20, slat=71, slon=-70, hemi='s'):
"""Plot the mask.
"""
try:
import enthought.mayavi.mlab as ml
mayavi = True
print 'using mayavi'
except:
import pylab as pl
mayavi = False
print 'using matplotlib'
if self.maskfile is None:
print 'error: plotmask: get mask file first:'
print '>>> m = Mask()'
print ">>> m.getmask('fname')"
print 'then you can do:'
print ">>> m.plotmask(region='left/right/bottom/top', resolution=20)"
sys.exit()
m_mask = self.m_mask
x_mask = self.x_mask
y_mask = self.y_mask
if region is not None:
left, right, bottom, top = str.split(region, '/')
left, bottom = self.mapll(left, bottom, slat=slat, slon=slon, hemi=hemi)
right, top = self.mapll(right, top, slat=slat, slon=slon, hemi=hemi)
jmin, = np.where(x_mask == np.rint(left))
jmax, = np.where(x_mask == np.rint(right))
imin, = np.where(y_mask == np.rint(bottom))
imax, = np.where(y_mask == np.rint(top))
#x_mask = x_mask[jmin:jmax+1:resolution]
#y_mask = y_mask[imin:imax+1:resolution]
m_mask = m_mask[imin:imax+1:resolution, jmin:jmax+1:resolution]
else:
#x_mask = x_mask[::resolution]
#y_mask = y_mask[::resolution]
m_mask = m_mask[::resolution,::resolution]
print 'plotting mask ...'
if mayavi:
ml.figure()
ml.imshow(m_mask)
ml.show()
else:
pl.figure()
pl.imshow(m_mask, origin='lower', interpolation='nearest')
pl.show()
print 'done!'
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()
################################################################################
# Plot the data
from enthought.mayavi import mlab
# A first plot in 3D
fig = mlab.figure(1)
mlab.clf()
mesh = mlab.mesh(x, y, z, scalars=s)
cursor3d = mlab.points3d(0.0, 0.0, 0.0, mode="axes", color=(0, 0, 0), scale_factor=0.5)
mlab.title("Click on the ball")
# A second plot, flat
fig2d = mlab.figure(2)
mlab.clf()
im = mlab.imshow(s)
cursor = mlab.points3d(0, 0, 0, mode="2dthick_cross", color=(0, 0, 0), scale_factor=10)
mlab.view(90, 0)
################################################################################
# Some logic to select 'mesh' and the data index when picking.
def picker_callback(picker_obj):
picked = picker_obj.actors
if mesh.actor.actor._vtk_obj in [o._vtk_obj for o in picked]:
# m.mlab_source.points is the points array underlying the vtk
# dataset. GetPointId return the index in this array.
x_, y_ = np.lib.index_tricks.unravel_index(picker_obj.point_id, s.shape)
print "Data indices: %i, %i" % (x_, y_)
n_x, n_y = s.shape
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()
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()
matrix=assembleMatrix(mToB,kernel,quadRule=quadrule)
identity=assembleIdentity(mToB,quadrule)
rhs=projRhs(mToB,[lambda t,x,normals: -numpy.exp(1j*k*x[1])],quadrule)
coeffs=numpy.linalg.solve(.5*identity+matrix,rhs)
gx,gy=numpy.mgrid[-2:4:200j,-2:2:200j]
points=numpy.array([gx.ravel(),gy.ravel()])
res=evaluate(points,mToB,kernel,quadrule,coeffs)
res=res.reshape(200,200)
res+=numpy.exp(1j*k*gy)
from enthought.mayavi import mlab
mlab.imshow(gx,gy,numpy.real(res),vmin=-1,vmax=1)
mlab.axes()
mlab.view(0,0)
mlab.show()
print "Finished"
# Plot the data
from enthought.mayavi import mlab
# A first plot in 3D
fig = mlab.figure(1)
mlab.clf()
mesh = mlab.mesh(x, y, z, scalars=s)
cursor3d = mlab.points3d(0., 0., 0., mode='axes',
color=(0, 0, 0),
scale_factor=0.5)
mlab.title('Click on the ball')
# A second plot, flat
fig2d = mlab.figure(2)
mlab.clf()
im = mlab.imshow(s)
cursor = mlab.points3d(0, 0, 0, mode='2dthick_cross',
color=(0, 0, 0),
scale_factor=10)
mlab.view(90, 0)
################################################################################
# Some logic to select 'mesh' and the data index when picking.
def picker_callback(picker_obj):
picked = picker_obj.actors
if mesh.actor.actor._vtk_obj in [o._vtk_obj for o in picked]:
# m.mlab_source.points is the points array underlying the vtk
# dataset. GetPointId return the index in this array.
x_, y_ = np.lib.index_tricks.unravel_index(picker_obj.point_id,
s.shape)
# -*- coding: utf-8 -*- import numpy as np from enthought.mayavi import mlab x, y = np.ogrid[-2:2:20j, -2:2:20j] z = x * np.exp(-x**2 - y**2) mlab.figure(1) pl = mlab.imshow(x, y, z) mlab.figure(2) pl = mlab.contour_surf(x, y, z, contours=20) mlab.show()
# -*- coding: utf-8 -*- import numpy as np from enthought.mayavi import mlab x, y = np.ogrid[-2:2:20j, -2:2:20j] z = x * np.exp( - x**2 - y**2) mlab.figure(1) pl = mlab.imshow(x, y, z) mlab.figure(2) pl = mlab.contour_surf(x, y, z, contours=20) mlab.show()
"""
Script to generate the preview images for the mayavi2 LUTs.
Requires ImageMagick.
"""
import os
from enthought.mayavi import mlab
from enthought.mayavi.core.lut_manager import lut_mode_list, lut_image_dir
import numpy as np
# Create some data
X = np.arange(0, 255)
X = X * np.ones((200, 1))
mlab.clf()
image = mlab.imshow(X.T)
mlab.view(0, 0, 118)
# Make a preview for each possible lut
for lut in lut_mode_list():
filebasename = os.path.join(lut_image_dir, lut.lower())
if not lut == 'file':
image.module_manager.scalar_lut_manager.lut_mode = lut
mlab.savefig(filebasename + '.png', size=(80, 20))
#os.system('convert %s.png %s.gif &' %(filebasename, filebasename))
os.system('montage -geometry -0-0 -label "%s" %s.png %s.gif &' %
(lut, filebasename, filebasename))
"""
Script to generate the preview images for the mayavi2 LUTs.
Requires ImageMagick.
"""
import os
from enthought.mayavi import mlab
from enthought.mayavi.core.lut_manager import lut_mode_list, lut_image_dir
import numpy as np
# Create some data
X = np.arange(0, 255)
X = X * np.ones((200, 1))
mlab.clf()
image = mlab.imshow(X.T)
mlab.view(0, 0, 118)
# Make a preview for each possible lut
for lut in lut_mode_list():
filebasename = os.path.join(lut_image_dir, lut.lower())
if not lut == 'file':
image.module_manager.scalar_lut_manager.lut_mode = lut
mlab.savefig(filebasename + '.png', size=(80, 20))
#os.system('convert %s.png %s.gif &' %(filebasename, filebasename))
os.system('montage -geometry -0-0 -label "%s" %s.png %s.gif &'
% (lut, filebasename, filebasename) )
