def viewImg(img, spacing, contours):
mlab.figure(bgcolor=(0, 0, 0), size=(400, 400))
src = mlab.pipeline.scalar_field(img)
# Our data is not equally spaced in all directions:
src.spacing = [1, 1, 1]
src.update_image_data = True
# Extract some inner structures: the ventricles and the inter-hemisphere
# fibers. We define a volume of interest (VOI) that restricts the
# iso-surfaces to the inner of the brain. We do this with the ExtractGrid
# filter.
blur = mlab.pipeline.user_defined(blur, filter='ImageGaussianSmooth')
print("blur type is",type(blur),blur.max())
#voi = mlab.pipeline.extract_grid(blur)
#voi.set(x_min=125, x_max=193, y_min=92, y_max=125, z_min=34, z_max=75)
#mlab.pipeline.iso_surface(src, contours=[1,2], colormap='Spectral')
mlab.pipeline.contour3d(blur)
mlab.view(-125, 54, 'auto','auto')
mlab.roll(-175)
mlab.show()
def mesh_plot(X,T,eta):
from enthought.mayavi import mlab
X = 2000*X/np.abs(x.min())
eta = 1000*eta
mlab.figure(1)
mlab.clf()
mlab.mesh(X,T,eta)
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 _plot_max_Value( self ):
X = self.X_hf[:, 0]
Y = self.Y_hf[:, 0]
Z = self.Z_hf[:, 0]
plot_col = getattr( self, self.plot_column )[:, 0]
scale = 1 / max( plot_col )
# if self.plot_column == 'n_tex':
# plot_col = where( plot_col < 0, 0, plot_col )
mlab.figure( figure = "SFB532Demo",
bgcolor = ( 1.0, 1.0, 1.0 ),
fgcolor = ( 0.0, 0.0, 0.0 ) )
mlab.points3d( X, Y, ( -1.0 ) * Z, plot_col,
# colormap = "gist_rainbow",
# colormap = "Reds",
colormap = "copper",
mode = "cube",
scale_factor = scale )
mlab.outline()
mlab.scalarbar( title = self.plot_column, orientation = 'vertical' )
mlab.show
def spin(fcm, idx0, idx1, idx2):
"""Plots 3D data as points in space."""
x = fcm[:, idx0]
y = fcm[:, idx1]
z = fcm[:, idx2]
s = trilinear_interpolate(x, y, z)
# bins = int(len(x)**(1/3.0))
# xfrac, xint = numpy.modf((x - numpy.min(x))/
# (numpy.max(x)-numpy.min(x))*(bins-1))
# yfrac, yint = numpy.modf((y - numpy.min(y))/
# (numpy.max(y)-numpy.min(y))*(bins-1))
# zfrac, zint = numpy.modf((z - numpy.min(z))/
# (numpy.max(z)-numpy.min(z))*(bins-1))
# xint = xint.astype('i')
# yint = yint.astype('i')
# zint = zint.astype('i')
# not interpolated - kiv write trilinear_interpolate function
# h, edges = numpy.histogramdd(fcm[:,[idx0, idx1, idx2]], bins=bins)
# v = h[xint, yint, zint]
mlab.figure()
mlab.points3d(x, y, z, s, mode='point')
mlab.xlabel(fcm.channels[idx0])
mlab.ylabel(fcm.channels[idx1])
mlab.zlabel(fcm.channels[idx2])
def m2screenshot(mayavi_fig=None, mpl_axes=None, autocrop=True):
""" Capture a screeshot of the Mayavi figure and display it in the
matplotlib axes.
"""
import pylab as pl
# Late import to avoid triggering wx imports before needed.
from enthought.mayavi import mlab
if mayavi_fig is None:
mayavi_fig = mlab.gcf()
else:
mlab.figure(mayavi_fig)
if mpl_axes is not None:
pl.axes(mpl_axes)
filename = tempfile.mktemp(".png")
mlab.savefig(filename, figure=mayavi_fig)
image3d = pl.imread(filename)
if autocrop:
bg_color = mayavi_fig.scene.background
image3d = autocrop_img(image3d, bg_color)
pl.imshow(image3d)
pl.axis("off")
os.unlink(filename)
def run():
dp = DatasetProcessor("L2_22aug.dat")
#dp = DatasetProcessor("L2_22aug.pickle")
dp.D -= dp.D.min()
mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
mlab.clf()
#dp.show_imgs()
sigmas = [1.5, 3.0]
sigmas2 = [0.0, 0.0]
cms = ["bone", "Spectral"]
for sigma, sigma2, cm in zip(sigmas, sigmas2, cms):
gc.collect()
if True:
D = gaussian_filter(dp.D, sigma)
l1, l2, l3 = get_ev(D, sigma2)
save_lambdas([l1, l2, l3])
else:
l1, l2, l3 = load_lambdas()
dp.S = select_struct(l1, l2, l3, 0.25, 0.5, 0.5)
gc.collect()
dp.S -= dp.S.min()
dp.show_found(cm)
plt.show()
def viewImgWithNodes(img, spacing, contours,g, title=''):
mlab.figure(bgcolor=(0, 0, 0), size=(900, 900))
#src = mlab.pipeline.scalar_field(img)
## Our data is not equally spaced in all directions:
#src.spacing = [1, 1, 1]
#src.update_image_data = True
#
#mlab.pipeline.iso_surface(src, contours=contours, opacity=0.2)
nodes = np.array(g.nodes())
dsize = 4*np.ones(nodes.shape[0],dtype='float32')
print(dsize.shape,nodes.shape)
#mlab.points3d(nodes[:,0],nodes[:,1],nodes[:,2],color=(0.0,1.0,0.0))
mlab.points3d(nodes[:,2],nodes[:,1],nodes[:,0],dsize,color=(0.0,0.0,1.0), scale_factor=0.25)
for n1, n2, edge in g.edges(data=True):
path = [n1]+edge['path']+[n2]
pa = np.array(path)
#print pa
mlab.plot3d(pa[:,2],pa[:,1],pa[:,0],color=(0,1,0),tube_radius=0.25)
mlab.view(-125, 54, 'auto','auto')
mlab.roll(-175)
mlab.title(title, height=0.1)
mlab.show()
def test_close(self):
""" Various tests for mlab.close().
"""
f = mlab.figure()
self.assert_(f.running)
mlab.close(f)
self.assertFalse(f.running)
f = mlab.figure(314)
self.assert_(f.running)
mlab.close(314)
self.assertFalse(f.running)
f = mlab.figure('test_figure')
self.assert_(f.running)
mlab.close('test_figure')
self.assertFalse(f.running)
f = mlab.figure()
self.assert_(f.running)
mlab.close()
self.assertFalse(f.running)
figs = [mlab.figure() for i in range(5)]
for f in figs:
self.assert_(f.running)
mlab.close(all=True)
for f in figs:
self.assertFalse(f.running)
def rotate():
mlab.figure(3)
for tt in linspace(30,160,14):
mlab.view(0,tt)
mlab.draw()
mlab.savefig('sphere-rotate%s.png' % str(int(tt)).zfill(3))
os.system("convert sphere-rotate*.png sphere.gif")
def test_close(self):
""" Various tests for mlab.close().
"""
f = mlab.figure()
self.assert_(f.running)
mlab.close(f)
self.assertFalse(f.running)
f = mlab.figure(314)
self.assert_(f.running)
mlab.close(314)
self.assertFalse(f.running)
f = mlab.figure('test_figure')
self.assert_(f.running)
mlab.close('test_figure')
self.assertFalse(f.running)
f = mlab.figure()
self.assert_(f.running)
mlab.close()
self.assertFalse(f.running)
figs = [mlab.figure() for i in range(5)]
for f in figs:
self.assert_(f.running)
mlab.close(all=True)
for f in figs:
self.assertFalse(f.running)
def plotSurface(surface):
"""plot a surface using mayavi"""
# create a figure
mlab.figure()
# create a surface plot
surfPlot = mlab.mesh(surface['x'], surface['y'], surface['value'])
def __call__(self, **options):
my_options = dict(size=(400, 300), bgcolor=(1, 1, 1),
fgcolor=(0, 0, 0), )
my_options.update(options)
mlab.figure(self.n, **my_options)
mlab.clf()
self.n += 1
def run():
dp = DatasetProcessor("L2_22aug.dat")
#dp = DatasetProcessor("L2_22aug.pickle")
dp.D -= dp.D.min()
mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
mlab.clf()
#dp.show_imgs()
sigmas = [1.5, 3.0]
sigmas2 = [0.0, 0.0]
cms = ["bone", "Spectral"]
for sigma, sigma2, cm in zip(sigmas,sigmas2,cms):
gc.collect()
if True:
D = gaussian_filter(dp.D, sigma)
l1,l2,l3 = get_ev(D, sigma2)
save_lambdas([l1,l2,l3])
else:
l1,l2,l3 = load_lambdas()
dp.S = select_struct(l1,l2,l3,0.25,0.5,0.5)
gc.collect()
dp.S -= dp.S.min()
dp.show_found(cm)
plt.show()
def viewImg2(img, spacing, contours):
print("In viewImg2: (min,max)=(%f,%f)"%(img.min(),img.max()))
print("contours=",contours)
mlab.figure(bgcolor=(0, 0, 0), size=(400, 400))
src = mlab.pipeline.scalar_field(img)
# Our data is not equally spaced in all directions:
src.spacing = [1, 1, 1]
src.update_image_data = True
# Extract some inner structures: the ventricles and the inter-hemisphere
# fibers. We define a volume of interest (VOI) that restricts the
# iso-surfaces to the inner of the brain. We do this with the ExtractGrid
# filter.
blur = mlab.pipeline.user_defined(src, filter='ImageGaussianSmooth')
#mlab.pipeline.volume(blur, vmin=0.2, vmax=0.8)
mlab.pipeline.iso_surface(src, contours=contours)
#mlab.pipeline.image_plane_widget(blur,
# plane_orientation='z_axes',
# slice_index=img.shape[0]/2,
# )
#voi = mlab.pipeline.extract_grid(blur)
#voi.set(x_min=125, x_max=193, y_min=92, y_max=125, z_min=34, z_max=75)
#mlab.pipeline.iso_surface(src, contours=[1,2], colormap='Spectral')
#mlab.pipeline.contour3d(blur)
mlab.view(-125, 54, 'auto','auto')
mlab.roll(-175)
mlab.show()
def plotSurface(surface): """plot a surface using mayavi""" # create a figure mlab.figure() # create a surface plot surfPlot = mlab.mesh(surface['x'], surface['y'], surface['value'])
def show_convection3(new_fig=True):
x, y, z = np.mgrid[0:1:20j, 0:1:20j, 0:1:20j]
u, v, w = convection_cell(x, y, z)
if new_fig:
mlab.figure(fgcolor=(0., 0., 0.), bgcolor=(0.5, 0.5, 0.5),
size=(600,600))
src = mlab.pipeline.vector_field(u, v, w)
magnitude = mlab.pipeline.extract_vector_norm(src)
mlab.outline()
# We apply the following modules on the magnitude object, in order to
# be able to display the norm of the vectors, eg as the color.
iso = mlab.pipeline.iso_surface(magnitude, contours=[1.9, ],
opacity=0.3)
vec = mlab.pipeline.vectors(magnitude,
mask_points=40,
line_width=1,
color=(1, 1, 1),
scale_factor=4.)
flow = mlab.pipeline.streamline(magnitude,
seedtype='plane',
seed_visible=True,
seed_scale=0.5,
seed_resolution=1,
linetype='ribbon',)
vcp = mlab.pipeline.vector_cut_plane(magnitude, mask_points=2,
scale_factor=4,
colormap='jet',
plane_orientation='x_axes')
def spin(fcm, idx0, idx1, idx2):
"""Plots 3D data as points in space."""
x = fcm[:, idx0]
y = fcm[:, idx1]
z = fcm[:, idx2]
s = trilinear_interpolate(x, y, z)
# bins = int(len(x)**(1/3.0))
# xfrac, xint = numpy.modf((x - numpy.min(x))/
# (numpy.max(x)-numpy.min(x))*(bins-1))
# yfrac, yint = numpy.modf((y - numpy.min(y))/
# (numpy.max(y)-numpy.min(y))*(bins-1))
# zfrac, zint = numpy.modf((z - numpy.min(z))/
# (numpy.max(z)-numpy.min(z))*(bins-1))
# xint = xint.astype('i')
# yint = yint.astype('i')
# zint = zint.astype('i')
# # not interpolated - kiv write trilinear_interpolate function
# h, edges = numpy.histogramdd(fcm[:,[idx0, idx1, idx2]], bins=bins)
# v = h[xint, yint, zint]
mlab.figure()
mlab.points3d(x, y, z, s, mode='point')
mlab.xlabel(fcm.channels[idx0])
mlab.ylabel(fcm.channels[idx1])
mlab.zlabel(fcm.channels[idx2])
def ChargeDensityFog3D(directory, save_file=None , DrawAtoms=True, DrawCell=True, opacity_range=[0,1]):
# Get data from calculation files
with jasp(directory) as calc:
atoms = calc.get_atoms()
x, y, z, cd = calc.get_charge_density()
mlab.figure(bgcolor=(0,0,0), size=(640,480))
# Draw atoms
if DrawAtoms == True:
for atom in atoms:
mlab.points3d(atom.x,
atom.y,
atom.z,
scale_factor=vdw_radii[atom.number]/10.,
resolution=16,
color=tuple(cpk_colors[atom.number]),
scale_mode='none')
# Draw unit cell
if DrawCell == True:
a1, a2, a3 = atoms.get_cell()
origin = [0,0,0]
cell_matrix = [[origin, a1],
[origin, a2],
[origin, a3],
[a1, a1+a2],
[a1, a1+a3],
[a2, a2+a1],
[a2, a2+a3],
[a3, a1+a3],
[a3, a2+a3],
[a1+a2, a1+a2+a3],
[a2+a3, a1+a2+a3],
[a1+a3, a1+a3+a2]] # contains all points on the box
for p1, p2 in cell_matrix:
mlab.plot3d([p1[0], p2[0]], # x-coords of box
[p1[1], p2[1]], # y-coords
[p1[2], p2[2]]) # z-coords
# Plot the charge density
src = mlab.pipeline.scalar_field(x, y, z, cd) #Source data
vmin = cd.min() #find minimum and maximum value of CD data
vmax = cd.max()
vol = mlab.pipeline.volume(src) # Make a volumetric representation of the data
# Set opacity transfer function
from tvtk.util.ctf import PiecewiseFunction
otf = PiecewiseFunction()
otf.add_point(vmin, opacity_range[0]) #Transparency at zero electron density
otf.add_point(vmax*1, opacity_range[1]) #Transparency at max electron density
vol._otf=otf
vol._volume_property.set_scalar_opacity(otf)
#Show a legend
mlab.colorbar(title="e- density\n(e/Ang^3)", orientation="vertical", nb_labels=5, label_fmt='%.2f')
mlab.view(azimuth=-90, elevation=90, distance='auto') # Set viewing angle
mlab.show()
if save_file != None:
mlab.savefig(save_file)
def show_convection1(new_fig=True):
x, y, z = np.mgrid[0:1:20j, 0:1:20j, 0:1:20j]
u, v, w = convection_cell(x, y, z)
if new_fig:
mlab.figure(size=(600, 600))
mlab.quiver3d(u, v, w)
mlab.outline()
def _show_button_fired( self ):
p, r = self.data.slice
mlab.figure( 'Continuous fibers' )
mlab.plot3d( p[:, 0], p[:, 1], p[:, 2], r,
tube_radius=20, tube_sides=20, colormap='Spectral' )
def show_convection1(new_fig=True):
x, y, z = np.mgrid[0:1:20j, 0:1:20j, 0:1:20j]
u, v, w = convection_cell(x, y, z)
if new_fig:
mlab.figure(size=(600,600))
mlab.quiver3d(u, v, w)
mlab.outline()
def show_convection2(new_fig=True):
x, y, z = np.mgrid[0:1:20j, 0:1:20j, 0:1:20j]
u, v, w = convection_cell(x, y, z)
if new_fig:
mlab.figure(size=(600,600))
src = mlab.pipeline.vector_field(u, v, w)
mlab.pipeline.vectors(src, mask_points=20, scale_factor=3.)
mlab.outline()
def show_convection2(new_fig=True):
x, y, z = np.mgrid[0:1:20j, 0:1:20j, 0:1:20j]
u, v, w = convection_cell(x, y, z)
if new_fig:
mlab.figure(size=(600, 600))
src = mlab.pipeline.vector_field(u, v, w)
mlab.pipeline.vectors(src, mask_points=20, scale_factor=3.)
mlab.outline()
def main():
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 400))
print "Generating the mlab images..."
illustrate_module(mlab)
mayavi2.close()
print "Done generating the mlab images"
print "Generating the example pages"
from render_examples import render_examples
render_examples(render_images=True)
print "Done generating the example pages"
def main():
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 400))
print "Generating the mlab images..."
illustrate_module(mlab)
mayavi2.close()
print "Done generating the mlab images"
print "Generating the example pages"
from render_examples import render_examples
render_examples(render_images=True)
print "Done generating the example pages"
def plotThisThing(state, proc):
react = sData.getInitReactant(state)
prod = sData.getInitReactant(proc)
#react, prod = sData.getReacProd(state,proc)
cn = ChangingNeighbors(react, prod, cutoffs)
ln = cn.nrValues(cn.lostNeighbors)
indxs = np.where(ln > 0)
ln = ln[indxs]
pos = react.get_positions()[indxs]
t = react.get_atomic_numbers()[indxs]
pts = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],t,t,t,scalars=ln, mode='sphere')
pts.glyph.color_mode = 'color_by_scalar'
pts.glyph.glyph_source.glyph_source.center = [0,0,0]
indx2 = np.setdiff1d(np.array(range(prod.get_number_of_atoms())),indxs[0])
p = react.get_positions()[indx2]
t2 = react.get_atomic_numbers()[indx2]
pt = mlab.quiver3d(p[:,0],p[:,1],p[:,2],t2,t2,t2, opacity=.06, mode='sphere')
mlab.figure()
ln = cn.nrValues(cn.newNeighbors)
indxs = np.where(ln > 0)
ln = ln[indxs]
pos = react.get_positions()[indxs]
t = react.get_atomic_numbers()[indxs]
pts = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],t,t,t,scalars=ln, mode='sphere')
pts.glyph.color_mode = 'color_by_scalar'
pts.glyph.glyph_source.glyph_source.center = [0,0,0]
indx2 = np.setdiff1d(np.array(range(prod.get_number_of_atoms())),indxs[0])
p = react.get_positions()[indx2]
t2 = react.get_atomic_numbers()[indx2]
pt = mlab.quiver3d(p[:,0],p[:,1],p[:,2],t2,t2,t2, opacity=.06, mode='sphere')
mlab.figure()
ln = cn.nrValues(cn.lostNeighbors) + cn.nrValues(cn.newNeighbors)
indxs = np.where(ln > 0)
ln = ln[indxs]
pos = react.get_positions()[indxs]
t = react.get_atomic_numbers()[indxs]
pts = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],t,t,t,scalars=ln, mode='sphere')
pts.glyph.color_mode = 'color_by_scalar'
pts.glyph.glyph_source.glyph_source.center = [0,0,0]
indx2 = np.setdiff1d(np.array(range(prod.get_number_of_atoms())),indxs[0])
p = react.get_positions()[indx2]
t2 = react.get_atomic_numbers()[indx2]
pt = mlab.quiver3d(p[:,0],p[:,1],p[:,2],t2,t2,t2, opacity=.06, mode='sphere')
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 plotframe(frameno,level=1):
plotdata = ClawPlotData()
plotdata.outdir = "_output"
print "Plotting solution from ",plotdata.outdir
plotdata = setplot(plotdata)
try:
frame = plotdata.getframe(frameno)
except:
print "Unable to get frame"
return
mlab.figure(1,bgcolor=(1,1,1),size=(700,600))
mlab.clf()
for grid in frame.grids:
if grid.level <= level:
y = grid.c_center[1]
x = grid.c_center[0]
q = grid.q
eta = q[:,:,3]
h = q[:,:,0]
#return x,y,eta
#eta = where(q[:,:,0] > 1.,eta,nan)
#import pdb; pdb.set_trace()
topo = eta - h
cutoff = 0.5
#cutoff2 = -500.
shift = 0.
scale = 10.
topo1 = scale*where(topo<cutoff, topo-shift, cutoff-shift)
#topo1 = scale*where(topo>cutoff2, topo1, nan)
eta1 = scale*where(eta<cutoff, eta-shift, cutoff-shift)
water1 = where(h>=1.e-3, eta1, nan)
mlab.mesh(x,y,topo1,colormap='Greens',vmin=-1.0, vmax=0.8)
mlab.mesh(x,y,water1,colormap='Blues',vmin=-0.8, vmax=0.8)
#mlab.surf(x,y,topo1,colormap='Greens',warp_scale=10,vmin=-0.8,vmax=0.5)
#mlab.surf(x,y,water1,colormap='Blues',warp_scale=10,vmin=-0.8,vmax=0.5)
V = (150.95115856920216,\
80.12676623482308,\
13.359093592227218,\
array([ 2.744 , 1.70099999, -0.04745156]))
V = (-108.612973405259,\
62.96905073871072,\
13.359093592227456,\
array([ 2.744 , 1.70099999, -0.04745156]))
mlab.view(*V)
t = frame.t
mlab.title('Time = %5.2f' % t,color=(0,0,0),height=0.1,size=0.5)
def plotframe(frameno, level=1):
plotdata = ClawPlotData()
plotdata.outdir = "_output"
print "Plotting solution from ", plotdata.outdir
plotdata = setplot(plotdata)
try:
frame = plotdata.getframe(frameno)
except:
print "Unable to get frame"
return
mlab.figure(1, bgcolor=(1, 1, 1), size=(700, 600))
mlab.clf()
for grid in frame.grids:
if grid.level <= level:
y = grid.c_center[1]
x = grid.c_center[0]
q = grid.q
eta = q[:, :, 3]
h = q[:, :, 0]
#return x,y,eta
#eta = where(q[:,:,0] > 1.,eta,nan)
#import pdb; pdb.set_trace()
topo = eta - h
cutoff = 0.5
#cutoff2 = -500.
shift = 0.
scale = 10.
topo1 = scale * where(topo < cutoff, topo - shift, cutoff - shift)
#topo1 = scale*where(topo>cutoff2, topo1, nan)
eta1 = scale * where(eta < cutoff, eta - shift, cutoff - shift)
water1 = where(h >= 1.e-3, eta1, nan)
mlab.mesh(x, y, topo1, colormap='Greens', vmin=-1.0, vmax=0.8)
mlab.mesh(x, y, water1, colormap='Blues', vmin=-0.8, vmax=0.8)
#mlab.surf(x,y,topo1,colormap='Greens',warp_scale=10,vmin=-0.8,vmax=0.5)
#mlab.surf(x,y,water1,colormap='Blues',warp_scale=10,vmin=-0.8,vmax=0.5)
V = (150.95115856920216,\
80.12676623482308,\
13.359093592227218,\
array([ 2.744 , 1.70099999, -0.04745156]))
V = (-108.612973405259,\
62.96905073871072,\
13.359093592227456,\
array([ 2.744 , 1.70099999, -0.04745156]))
mlab.view(*V)
t = frame.t
mlab.title('Time = %5.2f' % t, color=(0, 0, 0), height=0.1, size=0.5)
def plotsquare3d(us):
from enthought.mayavi.mlab import surf, show, colorbar, xlabel, ylabel, figure
for u in us:
figure()
Np = 60
points = uniformsquarepoints(Np)
x = points[:,0].reshape(Np,Np)
y = points[:,1].reshape(Np,Np)
up = np.real(u(points))
surf(x,y, up.reshape(Np,Np))
colorbar()
xlabel('x')
ylabel('y')
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 render_vol(data, new_window = True):
""" Renders simple volume data such as ROI """
from enthought.mayavi import mlab
import numpy as np
if new_window:
mlab.figure(1,fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
mlab.clf()
src = mlab.pipeline.scalar_field(data)
src.image_data.update_data()
volume = mlab.pipeline.contour(src)
volume_surf = mlab.pipeline.surface(volume)
def surface(fcm, idx0, idx1):
"""Plots a surface plot for 2D data."""
x = fcm[:, idx0]
y = fcm[:, idx1]
bins = int(numpy.sqrt(len(x)))
z, xedge, yedge = numpy.histogram2d(y, x, bins=[bins, bins],
range=[(numpy.min(y), numpy.max(y)),
(numpy.min(x), numpy.max(x))]
)
mlab.figure()
mlab.surf(xedge, yedge, z, warp_scale='auto')
mlab.xlabel(fcm.channels[idx0])
mlab.ylabel(fcm.channels[idx1])
mlab.zlabel('Density')
def plotframe(frameno,level=1, water_opacity=1.):
plotdata = ClawPlotData()
plotdata.outdir = outdir
print "Plotting solution from ",plotdata.outdir
plotdata = setplot(plotdata)
try:
frame = plotdata.getframe(frameno)
except:
print "Unable to get frame"
return
mlab.figure(1,bgcolor=(1,1,1),size=(700,600))
mlab.clf()
for grid in frame.grids:
if grid.level == level:
y = grid.c_center[1]
x = grid.c_center[0]
q = grid.q
eta = q[:,:,3]
h = q[:,:,0]
topo = eta - h
cutoff = 0.5
#cutoff2 = -500.
shift = 0.
scale = 1.
topo1 = scale*where(topo<cutoff, topo-shift, cutoff-shift)
#topo1 = scale*where(topo>cutoff2, topo1, nan)
eta1 = scale*where(eta<cutoff, eta-shift, cutoff-shift)
water1 = where(h>=1.e-3, eta1, nan)
scale = 12.
#mlab.mesh(x,y,topo1,colormap='Greens',vmin=-1.0, vmax=0.8)
#mlab.mesh(x,y,water1,colormap='Blues',vmin=-0.8, vmax=0.8)
mlab.surf(x,y,topo1,colormap='YlGn',warp_scale=scale,\
vmin=-0.3,vmax=0.3)
mlab.surf(x,y,water1,colormap='Blues',warp_scale=scale,\
vmin=-0.2,vmax=0.3, opacity=water_opacity)
# set the view: (Do V = view() to figure out the current view)
V = (29.157490879985176,\
67.560491214404507,\
79.798910042690324,\
array([ 0. , 1. , -0.07500005]))
mlab.view(*V)
t = frame.t
mlab.title('Time = %5.2f' % t,color=(0,0,0),height=0.1,size=0.5)
def show_3d(self):
"""SHOW_3D - Use mayavi2 to visualize point cloud.
Usage: obj.show_3d()
"""
from enthought.mayavi import mlab
# I want at most 50K points
stride = 1 + len(self) / 50000
pts = self[:, ::stride]
colors = np.ones(pts.shape[1], dtype=np.uint8)
# Draw clusters in point cloud
fig = mlab.figure()
mlab.points3d(
pts[0, :],
pts[1, :],
pts[2, :],
colors,
colormap="spectral",
figure=fig,
scale_mode="none",
scale_factor=0.02,
)
mlab.view(180, 180)
mlab.show()
def __init__(self, objects, objectsOverlay, objectInputOverlay, outputdir):
self.objects = objects
self.objectsOverlay = objectsOverlay
self.objectsInputOverlay = objectInputOverlay
self.outputdir = outputdir
self.counter = 0
self.max_spread_x = 0
self.max_spread_y = 0
self.max_spread_z = 0
for iobj in self.objects.values():
spread_x = max(iobj[0]) - min(iobj[0])
if self.max_spread_x < spread_x:
self.max_spread_x = spread_x
spread_y = max(iobj[1]) - min(iobj[1])
if self.max_spread_y < spread_y:
self.max_spread_y = spread_y
spread_z = max(iobj[2]) - min(iobj[2])
if self.max_spread_z < spread_z:
self.max_spread_z = spread_z
self.max_spread_x = self.max_spread_x / 2 + 10
self.max_spread_y = self.max_spread_y / 2 + 10
self.max_spread_z = self.max_spread_z / 2
from enthought.mayavi import mlab
# Returns the current scene.
self.scene = mlab.figure(size=(2 * self.max_spread_x,
2 * self.max_spread_y),
bgcolor=(1., 1., 1.))
self.engine = mlab.get_engine()
def surface(fcm, idx0, idx1):
"""Plots a surface plot for 2D data."""
x = fcm[:, idx0]
y = fcm[:, idx1]
bins = int(numpy.sqrt(len(x)))
z, xedge, yedge = numpy.histogram2d(y,
x,
bins=[bins, bins],
range=[(numpy.min(y), numpy.max(y)),
(numpy.min(x), numpy.max(x))])
mlab.figure()
mlab.surf(xedge, yedge, z, warp_scale='auto')
mlab.xlabel(fcm.channels[idx0])
mlab.ylabel(fcm.channels[idx1])
mlab.zlabel('Density')
def getData(self, data_D, data_H, name):
"""
plot passed in data as a surface
"""
#plotting
fig = mlab.figure(size=(512, 512))
cl.enqueue_read_buffer(lbm.queue, data_D, data_H).wait()
# retrieve mid cell points from cell node data
Nx = lbm.X.size - 1
Ny = lbm.Y.size - 1
x = np.zeros((Nx))
y = np.zeros((Ny))
for i in range(1, lbm.X.size):
x[i - 1] = (lbm.X[i] - lbm.X[i - 1]) / 2.0 + lbm.X[i - 1]
for i in range(1, lbm.Y.size):
y[i - 1] = (lbm.Y[i] - lbm.Y[i - 1]) / 2.0 + lbm.Y[i - 1]
s = mlab.surf(x, y, data_H, warp_scale='auto', colormap="jet")
mlab.axes(s)
sb = mlab.scalarbar(s, title=name)
self.s = s
self.data_D = data_D
self.data_H = data_H
def animateVTKFiles_2D(folder):
if not os.path.isdir(folder):
return
vtkFiles = []
for f in sorted(os.listdir(folder)):
if f.endswith(".vtk"):
vtkFiles.append(os.path.join(folder, f))
if len(vtkFiles) == 0:
return
figure = mlab.figure(size=(800, 600))
figure.scene.disable_render = True
vtkSource = VTKFileReader()
vtk_file = vtkFiles[0]
vtkSource.initialize(vtk_file)
surface = mlab.pipeline.surface(vtkSource)
axes = mlab.axes()
colorbar = mlab.colorbar(object=surface, orientation='horizontal')
mlab.view(0, 0)
figure.scene.disable_render = False
mlab.draw()
a = animateVTKFiles(figure, vtkSource, vtkFiles)
def test_den():
P = np.random.random((10,10))
#P=np.load(r'c:\maxdenP.np.npy')
#myfilestr=r'c:\structfactors_density.dat'
#x,y,z=np.loadtxt(myfilestr).T
#P=z.reshape((101,101))
#print P.shape
fig=mlab.figure()
x,y,z=gen_as()
#view along z-axis
pts_as=mlab.points3d(x,y,z-.125,color=(1,0,0),colormap='gist_rainbow',figure=fig,scale_factor=.1)
x,y,z=gen_fe()
print 'x',x
print 'y',y
print 'z',z
pts_fe=mlab.points3d(x,y,z-.125,color=(0,1,0),colormap='gist_rainbow',figure=fig,scale_factor=.02)
x,y,z=gen_sr()
pts_sr=mlab.points3d(x,y,z-.125,color=(0,0,1),colormap='gist_rainbow',figure=fig)
outline=mlab.outline(figure=fig,extent=[0,1,0,1,-1,0])
mlab.orientation_axes(figure=fig,xlabel='a',ylabel='b',zlabel='c')
print 'shape',P.shape
P = P[:,:, np.newaxis]
print 'after',P.shape
src = mlab.pipeline.scalar_field(P)
#src = mlab.pipeline.array2d_source(P)
surf = mlab.pipeline.surface(src,figure=fig,extent=[0,1,0,1,-1,0],name='surf2',opacity=0.4)
#surf.transform.GetTransform().RotateX(90)
print 'done'
def showCell(self, number, firstFrame=True):
"""
Show cell with label 'number', its surrounding, descendants, etc.
The second argument specifies whether the given cell label corresponds to a cell
from the first of the two time frames (default) or to a cell from the second frame
"""
# set the bounding box of the volume to be displayed
if firstFrame:
bbox = self.file1["features"][str(number)]["bbox"][:]
else:
bbox = self.file2["features"][str(number)]["bbox"][:]
bbox[0] = bbox[0] - min(self.borderSize, bbox[0]) # make sure that bbox[0] >= 0 (no array bound violation)
bbox[1] = bbox[1] - min(self.borderSize, bbox[1])
bbox[2] = bbox[2] - min(self.borderSize, bbox[2])
bbox[3] = bbox[3] + min(self.borderSize, self.shape[2]-bbox[3]) # make sure that bbox[3] <= self.shape[0] (no array bound violation)
bbox[4] = bbox[4] + min(self.borderSize, self.shape[1]-bbox[4])
bbox[5] = bbox[5] + min(self.borderSize, self.shape[0]-bbox[5])
if firstFrame:
partner = self.findDescendent(number)
number1 = number
number2 = partner
partnerStr = "descendent(s) "
else:
partner = self.findAncestor(number)
number1 = partner
number2 = number
partnerStr = "ancestor "
# load the data
seg1 = self.file1["segmentation"]["labels"][bbox[2]:bbox[5],bbox[1]:bbox[4],bbox[0]:bbox[3]]
seg2 = self.file2["segmentation"]["labels"][bbox[2]:bbox[5],bbox[1]:bbox[4],bbox[0]:bbox[3]]
raw1 = self.file1["raw"]["volume"][bbox[2]:bbox[5],bbox[1]:bbox[4],bbox[0]:bbox[3]]
raw2 = self.file2["raw"]["volume"][bbox[2]:bbox[5],bbox[1]:bbox[4],bbox[0]:bbox[3]]
print "Drawing cell number ",number," and its ", partnerStr ,partner
t0 = time.time()
#draw everything
fig1 = mlab.figure(1, size=(500,450))
mlab.clf(fig1)
self.drawImagePlane(fig1, raw1, 'gray')
self.drawImagePlane(fig1, raw2, 'copper', 'y_axes')
self.drawVolumeWithoutReferenceCell(fig1, seg1, number1, (0,0,1),0.2)
self.drawVolumeWithoutReferenceCell(fig1, seg2, number2, (0.2,0,0.8),0.2)
self.drawReferenceCell(fig1, seg1, number1, (0.5,1,0), 0.4)
self.drawReferenceCell(fig1, seg2, number2, (0.8,0.8,0),0.3)
self.drawArrows(fig1, bbox, number1, number2)
if firstFrame:
allneighbors = self.getNeighbors(seg1, number1)
for i in allneighbors:
p = self.findDescendent(i)
self.drawArrows(fig1, bbox, i, p)
else:
allneighbors = self.getNeighbors(seg2, number2)
for i in allneighbors:
p = self.findAncestor(i)
self.drawArrows(fig1, bbox, p, i)
t = time.time() - t0
print "Time for drawing:",t
def __init__(self, stim_xml_filename=None, num_points=5, scale_factor=0.01, ghost_length=20, resolution=4):
# initialize the viewer window
self.f = mlab.figure(size=(800, 600))
self.f.scene.anti_aliasing_frames = 1
visual.set_viewer(self.f)
################# moving fly objects ################
self.num_points = num_points
self.plot_objects = [
Object(ghost_length=ghost_length, scale_factor=scale_factor, resolution=resolution)
for i in range(self.num_points)
]
######################################################
self.objects = None
self.speed = np.zeros(self.num_points)
################### stimulus xml #####################
if stim_xml_filename is not None:
actors = []
stim_xml = xml_stimulus.xml_stimulus_from_filename(stim_xml_filename)
actors.extend(stim_xml.get_tvtk_actors())
for a in actors:
visual.show_actor(a)
######################################################
# ROS stuff
rospy.Subscriber("flydra_mainbrain_super_packets", flydra_mainbrain_super_packet, self.callback)
rospy.init_node("listener", anonymous=True)
# run the animation
self.animate()
def __init__(self,channels, thresholds, pixelsize = (1., 1., 1.)):
self.f = mlab.figure()
self.isos = []
self.projs = []
for im, th, i in zip(channels, thresholds, range(len(channels))):
c = mlab.contour3d(im, contours=[th], color = pylab.cm.gist_rainbow(float(i)/len(channels))[:3])
c.mlab_source.dataset.spacing = pixelsize
self.isos.append(c)
ps = []
thf = th*1.5
pr = im.mean(2)
#f = im.max()/pr.max()
#pr *= im.max()/pr.max()
ps.append(self.drawProjection((255*pylab.minimum(pr/(1.*thf), 1)).astype('uint8'), 'z', c))
pr = im.mean(0)
#pr *= im.max()/pr.max()
ps.append(self.drawProjection((255*pylab.minimum(pr/(.6*thf), 1)).astype('uint8'), 'x', c))
pr = im.mean(1)
#pr *= im.max()/pr.max()
ps.append(self.drawProjection((255*pylab.minimum(pr/(.8*thf), 1)).astype('uint8'), 'y', c))
self.projs.append(ps)
def draw_struct():
fig = mlab.figure()
r = gen_fe()
s = gen_spins()
#view along z-axis
x = r[:, 0]
y = r[:, 1]
z = r[:, 2]
u = s[:, 0]
v = s[:, 1]
w = s[:, 2]
#print x.shape
#print y.shape
#print z.shape
pts_as = mlab.points3d(x,
y,
z,
color=(0, 0, 1),
colormap='gist_rainbow',
figure=fig,
scale_factor=.1)
mlab.quiver3d(x, y, z, u, v, w, line_width=3, scale_factor=.3, figure=fig)
outline = mlab.outline(figure=fig, extent=[0, 1, 0, 1, 0, 1])
mlab.orientation_axes(figure=fig, xlabel='a', ylabel='b', zlabel='c')
print 'done'
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 showVTKFile_2D(filename):
figure = mlab.figure(size=(800, 600))
vtkSource = VTKFileReader()
vtkSource.initialize(filename)
surface = mlab.pipeline.surface(vtkSource)
axes = mlab.axes()
colorbar = mlab.colorbar(object=surface, orientation='horizontal')
mlab.view(0, 0)
mlab.show()
def test_den():
#P = np.random.random((10,10))
#P=np.load(r'c:\maxdenP.np.npy')
#myfilestr=r'c:\structfactors_density.dat'
#x,y,z=np.loadtxt(myfilestr).T
#P=z.reshape((101,101))
#print P.shape
fig = mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
x, y, z = np.array([[1, 0, 1], [0, -1, 1], [-1, 0, -1], [0, 1, -1],
[1, 1, 0], [-1, -1, 0]], 'float64').T
#view along z-axis
pts_as = mlab.points3d(x,
y,
z,
color=(1, 0, 0),
colormap='gist_rainbow',
figure=fig,
scale_factor=.3)
#x,y,z=gen_fe()
print 'x', x, x.shape
print 'y', y, y.shape
print 'z', z, z.shape
x, y, z = np.array([
[0, 1, 1],
[0, -1, -1],
], 'float64').T
pts_FE = mlab.points3d(x,
y,
z,
color=(0, 1, 0),
colormap='gist_rainbow',
figure=fig,
scale_factor=.3)
mlab.text3d(0, 1, 1, '011', scale=.2, color=(1, 1, 0))
mlab.text3d(1, 1, 0, '110', scale=.2)
mlab.text3d(-1, -1, 0, '-1-10', scale=.2)
mlab.text3d(1, 0, 1, '101', scale=.2)
mlab.text3d(0, -1, 1, '0-11', scale=.2)
mlab.text3d(-1, 0, -1, '-10-1', scale=.2)
mlab.text3d(0, 1, -1, '01-1', scale=.2)
#pts_fe=mlab.points3d(x,y,z-.125,color=(0,1,0),colormap='gist_rainbow',figure=fig,scale_factor=.02)
#x,y,z=gen_sr()
#print 'x',x,x.shape
#pts_sr=mlab.points3d(x,y,z-.125,color=(0,0,1),colormap='gist_rainbow',figure=fig)
#outline=mlab.outline(figure=fig,extent=[0,1,0,1,-1,0])
outline = mlab.outline(figure=fig)
mlab.orientation_axes(figure=fig, xlabel='a', ylabel='b', zlabel='c')
#print 'shape',P.shape
#P = P[:,:, np.newaxis]
#print 'after',P.shape
#src = mlab.pipeline.scalar_field(P)
#src = mlab.pipeline.array2d_source(P)
#surf = mlab.pipeline.surface(src,figure=fig,extent=[0,1,0,1,-1,0],name='surf2',opacity=0.4)
#surf.transform.GetTransform().RotateX(90)
print 'done'
def disp_odf(sph_map, theta_res=64, phi_res=32, colormap='RGB', colors=256):
pi = np.pi
sin = np.sin
cos = np.cos
theta, phi = np.mgrid[0:2 * pi:theta_res * 1j, 0:pi:phi_res * 1j]
x = sin(phi) * cos(theta)
y = sin(phi) * sin(theta)
z = cos(phi)
nvox = np.prod(sph_map.shape)
x_cen, y_cen, z_cen = _3grid(sph_map.shape)
odf_values = sph_map.evaluate_at(theta, phi)
max_value = odf_values.max()
mlab.figure()
for ii in range(nvox):
odf_ii = odf_values.reshape(nvox, theta_res, phi_res)[ii, :, :]
odf_ii /= max_value * 2
if colormap == 'RGB':
rgb = np.r_['-1,3,0', x * odf_ii, y * odf_ii, z * odf_ii]
rgb = np.abs(rgb * 255 / rgb.max()).astype('uint8')
odf_im = Image.fromarray(rgb, mode='RGB')
odf_im = odf_im.convert('P', palette=Image.ADAPTIVE, colors=colors)
lut = np.empty((colors, 4), 'uint8')
lut[:, 3] = 255
lut[:, 0:3] = np.reshape(odf_im.getpalette(), (colors, 3))
oo = mlab.mesh(x * odf_ii + x_cen.flat[ii],
y * odf_ii + y_cen.flat[ii],
z * odf_ii + z_cen.flat[ii],
scalars=np.int16(odf_im))
oo.module_manager.scalar_lut_manager.lut.table = lut
else:
mlab.mesh(x * odf_ii + x_cen.flat[ii],
y * odf_ii + y_cen.flat[ii],
z * odf_ii + z_cen.flat[ii],
scalars=odf_ii,
colormap=colormap)
def plot_connections(data_file, min, max, bins, params=None, output=''):
print("Creating connection profile graphs.")
# Read data points from file
f = open(data_file, 'r')
# Ignore first line
f.readline()
data = []
for line in f:
temp = line.split(' ')
if params != None:
if check_node([int(temp[1])], params):
data.append([float(temp[4]), float(temp[5])])
else:
data.append([float(temp[4]), float(temp[5])])
# Create histogram data based on the retrieved data.
histogram_data = histogram2d(data, min, max, bins)
# Open a new Mayavi2 figure
f = mlab.figure()
# Convert histogram bin count to relative densities.
m = np.max(histogram_data[2].max(axis=0))
histogram_data[2] = histogram_data[2] / float(m)
# Plot histogram data
mlab.mesh(histogram_data[0], histogram_data[1], histogram_data[2])
#surf(histogram_data[0], histogram_data[1], histogram_data[2])
# Create and save various viewpoints of histogram figure
mlab.axes(z_axis_visibility=False)
mlab.view(azimuth=0, elevation=90) # X
mlab.savefig(output + "xaxis.eps", size=[600, 400])
mlab.view(azimuth=90, elevation=270) # Y
mlab.savefig(output + "yaxis.eps", size=[600, 400])
mlab.view(azimuth=45, elevation=45) # Perspective
mlab.savefig(output + "perspective.eps", size=[600, 400])
mlab.colorbar(orientation="vertical")
mlab.view(azimuth=0, elevation=0) # Z
mlab.savefig(output + "above.eps", size=[600, 400])
def view(dataset):
""" Open up a mayavi scene and display the dataset in it.
"""
fig = mlab.figure(bgcolor=(1, 1, 1),
fgcolor=(0, 0, 0),
figure=dataset.class_name[3:])
surf = mlab.pipeline.surface(dataset, opacity=0.1)
mlab.pipeline.surface(
mlab.pipeline.extract_edges(surf),
color=(0, 0, 0),
)
def show_lorenz(new_fig=True):
x, y, z = np.mgrid[-50:50:100j, -50:50:100j, -10:60:70j]
u, v, w = lorenz(x, y, z)
if new_fig:
fig = mlab.figure(size=(600, 600), bgcolor=(0, 0, 0))
# Plot the flow of trajectories with suitable parameters.
f = mlab.flow(x, y, z, u, v, w, line_width=3, colormap='Paired')
f.module_manager.scalar_lut_manager.reverse_lut = True
f.stream_tracer.integration_direction = 'both'
f.stream_tracer.maximum_propagation = 200
def On3DVolume(self, event):
try:
from enthought.mayavi import mlab
except ImportError:
from mayavi import mlab
self.dsviewer.f3d = mlab.figure()
self.dsviewer.f3d.scene.stereo = True
for i in range(self.image.data.shape[3]):
#c = mlab.contour3d(im.img, contours=[pylab.mean(ivp.clim)], color = pylab.cm.gist_rainbow(float(i)/len(self.images))[:3])
v = mlab.pipeline.volume(mlab.pipeline.scalar_field(numpy.minimum(255*(self.image.data[:,:,:,i] -self.do.Offs[i])*self.do.Gains[i], 254).astype('uint8')))
def feature_plot(unit,size,delta_xyz):
'''
A three dimensional plot the the voxilized feature. Does what spy_on but
does a three dimensional plot rather then z slices.
feature (type: Lego_Model): contains all of the information required to
calculate the scattering off the feature
'''
from enthought.mayavi import mlab
dumbie_unit = roll(unit,1,axis = 0)
dumbie_unit = roll(dumbie_unit,1,axis = 1)
dumbie_unit[:,0,:] = 0.0
dumbie_unit[:,-1,:] = 0.0
dumbie_unit[0,:,:] = 0.0
dumbie_unit[-1,:,:] = 0.0
xyz = indices((unit.shape), 'd')
xyz[0] *= delta_xyz[0]
xyz[1] *= delta_xyz[1]
xyz[2] *= delta_xyz[2]
feature_size = shape(unit)
mlab.figure(0)
s = mlab.contour3d(xyz[0],xyz[1],xyz[2],dumbie_unit,opacity=.07,contours = 20)
mlab.figure(1)
t = mlab.contour3d(xyz[0],xyz[1],xyz[2]*10,dumbie_unit,opacity=.07,contours = 20)
mlab.figure(2)
u = mlab.contour3d(dumbie_unit,opacity=.05,contours = 20)
mlab.show()
return
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 On3DIsosurf(self, event):
try:
from enthought.mayavi import mlab
except ImportError:
from mayavi import mlab
self.dsviewer.f3d = mlab.figure()
self.dsviewer.f3d.scene.stereo = True
for i in range(self.image.data.shape[3]):
c = mlab.contour3d(self.image.data[:,:,:,i].squeeze().astype('f'), contours=[self.do.Offs[i] + .5/self.do.Gains[i]], color = matplotlib.cm.gist_rainbow(float(i)/self.image.data.shape[3])[:3])
self.lastSurf = c
c.mlab_source.dataset.spacing = self.image.voxelsize