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 _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 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_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 Show_Phase(phase_numpy):
dims=phase_numpy.shape
fig=mlab.figure()
src = mlab.pipeline.scalar_field(phase_numpy)
mlab.outline()
mlab.orientation_axes()
#v= mlab.pipeline.vector_field(velx_numpy,vely_numpy,velz_numpy)
#vx=mlab.pipeline.scalar_field(velx_numpy)
#vy=mlab.pipeline.scalar_field(vely_numpy)
#vz=mlab.pipeline.scalar_field(velz_numpy)
extract=mlab.pipeline.extract_grid(src)
extract.set(x_min=1,x_max=dims[0]-2,y_min=1,y_max=dims[1]-2)
surf = mlab.pipeline.contour_surface(extract)
def _plotbutton1_fired(self):
mlab.clf()
self.loaddata()
field=mlab.pipeline.scalar_field(self.sregion) # Generate a scalar field
mlab.pipeline.volume(field,vmax=self.datamax,vmin=self.datamin) # Render the field with dots
mlab.outline()
mlab.xlabel('RA(J2000)')
mlab.ylabel('DEC(J2000)')
mlab.zlabel('Velocity')
mlab.view(azimuth=0, elevation=0)
mlab.show()
self.field = field
def save(self,it):
msh = self.__msh
w = (self.__eqn).get(self.__var)()
mlab.clf()
mlab.points3d(msh.x[:,0], msh.x[:,1], zeros(w.shape), w,
scale_factor=self.__scale, scale_mode=self.__mode)
mlab.outline(extent=[msh.BB[0,0],msh.BB[1,0],msh.BB[0,1],msh.BB[1,1],
0,0])
mlab.view(0,0,self.__zoom,self.__xView)
mlab.colorbar()
fSol = ''.join([self.__figDir,'/',self.__name,'%04d.png'% it])
#print fSol
mlab.savefig(fSol)
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 _plotbutton2_fired(self):
mlab.clf()
self.loaddata()
field=mlab.contour3d(self.sregion,colormap='gist_ncar') # Generate a scalar field
field.contour.maximum_contour = self.datamax
field.contour.minimum_contour = self.datamin
field.actor.property.opacity = self.opacity
mlab.outline()
mlab.xlabel('RA(J2000)')
mlab.ylabel('DEC(J2000)')
mlab.zlabel('Velocity')
mlab.view(azimuth=0, elevation=0)
mlab.show()
self.field = field
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 add_cell(at,
pts,
origin=[0., 0., 0.],
shift=[.5, .5, .5],
supercell=[1, 1, 1]):
if not at.is_orthorhombic:
raise ValueError('Cannot draw cell outline for non-orthorhombic cells')
na, nb, nc = supercell
extent = np.array(
[0., at.lattice[1, 1], 0., at.lattice[2, 2], 0., at.lattice[3, 3]])
shift = np.dot(np.array(at.lattice), shift)
shift = np.array(
[shift[0], shift[0], shift[1], shift[1], shift[2], shift[2]])
origin = np.array(origin)
origin = np.array(
[origin[0], origin[0], origin[1], origin[1], origin[2], origin[2]])
print 'origin', origin
print 'shift', shift
for i in range(na):
for j in range(nb):
for k in range(nc):
disp = np.array([
i * at.lattice[1, 1], i * at.lattice[1, 1],
j * at.lattice[2, 2], j * at.lattice[2, 2],
k * at.lattice[3, 3], k * at.lattice[3, 3]
])
return mlab.outline(pts,
extent=origin + disp + extent - shift,
name='outline_%d_%d_%d' % (i, j, k))
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 cutPlanes(volume, colormap='gist_ncar'):
'''Display a 3D volume of scalars with two cut planes.
volume: a three dimensional array of scalars
'''
scalarField = mlab.pipeline.scalar_field(volume)
mlab.pipeline.image_plane_widget(scalarField,
plane_orientation='z_axes',
slice_index=10,
colormap=colormap)
mlab.pipeline.image_plane_widget(scalarField,
plane_orientation='y_axes',
slice_index=10,
colormap=colormap)
mlab.outline()
mlab.axes()
mlab.colorbar(orientation='vertical')
def _flow_default(self):
x, y, z = self.points
u, v, w = self.get_uvw()
f = self.scene.mlab.flow(x, y, z, u, v, w)
f.stream_tracer.integration_direction = 'both'
f.stream_tracer.maximum_propagation = 200
src = f.mlab_source.m_data
o = mlab.outline()
mlab.view(120, 60, 150)
return f
def Show_Phase(phase_numpy):
dims = phase_numpy.shape
fig = mlab.figure()
src = mlab.pipeline.scalar_field(phase_numpy)
mlab.outline()
mlab.orientation_axes()
#v= mlab.pipeline.vector_field(velx_numpy,vely_numpy,velz_numpy)
#vx=mlab.pipeline.scalar_field(velx_numpy)
#vy=mlab.pipeline.scalar_field(vely_numpy)
#vz=mlab.pipeline.scalar_field(velz_numpy)
extract = mlab.pipeline.extract_grid(src)
extract.set(x_min=1, x_max=dims[0] - 2, y_min=1, y_max=dims[1] - 2)
surf = mlab.pipeline.contour_surface(extract)
#mlab.pipeline.image_plane_widget(vx, plane_orientation='x_axes', slice_index=250)
#mlab.pipeline.vectors(v, mask_points=20, scale_factor=3.)
#mlab.pipeline.vector_cut_plane(v, mask_points=2, scale_factor=3)
mlab.show()
def main(self):
from enthought.mayavi import mlab
self._iter = 1
self.density = mlab.pipeline.scalar_field(self.sim.rho.transpose())
self.vx = mlab.pipeline.scalar_field((self.sim.vx**2 + self.sim.vy**2 + self.sim.vz**2).transpose())
self.velocity = mlab.pipeline.vector_field(self.sim.vx.transpose(), self.sim.vy.transpose(), self.sim.vz.transpose())
self.trc = mlab.points3d(self.sim.tracer_x, self.sim.tracer_y, self.sim.tracer_z, scale_factor=0.75)
mlab.pipeline.image_plane_widget(self.vx, plane_orientation='x_axes', slice_index=10)
mlab.pipeline.image_plane_widget(self.vx, plane_orientation='y_axes', slice_index=10)
mlab.pipeline.image_plane_widget(self.vx, plane_orientation='z_axes', slice_index=10)
mlab.axes()
# mlab.pipeline.vector_cut_plane(self.velocity, mask_points=2, scale_factor=3, plane_orientation='y_axes')
# mlab.pipeline.vectors(self.velocity, mask_points=20, scale_factor=3.)
mlab.outline()
while 1:
self.visualize()
def cutPlanes( volume, colormap='gist_ncar' ):
'''Display a 3D volume of scalars with two cut planes.
volume: a three dimensional array of scalars
'''
scalarField = mlab.pipeline.scalar_field( volume )
mlab.pipeline.image_plane_widget(scalarField,
plane_orientation='z_axes',
slice_index=10,
colormap = colormap
)
mlab.pipeline.image_plane_widget(scalarField,
plane_orientation='y_axes',
slice_index=10,
colormap = colormap
)
mlab.outline()
mlab.axes()
mlab.colorbar(orientation='vertical')
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 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 show(self):
"""show Vm in a graph. Works for 1D projects only"""
if self.Vm.ndim == 2:
pylab.imshow(self.Vm,aspect='auto',cmap=cm.jet)
pylab.show()
elif self.Vm.ndim == 3:
s = mlab.surf(self.Vm[...,0])
raw_input("Press Enter to lauch the simulation...")
for i in range(self.Vm.shape[-1]):
s.mlab_source.scalars = self.Vm[...,i]
elif self.Vm.ndim == 4:
p = mlab.pipeline.scalar_field(self.Vm[...,0])
s = mlab.pipeline.image_plane_widget( p,
plane_orientation='x_axes',
slice_index=self.mdl.stimCoord[0],
vmin = self.Vm.min(),
vmax = self.Vm.max()
)
s2 = mlab.pipeline.image_plane_widget(p,
plane_orientation='y_axes',
slice_index=self.mdl.stimCoord[2],
vmin = self.Vm.min(),
vmax = self.Vm.max()
)
s3 = mlab.pipeline.image_plane_widget( p,
plane_orientation='z_axes',
slice_index=self.mdl.stimCoord[4],
vmin = self.Vm.min(),
vmax = self.Vm.max()
)
mlab.scalarbar(s,orientation='vertical',nb_labels=4,label_fmt='%.3f')
mlab.outline(color=(1,1,1))
raw_input("Press Enter to lauch the simulation...")
for i in range(self.Vm.shape[-1]):
p.mlab_source.scalars = self.Vm[...,i]
def plot(self):
"绘制场景"
# ball.up.x = 5
# ball.up.y = 6
# ball.up.z = 7
# ball.up.x = -4
# ball.up.y = -3
# ball.up.z = -2
x = [[-1,1,1,-1,-1],
[-1,1,1,-1,-1]]
y = [[-1,-1,-1,-1,-1],
[1,1,1,1, 1]]
z = [[1,1,-1,-1,1],
[1,1,-1,-1,1]]
# s = mlab.mesh(x, y, z, representation="wireframe", line_width=1.0 )
# mlab.show()
pl = mlab.surf(x,y,z,warp_scale="auto")
mlab.axes(xlabel = 'x',ylabel= 'y',zlabel= 'z')
mlab.outline(pl)
def test_quiver3d():
fig = mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
x, y, z = numpy.mgrid[-2:3, -2:3, -2:3]
print 'almost'
#mlab.quiver3d(x, y, z,f, line_width=3, scale_factor=1,figure=fig)
d = 0.2
x = d * numpy.arange(0, 25)
y = -d * numpy.arange(0, 25)
z = numpy.zeros(len(x))
if 0:
func = spiral
if 1:
func = amplitude
mlab.quiver3d(x, y, z, func, line_width=3, scale_factor=1, figure=fig)
outline = mlab.outline(figure=fig, extent=[-1, 1, -1, 1, -1, 1])
mlab.orientation_axes(figure=fig, xlabel='a', ylabel='b', zlabel='c')
print 'done'
def test_quiver3d():
fig=mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
x, y, z = numpy.mgrid[-2:3, -2:3, -2:3]
print 'almost'
#mlab.quiver3d(x, y, z,f, line_width=3, scale_factor=1,figure=fig)
d=0.2
x=d*numpy.arange(0,25)
y=-d*numpy.arange(0,25)
z=numpy.zeros(len(x))
if 0:
func=spiral
if 1:
func=amplitude
mlab.quiver3d(x, y, z,func, line_width=3, scale_factor=1,figure=fig)
outline=mlab.outline(figure=fig,extent=[-1,1,-1,1,-1,1])
mlab.orientation_axes(figure=fig,xlabel='a',ylabel='b',zlabel='c')
print 'done'
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'
mlab.contour3d(ras,
decs,
vs,
HI_cube * cm_tweak,
contours=[level * cm_tweak],
opacity=op,
vmin=color_scale[1] * cm_tweak,
vmax=color_scale[0] * cm_tweak,
name='I: ' + np.str(level),
colormap='Set1')
# Draw a box around the cube to aid in the visualization
mlab.outline(extent=[
np.min(ras),
np.max(ras),
np.min(decs),
np.max(decs), c_v[slice_min, 1], c_v[slice_max, 1]
],
color=(0, 0, 0),
line_width=2.0) # Draw a box around it
# Now, add some axes
ax = mlab.axes(extent=[
np.min(ras),
np.max(ras),
np.min(decs),
np.max(decs), c_v[slice_min, 1], c_v[slice_max, 1]
],
nb_labels=3,
color=(0, 0, 0))
# Fine tune the look of the axis
temperature[i,j,k] = (f[:,i,j,k]* v[2:-2]**2).sum() / f.sum(axis=0)[i,j,k]
return temperature
for frame in range(len(fileh.root.Time[:])):
# 3D Movie
frame = 4*frame
mlab.clf(figure=fig3d)
scene = mlab.contour3d(Temperature(fileh.root.Phasespace[:,:,:,:,frame], fileh.root.Phasespace.attrs.v), contours=50)
# Density profile
#scene = mlab.contour3d(fileh.root.Phasespace[:,:,:,:,frame].sum(axis=0), contours=50)
#source = mlab.pipeline.scalar_field(Temperature(fileh.root.Phasespace[:,:,:,:,frame], fileh.root.Phasespace.attrs.v))
#vol = mlab.pipeline.volume(source)
angle = 50.
mlab.view(azimuth=angle%360, distance=72.0)
#mlab.axes(color=(0.0,0.0,0.),extent=ext,xlabel='X',ylabel='Y',zlabel='Z')
#a = array(gradient(fileh.root.Phi[:,:,:,frame]))**2
#mlab.quiver3d(a[0],a[1],a[2])
mlab.colorbar(orientation='vertical')#, label_fmt="%.2f", nb_labels=3)
mlab.outline()
mlab.savefig("Rho_" + string.zfill(frame, 4) + ".jpg")#, size=(600,600))
print "[", string.zfill(frame, 4),"/", len(fileh.root.Time[:]), "]"
#mencoder "mf://*.jpg" -mf fps=10 -ovc lavc -o mymovie.av
# -*- coding: utf-8 -*- from enthought.mayavi import mlab x = [[-1, 1, 1, -1, -1], [-1, 1, 1, -1, -1]] y = [[-1, -1, -1, -1, -1], [1, 1, 1, 1, 1]] z = [[1, 1, -1, -1, 1], [1, 1, -1, -1, 1]] box = mlab.mesh(x, y, z, representation="surface") mlab.axes(xlabel='x', ylabel='y', zlabel='z') mlab.outline(box) mlab.show()
fig.scene.background = (1, 1, 1)
p = vis.plot_prism_mesh(mesh.vfilter(res, 900, 2001),
style='surface',
opacity=1)
mlab.get_engine().scenes[0].children[-1].children[0].children[0].children[
0].scalar_lut_manager.data_range = [0, 2000]
#a = mlab.axes(p, nb_labels=0, extent=extent, color=(0,0,0))
#a.label_text_property.color = (0,0,0)
#a.title_text_property.color = (0,0,0)
#a.axes.label_format = ""
#a.axes.x_label, a.axes.y_label, a.axes.z_label = "", "", ""
#a.property.line_width = 1
mlab.outline(p, extent=extent, color=(0, 0, 0))
pos = 1000
field = 'gz'
scale = 200
Y, X, Z = utils.extract_matrices(data[field])
p = mlab.contour_surf(X, Y, Z, contours=10, colormap='jet')
p.contour.filled_contours = True
p.actor.actor.position = (0, 0, pos)
p.actor.actor.scale = (1, 1, scale)
a = mlab.axes(p,
nb_labels=0,
extent=[0, 3000, 0, 3000, pos, pos + scale * Z.max()],
color=(0, 0, 0))
a.label_text_property.color = (0, 0, 0)
#!/usr/bin/env python
import numpy as np
from enthought.mayavi import mlab
x,y=np.ogrid [-2:2:160j,-2:2:160j]
z=abs(x)*np.exp(-x**2-(y/.75)**2)
pl=mlab.surf(x,y,z,warp_scale=2)
mlab.axes(xlabel='x',ylabel='y',zlabel='z')
mlab.outline(pl)
mlab.show()
self.filter.extent = (self.x_min, self.x_max,
self.y_min, self.y_max,
self.z_min, self.z_max)
view = View('x_min', 'x_max', 'y_min', 'y_max', 'z_min', 'z_max',
title='Edit extent', resizable=True)
################################################################################
# Now build the visualization using mlab
from enthought.mayavi import mlab
fig = mlab.figure(1, bgcolor=(1, 1, 1))
# Create unconnected points
pts = mlab.pipeline.scalar_scatter(x, y, z, s)
mlab.outline(pts)
# Use a geometry_filter to filter with a bounding box
geometry_filter = mlab.pipeline.user_defined(pts,
filter='GeometryFilter')
geometry_filter.filter.extent_clipping = True
# Connect our dialog to the filter
extent_dialog = ExtentDialog(
data_x_min=0, data_x_max=1,
data_y_min=0, data_y_max=1,
data_z_min=0, data_z_max=1,
filter=geometry_filter.filter)
# We need to use 'edit_traits' and not 'configure_traits()' as we do
# not want to start the GUI event loop (the call to mlab.show())
# at the end of the script will do it.
extent_dialog.edit_traits()
# Plot the said contour - and flip the colorscheme for aesthetic purposes.
cm_tweak = -1.0
mlab.contour3d(ras,decs,vs,
HI_cube*cm_tweak,
contours = [level*cm_tweak],
opacity =op,
vmin =color_scale[1]*cm_tweak, vmax = color_scale[0]*cm_tweak,
name = 'I: '+np.str(level),
colormap = 'Set1')
# Draw a box around the cube to aid in the visualization
mlab.outline(extent=[ np.min(ras),np.max(ras),
np.min(decs), np.max(decs),
c_v[slice_min,1],
c_v[slice_max,1]],
color=(0,0,0),
line_width = 2.0) # Draw a box around it
# Now, add some axes
ax = mlab.axes(extent=[np.min(ras),np.max(ras),
np.min(decs), np.max(decs),
c_v[slice_min,1],
c_v[slice_max,1]],
nb_labels=3, color = (0,0,0))
# Fine tune the look of the axis
ax.axes.fly_mode = 'outer_edges'
ax.title_text_property.font_size = 10
ax.title_text_property.font_family = 'courier'
scale_error = 1000.0
def S(k, b):
"计算直线y=k*x+b和原始数据X、Y的误差的平方和"
error = np.zeros(k.shape)
for x, y in zip(X, Y):
error += (y - (k * x + b))**2
return error
ks, bs = np.mgrid[k - scale_k:k + scale_k:40j, b - scale_b:b + scale_b:40j]
error = S(ks, bs) / scale_error
from enthought.mayavi import mlab
surf = mlab.surf(ks, bs / scale_b, error)
mlab.axes(xlabel="k",
ylabel="b",
zlabel="error",
ranges=[
k - scale_k, k + scale_k, b - scale_b, b + scale_b,
np.min(error) * scale_error,
np.max(error) * scale_error
])
mlab.outline(surf)
mlab.points3d([k], [b / scale_b], [S(k, b) / scale_error],
scale_factor=0.1,
color=(1, 1, 1))
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) face = mlab.surf(x, y, z, warp_scale=2) mlab.axes(xlabel='x', ylabel='y', zlabel='z') mlab.outline(face) from enthought.tvtk.pipeline.browser import PipelineBrowser b = PipelineBrowser() b.root_object = [mlab.gcf().scene.render_window] b.show() mlab.show() mscene = mlab.gcf() tscene = mscene.scene rw = tscene.render_window a1 = rw.renderers[0].view_props[0]
new_fig = NewFig()
options = dict(scale_factor=0.9, mode='cube', scale_mode='none',
vmin=0, vmax=10, opacity=0.3)
################################################################################
# Broadcasting first figure
################################################################################
new_fig()
a = 0*np.ones((8, 5, 3), np.int)
a = np.atleast_3d(a)
x, y, z = -np.indices(a.shape)
z *= -1
mat1 = mlab.points3d(x, y, z, a, **options)
mlab.outline(mat1)
a = 10*np.ones((8, 1, 1), np.int)
a = np.atleast_3d(a)
x, y, z = -np.indices(a.shape)
y += 2
z *= -1
mat2 = mlab.points3d(x, y, z, a, **options)
o = mlab.outline(mat1)
mlab.pipeline.set_extent(o, mat2.actor.actor.bounds)
a = 5*np.ones((1, 5, 3), np.int)
a = np.atleast_3d(a)
x, y, z = -np.indices(a.shape)
x += 2
z *= -1
# -*- coding: utf-8 -*-
from enthought.mayavi import mlab
x = [[-1,1,1,-1,-1],
[-1,1,1,-1,-1]]
y = [[-1,-1,-1,-1,-1],
[ 1, 1, 1, 1, 1]]
z = [[1,1,-1,-1,1],
[1,1,-1,-1,1]]
box = mlab.mesh(x, y, z, representation="surface")
mlab.axes(xlabel='x', ylabel='y', zlabel='z')
mlab.outline(box)
mlab.show()
pl.gca().add_patch(rect)
pl.gca().set_aspect("equal")
pl.show()
####### 误差曲面 #######
scale_k = 1.0
scale_b = 10.0
scale_error = 1000.0
def S(k, b):
"计算直线y=k*x+b和原始数据X、Y的误差的平方和"
error = np.zeros(k.shape)
for x, y in zip(X, Y):
error += (y - (k*x + b))**2
return error
ks, bs = np.mgrid[k-scale_k:k+scale_k:40j, b-scale_b:b+scale_b:40j]
error = S(ks, bs)/scale_error
from enthought.mayavi import mlab
surf = mlab.surf(ks, bs/scale_b, error)
mlab.axes(xlabel="k", ylabel="b", zlabel="error",
ranges=[k-scale_k,k+scale_k,
b-scale_b,b+scale_b,
np.min(error)*scale_error, np.max(error)*scale_error])
mlab.outline(surf)
mlab.points3d([k],[b/scale_b],[S(k,b)/scale_error],scale_factor=0.1, color=(1,1,1))
mlab.show()
import numpy
from enthought.mayavi import mlab
from fatiando import vis
f = open("mesh.pickle")
mesh = pickle.load(f)
f.close()
f = open("seeds.pickle")
seeds = pickle.load(f)
f.close()
f = open("model.pickle")
model = pickle.load(f)
f.close()
seed_mesh = numpy.array([seed['cell'] for seed in seeds])
fig = mlab.figure()
fig.scene.camera.yaw(230)
fig.scene.background = (1, 1, 1)
vis.plot_prism_mesh(model, style='wireframe', xy2ne=True)
plot = vis.plot_prism_mesh(mesh, xy2ne=True)
vis.plot_prism_mesh(seed_mesh, xy2ne=True)
axes = mlab.axes(plot, nb_labels=5, color=(0,0,0))
axes.label_text_property.color = (0,0,0)
axes.title_text_property.color = (0,0,0)
axes.axes.label_format = "%-#.0f"
mlab.outline(color=(0,0,0))
mlab.show()
right_ys = []
for line in data:
cm = line.split()
top_xs.append(float(cm[0]))
left_zs.append(float(cm[2]))
right_ys.append(float(cm[1]))
dms.append(float(cm[9]))
# diameters
mlab.options.offscreen = True
mlab.figure(1, bgcolor=(1, 1, 1), size=(1280, 720))
dms = [x / 2.0 for x in dms]
q = mlab.quiver3d(top_xs, right_ys, left_zs, dms, dms, dms, mode='sphere', scale_factor=1)
mlab.orientation_axes()
mlab.outline(color=(0,0,0), line_width=1.0)
a = mlab.axes(color=(0.1,0.1,0.1), nb_labels=3, line_width=1.0)
a.axes.axis_label_text_property.color = (0,0,0)
q.scene.isometric_view()
count = 1
while count < 360:
q.scene.camera.azimuth(1)
mlab.draw()
mlab.savefig('animate/' + stage + '-dms-animated-' + str(count).zfill(3) + '.png')
count += 1
print(count)
density *= pdf_z
a = x + y
b = 2 * y
c = a - b + z
norm = np.sqrt(a.var() + b.var())
a /= norm
b /= norm
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
mlab.clf()
pts = mlab.points3d(a, b, c, density, colormap='jet', mode='2dvertex')
mlab.outline(extent=[
-3 * a.std(), 3 * a.std(), -3 * b.std(), 3 * b.std(), -3 * c.std(),
3 * c.std()
],
line_width=2)
Y = np.c_[a, b, c]
U, pca_score, V = np.linalg.svd(Y, full_matrices=False)
x_pca_axis, y_pca_axis, z_pca_axis = V.T * pca_score / pca_score.min()
mlab.view(-20.8, 83, 9, [0.18, 0.2, -0.24])
#mlab.savefig('pca_3d.jpg')
mlab.quiver3d(0.1 * x_pca_axis,
0.1 * y_pca_axis,
0.1 * z_pca_axis,
2 * x_pca_axis,
2 * y_pca_axis,
2 * z_pca_axis,
scene.scene.background = (1, 1, 1) p = vis.plot_prism_mesh(res, style='surface', xy2ne=True) p.actor.property.line_width = 2 mlab.get_engine().scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.data_range = [0,1200] p = vis.plot_prism_mesh(seeds, style='surface', xy2ne=True) p.actor.property.line_width = 2 mlab.get_engine().scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.data_range = [0,1200] p = vis.plot_prism_mesh(model, style='surface', xy2ne=True) p.actor.property.line_width = 2 p = vis.plot_prism_mesh(mesh.vfilter(model,1100,1201), style='wireframe', xy2ne=True) p.actor.property.color = (0,0,0) p.actor.property.line_width = 3 mlab.outline(color=(0,0,0), extent=extent) a = mlab.axes(p, nb_labels=5, extent=extent, ranges=ranges, color=(0,0,0)) a.label_text_property.color = (0,0,0) a.title_text_property.color = (0,0,0) a.property.line_width = 1 a.axes.label_format = "%-#.1f" a.axes.x_label, a.axes.y_label, a.axes.z_label = "Y (km)", "X (km)", "Z (km)" view.bbox(mlab) view.set(scene) mlab.show()
cat1 = cat(x, y, 1)
cat2 = cat(x, y, 2)
cat3 = cat(x, y, 3)
# The cats lie in a [0, 1] interval, with .5 being the assymptotique
# value. We want to reposition this value to 0, so as to put it in the
# center of our extents.
cat1 -= 0.5
cat2 -= 0.5
cat3 -= 0.5
cat1_extent = (-14,-6, -4,4, 0,5)
surf_cat1 = mlab.surf(x-10, y, cat1, colormap='Spectral', warp_scale=5,
extent=cat1_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat1, color=(.7, .7, .7))
mlab.axes(surf_cat1, color=(.7, .7, .7), extent=cat1_extent,
ranges=(0,1, 0,1, 0,1), xlabel='', ylabel='',
zlabel='Probability',
x_axis_visibility=False, z_axis_visibility=False)
mlab.text(-18, -4, '1 photon', z=-4, width=0.13)
cat2_extent = (-4,4, -4,4, 0,5)
surf_cat2 = mlab.surf(x, y, cat2, colormap='Spectral', warp_scale=5,
extent=cat2_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat2, color=(0.7, .7, .7), extent=cat2_extent)
mlab.text(-4, -3, '2 photons', z=-4, width=0.14)
cat3_extent = (6,14, -4,4, 0,5)
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) pl = mlab.surf(x, y, z, warp_scale='auto') mlab.axes(xlabel='x', ylabel='y', zlabel='z') mlab.outline(pl)
s2 = mlab.pipeline.image_plane_widget(p,
plane_orientation='y_axes',
slice_index=ymax,
vmin = -60,
vmax = 10
)
s3 = mlab.pipeline.image_plane_widget( p,
plane_orientation='z_axes',
slice_index=zmax,
vmin = -60,
vmax = 10
)
mlab.scalarbar(s,orientation='vertical',nb_labels=4,label_fmt='%.3f')
mlab.outline(color=(1,1,1))
filename = str('png/2F_%04d' % i) + '.png'
mlab.savefig(filename)
for i in range(int(tstep/round(max(t)/len(t))),len(t),int(tstep/round(max(t)/len(t)))) :
#
# The next four lines are just like MATLAB.
#
p.mlab_source.scalars = Y[...,i]
titl.text=str("Potential at time: %.0f ms."% t[i])
filename = str('png/2F_%04d' % i) + '.png'
mlab.savefig(filename)
if showbar:
pbar.update(i)
pdf_z = pdf(5*z)
density *= pdf_z
a = x+y
b = 2*y
c = a-b+z
norm = np.sqrt(a.var() + b.var())
a /= norm
b /= norm
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
mlab.clf()
pts = mlab.points3d(a, b, c, density, colormap='jet', mode='2dvertex')
mlab.outline(extent=[-3*a.std(), 3*a.std(), -3*b.std(), 3*b.std(),
-3*c.std(), 3*c.std()], line_width=2)
Y = np.c_[a, b, c]
U, pca_score, V = np.linalg.svd(Y, full_matrices=False)
x_pca_axis, y_pca_axis, z_pca_axis = V.T*pca_score/pca_score.min()
mlab.view(-20.8, 83, 9, [0.18, 0.2, -0.24])
#mlab.savefig('pca_3d.jpg')
mlab.quiver3d(0.1*x_pca_axis, 0.1*y_pca_axis, 0.1*z_pca_axis,
2*x_pca_axis, 2*y_pca_axis, 2*z_pca_axis,
color=(0.6, 0, 0), line_width=2)
x_pca_axis, y_pca_axis, z_pca_axis = 3*V.T
x_pca_plane = np.r_[x_pca_axis[:2], - x_pca_axis[1::-1]]
y_pca_plane = np.r_[y_pca_axis[:2], - y_pca_axis[1::-1]]
z_pca_plane = np.r_[z_pca_axis[:2], - z_pca_axis[1::-1]]
y1, y2 = 0, 3000 z1, z2 = 0, 3000 extent = [x1, x2, y1, y2, -z2, -z1] # Plot the adjusted model plus the skeleton of the synthetic model fig = mlab.figure(size=(600,730)) fig.scene.background = (1, 1, 1) p = vis.plot_prism_mesh(mesh.vfilter(res,900,2001), style='surface', opacity=1) mlab.get_engine().scenes[0].children[-1].children[0].children[0].children[0].scalar_lut_manager.data_range = [0,2000] #a = mlab.axes(p, nb_labels=0, extent=extent, color=(0,0,0)) #a.label_text_property.color = (0,0,0) #a.title_text_property.color = (0,0,0) #a.axes.label_format = "" #a.axes.x_label, a.axes.y_label, a.axes.z_label = "", "", "" #a.property.line_width = 1 mlab.outline(p, extent=extent, color=(0,0,0)) scene = fig scene.scene.camera.position = [5617.6246210610589, 9378.6744914189112, 1832.0425527256102] scene.scene.camera.focal_point = [1435.8921050117997, 1598.1461572098237, -1715.9026272606379] scene.scene.camera.view_angle = 30.0 scene.scene.camera.view_up = [-0.18883236854009863, -0.3216128720649955, 0.92785101019163696] scene.scene.camera.clipping_range = [4531.9434654515926, 15755.396726380868] scene.scene.camera.compute_view_plane_normal() scene.scene.render() mlab.show()
scene = mlab.contour3d((fileh.root.Phi[2:-2, 2:-2, 2:-2, frame]**2),
contours=50,
extent=ext,
vmax=fmax,
vmin=fmin) #, colormap='ylgn')
# Cutplane
#E = gradient(fileh.root.Phi[:,:,:,frame])
#mlab.quiver3d(E[0], E[1], E[2])
#src = mlab.pipeline.vector_field(E[2], E[1], E[0])
#mlab.pipeline.vectors(src, mask_points=20, scale_factor=3.)
#mlab.pipeline.vector_cut_plane(src, mask_points=1, scale_factor=5)
#src = mlab.pipeline.vector_field(E[0], E[1], E[2])
#magnitude = mlab.pipeline.extract_vector_norm(src)
#flow = mlab.pipeline.streamline(magnitude, seedtype='plane', seed_visible=False, seed_scale=1.0, seed_resolution=100, linetype='line',integration_direction='both')
#$mlab.axes(color=(0.0,0.0,0.),extent=ext,xlabel='X',ylabel='Y',zlabel='Z')
#source = mlab.pipeline.scalar_field(fileh.root.Phi[:,:,:,frame]**2)
#vol = mlab.pipeline.volume(source)
#a = array(gradient(fileh.root.Phi[:,:,:,frame]))**2
#mlab.quiver3d(a[0],a[1],a[2])
# mlab.view(azimuth=angle%360, distance=128.0)
mlab.view(azimuth=angle % 360, distance=10.0)
angle += 1.0
mlab.colorbar(orientation='vertical', label_fmt="%.2e", nb_labels=5)
mlab.outline()
mlab.savefig("Phi3D_" + filename + "_" + string.zfill(frame, 4) +
".jpg") #, size=(600,600))
print "[", string.zfill(frame, 4), "/", len(fileh.root.Time[:]), "]"
#mencoder "mf://*.jpg" -mf fps=10 -ovc lavc -o mymovie.av
mlab.clf()
figure.scene.disable_render = True
# Creates two set of points using mlab.points3d: red point and
# white points
x1, y1, z1 = np.random.random((3, 10))
red_glyphs = mlab.points3d(x1, y1, z1, color=(1, 0, 0),
resolution=20)
x2, y2, z2 = np.random.random((3, 10))
white_glyphs = mlab.points3d(x2, y2, z2, color=(0.9, 0.9, 0.9),
resolution=20)
# Add an outline to show the selected point and center it on the first
# data point.
outline = mlab.outline(line_width=3)
outline.outline_mode = 'cornered'
outline.bounds = (x1[0]-0.1, x1[0]+0.1,
y1[0]-0.1, y1[0]+0.1,
z1[0]-0.1, z1[0]+0.1)
# Every object has been created, we can reenable the rendering.
figure.scene.disable_render = False
################################################################################
# Here, we grab the points describing the individual glyph, to figure
# out how many points are in an individual glyph.
glyph_points = red_glyphs.glyph.glyph_source.glyph_source.output.points.to_array()