def show(self, sln, show=True, lib="mpl", notebook=False, filename="a.png",
**options):
"""
Shows the solution.
show ... should it actually plot the window? Set to False in tests.
lib .... which library to use for the plotting? either "mpl" or "mayavi"
notebook ... are we running inside Sage notebook? If True, just save
the image to a.png
filename ... the name of the filename if we are saving the image (e.g.
notebook == False)
"""
self._lib = lib
self._notebook = notebook
if lib == "mpl":
plot_sln_mpl(sln, **options)
import pylab
if show:
if notebook:
pylab.savefig(filename)
else:
pylab.ion()
pylab.draw()
pylab.ioff()
elif lib == "mayavi":
plot_sln_mayavi(sln, notebook=notebook)
from enthought.mayavi import mlab
if show:
if notebook:
mlab.savefig(filename)
else:
mlab.show()
else:
raise NotImplementedError("Unknown library '%s'" % lib)
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 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 main(argv=None):
"""docstring for main"""
from enthought.mayavi import mlab
if argv is None:
argv = sys.argv
x1 = x2 = y1 = y2 = 0
fname = ''
try:
fname = argv[1]
x1, x2, y1, y2 = [int(i) for i in argv[2:]]
except:
print('Usage: {0} fname x1 x2 y1 y2'.format(argv[0]))
print(x1, x2, y1, y2)
ds = gdal.Open(fname)
band = ds.GetRasterBand(1)
STORED_VALUE = band.ReadAsArray(x1, y1, x2 - x1, y2 - y1)
ds = 0
# PDS label infos:
SCALING_FACTOR = 0.25
OFFSET = -8000
topo = (STORED_VALUE * SCALING_FACTOR) + OFFSET
mlab.surf(topo, warp_scale=1 / 115., vmin=1700)
mlab.colorbar(orientation='vertical',
title='Height [m]',
label_fmt='%4.0f')
mlab.show()
def etest(self):
from enthought.mayavi import mlab
from numpy import pi, sin, cos, exp, arange, array
ni=10
dr, dphi, dtheta = 1.0/ni, 2*pi/ni, pi/ni
rlin = arange(0.0,1.0+dr,dr)
plin = arange(0.0,2*pi+dphi,dphi)
tlin = arange(0.0,pi+dtheta,dtheta)
r,phi,theta = ndgrid(rlin,plin,tlin)
a=1
fr = .5*exp(-r/a)*(cos(2*pi*r/a)+1.0)
fp = (1.0/6.0)*(cos(3.0*phi)+5.0)
ft = (1.0/6.0)*(cos(10.0*theta)+5.0)
f = fr*fp*ft
x = r*sin(theta)*cos(phi)
y = r*sin(theta)*sin(phi)
z = r*cos(theta)
#mayavi
#mlab.contour3d(x,y,z,f)
#mlab.contour3d(r,phi,theta,f)
i=7
#mlab.mesh(x[i],y[i],z[i],scalars=f[i])
mlab.mesh(f[i]*x[i],f[i]*y[i],f[i]*z[i],scalars=f[i])
mlab.show()
return
def _plot_fired( self ):
X = self.info_shell.X[:, 0]
Y = self.info_shell.Y[:, 0]
Z = self.info_shell.Z[:, 0]
plot_col = getattr( self, self.plot_column )[:, 0]
mlab.points3d( X, Y, Z, plot_col )
mlab.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 plot_atoms(atoms):
for atom in atoms:
pos = atom.position
mlab.points3d(
[pos[0]],
[pos[1]],
[pos[2]],
scale_factor=4,
resolution=20,
color=(1, 0, 0), # this should get species specifuc
scale_mode="none",
)
(u0, u1, u2) = atoms.get_cell()
origin = np.array([0.0, 0.0, 0.0])
plot_cylinder(origin, u0)
plot_cylinder(origin, u1)
plot_cylinder(origin, u2)
plot_cylinder(u0, u0 + u1)
plot_cylinder(u0, u0 + u2)
plot_cylinder(u1, u1 + u0)
plot_cylinder(u1, u1 + u2)
plot_cylinder(u2, u2 + u0)
plot_cylinder(u2, u2 + u1)
plot_cylinder(u0 + u1, u0 + u1 + u2)
plot_cylinder(u1 + u2, u0 + u1 + u2)
plot_cylinder(u0 + u2, u0 + u1 + u2)
mlab.show()
def initialize_rope_from_cloud(xyzs, plotting=False):
xyzs = xyzs.reshape(-1,3)
if len(xyzs) > 500: xyzs = xyzs[::len(xyzs)//500]
pdists = ssd.squareform(ssd.pdist(xyzs,'sqeuclidean'))
G = nx.Graph()
for (i_from, row) in enumerate(pdists):
to_inds = np.flatnonzero(row[:i_from] < MAX_DIST**2)
for i_to in to_inds:
G.add_edge(i_from, i_to, weight = pdists[i_from, i_to])
A = nx.floyd_warshall_numpy(G)
A[np.isinf(A)] = 0
(i_from_long, i_to_long) = np.unravel_index(A.argmax(), A.shape)
nodes = G.nodes();
path = nx.shortest_path(G, source=nodes[i_from_long], target=nodes[i_to_long])
xyz_path = xyzs[path,:]
xyzs_unif = curves.unif_resample(xyz_path,N_CTRL_PTS,tol=.005)
labels = np.ones(len(xyzs_unif)-1,'int')
labels[[0,-1]] = 2
if plotting:
import enthought.mayavi.mlab as mlab
mlab.plot3d(*xyzs_unif.T, tube_radius=.001)
mlab.points3d(*xyzs.T, scale_factor=.01)
mlab.show()
return xyzs_unif, labels
def isosurface(self):
from enthought.mayavi import mlab
npoints,ndim = self.points.shape
dimensions = array([20,20,20])
x = zeros(dimensions)
y = zeros(dimensions)
z = zeros(dimensions)
s = zeros(dimensions)
ipoint = 0
for i in range(dimensions[0]):
for j in range(dimensions[1]):
for k in range(dimensions[2]):
r = self.points[ipoint,:]
u = dot(self.axinv,r)
#u=r
x[i,j,k] = u[0]
y[i,j,k] = u[1]
z[i,j,k] = u[2]
s[i,j,k] = self.E[ipoint]
ipoint+=1
#end for
#end for
#end for
mlab.contour3d(x,y,z,s)
mlab.show()
return
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 show_3d(self):
"""SHOW - Use mayavi2 to visualize point cloud.
Usage: DepthImage.show()
"""
from enthought.mayavi import mlab
# Get 3D points
pts = self.tocloud()
# I want at most 50K points
stride = 1 + pts.shape[1]/50000
pts = np.c_[pts[:,::stride], pts[:,::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 main(argv=None):
"""docstring for main"""
from enthought.mayavi import mlab
if argv is None:
argv = sys.argv
x1 = x2 = y1 = y2 = 0
fname = ""
try:
fname = argv[1]
x1, x2, y1, y2 = [int(i) for i in argv[2:]]
except:
print("Usage: {0} fname x1 x2 y1 y2".format(argv[0]))
print(x1, x2, y1, y2)
ds = gdal.Open(fname)
band = ds.GetRasterBand(1)
STORED_VALUE = band.ReadAsArray(x1, y1, x2 - x1, y2 - y1)
ds = 0
# PDS label infos:
SCALING_FACTOR = 0.25
OFFSET = -8000
topo = (STORED_VALUE * SCALING_FACTOR) + OFFSET
mlab.surf(topo, warp_scale=1 / 115.0, vmin=1700)
mlab.colorbar(orientation="vertical",
title="Height [m]",
label_fmt="%4.0f")
mlab.show()
def display_points_func():
from enthought.mayavi import mlab
scf = shelf["scf"]
mo = MolecularOrbital(scf, 0)
x, y, z = make_grid(np.mgrid)
obj = mlab.contour3d(x, y, z, mo(x, y, z))
mlab.show()
def display_points(val=0.3):
scalars = shelf["scalars"]
scalars = generate_points()
shelf["scalars"] = scalars
min = scalars.min()
max = scalars.max()
from enthought.mayavi import mlab
x, y, z = make_grid(np.mgrid)
contours = list(np.arange(-val, val, 0.02))
obj = mlab.contour3d(x,
y,
z,
scalars,
contours=contours,
opacity=0.3,
colormap="PRGn",
transparent=True)
display_atoms()
# source = mlab.pipeline.scalar_field(x,y,z,scalars)
# vol = mlab.pipeline.volume(source, vmin=min+0.65*(max-min),
# vmax=min+0.9*(max-min))
# mlab.view(132, 54, 45, [21, 20, 21.5])
mlab.show()
def display_points_func():
from enthought.mayavi import mlab
scf = shelf["scf"]
mo = MolecularOrbital(scf,0)
x,y,z = make_grid(np.mgrid)
obj = mlab.contour3d(x,y,z,mo(x,y,z) )
mlab.show()
def _viztest3d(self, extra_bb):
"""
3D vizualization of CPRep for obj
"""
from enthought.mayavi import mlab
import numpy as np
f = mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1), size=(640,640))
mlab.clf()
if self._hasParam:
L = self.ParamGrid()
x,y,z = L
#print x,y,z
#s = mlab.mesh(xb, yb, zb, scalars=real((Eplot_bulb*E*uxx).reshape(xb.shape)))
# TODO: mayavi bug, opacity<1
s = mlab.mesh(x, y, z, scalars=z, opacity=1.0)
a,b = self.getBB()
ll = (b+a)/2 - (extra_bb)*(b-a)/2
rr = (b+a)/2 + (extra_bb)*(b-a)/2
for i in range(0,40):
x = np.random.uniform( ll, rr )
cp,dist,bdy,other = self.cp(x)
colt = (0.5,0.5,0.5)
op = 0.3
l = mlab.plot3d([x[0],cp[0]], [x[1],cp[1]], [x[2], cp[2]], color=colt, opacity=op, tube_radius=0.1)
#mlab.title('3d viz test')
mlab.show()
def mtest(self):
from enthought.mayavi import mlab
# Create the data.
from numpy import pi, sin, cos, mgrid, arange, array
ni = 100.0
dtheta, dphi = pi/ni, pi/ni
#[theta,phi] = mgrid[0:pi+dtheta:dtheta,0:2*pi+dphi:dphi]
#tlin = arange(0,pi+dtheta,dtheta)
#plin = arange(0,2*pi+dphi,dphi)
tlin = pi*array([0,.12,.2,.31,.43,.56,.63,.75,.87,.92,1])
plin = 2*pi*array([0,.11,.22,.34,.42,.58,.66,.74,.85,.97,1])
theta,phi = ndgrid(tlin,plin)
fp = (1.0/6.0)*(cos(3.0*phi)+5.0)
ft = (1.0/6.0)*(cos(10.0*theta)+5.0)
r = fp*ft
x = r*sin(theta)*cos(phi)
y = r*sin(theta)*sin(phi)
z = r*cos(theta)
# View it.
s = mlab.mesh(x, y, z, scalars=r)
mlab.show()
return
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 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_atoms(atoms):
for atom in atoms:
pos = atom.get_position()
mlab.points3d(
[pos[0]],
[pos[1]],
[pos[2]],
scale_factor=4,
resolution=20,
color=(1, 0, 0), #this should get species specifuc
scale_mode='none')
(u0, u1, u2) = atoms.get_cell()
origin = np.array([0.0, 0.0, 0.0])
plot_cylinder(origin, u0)
plot_cylinder(origin, u1)
plot_cylinder(origin, u2)
plot_cylinder(u0, u0 + u1)
plot_cylinder(u0, u0 + u2)
plot_cylinder(u1, u1 + u0)
plot_cylinder(u1, u1 + u2)
plot_cylinder(u2, u2 + u0)
plot_cylinder(u2, u2 + u1)
plot_cylinder(u0 + u1, u0 + u1 + u2)
plot_cylinder(u1 + u2, u0 + u1 + u2)
plot_cylinder(u0 + u2, u0 + u1 + u2)
mlab.show()
def plot_normals(pts, normals, curvature=None):
x = pts[0, :].A1
y = pts[1, :].A1
z = pts[2, :].A1
u = normals[0, :].A1
v = normals[1, :].A1
w = normals[2, :].A1
if curvature != None:
#mlab.points3d(x,y,z,curvature,mode='point',scale_factor=1.0)
mlab.points3d(x,
y,
z,
curvature,
mode='sphere',
scale_factor=0.1,
mask_points=1)
mlab.colorbar()
else:
mlab.points3d(x, y, z, mode='point')
mlab.quiver3d(x, y, z, u, v, w, mask_points=16, scale_factor=0.1)
# mlab.axes()
mlab.show()
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 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 testVerticalSurface():
import pylab, numpy
pylab.clf()
no_of_phenotypes = 5
x, y = numpy.mgrid[
0:2 * no_of_phenotypes:1, 0:10:
1] #added a gap of 1 column between two phenotypes. one phenotype occupies two rows & two columns.
#remove the gap in x & y
needed_index_ls = [0, 5]
#for i in [0, 5]:
# for j in range(no_of_phenotypes):
# needed_index_ls.append(no_of_phenotypes*i+j)
#for i in range(0, no_of_phenotypes):
# needed_index_ls.append(2*i)
#needed_index_ls.append(3*i+1)
#y[3*i+1][1]=2
x = x[:, needed_index_ls]
y = y[:, needed_index_ls]
enrichment_matrix = numpy.ones(x.shape, numpy.float)
enrichment_matrix[:, :] = 10
enrichment_matrix[0, 0] = 3
from enthought.mayavi import mlab
mlab.clf()
#from palos.yh_mayavi import customBarchart
bar = customBarchart(x,
y,
enrichment_matrix,
x_scale=0.9,
y_scale=4.5,
opacity=1,
mode='cube',
color=(0, 1, 0),
scale_factor=1.0)
"""
#mlab.ylabel("KW")
#mlab.xlabel("Emma")
#mlab.zlabel("Enrichment Ratio")
from palos.DrawMatrix import get_font
font = get_font()
for i in range(len(xlabel_ls)):
label = xlabel_ls[i]
char_width, char_height = font.getsize(label) #W is the the biggest(widest)
mlab.text(2*i, 0, label, z=0, width=char_width/1500.) #min(0.0075*len(label), 0.04))
"""
s = numpy.zeros(x.shape, numpy.int)
#s[0,1]=0.5
surf = mlab.surf(x,
y,
s,
opacity=0.6,
extent=[-1, 2 * no_of_phenotypes, -1, 10, 0.0, 0.0])
mlab.show()
def mlab3Dview():
e_arr = orthogonalize([x, w])
n_e_arr = [ e / np.max(np.fabs(e)) for e in e_arr ]
n_mu_q_arr = mu_q_arr / np.max(np.fabs(mu_q_arr))
m.surf(n_e_arr[0], n_e_arr[1], n_mu_q_arr)
m.show()
def downsample_cb(cloud_down):
print "downsample_cb got called."
pts = ros_pts_to_np(cloud_down.pts)
x = pts[0, :].A1
y = pts[1, :].A1
z = pts[2, :].A1
mlab.points3d(x, y, z, mode="point")
mlab.show()
def downsample_cb(cloud_down):
print 'downsample_cb got called.'
pts = ros_pts_to_np(cloud_down.pts)
x = pts[0, :].A1
y = pts[1, :].A1
z = pts[2, :].A1
mlab.points3d(x, y, z, mode='point')
mlab.show()
def plot(self, lib="mayavi", show=True):
if lib == "mayavi":
self._plot_mayavi_()
if show:
from enthought.mayavi import mlab
mlab.show()
else:
raise NotImplementedError()
def _viztest3d(self):
"""
3D vizualization of CPRep for obj with boundary
"""
from enthought.mayavi import mlab
import numpy as np
f = mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1), size=(500,700))
mlab.clf()
#s = mlab.mesh(xb, yb, zb, scalars=real(evec_b.reshape(xb.shape)))
#l = mlab.plot3d(xs, ys, zs, real(evec_s))
#mlab.title(str(ii) + ' ew=' + str(eval), size=0.2)
#mlab.show()
#mlab.savefig('b_horn' + str(ii) + '_' + str(eval) + '.png')
#s = mlab.mesh(xb, yb, zb, scalars=real((Eplot_bulb*E*uxx).reshape(xb.shape)))
#l = mlab.plot3d(xs, ys, zs, real((Eplot_stem*E*uxx).reshape(xs.shape)))
#(x1,y1),(x2,y2),(x3,y3) = mesh2d(resolution=3)
# TODO: is has param:
if self._hasParam:
L = self.ParamGrid()
x,y,z = L
#print x,y,z
#s = mlab.mesh(xb, yb, zb, scalars=real((Eplot_bulb*E*uxx).reshape(xb.shape)))
# TODO: mayavi bug, opacity<1
s = mlab.mesh(x, y, z, scalars=z, opacity=1.0)
a,b = self.getBB()
ll = (b+a)/2 - (extra_bb)*(b-a)/2
rr = (b+a)/2 + (extra_bb)*(b-a)/2
for i in range(0,200):
x = np.random.uniform( ll, rr )
cp,dist,bdy,other = self.cp(x)
drawplot = False
if bdy==1:
if (np.random.random(1) < 1.0):
drawplot = True
col = 'g'
colt = (0.5,1,.2)
op = 0.9
elif bdy==2:
if (np.random.random(1) < 1.0):
drawplot = True
col = 'b'
colt = (.2,.5,1)
op = 0.9
else:
if ( (np.random.random(1) < 0.5) and (dist <= max((b-a)/10)) ):
drawplot = True
col = 'k'
colt = (0.5,0.5,0.5)
op = 0.3
if drawplot:
l = mlab.plot3d([x[0],cp[0]], [x[1],cp[1]], [x[2], cp[2]], color=colt, opacity=op)#, tube_radius=0.1)
#mlab.title('3d viz test')
mlab.show()
def show(self, sln, show=True, lib="mayavi", notebook=None,
filename="scalar.png", **options):
"""
Shows the solution.
show ... should it actually plot the window? Set to False in tests.
lib .... which library to use for the plotting? either "mpl" or "mayavi"
notebook ... are we running inside Sage notebook? If True, just save
the image to a.png
filename ... the name of the filename if we are saving the image (e.g.
notebook == False)
Example:
>>> 1 + 1
2
>>> 1 + 2
3
"""
if notebook is None:
try:
from sagenb.misc.support import EMBEDDED_MODE
except ImportError:
EMBEDDED_MODE = False
notebook = EMBEDDED_MODE
self._lib = lib
self._notebook = notebook
if lib == "glut":
if self._glut_view is None:
from _hermes2d import ScalarView
self._glut_view = ScalarView(self._name)
self._glut_view.show(sln)
elif lib == "mpl":
plot_sln_mpl(sln, **options)
import pylab
if show:
if notebook:
pylab.savefig(filename)
else:
pylab.ion()
pylab.draw()
pylab.ioff()
elif lib == "mayavi":
plot_sln_mayavi(sln, notebook=notebook)
from enthought.mayavi import mlab
if show:
engine = mlab.get_engine()
image = engine.current_scene
image.scene.background = (1.0, 1.0, 1.0)
image.scene.foreground = (0.0, 0.0, 0.0)
#mlab.colorbar(orientation="vertical")
if notebook:
mlab.savefig(filename)
else:
mlab.show()
else:
raise NotImplementedError("Unknown library '%s'" % lib)
def demo2():
from enthought.mayavi import mlab
pts = generate_pts()
direction, magnitudes = gaussian_curvature(pts)
print 'eigen values', magnitudes.T
mlab.points3d(pts[0,:].A1, pts[1,:].A1, pts[2,:].A1, mode='sphere', scale_factor=.05)
plot_axis(0,0,0, direction)
plot_axis(2,0,0, np.eye(3))
mlab.show()
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 demo1():
from enthought.mayavi import mlab
pts = generate_pts()
directions, magnitudes = gaussian_curvature(pts)
print directions.T.A[0].tolist()
print magnitudes.tolist()
mlab.points3d(pts[0,:].A1, pts[1,:].A1, pts[2,:].A1, mode='sphere', scale_factor=.05)
plot_axis(0,0,0, directions)
plot_axis(2,0,0, np.eye(3))
mlab.show()
def test_segmentation():
rospy.wait_for_service('segment_pointcloud')
try:
pts3d, intensities, labels, image, image_angle, polygon = get_test_data(
)
ROS_pointcloud = convert_pointcloud_to_ROS(pts3d, intensities, labels)
ROS_image = convert_cvimage_to_ROS(image)
imageSize = cv.cvGetSize(image)
ROS_polygon = convert_polygon_to_ROS(polygon)
segment_pointcloud = rospy.ServiceProxy('segment_pointcloud',
Segmentation)
request = SegmentationRequest()
request.imageWidth = 41
request.pointcloud = ROS_pointcloud
request.image = ROS_image
request.imageWidth = imageSize.width
request.imageHeight = imageSize.height
request.imageAngle = image_angle
request.regionOfInterest2D = ROS_polygon
request.laserHeightAboveGround = 1.32
request.numberOfPointsToClassify = 1000 #-1 == all
response = segment_pointcloud(request)
pts3d_bound, intensities, labels = convert_ROS_pointcloud_to_pointcloud(
response.pointcloud)
cfg = configuration.configuration('../data/ROS_test_client/',
'dummyScanner')
pc = processor.processor(cfg)
pc.scan_dataset = scan_dataset()
pc.scan_dataset.image_artag_filename = ''
pc.scan_dataset.table_plane_translation = np.matrix([0, 0, 0]).T
pc.scan_dataset.ground_plane_translation = np.matrix(
[0, 0, request.laserHeightAboveGround]).T
pc.scan_dataset.ground_plane_rotation = ''
pc.scan_dataset.is_labeled = True
pc.scan_dataset.id = 'ROStest'
pc.image_angle = ''
pc.pts3d_bound = pts3d_bound
pc.map_polys = labels
pc.scan_dataset.ground_plane_normal = np.matrix([0., 0., 1.]).T
from enthought.mayavi import mlab
pc.display_3d('labels')
mlab.show()
return response
except rospy.ServiceException, e:
print "Service call failed: %s" % e
def plot_3d(lats,lons,depth,vs,runlat,runlon,vtkname='rayleigh_c_u.vtk',
annotate_depth=False,coastline=False,annotate_lat=True,annotate_lon=True):
"""
plot 3d results using mayavi
"""
if coastline:
plot_canada_map()
sgrid = get_tvtk_grid(lats,lons,depth,vs)
d = mlab.pipeline.add_dataset(sgrid)
#sf = mlab.pipeline.surface(d)
#gx = mlab.pipeline.grid_plane(d)
#gx.grid_plane.axis = 'x'
gy = mlab.pipeline.grid_plane(d)
gy.grid_plane.axis = 'x'
gy.module_manager.scalar_lut_manager.show_scalar_bar = True
gy.module_manager.scalar_lut_manager.lut_mode = 'jet'
gy.module_manager.scalar_lut_manager.data_range = np.array([ 2. , 4.8])
gy.module_manager.scalar_lut_manager.scalar_bar_representation.maximum_size = np.array([100000, 100000])
gy.module_manager.scalar_lut_manager.scalar_bar_representation.minimum_size = np.array([1, 1])
gy.module_manager.scalar_lut_manager.scalar_bar_representation.position2 = np.array([ 0.08796009, 0.56264591])
gy.module_manager.scalar_lut_manager.scalar_bar_representation.position = np.array([ 0.03396896, 0.39182879])
gy.actor.mapper.progress = 1.0
gy.actor.mapper.scalar_range = np.array([ 0., 1.])
gy.actor.mapper.scalar_visibility = True
gy.actor.property.representation = 'surface'
gy.grid_plane.position = 6
#gz = mlab.pipeline.grid_plane(d)
#gz.grid_plane.axis = 'z'
if annotate_lat:
for lat in runlat:
x,y,z = convert_pt(lat,-58.,10.)
txt = mlab.text3d(x,y,z,'%d'%(lat),color=(0,0,0),line_width=10.0)
txt.scale = [20,20,20]
if annotate_lon:
for lon in runlon[1::]:
x,y,z = convert_pt(49.,lon,10.)
txt = mlab.text3d(x,y,z,'%d'%(lon),color=(0,0,0),line_width=10.0)
txt.scale = [20,20,20]
if annotate_depth:
for dp in [-10,-40,-80,-120]:
x,y,z = convert_pt(49,-68.,dp)
txt = mlab.text3d(x,y,z,'%d km'%(dp),color=(0,0,0),line_width=10.0)
txt.scale = [20,20,20]
### Include 3D screenshot in matplotlib
#arr = mlab.screenshot()
#import pylab as pl
#pl.imshow(arr)
#pl.show()
mlab.text(0.76,0.86,'49N',width=0.1)
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 Analyze_Phase(name):
#parameters of the binary liquid model
k = 0.04
a = 0.04
phase_numpy, velx_numpy, vely_numpy, velz_numpy = Read_Phase(name)
Show_Phase(phase_numpy, velx_numpy, vely_numpy, velz_numpy)
dims = phase_numpy.shape
print dims
center = phase_numpy[dims[0] / 2, dims[1] / 2, :]
z1 = numpy.min(numpy.where(center < 0.0))
z2 = numpy.max(numpy.where(center < 0.0))
if z1 == 0:
z2 = numpy.min(numpy.where(center > 0.0)) + dims[2]
z1 = numpy.max(numpy.where(center > 0.0))
pylab.figure()
pylab.plot(center)
print z1, z2
mid = ((z1 + z2) / 2) % dims[2]
print mid
slice = phase_numpy[:, :, mid]
shape = slice.shape
prof_axis = slice[shape[0] / 2, :]
prof_diag = numpy.diag(slice)
#axis_zero=(1.0-Get_Zero(prof_axis)/(0.5*(shape[1]-2)))
#diag_zero=(math.sqrt(2.0)-math.sqrt(2.0)*Get_Zero(prof_diag)/(0.5*(shape[1]-2)))
print "Velx", velx_numpy[dims[0] / 2, dims[1] / 2, mid]
print "Vely", vely_numpy[dims[0] / 2, dims[1] / 2, mid]
print "Velz", velz_numpy[dims[0] / 2, dims[1] / 2, mid]
#Calculation of the capillary number
capillary = velx_numpy[dims[0] / 2, dims[1] / 2,
mid] * (2.0 / 3.0) / math.sqrt(8.0 * k * a / 9.0)
# pylab.figure()
# pylab.imshow(phase_numpy[:,:,mid])
# pylab.figure()
# pylab.plot(prof_axis)
# pylab.figure()
# pylab.plot(prof_diag)
mlab.show()
#print axis_zero,diag_zero
print "Capillary=", capillary
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 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 start_vtk(component):
f = mlab.figure(size=(700,500))
m = mlab.test_mesh()
scene = mlab.gcf().scene
render_window = scene.render_window
renderer = scene.renderer
rwi = scene.interactor
window = EnableVTKWindow(rwi, renderer,
component = component,
istyle_class = tvtk.InteractorStyleTrackballCamera,
bgcolor = "transparent",
event_passthrough = True,
)
mlab.show()
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 test_segmentation():
rospy.wait_for_service("segment_pointcloud")
try:
pts3d, intensities, labels, image, image_angle, polygon = get_test_data()
ROS_pointcloud = convert_pointcloud_to_ROS(pts3d, intensities, labels)
ROS_image = convert_cvimage_to_ROS(image)
imageSize = cv.cvGetSize(image)
ROS_polygon = convert_polygon_to_ROS(polygon)
segment_pointcloud = rospy.ServiceProxy("segment_pointcloud", Segmentation)
request = SegmentationRequest()
request.imageWidth = 41
request.pointcloud = ROS_pointcloud
request.image = ROS_image
request.imageWidth = imageSize.width
request.imageHeight = imageSize.height
request.imageAngle = image_angle
request.regionOfInterest2D = ROS_polygon
request.laserHeightAboveGround = 1.32
request.numberOfPointsToClassify = 1000 # -1 == all
response = segment_pointcloud(request)
pts3d_bound, intensities, labels = convert_ROS_pointcloud_to_pointcloud(response.pointcloud)
cfg = configuration.configuration("../data/ROS_test_client/", "dummyScanner")
pc = processor.processor(cfg)
pc.scan_dataset = scan_dataset()
pc.scan_dataset.image_artag_filename = ""
pc.scan_dataset.table_plane_translation = np.matrix([0, 0, 0]).T
pc.scan_dataset.ground_plane_translation = np.matrix([0, 0, request.laserHeightAboveGround]).T
pc.scan_dataset.ground_plane_rotation = ""
pc.scan_dataset.is_labeled = True
pc.scan_dataset.id = "ROStest"
pc.image_angle = ""
pc.pts3d_bound = pts3d_bound
pc.map_polys = labels
pc.scan_dataset.ground_plane_normal = np.matrix([0.0, 0.0, 1.0]).T
from enthought.mayavi import mlab
pc.display_3d("labels")
mlab.show()
return response
except rospy.ServiceException, e:
print "Service call failed: %s" % e
def initialize_rope(label_img, xyz,bgr, plotting=False):
# XXX that sucks
rope_mask = (label_img==1) | ndi.morphology.binary_dilation(label_img==2,np.ones((5,5)))
rope_mask = ndi.binary_opening(rope_mask, np.ones((3,3)))
rope_mask = ndi.binary_dilation(rope_mask, np.ones((15,15)))
skel = mip.skeletonize(rope_mask)
if plotting:
cv2.imshow('bgr',bgr.copy())
cv2.imshow('labels',label_img.astype('uint8')*50)
cv2.imshow("skel", skel.astype('uint8')*50)
cv2.imshow("rope_mask", rope_mask.astype('uint8')*50)
cv2.waitKey(5)
#start_end = (uv_start, uv_end) = get_cc_centers(label_img==2,2)
G = skel2graph(skel)
path2d = longest_shortest_path(G)
path3d = to3d(path2d,xyz)
xyzs_unif = curves.unif_resample(path3d,N_CTRL_PTS,tol=.0025)
#us,vs = xyz2uv(xyzs_unif).T
#labels = label_img[us,vs]
labels = np.ones(xyzs_unif.shape[0]-1,'int')
labels[0] = labels[-1] = 2
if plotting:
xs,ys,zs = xyzs_unif.T
us_skel, vs_skel = np.nonzero(skel)
xs_skel, ys_skel, zs_skel = to3d(np.c_[us_skel, vs_skel], xyz).T
import matplotlib.pyplot as plt, enthought.mayavi.mlab as mlab
mlab.plot3d(xs,ys,zs,np.arange(len(xs)),tube_radius=.0025, colormap='spectral')
mlab.points3d(xs_skel, ys_skel, zs_skel, scale_factor=.02, color=(1,1,1),opacity=.1)
for (i,path) in enumerate(path3d):
mlab.text3d(path[0,0],path[0,1],path[0,2],str(2*i),scale=.01,color=(0,0,0))
mlab.text3d(path[-1,0],path[-1,1],path[-1,2],str(2*i+1),scale=.01,color=(0,0,0))
mlab.show()
return xyzs_unif, labels
def read(name):
from enthought.tvtk.api import tvtk
from enthought.mayavi import mlab
import numpy
gridreader = tvtk.XMLStructuredGridReader()
gridreader.file_name = name
gridreader.update()
grid = gridreader.output
data = grid.point_data
points = grid.points
dims = grid.dimensions
dims = dims.tolist()
dims.reverse()
phase = numpy.array(data.get_array("Phase"))
velocity = numpy.array(data.get_array("Velocity"))
velx = velocity[:, 0]
vely = velocity[:, 1]
velz = velocity[:, 2]
phase_numpy = phase.reshape(dims)
velx_numpy = velx.reshape(dims)
vely_numpy = vely.reshape(dims)
velz_numpy = velz.reshape(dims)
fig = mlab.figure()
src = mlab.pipeline.scalar_field(phase_numpy)
mlab.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(z_min=1, z_max=dims[2] - 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 marsyasplay(sfname):
mng = marsyas.MarSystemManager()
net = mng.create("Series", "series")
net.addMarSystem(mng.create("SoundFileSource", "src"))
net.addMarSystem(mng.create("BinauralCARFAC", "carfac"))
net.updControl("SoundFileSource/src/mrs_string/filename",
marsyas.MarControlPtr.from_string(sfname))
outData = net.getControl("mrs_realvec/processedData")
data = np.zeros((96, 200, 1))
while net.getControl("SoundFileSource/src/mrs_bool/hasData").to_bool():
net.tick()
a = np.array(outData.to_realvec()).reshape((96, 200, 1))
data = np.append(data, a, axis=2)
obj = mlab.contour3d(data, contours=4, transparent=True)
mlab.show()
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 _plot3d(self, a, showCP, npoints):
"""
Internal plot function, use Spline.plot().
"""
if not isplot3d:
raise NotImplementedError('Lacking Mayavi to do the plotting.')
x = self(np.linspace(self.knots[2],self.knots[-3],npoints,endpoint=0))
k = self(np.hstack((self.knots[2:-3],self.knots[-3]-1e-15)))
ml.plot3d(x[:,a[0]],x[:,a[1]],x[:,a[2]],color=(.5,.2,.3))
ml.points3d(k[:,a[0]],k[:,a[1]],k[:,a[2]],
color=(.5,.2,.3),scale_factor=.3)
if showCP:
ml.plot3d(self.cp[:,a[0]], self.cp[:,a[1]],
self.cp[:,a[2]], color=(.2,.4,.4))
ml.points3d(self.cp[:,a[0]], self.cp[:,a[1]],
self.cp[:,a[2]], color=(.2,.4,.4), scale_factor=.3)
ml.show()
def Analyze_NoForce_Consequence():
ax_zeros = []
diag_zeros = []
capillaries = []
os.chdir("NoForce")
for i in range(1, 5):
print os.getcwd()
name = "steady" + str(2 * i) + "00000_0.vti"
print name
axis_zero, diag_zero, capillary = Analyze_Phase(name)
ax_zeros.append(axis_zero)
diag_zeros.append(diag_zero)
capillaries.append(capillary)
#Calculate the capillary number
os.chdir("..")
pylab.plot(capillaries, ax_zeros)
pylab.plot(capillaries, diag_zeros)
#numpy.savetxt("steady.txt",zip(capillaries,ax_zeros,diag_zeros))
pylab.show()
mlab.show()
