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 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 plot_sln_mayavi(x, y, mesh, sln_values, colorbar=False):
"""
Plot a solution using mayavi.
Example:
>>> from numpy import array
>>> from femhub.plot import plot_sln_mayavi
>>> f = plot_sln_mayavi([0, 1, 1], [0, 0, 1], [1, 2, 3])
>>> f.savefig("a.png")
"""
from enthought.mayavi import mlab
#mlab.options.offscreen = True
mlab.clf()
#mlab.options.show_scalar_bar = False
z = [0] * len(x)
mlab.triangular_mesh(x, y, z, mesh, scalars=sln_values)
engine = mlab.get_engine()
image = engine.current_scene
image.scene.background = (1.0, 1.0, 1.0)
image.scene.foreground = (0.0, 0.0, 0.0)
if colorbar:
mlab.colorbar(orientation="vertical")
mlab.view(0, 0)
return mlab
def show_votegrid(vg, color=(1, 0, 0), opacity=1):
from enthought.mayavi import mlab
mlab.clf()
x, y, z = np.nonzero(vg > 0)
if not color is None:
mlab.points3d(x,
y,
z,
opacity=opacity,
color=color,
scale_factor=1,
scale_mode='none',
mode='cube')
else:
mlab.points3d(x,
y,
z,
vg[vg > 0],
opacity=opacity,
scale_factor=1,
scale_mode='none',
mode='cube')
gridmin, gridmax = config.bounds
X, Y, Z = np.array(gridmax) - gridmin
#mlab.axes(extent=[0,0,0,X,Y,Z])
mlab.draw()
def plot(self):
"绘制场景"
# 产生三维网格
x, y, z = np.mgrid[
self.x0 : self.x1 : 1j * self.points,
self.y0 : self.y1 : 1j * self.points,
self.z0 : self.z1 : 1j * self.points,
]
# 根据函数计算标量场的值
scalars = eval(self.function)
mlab.clf() # 清空当前场景
# 绘制等值平面
g = mlab.contour3d(x, y, z, scalars, contours=8, transparent=True)
g.contour.auto_contours = self.autocontour
mlab.axes() # 添加坐标轴
# 添加一个X-Y的切面
s = mlab.pipeline.scalar_cut_plane(g)
cutpoint = (self.x0 + self.x1) / 2, (self.y0 + self.y1) / 2, (self.z0 + self.z1) / 2
s.implicit_plane.normal = (0, 0, 1) # x cut
s.implicit_plane.origin = cutpoint
self.g = g
self.scalars = scalars
# 计算标量场的值的范围
self.v0 = np.min(scalars)
self.v1 = np.max(scalars)
def plot(self):
"绘制场景"
# 产生三维网格
x, y, z = np.mgrid[self.x0:self.x1:1j * self.points,
self.y0:self.y1:1j * self.points,
self.z0:self.z1:1j * self.points]
# 根据函数计算标量场的值
scalars = eval(self.function)
mlab.clf() # 清空当前场景
# 绘制等值平面
g = mlab.contour3d(x, y, z, scalars, contours=8, transparent=True)
g.contour.auto_contours = self.autocontour
mlab.axes() # 添加坐标轴
# 添加一个X-Y的切面
s = mlab.pipeline.scalar_cut_plane(g)
cutpoint = (self.x0 + self.x1) / 2, (self.y0 + self.y1) / 2, (
self.z0 + self.z1) / 2
s.implicit_plane.normal = (0, 0, 1) # x cut
s.implicit_plane.origin = cutpoint
self.g = g
self.scalars = scalars
# 计算标量场的值的范围
self.v0 = np.min(scalars)
self.v1 = np.max(scalars)
def plot_orbits3d(self,xaxis='x',yaxis='y',clf=True,**kwargs):
"""
Plots a 3D plot of the particles and their orbits with mayavi.
:param bool clf: If True, clear figure before plotting.
kwargs are passed into :func:`enthough.mayavi.mlab.plot3d`.
:returns:
the result of :func:`enthough.mayavi.mlab.plot3d` and the result of
:func:`enthough.mayavi.mlab.quiver3d`
"""
from enthought.mayavi import mlab as M
if clf:
M.clf()
orbarr = self.getOrbits()[1].transpose((1,2,0))
t = self._orbt
pntress = []
for o in orbarr:
pntress.append(M.plot3d(o[0],o[1],o[2],t,**kwargs))
xp,yp,zp = self.particles.position.T
vx,vy,vz = self.particles.velocity.T
quiverres = M.quiver3d(xp,yp,zp,vx,vy,vz)
return pntress,quiverres
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 evolve_visual3d(msnake, levelset=None, num_iters=20):
"""
Visual evolution of a three-dimensional morphological snake.
Parameters
----------
msnake : MorphGAC or MorphACWE instance
The morphological snake solver.
levelset : array-like, optional
If given, the levelset of the solver is initialized to this. If not
given, the evolution will use the levelset already set in msnake.
num_iters : int, optional
The number of iterations.
"""
from enthought.mayavi import mlab
if levelset is not None:
msnake.levelset = levelset
fig = mlab.gcf()
mlab.clf()
src = mlab.pipeline.scalar_field(msnake.data)
mlab.pipeline.image_plane_widget(src, plane_orientation='x_axes', colormap='gray')
cnt = mlab.contour3d(msnake.levelset, contours=[0.5])
for i in xrange(num_iters):
msnake.step()
cnt.mlab_source.scalars = msnake.levelset
# Return the last levelset.
return msnake.levelset
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 _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 plotsln(mesh, z=None, sln=None, colorbar=False, view=(0,0),
filename="a.png"):
"""
Plot a solution for the mesh editor in the online lab.
"""
x = [n[0] for n in mesh.nodes]
y = [n[1] for n in mesh.nodes]
if z == None:
try:
z = [n[2] for n in mesh.nodes]
except IndexError:
z = [0]*len(y)
from enthought.mayavi import mlab
mlab.options.offscreen = True
mlab.clf()
#mlab.options.show_scalar_bar = False
mlab.triangular_mesh(x, y, z, mesh.elems, scalars=sln)
engine = mlab.get_engine()
image = engine.current_scene
image.scene.background = (1.0, 1.0, 1.0)
image.scene.foreground = (0.0, 0.0, 0.0)
if colorbar:
mlab.colorbar(orientation="vertical")
if view:
mlab.view(view[0], view[1])
mlab.savefig(filename)
def __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 test_test_backend(self):
"""Test if setting the backend to 'test' works."""
mlab.options.backend = 'test'
mlab.test_contour3d()
mlab.clf()
mlab.pipeline.open(get_example_data('cube.vti'))
mlab.clf()
def evolve_visual3d(msnake, levelset=None, num_iters=20):
"""
Visual evolution of a three-dimensional morphological snake.
Parameters
----------
msnake : MorphGAC or MorphACWE instance
The morphological snake solver.
levelset : array-like, optional
If given, the levelset of the solver is initialized to this. If not
given, the evolution will use the levelset already set in msnake.
num_iters : int, optional
The number of iterations.
"""
from enthought.mayavi import mlab
if levelset is not None:
msnake.levelset = levelset
fig = mlab.gcf()
mlab.clf()
src = mlab.pipeline.scalar_field(msnake.data)
mlab.pipeline.image_plane_widget(src,
plane_orientation='x_axes',
colormap='gray')
cnt = mlab.contour3d(msnake.levelset, contours=[0.5])
for i in xrange(num_iters):
msnake.step()
cnt.mlab_source.scalars = msnake.levelset
# Return the last levelset.
return msnake.levelset
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 do_mlab():
############################################################
# run all the "test_foobar" functions in the mlab module.
for name, func in getmembers(mlab):
if not callable(func) or not name[:4] in ('test', 'Test'):
continue
mlab.clf()
func()
def do_mlab():
############################################################
# run all the "test_foobar" functions in the mlab module.
for name, func in getmembers(mlab):
if not callable(func) or not name[:4] in ('test', 'Test'):
continue
mlab.clf()
func()
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 capture_image(func, filename):
""" Runs a function doing some mayavi drawing and save the resulting
scene to a file.
"""
mlab.clf()
func()
if not filename[-4:] in ('.jpg', '.png'):
filename = '%s.jpg' % filename
mlab.savefig(filename, size=(400, 400))
os.system('convert %s -trim %s' % (filename, filename))
def capture_image(func, filename):
""" Runs a function doing some mayavi drawing and save the resulting
scene to a file.
"""
mlab.clf()
func()
if not filename[-4:] in ('.jpg', '.png'):
filename = '%s.jpg' % filename
mlab.savefig(filename , size=(400, 400) )
os.system('convert %s -trim %s' % (filename, filename))
def run(self):
MayaviDaemon._viewers.append(self)
mlab.clf()
bounds = zeros((0, 6))
self.cellsource = self.setup_source(self.cellfname)
if self.cellsource is not None:
tmp = [out.cell_data.scalars for out in self.cellsource.outputs \
if out.cell_data.scalars is not None]
self.has_cell_scalars = (len(tmp) > 0)
tmp = [out.cell_data.vectors for out in self.cellsource.outputs \
if out.cell_data.vectors is not None]
self.has_cell_vectors = (len(tmp) > 0)
tmp = [out.cell_data.tensors for out in self.cellsource.outputs \
if out.cell_data.tensors is not None]
self.has_cell_tensors = (len(tmp) > 0)
bounds = concatenate((bounds,
[out.bounds for out in self.cellsource.outputs]),
axis=0)
self.facesource = self.setup_source(self.facefname)
if self.facesource is not None:
tmp = [out.point_data.scalars for out in self.facesource.outputs \
if out.point_data.scalars is not None]
self.has_face_scalars = (len(tmp) > 0)
tmp = [out.point_data.vectors for out in self.facesource.outputs \
if out.point_data.vectors is not None]
self.has_face_vectors = (len(tmp) > 0)
tmp = [out.point_data.tensors for out in self.facesource.outputs \
if out.point_data.tensors is not None]
self.has_face_tensors = (len(tmp) > 0)
bounds = concatenate((bounds,
[out.bounds for out in self.facesource.outputs]),
axis=0)
boundsmin = bounds.min(axis=0)
boundsmax = bounds.max(axis=0)
bounds = (boundsmin[0], boundsmax[1],
boundsmin[2], boundsmax[3],
boundsmin[4], boundsmax[5])
self.bounds = where(self.bounds == array((None,)),
bounds,
self.bounds).astype(float)
self.view_data()
# Poll the lock file.
self.timer = Timer(1000 / self.fps, self.poll_file)
def plotfield(ufunc):
d = ufunc(x, y, z, t)
print "field: min, max, mean, std = %g, %g, %g, %g" % (
d.min(), d.max(), d.mean(), d.std() )
print "lwall: min, max, mean, std = %g, %g, %g, %g" % (
d[:,0,:].min(), d[:,0,:].max(), d[:,0,:].mean(), d[:,0,:].std() )
print "uwall: min, max, mean, std = %g, %g, %g, %g" % (
d[:,-1,:].min(), d[:,-1,:].max(), d[:,-1,:].mean(), d[:,-1,:].std() )
mlab.clf()
f = mlab.contour3d(x, y, z, d, transparent = True)
return (d, f)
def plotfield(ufunc):
d = ufunc(x, y, z, t)
print "field: min, max, mean, std = %g, %g, %g, %g" % (d.min(), d.max(),
d.mean(), d.std())
print "lwall: min, max, mean, std = %g, %g, %g, %g" % (
d[:, 0, :].min(), d[:, 0, :].max(), d[:, 0, :].mean(), d[:,
0, :].std())
print "uwall: min, max, mean, std = %g, %g, %g, %g" % (d[:, -1, :].min(
), d[:, -1, :].max(), d[:, -1, :].mean(), d[:, -1, :].std())
mlab.clf()
f = mlab.contour3d(x, y, z, d, transparent=True)
return (d, f)
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 show_votegrid(vg, color=(1, 0, 0), opacity=1):
from enthought.mayavi import mlab
mlab.clf()
x, y, z = np.nonzero(vg > 0)
if not color is None:
mlab.points3d(x, y, z, opacity=opacity, color=color, scale_factor=1, scale_mode="none", mode="cube")
else:
mlab.points3d(x, y, z, vg[vg > 0], opacity=opacity, scale_factor=1, scale_mode="none", mode="cube")
gridmin, gridmax = config.bounds
X, Y, Z = np.array(gridmax) - gridmin
# mlab.axes(extent=[0,0,0,X,Y,Z])
mlab.draw()
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 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 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 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 run_mlab_file(filename, image_file):
## XXX: Monkey-patch mlab.show, so that we keep control of the
## the mainloop
old_show = mlab.show
def my_show(func=None):
pass
mlab.show = my_show
mlab.clf()
e = mlab.get_engine()
e.close_scene(mlab.gcf())
execfile(filename, {'__name__': '__main__'})
mlab.savefig(image_file)
size = mlab.gcf().scene.get_size()
for scene in e.scenes:
e.close_scene(scene)
mlab.show = old_show
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 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 xrange(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 pymodule.yh_mayavi import customBarchart
bar = customBarchart(x, y , enrichment_matrix, y_scale=4.5, opacity=1, mode='cube', color=(0,1,0), scale_factor=1.0, x_scale=0.9)
"""
#mlab.ylabel("KW")
#mlab.xlabel("Emma")
#mlab.zlabel("Enrichment Ratio")
from pymodule.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 plot(self):
"""
Visualize the current data volume with two perpendicular
image plane widgets.
"""
mlab.clf(figure=self.slices_scene.mayavi_scene)
self.slices_scene.scene.background = (0, 0, 0)
self.s = mlab.pipeline.scalar_field(\
self.dataset[self.dataset.time].astype(np.float),
figure=self.slices_scene.mayavi_scene)
self.ipw_x = mlab.pipeline.image_plane_widget(self.s,
figure=self.slices_scene.mayavi_scene,
plane_orientation = 'x_axes')
self.ipw_y = mlab.pipeline.image_plane_widget(self.s,
figure=self.slices_scene.mayavi_scene,
plane_orientation = 'y_axes')
self.ipw_x.parent.scalar_lut_manager.lut_mode = 'gist_gray'
def make_3d_graph(scalarfield, filename):
mlab.figure(bgcolor=(1,1,1),fgcolor=(0,0,0))
#mlab.options.offscreen = True
#win = e.new_scene()
#src = mlab.pipeline.scalar_scatter(Phi_graph)
#e.add_source(src)
#e.add_module(IsoSurface())
#e.add_module(
#win.scene.isometric_view()
#win.scene.save(filename,size=(800,600))
mlab.clf()
mlab.contour3d(scalarfield,opacity=.5,transparent=True)#,contours=[1.0])
# mlab.outline()
mlab.zlabel('Z')
mlab.xlabel('X')
mlab.ylabel('Y')
print 'saving %s' % filename
mlab.savefig(filename)
def run_mlab_file(filename, image_file):
## XXX: Monkey-patch mlab.show, so that we keep control of the
## the mainloop
old_show = mlab.show
def my_show(func=None):
pass
mlab.show = my_show
mlab.clf()
e = mlab.get_engine()
e.close_scene(mlab.gcf())
execfile(filename, {'__name__': '__main__'})
mlab.savefig(image_file)
size = mlab.gcf().scene.get_size()
for scene in e.scenes:
e.close_scene(scene)
mlab.show = old_show
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 = 100.
#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 = 10.
#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)
if 0:
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)
#mlab.view(*V)
t = frame.t
def mcrtmv(frames, dt, Lx, Ly, Nx, Ny, savemovie=False, mvname='test'):
x = np.linspace(0, Lx, Nx)
y = np.linspace(0, Lx, Nx)
X, Y = np.meshgrid(x, y)
size = 500, 500
fig = ml.figure(size=size, bgcolor=(1., 1., 1.))
#fig.scene.anti_aliasing_frames=07
#extent = [0,Nx-1,0,Ny-1,-30,30]
ml.clf(figure=fig)
u = np.loadtxt('data/solution_%06d.txt' % 1)
fname = 'data/_tmp%07d.png' % 1
s = ml.surf(x, y, u, figure=fig, vmin=-1, vmax=1)
ml.axes(extent=[0, Lx, 0, Ly, -2, 2])
ml.colorbar()
ml.xlabel('x position')
ml.ylabel('y position')
ml.zlabel('wave amplitude')
if savemovie == True:
pl.ion()
arr = ml.screenshot()
img = pl.imshow(arr)
pl.axis('off')
for i in range(2, frames):
u = np.loadtxt('data/solution_%06d.txt' % i)
s.mlab_source.scalars = u
fname = 'data/_tmp%07d.png' % i
if savemovie == True:
arr = ml.screenshot()
img.set_array(arr)
pl.savefig(filename=fname) #,figure=fig)
print 'Saving frame', fname
pl.draw()
fig.scene.disable_render = False
os.system(
"mencoder 'mf://data/_tmp*.png' -mf type=png:fps=20 -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o %s.mpg"
% mvname)
def plot3d(self,ix=0,iy=1,iz=2,clf=True):
"""
Generates a 3-dimensional plot of the data set and principle components
using mayavi.
ix, iy, and iz specify which of the input p-dimensions to place on each of
the x,y,z axes, respectively (0-indexed).
"""
import enthought.mayavi.mlab as M
if clf:
M.clf()
z3=np.zeros(3)
v=(self.getEigenvectors()*self.getEigenvalues())
M.quiver3d(z3,z3,z3,v[ix],v[iy],v[iz],scale_factor=5)
M.points3d(self.N[:,ix],self.N[:,iy],self.N[:,iz],scale_factor=0.3)
if self.names:
M.axes(xlabel=self.names[ix]+'/sigma',ylabel=self.names[iy]+'/sigma',zlabel=self.names[iz]+'/sigma')
else:
M.axes()
def plot_velocities3d(self,clf=True,**kwargs):
"""
Plots the particle velocities in a 3d projection with mayavi.
:param bool clf: If True, clear figure before plotting.
kwargs are passed into :func:`enthought.mayavi.mlab.quiver3d`.
:returns: The result of the plotting command
"""
from enthought.mayavi import mlab as M
if clf:
M.clf()
args = list(self._pos.T)
args.extend(list(self._vel.T))
return M.quiver3d(*args,**kwargs)
def plotframe(frameno):
plotdata = ClawPlotData()
plotdata.outdir = "_output"
plotdata = setplot(plotdata)
frame = plotdata.getframe(frameno)
x = frame.grids[0].c_center[0]
y = frame.grids[0].c_center[1]
q = frame.grids[0].q
eta = q[:,:,3]
eta = where(q[:,:,0] > 1.,eta,nan)
#import pdb; pdb.set_trace()
mlab.figure(3,bgcolor=(1,1,1))
mlab.clf()
#plot_solid_sphere()
plot_lat_long_lines()
#x = 360 - x
X,Y,Z = plot_eta(x,y,eta)
def make_multiple_3d_graphs(data, filename):
'''data is a list of 3d scalar fields. filename is a string, probably ending in .png'''
mlab.figure(bgcolor=(1,1,1),fgcolor=(0,0,0),size=(1000,600))
# f = mlab.gcf()
# camera = f.scene.camera
# cam.parallel_scale = 9
# view = mlab.view()
# roll = mlab.roll()
# print 'camera view is:',view
# print 'camera roll is:',roll
#mlab.options.offscreen = True
#win = e.new_scene()
#src = mlab.pipeline.scalar_scatter(Phi_graph)
#e.add_source(src)
#e.add_module(IsoSurface())
#e.add_module(
#win.scene.isometric_view()
#win.scene.save(filename,size=(800,600))
i=0
# print 'mean is: ',data[i].mean()
# print 'min is: ',data[i].min()
for contourpercent in [.232,]: # [.16, .20,.22,.232,.25,.28,.32,.36,.40,.44,.48,.52,.56,.60,.64,.68,.72]:
mlab.clf()
# contourpercent = .232 # .28, .24 is good
value = (data[i].max()-data[i].min())*contourpercent+data[i].min()
print 'graphing contours at',contourpercent,'of min-max distance, where value is ',value,'(versus a max of',data[i].max(),'and a min of',data[i].min(),', and a range of ',data[i].max()-data[i].min(),').'
# print 'graphing data[%i].shape='%i,data[i].shape
mlab.contour3d(data[i],contours=[value],opacity=.36,transparent=True,colormap='bone')
for i in range(1,len(data)):
# print data[i]
# print 'graphing data[%i].shape='%i,data[i].shape
mlab.contour3d(data[i],contours=[(data[i].max()-data[i].min())*contourpercent+data[i].min()],opacity=.56,transparent=True)
# mlab.view(distance=36.0)
# mlab.outline()
# mlab.zlabel('Z')
# mlab.xlabel('X')
# mlab.ylabel('Y')
tosave = filename + '%.03f'%contourpercent + '.png'
print 'saving %s' % tosave
mlab.savefig(tosave)
def do(self):
############################################################
# Imports.
from enthought.mayavi import mlab
############################################################
# Create a new scene and set up the visualization.
s = self.new_scene()
############################################################
# run all the "test_foobar" functions in the mlab module.
for name, func in getmembers(mlab):
if not callable(func) or not name[:4] in ('test', 'Test'):
continue
mlab.clf()
func()
# Mayavi has become too fast: the operator cannot see if the
# Test function was succesful.
sleep(0.1)
############################################################
# Test some specific corner-cases
import numpy
x, y, z = numpy.mgrid[1:10, 1:10, 1:10]
u, v, w = numpy.mgrid[1:10, 1:10, 1:10]
s = numpy.sqrt(u**2 + v**2)
mlab.clf()
# Test the extra argument "scalars"
mlab.quiver3d(x, y, z, u, v, w, scalars=s)
# Test surf with strange-shaped inputs
X, Y = numpy.ogrid[-10:10, -10:10]
Z = X**2 + Y**2
mlab.surf(X, Y, Z)
mlab.surf(X.ravel(), Y.ravel(), Z)
x, y, z = numpy.mgrid[-10:10, -10:10, -3:2]
mlab.flow(x, y, z)
# Test glyphs with number-only coordinnates
mlab.points3d(0, 0, 0, resolution=50)
def plot_particles3d(self,clf=True,**kwargs):
"""
Plots the particle locations in a 3d projection with mayavi.
:param bool clf: If True, clear figure before plotting.
kwargs are passed into :func:`enthought.mayavi.mlab.points3d`.
:returns: The result of the plotting command
"""
from enthought.mayavi import mlab as M
if clf:
M.clf()
args = list(self._pos.T)
args.append(self._mass)
kwargs.setdefault('mode','point')
return M.points3d(*args,**kwargs)
def draw_atoms(at,
colours=None,
colorbar=None,
atom_scale_factor=1.0,
cell=True,
origin=[0., 0., 0.],
shift=[.5, .5, .5],
supercell=[1, 1, 1],
bonds=True,
cutoff_factor=1.2,
bond_radius=0.2,
bond_colour=(.55, .55, .55),
arrows=None,
arrow_colour=(0, 0, 0),
arrow_scale_factor=1.0,
clf=True):
if clf: mlab.clf()
fig = mlab.gcf()
fig.scene.disable_render = True
balls = add_balls(at, colours, colorbar, atom_scale_factor)
if cell:
add_cell(at, balls, origin, shift, supercell)
if bonds:
add_bonds(at, balls, cutoff_factor, bond_radius, bond_colour)
if arrows:
arrows = add_arrows(at, arrows, arrow_colour, arrow_scale_factor)
fig.scene.disable_render = False
if arrows:
return (balls, arrows)
else:
return balls
def plot_sln_mayavi(sln, offscreen=False, show_scale=True):
"""
Plots the Solution() instance sln using Linearizer() and matplotlib.
It takes the vertices from linearizer and interpolates them.
"""
lin = Linearizer()
lin.process_solution(sln)
vert = lin.get_vertices()
triangles = lin.get_triangles()
from numpy import zeros
from enthought.mayavi import mlab
x = vert[:, 0]
y = vert[:, 1]
z = zeros(len(y))
t = vert[:, 2]
if offscreen:
# the off screen rendering properly works only with VTK-5.2 or above:
mlab.options.offscreen = True
mlab.clf()
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.view(0, 0)
mlab.view(distance=4)
mlab.view(focalpoint=(.35, 0, 0))
mlab.colorbar(title="Solution", orientation="vertical")
#mlab.move(right=-1.0, up=-10.0)
# Below is a code that does exactly what the "View along the +Z axis"
# button does:
#scene = mlab.get_engine().current_scene.scene
#scene.camera.focal_point = [0, 0, 0]
#scene.camera.position = [0, 0, 1]
#scene.camera.view_up = [0, 1, 0]
#scene.renderer.reset_camera()
#scene.render()
# the above looks ok, but there is still quite a large margin, so we prefer
# to just call .view(0, 0), which seems to be working fine.
return mlab
def plot_sln_mayavi(sln, notebook=False):
"""
Plots the Solution() instance sln using Linearizer() and matplotlib.
Currently only a very simple version is implemented, that takes the
vertices from linearizer and interpolates them. More sophisticated version
should take the triangles.
"""
lin = Linearizer()
lin.process_solution(sln)
vert = lin.get_vertices()
triangles = lin.get_triangles()
from numpy import zeros
from enthought.mayavi import mlab
x = vert[:, 0]
y = vert[:, 1]
z = zeros(len(y))
t = vert[:, 2]
if notebook:
# the off screen rendering properly works only with VTK-5.2 or above:
mlab.options.offscreen = True
mlab.clf()
s = mlab.triangular_mesh(x, y, z, triangles, scalars=t)
mlab.view(0, 0)
# Below is a code that does exactly what the "View along the +Z axis"
# button does:
#scene = mlab.get_engine().current_scene.scene
#scene.camera.focal_point = [0, 0, 0]
#scene.camera.position = [0, 0, 1]
#scene.camera.view_up = [0, 1, 0]
#scene.renderer.reset_camera()
#scene.render()
# the above looks ok, but there is still quite a large margin, so we prefer
# to just call .view(0, 0), which seems to be working fine.
return s
Y = X
Z = 1.5 * X
ext = [min(X), max(X), min(Y), max(Y), min(Z), max(Z)]
fig3d = mlab.figure(bgcolor=(1.0, 1.0, 1.0),
fgcolor=(0.0, 0.0, 0.0),
size=(1280, 1280))
angle = 1.0 * 25
for frame in range(len(fileh.root.Time[1:])):
frame = frame_skip * frame
# 3D Movie
mlab.clf(figure=fig3d)
#Simple contour
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)
"""
Use Mayavi to visualize the structure of a VolumeImg
"""
from enthought.mayavi import mlab
import numpy as np
x, y, z = np.mgrid[-5:5:64j, -5:5:64j, -5:5:64j]
data = x*x*0.5 + y*y + z*z*2.0
mlab.figure(1, fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
mlab.clf()
src = mlab.pipeline.scalar_field(x, y, z, data)
mlab.pipeline.surface(src, opacity=0.4)
src2 = mlab.pipeline.scalar_field(x[::9, ::9, ::9],
y[::9, ::9, ::9],
z[::9, ::9, ::9],
data[::9, ::9, ::9])
mlab.pipeline.surface(mlab.pipeline.extract_edges(src2), color=(0, 0, 0))
mlab.pipeline.glyph(src2, mode='cube', scale_factor=0.4, scale_mode='none')
mlab.savefig('viz_volume_structure.png')
mlab.show()
def vector_movie_v0(timestamp,
tmin=50.0,
tmax=250.0,
nskip=100,
integrate=False,
showplot=False):
from frc_data_class import FrcData, FrcDataOld
from enthought.mayavi import mlab
from enthought.mayavi.mlab import quiver3d, savefig, view, clf
from scipy import arange, ndarray, array, zeros
from scipy.integrate import cumtrapz
import numpy
filename = 'mag.x1.' + timestamp + '.csv'
shot = FrcData(filename)
if tmin > tmax:
temp = tmin
tmin = tmax
tmax = temp
imin = tmin * 1e-6 / shot.deltat
imax = tmax * 1e-6 / shot.deltat
n = imax - imin
time = shot.timepoints[imin:imax]
Bx = ndarray((16, n), float)
By = ndarray((16, n), float)
Bz = ndarray((16, n), float)
Bdot = {}
# for ch in arange(16):
#
# for axis in ['x','y','z']:
#
# filename = 'mag.'+axis+str(ch+1)+'.'+timestamp+'.csv'
#
# shot = FrcData(filename)
#
# dB = shot.voltage[imin:imax]*calfactor
#
# Bdot[axis] = dB/numpy.max(abs(dB))
#
#
#
# if integrate:
#
# Bx[ch,1:] = cumtrapz(Bdot['x'],x=time)
#
# By[ch,1:] = cumtrapz(Bdot['y'],x=time)
#
# Bz[ch,1:] = cumtrapz(Bdot['z'],x=time)
#
# else:
#
# Bx[ch,:] = Bdot['x']
#
# By[ch,:] = Bdot['y']
#
# Bz[ch,:] = Bdot['z']
modB = numpy.sqrt(Bx**2 + By**2 + Bz**2)
Bmin = numpy.min(modB)
Bmax = numpy.max(modB)
modB = modB / Bmax
x = zeros(16)
y = zeros(16)
z = arange(16)
# if showplot:
# pass
# else:
# #mlab.options.offscreen=True
# pass
box = 0.5
fig = quiver3d(x,
y,
z,
Bx[:, 0],
By[:, 0],
Bz[:, 0],
mode='arrow',
scale_mode='scalar',
resolution=20,
color=(0.1, 1.0, 0.1),
vmin=Bmin,
vmax=Bmax,
scalars=modB[:, 0],
scale_factor=0.9,
extent=(-box, box, -box, box, -9, 9))
view(azimuth=0, elevation=90, distance=30)
source = fig.mlab_source
frames = range(int(round(n / nskip)))
for j in frames:
i = j * nskip
source.set(u=Bx[:, i], v=By[:, i], w=Bz[:, i], scalars=modB[:, i])
savefile = 'anim' + str(j) + '.png'
#print 'Creating image '+str(j)
#savefig(savefile)
clf()
def make_flash_bem(subject, subjects_dir, flash05, flash30, show=False):
"""Create 3-Layers BEM model from Flash MRI images
Parameters
----------
subject : string
Subject name
subjects_dir : string
Directory containing subjects data (Freesurfer SUBJECTS_DIR)
flash05 : string
Full path of the NIFTI file for the
FLASH sequence with a spin angle of 5 degrees
flash30 : string
Full path of the NIFTI file for the
FLASH sequence with a spin angle of 30 degrees
show : bool
Show surfaces in 3D to visually inspect all three BEM
surfaces (recommended)
Notes
-----
This program assumes that both Freesurfer/FSL, and MNE,
including MNE's Matlab Toolbox, are installed properly.
For reference please read the MNE manual and wiki, and Freesurfer's wiki:
http://www.nmr.mgh.harvard.edu/meg/manuals/
http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php
http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/MNE_register/index.php
http://surfer.nmr.mgh.harvard.edu/
http://surfer.nmr.mgh.harvard.edu/fswiki
References:
B. Fischl, D. H. Salat, A. J. van der Kouwe, N. Makris, F. Segonne,
B. T. Quinn, and A. M. Dale, "Sequence-independent segmentation of magnetic
resonance images," Neuroimage, vol. 23 Suppl 1, pp. S69-84, 2004.
J. Jovicich, S. Czanner, D. Greve, E. Haley, A. van der Kouwe, R. Gollub,
D. Kennedy, F. Schmitt, G. Brown, J. Macfall, B. Fischl, and A. Dale,
"Reliability in multi-site structural MRI studies: effects of gradient
non-linearity correction on phantom and human data," Neuroimage,
vol. 30, Epp. 436-43, 2006.
"""
os.environ['SUBJECT'] = subject
os.chdir(os.path.join(subjects_dir, subject, "mri"))
if not os.path.exists('flash'):
os.mkdir("flash")
os.chdir("flash")
# flash_dir = os.getcwd()
if not os.path.exists('parameter_maps'):
os.mkdir("parameter_maps")
print("--- Converting Flash 5")
os.system('mri_convert -flip_angle %s -tr 25 %s mef05.mgz' %
(5 * math.pi / 180, flash05))
print("--- Converting Flash 30")
os.system('mri_convert -flip_angle %s -tr 25 %s mef30.mgz' %
(30 * math.pi / 180, flash30))
print("--- Running mne_flash_bem")
os.system('mne_flash_bem --noconvert')
os.chdir(os.path.join(subjects_dir, subject, 'bem'))
if not os.path.exists('flash'):
os.mkdir("flash")
os.chdir("flash")
print("[done]")
if show:
fnames = ['outer_skin.surf', 'outer_skull.surf', 'inner_skull.surf']
head_col = (0.95, 0.83, 0.83) # light pink
skull_col = (0.91, 0.89, 0.67)
brain_col = (0.67, 0.89, 0.91) # light blue
colors = [head_col, skull_col, brain_col]
from enthought.mayavi import mlab
mlab.clf()
for fname, c in zip(fnames, colors):
points, faces = mne.read_surface(fname)
mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2], faces,
color=c, opacity=0.3)
mlab.show()
def dense_reconstruction(S, P1, P2, H1, H2, region=None, step=1, imbw1=None):
"""
Build and show a dense reconstruction.
Parameters
----------
S : ndarray
Matrix of disparities.
P1, P2 : ndarray
Projection matrices of the stereo pair.
H1, H2 : ndarray
Homographies which rectify the images of the stereo
pair. See projmat2rectify.
region : array-like, optional
Region of the matrix of disparities that will be used for
the dense reconstruction. It must be expressed
as [x_min, x_max, y_min, y_max].
If not given, the full matrix of disparities is considered.
step : integer, optional
Quality of the reconstruction. Only one of every 'step' pixels
inside the region of interest is taken for the reconstruction.
If not given, all pixels are used.
imbw1 : ndarray, optional
The image before rectification. This is used to assign a color
to the 3D vertices of the reconstruction. If not given, an arbitrary
color is assigned to vertices.
imbw1 should be a gray scale image.
"""
if region is None:
region = [0, S.shape[1], 0, S.shape[0]]
# The matrix of disparities is smoothed.
H = np.ones((3, 3)) / 9.0
S = ndimage.convolve(np.double(S), H, mode='constant')
# Point correspondences.
X, Y = np.mgrid[region[0]:region[1] + 1:step, region[2]:region[3] + 1:step]
num_points = np.prod(X.shape)
points1 = np.ones((3, num_points))
points1[0, :] = X.ravel()
points1[1, :] = Y.ravel()
X2 = X.ravel() + S[np.int32(points1[1, :]), np.int32(points1[0, :])]
points2 = np.copy(points1)
points2[0, :] = X2
# Colors
if imbw1 is not None:
points1back = hom2cart(np.dot(la.inv(H1), points1))
colors = ndimage.map_coordinates(imbw1,
[points1back[1], points1back[0]],
order=1)
else:
colors = None
# Polygons.
vlist = np.arange(0, num_points)
vlist = vlist.reshape(X.shape)
vlist1 = vlist[:-1, :-1]
vlist2 = vlist[:-1, 1:]
vlist3 = vlist[1:, 1:]
vlist4 = vlist[1:, :-1]
faces1 = np.array([vlist1.ravel(), vlist2.ravel(), vlist3.ravel()])
faces2 = np.array([vlist1.ravel(), vlist3.ravel(), vlist4.ravel()])
faces = matarray(faces1, faces2).T
# Reconstruction.
from stereo import reconstruct
M = reconstruct(points1, points2, np.dot(H1, P1), np.dot(H2, P2))
# Show the result.
try:
from enthought.mayavi import mlab
except ImportError:
from mayavi import mlab
mlab.clf()
mlab.triangular_mesh(M[0],
M[1],
M[2],
faces,
scalars=colors,
colormap='gray')
return M, faces, colors
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
# -*- coding: utf-8 -*-
def plot_3d_spectrum_mayavi(fs, fignum=None, vmin=None, vmax=None,
pop_ids=None):
"""
Logarithmic heatmap of single 3d FS.
This method relies on MayaVi2's mlab interface. See http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html . To edit plot
properties, click leftmost icon in the toolbar.
If you get an ImportError upon calling this function, it is likely that you
don't have mayavi installed.
fs: FS to plot
vmin: Values in fs below vmin are masked in plot.
vmax: Values in fs above vmax saturate the color spectrum.
fignum: Figure number to plot into. If None, a new figure will be created.
Note that these are MayaVi figures, which are separate from
matplotlib figures.
pop_ids: If not None, override pop_ids stored in Spectrum.
"""
from enthought.mayavi import mlab
fig = mlab.figure(fignum, bgcolor=(1,1,1))
mlab.clf(fig)
if vmin is None:
vmin = fs.min()
if vmax is None:
vmax = fs.max()
# Which entries should I plot?
toplot = numpy.logical_not(fs.mask)
toplot = numpy.logical_and(toplot, fs.data >= vmin)
# For the color mapping
normalized = (numpy.log(fs)-numpy.log(vmin))\
/(numpy.log(vmax)-numpy.log(vmin))
normalized = numpy.minimum(normalized, 1)
xs,ys,zs = numpy.indices(fs.shape)
flat_xs = xs.flatten()
flat_ys = ys.flatten()
flat_zs = zs.flatten()
flat_toplot = toplot.flatten()
mlab.barchart(flat_xs[flat_toplot], flat_ys[flat_toplot],
flat_zs[flat_toplot], normalized.flatten()[flat_toplot],
colormap='hsv', scale_mode='none', lateral_scale=1,
figure=fig)
if pop_ids is None:
if fs.pop_ids is not None:
pop_ids = fs.pop_ids
else:
pop_ids = ['pop0','pop1','pop2']
a = mlab.axes(xlabel=pop_ids[0],ylabel=pop_ids[1],zlabel=pop_ids[2],
figure=fig, color=(0,0,0))
a.axes.label_format = ""
a.title_text_property.color = (0,0,0)
mlab.text3d(fs.sample_sizes[0],fs.sample_sizes[1],fs.sample_sizes[2]+1,
'(%i,%i,%i)'%tuple(fs.sample_sizes), scale=0.75, figure=fig,
color=(0,0,0))
mlab.view(azimuth=-40, elevation=65, distance='auto', focalpoint='auto')
mlab.show()
fig1 = mlab.figure()
fig2 = mlab.figure()
#mlab.gcf().scene.y_plus_view()
view2=mlab.view(120,90)
print view2
#mlab.pitch(90)
span+=1
for j in range(skip,cnt-1):
if j%span!=0 and j!=0:
continue
print "TimeStep: %d" % (times[j])
xx=xs[j]
yy=ys[j]
zz=zs[j]
tt=ts[j]
mlab.clf(fig1)
mlab.clf(fig2)
mlab.points3d(xx,yy,zz,tt,colormap="gist_heat",scale_factor=0.2,figure=fig1)
mlab.mesh(xdummy,ydummy,pairdistro[j],figure=fig2)
mlab.show(stop=True)
mlab.close(all=True)
#Bond lengths
"""
print "Calculating histogram of bond lengths corresponding to the minimum energy."
n=pes.index(min(pes))
xs=xs[n][:N]
ys=ys[n][:N]
zs=zs[n][:N]
