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 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_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 PlotResultsVectorField(results, onSurf=False):
x, y = np.mgrid[0:512,0:512]
w = np.zeros((512, 512))
if onSurf:
mlab.quiver3d(x, y, results['combineMap'],
results['sU'], results['sV'], w)
else:
mlab.quiver3d(results['sU'], results['sV'], w)
def plot_quiver(pts, vecs, color=(0.,1.,0.), arrow_scale = 0.05,
point_mode='sphere', point_scale=0.002):
x = pts[0,:].A1
y = pts[1,:].A1
z = pts[2,:].A1
u = vecs[0,:].A1
v = vecs[1,:].A1
w = vecs[2,:].A1
plot_points(pts, mode=point_mode, scale_factor=point_scale)
mlab.quiver3d(x, y, z, u, v, w, scale_factor=arrow_scale,
color=color, scale_mode='vector')
def graph_plot(x, y, z, start_idx, end_idx, edge_scalars=None, **kwargs):
""" Show the graph edges using Mayavi
Parameters
-----------
x: ndarray
x coordinates of the points
y: ndarray
y coordinates of the points
z: ndarray
z coordinates of the points
edge_scalars: ndarray, optional
optional data to give the color of the edges.
kwargs:
extra keyword arguments are passed to quiver3d.
"""
vec = mlab.quiver3d(x[start_idx],
y[start_idx],
z[start_idx],
x[end_idx] - x[start_idx],
y[end_idx] - y[start_idx],
z[end_idx] - z[start_idx],
scalars=edge_scalars,
mode='2ddash',
scale_factor=1,
**kwargs)
if edge_scalars is not None:
vec.glyph.color_mode = 'color_by_scalar'
return vec
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 __init__(self, objects, t, zscale=1.0, **kwargs):
"""Invokes mlab.quiver3d. Mayavi 2 by Enthought has to be installed
objects: Either a cmf.node_list or a sequence of cells
t : A cmf.Time or a datetime.datetime object
keywords are passed to mlab.quiver3d
"""
try:
from enthought.mayavi import mlab
from numpy import asarray, meshgrid
except ImportError:
raise NotImplementedError(
"Raster.draw3d needs an installation of mayavi to work")
if isinstance(objects, cmf.node_list):
p = objects.get_positions()
f = objects.get_fluxes3d(t)
elif isinstance(objects, cmf.project):
p = cmf.cell_positions(objects.cells)
f = cmf.cell_fluxes(objects.cells, t)
elif isinstance(objects[0], cmf.Cell):
p = cmf.cell_positions(objects)
f = cmf.cell_fluxes(objects, t)
else:
raise ValueError(
"Given objects are not a nodelist or a sequence of cells")
self.objects = objects
self.zscale = zscale
self.quiver = mlab.quiver3d(p.X, p.Y, p.Z, f.X, f.Y, f.Z * zscale,
**kwargs)
def graph_plot(x, y, z, start_idx, end_idx, edge_scalars=None, **kwargs):
""" Show the graph edges using Mayavi
Parameters
-----------
x: ndarray
x coordinates of the points
y: ndarray
y coordinates of the points
z: ndarray
z coordinates of the points
edge_scalars: ndarray, optional
optional data to give the color of the edges.
kwargs:
extra keyword arguments are passed to quiver3d.
"""
vec = mlab.quiver3d(x[start_idx],
y[start_idx],
z[start_idx],
x[end_idx] - x[start_idx],
y[end_idx] - y[start_idx],
z[end_idx] - z[start_idx],
scalars=edge_scalars,
mode='2ddash',
scale_factor=1,
**kwargs)
if edge_scalars is not None:
vec.glyph.color_mode = 'color_by_scalar'
return vec
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 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 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_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 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 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 plot_normals(pts, normals, curvature=None, mask_points=1,
color=(0.,1.,0.), scale_factor = 0.1):
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:
curvature = np.array(curvature)
#idxs = np.where(curvature>0.03)
#mlab.points3d(x[idxs],y[idxs],z[idxs],curvature[idxs],mode='sphere',scale_factor=0.1,mask_points=1)
mlab.points3d(x,y,z,curvature,mode='sphere',scale_factor=0.1,mask_points=1, color=color)
# mlab.points3d(x,y,z,mode='point')
mlab.colorbar()
else:
mlab.points3d(x,y,z,mode='point')
mlab.quiver3d(x, y, z, u, v, w, mask_points=mask_points,
scale_factor=scale_factor, color=color)
def efield(name, sources, grid):
X, Y, Z = grid
Ex, Ey, Ez = (zeros_like(X), zeros_like(Y), zeros_like(Z))
for source in sources:
(Xp, Yp, Zp), q = source
if q < 0e0:
color = (0,0,1)
else:
color = (1,0,0)
mlab.points3d(Xp, Yp, Zp, color=color, scale_factor=q)
Xs, Ys, Zs = (X - Xp, Y - Yp, Z - Zp)
rminus3half = (Xs*Xs + Ys*Ys + Zs*Zs)**(-1.5e0)
#~ rminus3half[isnan(rminus3half)] = 0
Ex += q * rminus3half * Xs
Ey += q * rminus3half * Ys
Ez += q * rminus3half * Zs
# Run isnan on the data after adding all of the field contributions
Xs[isnan(Xs)] = 0
Ys[isnan(Ys)] = 0
Zs[isnan(Zs)] = 0
mlab.quiver3d(X, Y, Z, Ex, Ey, Ez, line_width=2, scale_factor=1)
mlab.show()
def plot_normals(pts,
normals,
curvature=None,
mask_points=1,
color=(0., 1., 0.),
scale_factor=0.1):
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:
curvature = np.array(curvature)
#idxs = np.where(curvature>0.03)
#mlab.points3d(x[idxs],y[idxs],z[idxs],curvature[idxs],mode='sphere',scale_factor=0.1,mask_points=1)
mlab.points3d(x,
y,
z,
curvature,
mode='sphere',
scale_factor=0.1,
mask_points=1,
color=color)
# mlab.points3d(x,y,z,mode='point')
mlab.colorbar()
else:
mlab.points3d(x, y, z, mode='point')
mlab.quiver3d(x,
y,
z,
u,
v,
w,
mask_points=mask_points,
scale_factor=scale_factor,
color=color)
def viz_BARY(data, j_data, file_name=None, labels=None, size=(600, 600)):
u"""Visualizes the barycenters of the electron density difference (for visualizing dipole moments).
** Parameters **
data : tuple(numpy.ndarray((3,N)), numpy.ndarray((3,N)))
Pair of column-major matrices containing the coordinates of the positive and negative barycenters, in that order.
j_data : dict
Data on the molecule, as deserialized from the scanlog format.
file_name : str, optional
Base name of the files in which to save the images.
labels : list(str), optional
Labels to append to `file_name` for each datum in `data`. If None, the position of the datum is appended.
size : tuple(int, int), optional
The size of the image to save.
** Returns **
figure : mayavi.core.scene.Scene
The MayaVi scene containing the visualization.
"""
figure = _init_scene(j_data)[0]
for i, D in enumerate(data):
#Pp = mlab.points3d(D[0][0], D[0][1], D[0][2], figure=figure, mode='axes', scale_factor=0.3, color=(0.0, 0.5, 0.5))
#Pm = mlab.points3d(D[1][0], D[1][1], D[1][2], figure=figure, mode='axes', scale_factor=0.3, color=(0.95, 0.95, 0.95))
## Chemistry convention (from negative to positive)
Mu = mlab.quiver3d(D[1][0],
D[1][1],
D[1][2],
D[0][0] - D[1][0],
D[0][1] - D[1][1],
D[0][2] - D[1][2],
figure=figure,
mode='arrow',
scale_factor=1.0,
color=(0.0, 0.5, 0.5))
if file_name is not None:
mlab.savefig("{}-BARY-{}.png".format(
file_name, labels[i] if labels is not None else i),
figure=figure,
size=size)
#Pp.remove()
#Pm.remove()
Mu.remove()
return figure
def plot_quiver(pts,
vecs,
color=(0., 1., 0.),
arrow_scale=0.05,
point_mode='sphere',
point_scale=0.002):
x = pts[0, :].A1
y = pts[1, :].A1
z = pts[2, :].A1
u = vecs[0, :].A1
v = vecs[1, :].A1
w = vecs[2, :].A1
plot_points(pts, mode=point_mode, scale_factor=point_scale)
mlab.quiver3d(x,
y,
z,
u,
v,
w,
scale_factor=arrow_scale,
color=color,
scale_mode='vector')
def bfield():
x = linspace(0, 2*np.pi, 15)
rho = linspace(1, 2.5, 3)
rhogrid, phigrid = meshgrid(rho, phi)
x = rhogrid * np.cos(phigrid)
y = rhogrid * np.sin(phigrid)
z = np.zeros_like(x)
# phihat = -sin(phi)*xhat + cos(phi)*yhat = -y/rho*xhat + x/rho*yhat
# Be careful when you divide by zero as the eps graphic will be corrupted
u = -y / rhogrid; u[np.isnan(u)] = 0
v = x / rhogrid; v[np.isnan(v)] = 0
w = np.zeros_like(z)
obj = quiver3d(x, y, z, u, v, w, line_width=3, scale_factor=0.3)
# outline()
return obj
def plotThisThing(state, proc):
react = sData.getInitReactant(state)
prod = sData.getInitReactant(proc)
#react, prod = sData.getReacProd(state,proc)
cn = ChangingNeighbors(react, prod, cutoffs)
ln = cn.nrValues(cn.lostNeighbors)
indxs = np.where(ln > 0)
ln = ln[indxs]
pos = react.get_positions()[indxs]
t = react.get_atomic_numbers()[indxs]
pts = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],t,t,t,scalars=ln, mode='sphere')
pts.glyph.color_mode = 'color_by_scalar'
pts.glyph.glyph_source.glyph_source.center = [0,0,0]
indx2 = np.setdiff1d(np.array(range(prod.get_number_of_atoms())),indxs[0])
p = react.get_positions()[indx2]
t2 = react.get_atomic_numbers()[indx2]
pt = mlab.quiver3d(p[:,0],p[:,1],p[:,2],t2,t2,t2, opacity=.06, mode='sphere')
mlab.figure()
ln = cn.nrValues(cn.newNeighbors)
indxs = np.where(ln > 0)
ln = ln[indxs]
pos = react.get_positions()[indxs]
t = react.get_atomic_numbers()[indxs]
pts = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],t,t,t,scalars=ln, mode='sphere')
pts.glyph.color_mode = 'color_by_scalar'
pts.glyph.glyph_source.glyph_source.center = [0,0,0]
indx2 = np.setdiff1d(np.array(range(prod.get_number_of_atoms())),indxs[0])
p = react.get_positions()[indx2]
t2 = react.get_atomic_numbers()[indx2]
pt = mlab.quiver3d(p[:,0],p[:,1],p[:,2],t2,t2,t2, opacity=.06, mode='sphere')
mlab.figure()
ln = cn.nrValues(cn.lostNeighbors) + cn.nrValues(cn.newNeighbors)
indxs = np.where(ln > 0)
ln = ln[indxs]
pos = react.get_positions()[indxs]
t = react.get_atomic_numbers()[indxs]
pts = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],t,t,t,scalars=ln, mode='sphere')
pts.glyph.color_mode = 'color_by_scalar'
pts.glyph.glyph_source.glyph_source.center = [0,0,0]
indx2 = np.setdiff1d(np.array(range(prod.get_number_of_atoms())),indxs[0])
p = react.get_positions()[indx2]
t2 = react.get_atomic_numbers()[indx2]
pt = mlab.quiver3d(p[:,0],p[:,1],p[:,2],t2,t2,t2, opacity=.06, mode='sphere')
def add_arrows(at,
arrows,
arrow_colour=(0, 0, 0),
arrow_scale_factor=1.0,
clf=True):
pos = np.array(at.pos)
arrows = np.array(arrows)
return mlab.quiver3d(pos[0, :],
pos[1, :],
pos[2, :],
arrows[0, :],
arrows[1, :],
arrows[2, :],
scale_factor=arrow_scale_factor,
color=arrow_colour,
name='arrows')
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 add_balls(at,
colours=None,
colorbar=False,
atom_scale_factor=1.0,
vmin=None,
vmax=None):
r = np.array([quippy.ElementCovRad[z] for z in at.z])
if colours is None:
scalars = at.z
vmin = 0
vmax = 110
else:
scalars = colours
pts = mlab.quiver3d(at.pos[1, :],
at.pos[2, :],
at.pos[3, :],
r, [0] * len(r), [0] * len(r),
scale_factor=atom_scale_factor,
scale_mode='vector',
scalars=scalars,
mode='sphere',
name='balls',
vmin=vmin,
vmax=vmax)
pts.glyph.color_mode = 'color_by_scalar'
#pts.glyph.glyph.scale_mode = 'scale_by_vector'
pts.glyph.glyph_source.glyph_source.center = [0, 0, 0]
if colours is None:
pts.module_manager.scalar_lut_manager.lut.table = atom_colours_lut
if colorbar: mlab.colorbar()
return pts
def plot_axis(x, y, z, directions):
from enthought.mayavi import mlab
mlab.quiver3d(x,
y,
z, [directions[0, 0]], [directions[1, 0]],
[directions[2, 0]],
scale_factor=1,
color=(1, 0, 0))
if directions.shape[1] > 1:
mlab.quiver3d(x,
y,
z, [directions[0, 1]], [directions[1, 1]],
[directions[2, 1]],
scale_factor=1,
color=(0, 1, 0))
mlab.quiver3d(x,
y,
z, [directions[0, 2]], [directions[1, 2]],
[directions[2, 2]],
scale_factor=1,
color=(0, 0, 1))
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]]
x_pca_plane.shape = (2, 2)
y_pca_plane.shape = (2, 2)
z_pca_plane.shape = (2, 2)
mlab.mesh(x_pca_plane, y_pca_plane, z_pca_plane, color=(0.6, 0, 0),
opacity=0.1)
mlab.mesh(x_pca_plane, y_pca_plane, z_pca_plane, color=(0.6, 0, 0),
representation='wireframe', line_width=3, opacity=0.3)
display.display(grid.get_grid())
# Choose the molecular orbitals to be calculated
selected_MO = ['1.1_a', '1.5_a']
qc.mo_spec = read.mo_select(qc.mo_spec, selected_MO, strict=True)['mo_spec']
# Calculate molecular orbitals
mo_list = core.rho_compute(qc,
calc_mo=True,
slice_length=slice_length,
drv=None,
numproc=numproc)
# Calculate analytic derivatives of molecular orbitals
mo_list_drv = core.rho_compute(qc,
calc_mo=True,
slice_length=slice_length,
drv='xyz',
numproc=numproc)
# Calculate Transition Electronic Flux Densities (time independent)
# Calculate the STEFD [in units of (i E_h/(hbar a_0^2))]
j_stefd = -0.5 * (mo_list[numpy.newaxis, 0] * mo_list_drv[:, 1] -
mo_list[numpy.newaxis, 1] * mo_list_drv[:, 0])
Z, Y, X = output.meshgrid2(*grid.tolist()[::-1])
mlab.quiver3d(X, Y, Z, *j_stefd)
mlab.show()
def animate(coords, mass, dipole, temp, F, cycles, w0=None, max_tstep=100000, no_anim=True):
"""
UNITS:
coords angstrom
mass amu
t ps
dipole e * angstrom
temp kelvin
F amu * angstrom / ps**2.0 / e
"""
cm_coords = coords - tile(center_of_mass(coords, mass), (coords.shape[0], 1))
mol_I, mol_Ix = eig(inertia_tensor(cm_coords, mass))
mol_I.sort()
print "principal moments of inertia"
print mol_I
q0, w0 = initial_cond(coords, mass, dipole, temp, F)
print "initial conditions"
print " q0 = ", q0, " norm(q0) = ", norm(q0)
print " w0 = ", w0, " norm(w0) = ", norm(w0)
# q0 = array([0., 0., 1., 0.])
# w0 = array([0.,1.,0.], dtype=float)
print "time in picoseconds"
# t0 = 2.0 * pi / norm(w0) * cycles[0]
# t1 = t0 + 2.0 * pi / norm(w0) * cycles[1]
# t2 = t1 + 2.0 * pi / norm(w0) * cycles[2]
t0 = cycles[0]
t1 = t0 + cycles[1]
t2 = t1 + cycles[2]
print "t0 = ", t0
print "t1 = ", t1
print "t2 = ", t2
print "total time = ", t0 + t1 + t2
print "integrating motion of rotor"
qt = ar.asym_rotor(t0, t1, t2, max_tstep, r_[q0, w0], F, dipole, mol_I)
print "qt.shape = ", qt.shape
# now extract the orientation quaternions
tsr = qt[:, 0]
qtr = qt[:, 1:5]
wtr = qt[:, 5:8]
# diagnostic plots
# field in rk time steps
ft = zeros(len(tsr))
ft[tsr < t0] = 0.0
ft[(tsr > t0) * (tsr < t1)] = F * tsr / (t1 - t0)
ft[tsr > t1] = F
fp = pylab.figure(figsize=(3, 4))
# total energy
pylab.subplot(4, 1, 1)
pylab.plot(tsr, array([total_energy(qt[i, :], ft[i], dipole, mol_I) for i in xrange(len(tsr))]))
pylab.title("Total Energy")
# angular momentum
pylab.subplot(4, 1, 2)
L = r_[[angular_momentum(qt[i, :], ft[i], dipole, mol_I) for i in xrange(len(tsr))]]
print "L.shape = ", L.shape
# pylab.plot(tsr, array([norm(L[i,:]) for i in xrange(len(tsr))]), 'r-')
pylab.plot(tsr, array([L[i, 0] for i in xrange(len(tsr))]), "b-")
pylab.plot(tsr, array([L[i, 1] for i in xrange(len(tsr))]), "g-")
pylab.plot(tsr, array([L[i, 2] for i in xrange(len(tsr))]), "r-")
pylab.title("Angular Momentum")
# dipole projection
pylab.subplot(4, 1, 3)
dp = array([dipole_projection(qt[i, :], ft[i], dipole, mol_I) for i in xrange(len(tsr))])
dpavg = cumsum(dp) / (arange(len(dp)) + 1.0)
pylab.plot(tsr, dpavg)
pylab.title("Projected Dipole (polarization)")
# field strength
pylab.subplot(4, 1, 4)
pylab.plot(tsr, ft)
pylab.title("Field Strength")
pylab.subplots_adjust(hspace=0.95)
pylab.show()
if no_anim:
return
# frame rate is 10 ps to 1 s
frames = floor(t2 / 10 * 30)
print "total time is ", t2, " rendering ", frames, " frames"
ts, qt = interpolate_quat(tsr, qtr, frames)
# field
ft = zeros(len(ts))
ft[ts < t0] = 0.0
ft[(ts > t0) * (ts < t1)] = F * ts / (t1 - t0)
ft[ts > t1] = F
# render the molecule
f = mlab.figure(size=(500, 500), bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
# # draw the molecule
molecule = mlab.points3d(
cm_coords[:, 0], cm_coords[:, 1], cm_coords[:, 2], scale_factor=2, color=(1, 1, 0), resolution=20
)
# and the dipole moment
dip = mlab.quiver3d(
[0], [0], [0], [dipole[0]], [dipole[1]], [dipole[2]], scale_factor=1, line_width=2.0, color=(0, 1, 0)
)
mlab.view(distance=45.0)
# timelab = mlab.text(0.1, 0.9, 'Time: 0 ps', width=0.4)
# fieldlab = mlab.text(0.5, 0.9, 'Field: 0 kV/cm', width=0.4)
ms = molecule.mlab_source
dms = dip.mlab_source
for t, fi, q in zip(ts, ft, qt):
# timelab.text = "Time: %d ps" % t
# fieldlab.text = "Field: %f" % fi
M = q_to_rotmatrix(q)
cp = dot(cm_coords, M)
dp = dot(dipole, M) * 12.0
ms.set(x=cp[:, 0], y=cp[:, 1], z=cp[:, 2])
dms.set(u=[dp[0]], v=[dp[1]], w=[dp[2]])
my_x = coords_plot[0][0]
my_y = coords_plot[0][1]
my_z = vel_n
lw = 5
# Plot the black crosses first ...
mlab.points3d([my_x], [my_y], [my_z], [1.0],
color=(0, 0, 0),
mode='axes',
scale_factor=sphere_size)
# ... and a sphere at the same location.
mlab.quiver3d([my_x], [my_y - sphere_size / 4.], [my_z], [0], [1], [0],
scalars=[1],
scale_factor=sphere_size / 2.,
scale_mode='scalar',
mode='sphere',
line_width=lw * 0.75,
name=gal,
color=gals[gal]['col'])
# Finally, add the galaxy name as 3D text.
#mlab.text3d(my_x,my_y,my_z,'HCG91'+gal[-1],scale=20,color = (0,0,0))
mlab.text(my_x,
my_y,
'HCG91' + gal[-1],
color=(0, 0, 0),
z=my_z,
width=0.1)
# Next, add peculiar HII regions of interest inside HCG 91c
# (See Vogt+, MNRAS (2015) for details)
points[:, 2],
faces,
color=(1, 1, 0),
opacity=0.3)
# show one cortical surface
mlab.triangular_mesh(lh_points[:, 0],
lh_points[:, 1],
lh_points[:, 2],
lh_faces,
color=(0.7, ) * 3)
# show dipole as small cones
dipoles = mlab.quiver3d(pos[:, 0],
pos[:, 1],
pos[:, 2],
ori[:, 0],
ori[:, 1],
ori[:, 2],
opacity=1.,
scale_factor=4e-4,
scalars=time,
mode='cone',
colormap='RdBu')
# revert colormap
dipoles.module_manager.scalar_lut_manager.reverse_lut = True
mlab.colorbar(dipoles, title='Dipole fit time (ms)')
# proper 3D orientation
mlab.get_engine().scenes[0].scene.x_plus_view()
J_x_temp = J_temp[:, 0]
J_y_temp = J_temp[:, 1]
J_z_temp = J_temp[:, 2]
J_x = reshape(J_x_temp, u2.shape)
J_y = reshape(J_y_temp, u2.shape)
J_z = reshape(J_z_temp, u2.shape)
#==============================================================================
# Plotting current vectors
#==============================================================================
#mlab.options.backend = 'envisage' # one way to save visualization
#f = mlab.figure()
c1 = mlab.points3d(0, 0, 0)
mlab.quiver3d(x, y, z2, J_x, J_y, J_z, colormap="jet", scale_factor=1)
#==============================================================================
# Plotting actual ***boids
#==============================================================================
if (eps1 == 1):
s2 = mlab.mesh(x, y, z2, colormap="gray", transparent=True)
# mlab.quiver3d(x, y, z2, u2, v2, w2, scale_factor=1)
else:
# sr = mlab.mesh(x_r, y_r, z_r, colormap="gray", transparent=True)
s2 = mlab.mesh(x, y, z2, colormap="gray", transparent=True)
# mlab.quiver3d(x, y, z2, u2, v2, w2,colormap="gray",transparent=True, scale_factor=1)
def genvec(vec,fig,color=None):
vmag=np.sqrt(vec[0]**2+vec[1]**2+vec[2]**2); vec=vec/vmag
u=[vec[0]];v=[vec[1]];w=[vec[2]]
mlab.quiver3d([0],[0],[0],u,v,w,line_width=3, scale_factor=1,figure=fig,color=color)
0,
0,
scale_mode='none',
scale_factor=2,
color=tango.aluminum3,
resolution=50,
opacity=0.7,
name='Poincare')
sphere.actor.property.specular = 0.45
sphere.actor.property.specular_power = 5
sphere.actor.property.backface_culling = True
# Display Cartesian axes
mlab.points3d((0, 0), (0, 0), (0, 0), mode='axes', scale_factor=1.2) # axes
mlab.quiver3d(*itertools.chain(rotations([1.2, 0, 0]), rotations([1, 0, 0])),
color=tango.black,
mode='arrow',
scale_factor=0.1) # axes arrows
# Display great circles
t = N.linspace(0, 2 * N.pi, 100, endpoint=True)
for coords in rotations([N.cos(t), N.sin(t), N.zeros_like(t)]):
mlab.plot3d(*coords, tube_radius=None)
# Plotting parameters
plots = zip(
['D', 'A', 'R', 'L'], # polarization
[(0, 1, 0), (0, -1, 0), (0, 0, 1), (0, 0, -1)], # incident stokes vector
['butter3', 'chameleon3', 'skyblue3', 'scarletred3'], # colors
[1, 1, 1, 1], # E_TM
[1, 1, 1, 1], # E_TE
[0, N.pi, N.pi / 2, -N.pi / 2], # psi
lw = 5
# Plot the black crosses first ...
mlab.points3d([my_x],[my_y],
[my_z],[1.0],
color=(0,0,0),
mode = 'axes',
scale_factor= sphere_size)
# ... and a sphere at the same location.
mlab.quiver3d([my_x],
[my_y - sphere_size/4.],
[my_z],
[0],[1],[0],
scalars = [1],
scale_factor = sphere_size/2.,
scale_mode = 'scalar',
mode = 'sphere',
line_width = lw*0.75,
name = gal,
color=gals[gal]['col'])
# Finally, add the galaxy name as 3D text.
#mlab.text3d(my_x,my_y,my_z,'HCG91'+gal[-1],scale=20,color = (0,0,0))
mlab.text(my_x,my_y,'HCG91'+gal[-1],color=(0,0,0),z=my_z,width=0.1)
# Next, add peculiar HII regions of interest inside HCG 91c
# (See Vogt+, MNRAS (2015) for details)
# First, compute their coordinates
coords_91c = [[ratodeg(gals['hcg91c']['ra']),
mos = orbkit.run_orbkit()
# Write a npz file with all the ouput
orbkit.output.main_output(mos, outputname='h2o_MO', otype='npz')
# plot derivative
x = orbkit.grid.x
y = orbkit.grid.y
z = orbkit.grid.z
# test if mayavi exists and possibly plot
maya = False
try:
from enthought.mayavi import mlab
maya = True
except ImportError:
pass
try:
from mayavi import mlab
maya = True
except Exception:
pass
if maya == False:
print('mayavi module could not be loaded and vector field cannot be shown')
else:
mo_num = 3
mlab.quiver3d(x,y,z,mos[1,mo_num,:],mos[2,mo_num,:],\
mos[3,mo_num,:],line_width=1.5,scale_factor=0.1)
mlab.show()
max_reflect=0)
mask_points = (len(outs) // 300) or None
rx = [out[0][0] for out in outs]
ry = [out[0][1] for out in outs]
rz = [out[0][2] for out in outs]
ru = [out[1][0] * (2 if out[3] else 1) for out in outs]
rv = [out[1][1] * (2 if out[3] else 1) for out in outs]
rw = [out[1][2] * (2 if out[3] else 1) for out in outs]
mlab.clf()
mlab.triangular_mesh(xs, ys, zs, triangles, color=(1, 1, 0), opacity=0.9)
mlab.triangular_mesh(xs,
ys,
zs,
triangles,
color=(1, .8, 0),
representation='wireframe',
line_width=4)
mlab.quiver3d(rx,
ry,
rz,
ru,
rv,
rw,
scale_factor=1.0,
mask_points=mask_points,
vmin=1,
vmax=2)
mlab.show()
# show brain surface after proper coordinate system transformation
points = brain_surface["rr"]
faces = brain_surface["tris"]
coord_trans = fwd["mri_head_t"]["trans"]
points = np.dot(coord_trans[:3, :3], points.T).T + coord_trans[:3, -1]
mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2], faces, color=(1, 1, 0), opacity=0.3)
# show one cortical surface
mlab.triangular_mesh(lh_points[:, 0], lh_points[:, 1], lh_points[:, 2], lh_faces, color=(0.7,) * 3)
# show dipole as small cones
dipoles = mlab.quiver3d(
pos[:, 0],
pos[:, 1],
pos[:, 2],
ori[:, 0],
ori[:, 1],
ori[:, 2],
opacity=1.0,
scale_factor=4e-4,
scalars=time,
mode="cone",
colormap="RdBu",
)
# revert colormap
dipoles.module_manager.scalar_lut_manager.reverse_lut = True
mlab.colorbar(dipoles, title="Dipole fit time (ms)")
# proper 3D orientation
mlab.get_engine().scenes[0].scene.x_plus_view()
def renderSnapshot(self, t):
px,py,pz,vx,vy,vz = self._generateDataVector(t)
return mlab.quiver3d(px,py,pz,vx,vy,vz, scale_factor=1,
*self._mlab_args, **self._mlab_kwargs).mlab_source
mlab.clf()
points = mlab.points3d(xyz[:, 0],
xyz[:, 1],
xyz[:, 2], [v for v in g],
scale_factor=0.1,
scale_mode='none',
colormap='summer',
resolution=20)
print points
edge_start = []
edge_end = []
for edge in g.edges():
edge_start.append(pos[edge[0]])
edge_end.append(pos[edge[1]])
edge_start = np.array(edge_start)
edge_end = np.array(edge_end)
components = edge_end - edge_start
mlab.quiver3d(edge_start[:, 0],
edge_start[:, 1],
edge_start[:, 2],
components[:, 0],
components[:, 1],
components[:, 2],
scale_factor=0.1)
mlab.show()
print xyz
#
# test_mayavi.py ends here
append='/', # where to append in file
data_only=True, # do not include grid, structure, and MO data
is_mo_output=False,
mo_spec=mo['mo_spec'])
# plot derivative
x = ok.grid.x
y = ok.grid.y
z = ok.grid.z
# test if mayavi exists and possibly plot
maya = False
try:
from enthought.mayavi import mlab
maya = True
except ImportError:
pass
try:
from mayavi import mlab
maya = True
except Exception:
pass
if maya == False:
print('mayavi module could not be loaded and vector field cannot be shown')
else:
mo_num = 3
mlab.quiver3d(x,y,z,delta_mo_list[0,mo_num,:],delta_mo_list[1,mo_num,:],\
delta_mo_list[2,mo_num,:],line_width=1.5,scale_factor=0.1)
mlab.show()
def ip_format(
a,
b,
A,
gamma=np.pi / 2,
plot=False,
color="b",
linewidth=1,
linestyle="-",
alpha=1,
show=True,
stem=False,
stemline="g--",
stemmarker="ro",
mayavi_app=False,
):
r"""
Function to generate the 'Arraytool' input format.
:param a: separation between elements along the x-axis in wavelengths
:param b: separation between elements along the y-axis in wavelengths
:param A: visual excitation matrix
:param gamma: lattice angle in radians
:param plot: if True, produces a 2D/3D plot of the array excitation
:param stem: if True, the array excitation is plotted as 'stem plot'
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
All other parameters are nothing but the 'Matplotlib' parameters. These
should be familiar to 'Matlab' or 'Matplotlib' users.
:rtype: array_ip, a Numpy array of size (Number_elements(A)*4)
"""
M = float(A.shape[1]) # no. of elements along the x-axis
N = float(A.shape[0]) # no. of elements along the y-axis
if M == 1: # i.e, linear array is along the y-direction
a = 0
gamma = np.pi / 2
if N == 1: # i.e, linear array is along the x-direction
b = 0
gamma = np.pi / 2
xlim = (M * a) / 2 # array is with in the x-limits [-xlim, +xlim]
# Grid generation
[x, y] = np.mgrid[0:M, 0:N]
x = (x - (M - 1) / 2).T
y = np.flipud((y - (N - 1) / 2).T)
x = x * a
y = y * b # rectangular grid is generated
# modifying the rect-grid according to the given lattice angle 'gamma'
if gamma != np.pi / 2:
x = x + (y / np.tan(gamma)) % a
# Adjusting the rows so that the array lies within [-xlim, +xlim]
for i1 in range(int(N)):
if x[i1, 0] < -xlim:
x[i1, :] = x[i1, :] + a # RIGHT shifting the row by 'a'
if x[i1, -1] > xlim:
x[i1, :] = x[i1, :] - a # LEFT shifting the row by 'a'
# Finally, arranging all the data into 'Arraytool' input format
x = np.reshape(x, (M * N, -1))
y = np.reshape(y, (M * N, -1))
z = np.zeros_like(x) # because only planar arrays are permitted here
A = np.reshape(A, (M * N, -1))
array_ip = np.hstack((x, y, z, A)) # finally, 'Arraytool' input format
# plotting the 'absolute' value of the array excitation (2D/3D)
if plot:
# checking whether 'A' has any imaginary values
if (A.imag > 1e-10).sum():
A_plt = abs(A) # if A.imag are significant, then '|A|' will be plotted
else:
A_plt = A.real # if A.imag are negligible, then 'A' will be plotted
if M == 1: # i.e, linear array is along the y-direction
plt.plot(y, A_plt, color=color, linewidth=linewidth, linestyle=linestyle, alpha=alpha)
if stem:
plt.stem(y, A_plt, linefmt=stemline, markerfmt=stemmarker)
plt.axis("tight")
plt.grid(True)
plt.xlabel(r"$y$", fontsize=16)
plt.ylabel(r"$\left|A_{n}\right|$", fontsize=16)
if show:
plt.title(r"$\mathrm{Array}\ \mathrm{Excitation}$", fontsize=18)
plt.show()
elif N == 1: # i.e, linear array is along the x-direction
plt.plot(x, A_plt, color=color, linewidth=linewidth, linestyle=linestyle, alpha=alpha)
if stem:
plt.stem(x, A_plt, linefmt=stemline, markerfmt=stemmarker)
plt.axis("tight")
plt.grid(True)
plt.xlabel(r"$x$", fontsize=16)
plt.ylabel(r"$\left|A_{m}\right|$", fontsize=16)
if show:
plt.title(r"$\mathrm{Array}\ \mathrm{Excitation}$", fontsize=18)
plt.show()
else:
if mayavi_app: # this option opens the 3D plot in MayaVi Application
mlab.options.backend = "envisage"
s1 = mlab.quiver3d(x, y, z, z, z, A_plt) # stem3D representation
ranges1 = [x.min(), x.max(), y.min(), y.max(), A_plt.min(), A_plt.max()]
mlab.axes(xlabel="x", ylabel="y", zlabel="Amn", ranges=ranges1, nb_labels=3)
mlab.colorbar(orientation="vertical", nb_labels=5)
s1.scene.isometric_view()
if show:
mlab.show()
return array_ip
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)
def plot3d(cf, vp1, vp2, precision, m1,m2,centerline,showbases,seq,mult,ghost): #DRAWS THE MOLECULE(S)
print "Loading mlab....."
from anim import stiffness, ACP
from draw import data3d
from enthought.mayavi import mlab
r, q, d, g, h, sequence = cf
nbbases = q.shape[0]
#mlab.options.backend = 'envisage' #to use the full Mayavi interface
fig = mlab.figure(1, bgcolor=(0, 0, 0), size=(1024,768)) #creates a new window
mlab.clf() #clears the window
if centerline >0 :
#DRAWS CENTRAL AXIS
#axis_nodes = mlab.points3d(q[:,0],q[:,1],q[:,2],scale_factor=0.4,color=(1,0,1))
ghost_axis = mlab.plot3d(q[:,0],q[:,1],q[:,2],tube_radius=0.05, color=(1,1,1)) #will never move
axis = mlab.plot3d(q[:,0],q[:,1],q[:,2],tube_radius=0.05, color=(1,0,1))
#DRAWS LAB FRAME e
ee = mlab.quiver3d([0,0,0],[0,0,0],[0,0,0],[1,0,0],[0,1,0],[0,0,1], \
mode='arrow',scale_factor=2, color=(1,1,1))
if showbases >0 :
#DRAWS BASES AND BASE FRAMES
points, triangles, centres, frames, i,j, repere, s = data3d(r,d,sequence) #see draw.py
if ghost >0 :
ghost = mlab.triangular_mesh(points[:,0],points[:,1],points[:,2],triangles,color=(1,1,1),opacity=0.4)
k = array(repere)[nbbases,0] #index i : points
l = array(repere)[nbbases,1] #index j : triangles
main_strand = mlab.triangular_mesh(points[:k,0],points[:k,1],points[:k,2],triangles[:l],\
scalars=s[:k], colormap="autumn") #main strand
comp_strand = mlab.triangular_mesh(points[k:i,0],points[k:i,1],points[k:i,2],triangles[l:j]-triangles[l,0],\
scalars=s[k:i], colormap="autumn") #complementary
baseframes = mlab.quiver3d(centres[:,0],centres[:,1],centres[:,2],frames[:,0],frames[:,1],frames[:,2], \
mode='arrow',scale_factor=2, colormap="Oranges")
#ANIMATION
if vp1 >0 :
if centerline >0 :
#axis_nodes_src = axis_nodes.mlab_source
axis_src = axis.mlab_source
if showbases >0 :
main_strand_src = main_strand.mlab_source
comp_strand_src = comp_strand.mlab_source
baseframes_src = baseframes.mlab_source
vecp = stiffness(m1,1,vp1-1) #chosen eigenvector 1
#mult = 2.0 #multiple of the added eigenvector
fig.scene.anti_aliasing_frames = 0 #antialiasing, default=8, off=0
for p in range(precision):
wmod = ACP(readParameters(),vecp,mult*(p+1)/precision) #modified configuration
cfmod = calculPoints(wmod) #modified coordinates
r_p, q_p, d_p, g_p, h_p, sequence = cfmod
fig.scene.disable_render = True #accelerates rendering
if showbases >0 :
points_p, triangles_p, centres_p, frames_p, i,j, repere, s = data3d(r_p,d_p,sequence) #modified graphics
main_strand_src.set(x=points_p[:k,0],y=points_p[:k,1],z=points_p[:k,2])
comp_strand_src.set(x=points_p[k:i,0],y=points_p[k:i,1],z=points_p[k:i,2])
baseframes_src.reset(x=centres_p[:,0],y=centres_p[:,1],z=centres_p[:,2],\
u=frames_p[:,0],v=frames_p[:,1],w=frames_p[:,2])
if centerline >0 :
#axis_nodes_src.set(x=q_p[:,0],y=q_p[:,1],z=q_p[:,2],scale_factor=0.7,color=(1,0,1))
axis_src.set(x=q_p[:,0],y=q_p[:,1],z=q_p[:,2],tube_radius=0.05, color=(1,0,1))
fig.scene.disable_render = False
if vp2 > 0:
if showbases >0 :
#new instance of the entire molecule
new_main_strand = mlab.triangular_mesh(points[:k,0],points[:k,1],points[:k,2],triangles[:l],\
scalars=s[:k], colormap="winter") #main strand
new_comp_strand = mlab.triangular_mesh(points[k:i,0],points[k:i,1],points[k:i,2],triangles[l:j]-triangles[l,0],\
scalars=s[k:i], colormap="winter") #complementary
new_baseframes = mlab.quiver3d(centres[:,0],centres[:,1],centres[:,2],frames[:,0],frames[:,1],frames[:,2], \
mode='arrow',scale_factor=2, colormap="Blues")
new_main_strand_src = new_main_strand.mlab_source
new_comp_strand_src = new_comp_strand.mlab_source
new_baseframes_src = new_baseframes.mlab_source
if centerline >0 :
new_axis = mlab.plot3d(q[:,0],q[:,1],q[:,2],tube_radius=0.05, color=(1,0,1))
new_axis_src = new_axis.mlab_source
vecp = stiffness(m2,1,vp2-1) #chosen eigenvector 2
#mult = 2.0 #multiple of the added eigenvector
for p in range(precision):
wmod = ACP(readParameters(),vecp,mult*(p+1)/precision) #modified configuration
cfmod = calculPoints(wmod) #modified coordinates
r_p, q_p, d_p, g_p, h_p, sequence = cfmod
if showbases >0 :
points_p, triangles_p, centres_p, frames_p, i,j, repere, s = data3d(r_p,d_p,sequence) #modified graphics
fig.scene.disable_render = True #accelerates rendering
new_main_strand_src.set(x=points_p[:k,0],y=points_p[:k,1],z=points_p[:k,2])
new_comp_strand_src.set(x=points_p[k:i,0],y=points_p[k:i,1],z=points_p[k:i,2])
new_baseframes_src.reset(x=centres_p[:,0],y=centres_p[:,1],z=centres_p[:,2],\
u=frames_p[:,0],v=frames_p[:,1],w=frames_p[:,2])
if centerline >0 :
#new_axis_nodes_src.set(x=q_p[:,0],y=q_p[:,1],z=q_p[:,2],scale_factor=0.7,color=(1,0,1))
new_axis_src.set(x=q_p[:,0],y=q_p[:,1],z=q_p[:,2],tube_radius=0.05, color=(1,0,1))
fig.scene.disable_render = False
mlab.orientation_axes()
mlab.show()
mlab.points3d(ecog_electrodes[[ecog_chan_idx],0],
ecog_electrodes[[ecog_chan_idx],1],
ecog_electrodes[[ecog_chan_idx],2],
opacity=0.5, scale_factor=8, color=(1,0,0))
###############################################################################
# MEG leadfield
mlab.figure(3)
mlab.clf()
meg_chan_idx = 30
cortex.plot(opacity=1, scalars=G_meg[meg_chan_idx,:])
# view MEG squids
mlab.quiver3d(squids[[meg_chan_idx], 0], squids[[meg_chan_idx], 1],
squids[[meg_chan_idx], 2], -squids[[meg_chan_idx], 3],
-squids[[meg_chan_idx], 4], -squids[[meg_chan_idx], 5],
opacity=0.5, scale_factor=10, mode='cone')
###############################################################################
# EIT leadfield
mlab.figure(4)
mlab.clf()
# Generate sample current injection set up
n_electrodes = eeg_electrodes.shape[0]
j_eit = np.zeros(n_electrodes)
idx_in = 10
idx_out = 13
j_eit[idx_in] = 1 # +1 current enters in idx_in electrode
j_eit[idx_out] = -1 # -1 current leaves in idx_out electrode
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]]
x_pca_plane.shape = (2, 2)
y_pca_plane.shape = (2, 2)
z_pca_plane.shape = (2, 2)
mlab.mesh(x_pca_plane,
y_pca_plane,
mlab.clf()
mlab.points3d(center.T[0],
center.T[1],
center.T[2],
colors_,
scale_factor=0.05,
scale_mode='none',
colormap='RdBu',
vmin=colors_.min(),
vmax=colors_.max())
#mlab.quiver3d(center.T[0],center.T[1],center.T[2],normal.T[0],normal.T[1],normal.T[2],colormap='spectral',scale_mode='none')
mlab.colorbar()
if i >= 1:
vx,vy,vz = center.T[0]-old_center.T[0],\
center.T[1]-old_center.T[1],\
center.T[2]-old_center.T[2]
v = np.sqrt(vx**2 + vy**2 + vz**2)
mlab.quiver3d(center.T[0],center.T[1],center.T[2],\
vx,vy,vz,scalars=v,colormap='spectral',scale_mode='scalar')
#mlab.show()
old_center = center.copy()
mlab.view(distance=5, azimuth=-90, elevation=90)
mlab.savefig('pulsation_lm%d%d_k%03d_%03d.png' %
(l, m, k, i))
mlab.close()
multimedia.make_movie(
'pulsation_lm%d%d_k%03d_*.png' % (l, m, k),
output='pulsation_lm%d%d_k%03d.avi' % (l, m, k))
faces,
color=(1, 1, 0),
opacity=0.1)
# show one cortical surface
mlab.triangular_mesh(rh_points[:, 0],
rh_points[:, 1],
rh_points[:, 2],
rh_faces,
color=(0.7, ) * 3,
opacity=0.3)
# show dipole as small cones
dipoles = mlab.quiver3d(dip.pos[:, 0],
dip.pos[:, 1],
dip.pos[:, 2],
dip.ori[:, 0],
dip.ori[:, 1],
dip.ori[:, 2],
opacity=0.8,
scale_factor=4e-3,
scalars=dip.times,
mode='cone',
colormap='RdBu')
# revert colormap
dipoles.module_manager.scalar_lut_manager.reverse_lut = True
mlab.colorbar(dipoles, title='Dipole fit time (ms)')
# proper 3D orientation
mlab.get_engine().scenes[0].scene.x_plus_view()
print "PE=%g" % (PE)
print "=" * 50
print ""
if not (disabled):
mlab.clf()
if md == True:
mlab.points3d(ax,
ay,
az,
types,
colormap="gist_heat",
scale_factor=0.7)
mlab.quiver3d(ax,
ay,
az,
afx,
afy,
afz,
line_width=3,
scale_factor=1)
else:
datoms, dtypes = duplicate26(zip(ax, ay, az), types,
zip(v1, v2, v3))
axd, ayd, azd = zip(*datoms)
mlab.points3d(axd,
ayd,
azd,
dtypes,
colormap="gist_heat",
scale_factor=0.7)
mlab.plot3d([0, v1[0]], [0, v1[1]], [0, v1[2]],
color=(0, 1, 0))
break
print "="*50
print count
print "Stress (in kB) XX YY ZZ XY YZ XZ:"
print stresskb
if md==True:
print "T=%g, PE=%g, TE=%g" % (T,PE,KE)
else:
print "PE=%g" % (PE)
print "="*50
print ""
if not(disabled):
mlab.clf()
if md==True:
mlab.points3d(ax,ay,az,types,colormap="gist_heat",scale_factor=0.7)
mlab.quiver3d(ax,ay,az,afx,afy,afz,line_width=3,scale_factor=1)
else:
datoms,dtypes=duplicate26(zip(ax,ay,az),types,zip(v1,v2,v3))
axd,ayd,azd=zip(*datoms)
mlab.points3d(axd,ayd,azd,dtypes,colormap="gist_heat",scale_factor=0.7)
mlab.plot3d([0,v1[0]],[0,v1[1]],[0,v1[2]],color=(0,1,0))
mlab.plot3d([v1[0],v1[0]+v2[0]],[v1[1],v1[1]+v2[1]],[v1[2],v1[2]+v2[2]],color=(0,1,0))
mlab.plot3d([v1[0],v1[0]+v3[0]],[v1[1],v1[1]+v3[1]],[v1[2],v1[2]+v3[2]],color=(0,1,0))
mlab.plot3d([v1[0]+v2[0],v1[0]+v2[0]+v3[0]],[v1[1]+v2[1],v1[1]+v2[1]+v3[1]],[v1[2]+v2[2],v1[2]+v2[2]+v3[2]],color=(0,1,0))
mlab.plot3d([0,v2[0]],[0,v2[1]],[0,v2[2]],color=(0,1,0))
mlab.plot3d([v2[0],v2[0]+v1[0]],[v2[1],v2[1]+v1[1]],[v2[2],v2[2]+v1[2]],color=(0,1,0))
mlab.plot3d([v2[0],v2[0]+v3[0]],[v2[1],v2[1]+v3[1]],[v2[2],v2[2]+v3[2]],color=(0,1,0))
mlab.plot3d([v2[0]+v3[0],v1[0]+v2[0]+v3[0]],[v2[1]+v3[1],v1[1]+v2[1]+v3[1]],[v2[2]+v3[2],v1[2]+v2[2]+v3[2]],color=(0,1,0))
mlab.plot3d([0,v3[0]],[0,v3[1]],[0,v3[2]],color=(0,1,0))
field.contour.minimum_contour = 2000
field.actor.property.opacity = 0.3
mlab.outline()
mlab.xlabel('RA(J2000)')
mlab.ylabel('DEC(J2000)')
mlab.zlabel('Velocity')
#mlab.axes()
#mlab.colorbar()
## Add 3-d text in the cube
mlab.text3d(30,80,23,'Outflows in L1551 IRS5',scale=2.)
#mlab.text(45,45,"outflows!",width=0.2,z=20)
## Draw arrows to show outflows etc.
vector1=mlab.quiver3d(55,55,30,1,1,0.2,mode='arrow',scale_factor=10.0,color=(0.0,0.0,1.0))
vector2=mlab.quiver3d(55,40,30,1,-1.2,0.2,mode='arrow',scale_factor=10.0,color=(0.0,0.0,1.0))
vector3=mlab.quiver3d(45,50,30,-0.8,1,0.2,mode='2dthick_arrow',scale_factor=10.0,color=(0.0,0.0,1.0))
vector4=mlab.quiver3d(49,48,30,0.2,0,1,mode='2darrow',scale_factor=8.0,color=(0.0,0.0,1.0))
vector5=mlab.quiver3d(47,58,20,-1,1.8,-0.2,mode='arrow',scale_factor=10.0,color=(1.0,0.0,0.0))
vector6=mlab.quiver3d(45,45,20,-1.2,-1,-0.2,mode='arrow',scale_factor=10.0,color=(1.0,0.0,0.0))
vector7=mlab.quiver3d(55,45,20,1,-1.4,-0.2,mode='2dthick_arrow',scale_factor=10.0,color=(1.0,0.0,0.0))
vector8=mlab.quiver3d(48,48,20,-0.2,0,-1,mode='2darrow',scale_factor=8.0,color=(1.0,0.0,0.0))
# Insert the continuum image as a background
img=pyfits.open('L1551.cont.fits') # Read the fits data
dat=img[0].data
dat0=dat[0]
channel=dat0[0]
region=channel[0:95,0:95]*1000 # Select a region. Use mJy unit
try:
from enthought.mayavi import mlab
except:
from mayavi import mlab
lh_points = src[0]['rr']
lh_faces = src[0]['use_tris']
mlab.figure(size=(600, 600), bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
# show brain surface after proper coordinate system transformation
points = brain_surface['rr']
faces = brain_surface['tris']
coord_trans = fwd['mri_head_t']['trans']
points = np.dot(coord_trans[:3,:3], points.T).T + coord_trans[:3,-1]
mlab.triangular_mesh(points[:, 0], points[:, 1], points[:, 2],
faces, color=(1, 1, 0), opacity=0.3)
# show one cortical surface
mlab.triangular_mesh(lh_points[:, 0], lh_points[:, 1], lh_points[:, 2],
lh_faces, color=(0.7, ) * 3)
# show dipole as small cones
dipoles = mlab.quiver3d(pos[:,0], pos[:,1], pos[:,2],
ori[:,0], ori[:,1], ori[:,2],
opacity=1., scale_factor=4e-4, scalars=time,
mode='cone')
mlab.colorbar(dipoles, title='Dipole fit time (ms)')
# proper 3D orientation
mlab.get_engine().scenes[0].scene.x_plus_view()
# Display a semi-transparent sphere
sphere = mlab.points3d(0, 0, 0,
scale_mode='none',
scale_factor=2,
color=tango.aluminum3,
resolution=50,
opacity=0.7,
name='Poincare')
sphere.actor.property.specular = 0.45
sphere.actor.property.specular_power = 5
sphere.actor.property.backface_culling = True
# Display Cartesian axes
mlab.points3d((0, 0), (0, 0), (0, 0), mode='axes', scale_factor=1.2) # axes
mlab.quiver3d(*itertools.chain(rotations([1.2, 0, 0]), rotations([1, 0, 0])),
color=tango.black,
mode='arrow',
scale_factor=0.1) # axes arrows
# Display great circles
t = N.linspace(0, 2 * N.pi, 100, endpoint=True)
for coords in rotations([N.cos(t), N.sin(t), N.zeros_like(t)]):
mlab.plot3d(*coords, tube_radius=None)
# Plotting parameters
plots = zip(
['D', 'A', 'R', 'L'], # polarization
[(0, 1, 0), (0, -1, 0), (0, 0, 1), (0, 0, -1)], # incident stokes vector
['butter3', 'chameleon3', 'skyblue3', 'scarletred3'], # colors
[1, 1, 1, 1], # E_TM
[1, 1, 1, 1], # E_TE
[0, N.pi, N.pi / 2, -N.pi / 2], # psi
def ip_format(a, b, A, gamma=np.pi / 2, plot=False, color='b', linewidth=1,
linestyle='-', alpha=1, show=True, stem=False, stemline='g--',
stemmarker='ro', mayavi_app=False):
r"""
Function to generate the 'Arraytool' input format.
:param a: separation between elements along the x-axis in wavelengths
:param b: separation between elements along the y-axis in wavelengths
:param A: visual excitation matrix
:param gamma: lattice angle in radians
:param plot: if True, produces a 2D/3D plot of the array excitation
:param stem: if True, the array excitation is plotted as 'stem plot'
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
All other parameters are nothing but the 'Matplotlib' parameters. These
should be familiar to 'Matlab' or 'Matplotlib' users.
:rtype: array_ip, a Numpy array of size (Number_elements(A)*4)
"""
M = float(A.shape[1]) # no. of elements along the x-axis
N = float(A.shape[0]) # no. of elements along the y-axis
if (M == 1): # i.e, linear array is along the y-direction
a = 0
gamma = np.pi / 2
if (N == 1): # i.e, linear array is along the x-direction
b = 0
gamma = np.pi / 2
xlim = (M * a) / 2 # array is with in the x-limits [-xlim, +xlim]
# Grid generation
[x, y] = np.mgrid[0:M, 0:N]
x = (x - (M - 1) / 2).T
y = np.flipud((y - (N - 1) / 2).T)
x = x * a
y = y * b # rectangular grid is generated
# modifying the rect-grid according to the given lattice angle 'gamma'
if (gamma != np.pi / 2):
x = x + (y / np.tan(gamma)) % a
# Adjusting the rows so that the array lies within [-xlim, +xlim]
for i1 in range(int(N)):
if (x[i1, 0] < -xlim):
x[i1, :] = x[i1, :] + a # RIGHT shifting the row by 'a'
if (x[i1, -1] > xlim):
x[i1, :] = x[i1, :] - a # LEFT shifting the row by 'a'
# Finally, arranging all the data into 'Arraytool' input format
x = np.reshape(x, (M * N, -1))
y = np.reshape(y, (M * N, -1))
z = np.zeros_like(x) # because only planar arrays are permitted here
A = np.reshape(A, (M * N, -1))
array_ip = np.hstack((x, y, z, A)) # finally, 'Arraytool' input format
# plotting the 'absolute' value of the array excitation (2D/3D)
if (plot):
# checking whether 'A' has any imaginary values
if((A.imag > 1e-10).sum()):
A_plt = abs(A) # if A.imag are significant, then '|A|' will be plotted
else:
A_plt = A.real # if A.imag are negligible, then 'A' will be plotted
if (M == 1): # i.e, linear array is along the y-direction
plt.plot(y, A_plt, color=color, linewidth=linewidth,
linestyle=linestyle, alpha=alpha)
if(stem): plt.stem(y, A_plt, linefmt=stemline, markerfmt=stemmarker)
plt.axis('tight'); plt.grid(True)
plt.xlabel(r'$y$', fontsize=16); plt.ylabel(r'$\left|A_{n}\right|$', fontsize=16)
if(show): plt.title(r'$\mathrm{Array}\ \mathrm{Excitation}$', fontsize=18); plt.show()
elif (N == 1): # i.e, linear array is along the x-direction
plt.plot(x, A_plt, color=color, linewidth=linewidth,
linestyle=linestyle, alpha=alpha)
if(stem): plt.stem(x, A_plt, linefmt=stemline, markerfmt=stemmarker)
plt.axis('tight'); plt.grid(True)
plt.xlabel(r'$x$', fontsize=16); plt.ylabel(r'$\left|A_{m}\right|$', fontsize=16)
if(show): plt.title(r'$\mathrm{Array}\ \mathrm{Excitation}$', fontsize=18); plt.show()
else:
if (mayavi_app): # this option opens the 3D plot in MayaVi Application
mlab.options.backend = 'envisage'
s1 = mlab.quiver3d(x, y, z, z, z, A_plt) # stem3D representation
ranges1 = [x.min(), x.max(), y.min(), y.max(), A_plt.min(), A_plt.max()]
mlab.axes(xlabel="x", ylabel="y", zlabel="Amn", ranges=ranges1, nb_labels=3)
mlab.colorbar(orientation="vertical", nb_labels=5)
s1.scene.isometric_view()
if(show): mlab.show()
return array_ip
def plot_axis(x,y, z, directions):
from enthought.mayavi import mlab
mlab.quiver3d(x, y, z, [directions[0,0]], [directions[1,0]], [directions[2,0]], scale_factor=1, color=(1,0,0))
if directions.shape[1] > 1:
mlab.quiver3d(x, y, z, [directions[0,1]], [directions[1,1]], [directions[2,1]], scale_factor=1, color=(0,1,0))
mlab.quiver3d(x, y, z, [directions[0,2]], [directions[1,2]], [directions[2,2]], scale_factor=1, color=(0,0,1))