def generate_plots_3d(self):
self.ax = mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(800, 600))
self.clf = mlab.clf()
minS, maxS = maxint, 0
contour_plots = []
for cond in self.conductors.itervalues():
minS, maxS, face_data = self.generate_plot_data_for_faces_3d(cond, minS, maxS)
for (x, y, z, s) in face_data:
if isinstance(cond, conductor_type_3d['Unstructured']):
pts = mlab.points3d(x, y, z, s, scale_mode='none', scale_factor=0.002)
mesh = mlab.pipeline.delaunay3d(pts)
contour_plots.append(mlab.pipeline.surface(mesh, colormap='viridis'))
else:
if np.min(s) < 0.0:
contour_plots.append(mlab.mesh(x, y, z, color=(0, 0, 0), colormap='viridis'))
else:
contour_plots.append(mlab.mesh(x, y, z, scalars=s, colormap='viridis'))
for cp in contour_plots:
cp.module_manager.scalar_lut_manager.trait_set(default_data_range=[minS * 0.95, maxS * 1.05])
mlab.draw()
mlab.colorbar(object=contour_plots[0], orientation='vertical')
mlab.show()
def PlotHorizon3d(tss):
"""
Plot a list of horizons.
Parameters
----------
tss : list of trappedsurface
All the trapped surfaces to visualize.
"""
from mayavi import mlab
cmaps = ['bone', 'jet', 'hot', 'cool', 'spring', 'summer', 'winter']
assert len(cmaps) > len(tss)
extents = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
for ts, cm in zip(tss, cmaps):
mlab.mesh(ts.X, ts.Y, ts.Z, colormap=cm, opacity=0.4)
extents[0] = min(extents[0], np.min(ts.X))
extents[1] = max(extents[1], np.max(ts.X))
extents[2] = min(extents[2], np.min(ts.Y))
extents[3] = max(extents[3], np.max(ts.Y))
extents[4] = min(extents[4], np.min(ts.Z))
extents[5] = max(extents[5], np.max(ts.Z))
mlab.axes(extent=extents)
mlab.outline(extent=extents)
mlab.show()
def draw_encoders(encoders, angle=None, colors=None):
"""Add encoders to the scene.
Can either supply a collection of encoders, or an integer giving the
number of encoders to randomly generate.
encoders: int or collection
angle: angle giving size of the cap drawn to represent an encoder
colors: collection of normalized rgb colors to paint the encoders
"""
if isinstance(encoders, int):
encoders = dists.UniformHypersphere(3, surface=True).sample(encoders)
if colors is None:
colors = [black for e in encoders]
if not angle:
angle = 0.01 * np.pi
for enc, color in zip(encoders, colors):
front = color
back = black
cap = threed.make_cap(r=1.01*radius, cap_angle=angle, direction=enc)
mlab.mesh(*cap, color=front, opacity=1.0)
cap = threed.make_cap(r=1.007*radius, cap_angle=angle, direction=enc)
mlab.mesh(*cap, color=back, opacity=1.0)
def plot(self, Ps):
from mayavi import mlab
mlab.figure(mlab.figure(), bgcolor=(1, 1, 1))
for s in range(len(Ps)):
mlab.mesh(Ps[s][:, :, 0], Ps[s][:, :, 1], Ps[s][:, :, 2], color=(65 / 256, 105 / 256, 225 / 256))
mlab.show()
def plotvfonsph3D(theta_rad, phi_rad, E_th, E_ph, freq=0.0,
vcoord='sph', projection='equirectangular'):
PLOT3DTYPE = "quiver"
(x, y, z) = sph2crtISO(theta_rad, phi_rad)
from mayavi import mlab
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300))
mlab.clf()
if PLOT3DTYPE == "MESH_RADIAL" :
r_Et = numpy.abs(E_th)
r_Etmx = numpy.amax(r_Et)
mlab.mesh(r_Et*(x)-1*r_Etmx, r_Et*y, r_Et*z, scalars=r_Et)
r_Ep = numpy.abs(E_ph)
r_Epmx = numpy.amax(r_Ep)
mlab.mesh(r_Ep*(x)+1*r_Epmx , r_Ep*y, r_Ep*z, scalars=r_Ep)
elif PLOT3DTYPE == "quiver":
##Implement quiver plot
s2cmat = getSph2CartTransfMatT(numpy.array([x,y,z]))
E_r = numpy.zeros(E_th.shape)
E_fldsph = numpy.rollaxis(numpy.array([E_r, E_ph, E_th]), 0, 3)[...,numpy.newaxis]
E_fldcrt = numpy.rollaxis(numpy.matmul(s2cmat, E_fldsph).squeeze(), 2, 0)
#print E_fldcrt.shape
mlab.quiver3d(x+1.5, y, z,
numpy.real(E_fldcrt[0]),
numpy.real(E_fldcrt[1]),
numpy.real(E_fldcrt[2]))
mlab.quiver3d(x-1.5, y, z,
numpy.imag(E_fldcrt[0]),
numpy.imag(E_fldcrt[1]),
numpy.imag(E_fldcrt[2]))
mlab.show()
def draw_ballast(self, fig, centerline, freeboard, h_section, r_nodes, h_perm, h_water):
from mayavi import mlab
npts = 40
th = np.linspace(0, 2*np.pi, npts)
z_nodes = np.flipud( freeboard - np.r_[0.0, np.cumsum(np.flipud(h_section))] )
# Permanent ballast
z_perm = z_nodes[0] + np.linspace(0, h_perm, npts)
r_perm = np.interp(z_perm, z_nodes, r_nodes)
R, TH = np.meshgrid(r_perm, th)
Z, _ = np.meshgrid(z_perm, th)
X = R*np.cos(TH) + centerline[0]
Y = R*np.sin(TH) + centerline[1]
ck = np.array([122, 85, 33]) / 255.0
ck = tuple(ck.tolist())
mlab.mesh(X, Y, Z, color=ck, figure=fig)
# Water ballast
z_water = z_perm[-1] + np.linspace(0, h_water, npts)
r_water = np.interp(z_water, z_nodes, r_nodes)
R, TH = np.meshgrid(r_water, th)
Z, _ = np.meshgrid(z_water, th)
X = R*np.cos(TH) + centerline[0]
Y = R*np.sin(TH) + centerline[1]
ck = (0.0, 0.1, 0.8) # Dark blue
mlab.mesh(X, Y, Z, color=ck, figure=fig)
def draw_column(self, fig, centerline, freeboard, h_section, r_nodes, spacingVec=None, ckIn=None):
from mayavi import mlab
npts = 20
nsection = h_section.size
z_nodes = np.flipud( freeboard - np.r_[0.0, np.cumsum(np.flipud(h_section))] )
th = np.linspace(0, 2*np.pi, npts)
for k in xrange(nsection):
rk = np.linspace(r_nodes[k], r_nodes[k+1], npts)
z = np.linspace(z_nodes[k], z_nodes[k+1], npts)
R, TH = np.meshgrid(rk, th)
Z, _ = np.meshgrid(z, th)
X = R*np.cos(TH) + centerline[0]
Y = R*np.sin(TH) + centerline[1]
# Draw parameters
if ckIn is None:
ck = (0.6,)*3 if np.mod(k,2) == 0 else (0.4,)*3
else:
ck = ckIn
#ax.plot_surface(X, Y, Z, alpha=0.5, color=ck)
mlab.mesh(X, Y, Z, opacity=0.7, color=ck, figure=fig)
if spacingVec is None: continue
z = z_nodes[k] + spacingVec[k]
while z < z_nodes[k+1]:
rk = np.interp(z, z_nodes[k:], r_nodes[k:])
#ax.plot(rk*np.cos(th), rk*np.sin(th), z*np.ones(th.shape), 'r', lw=0.25)
mlab.plot3d(rk*np.cos(th) + centerline[0], rk*np.sin(th) + centerline[1], z*np.ones(th.shape), color=(0.5,0,0), figure=fig)
z += spacingVec[k]
'''
def zoncaview(m):
"""
m is a healpix sky map, such as provided by WMAP or Planck.
"""
nside = hp.npix2nside(len(m))
vmin = -1e3; vmax = 1e3
# Set up some grids:
xsize = ysize = 1000
theta = np.linspace(np.pi, 0, ysize)
phi = np.linspace(-np.pi, np.pi, xsize)
longitude = np.radians(np.linspace(-180, 180, xsize))
latitude = np.radians(np.linspace(-90, 90, ysize))
# Project the map to a rectangular matrix xsize x ysize:
PHI, THETA = np.meshgrid(phi, theta)
grid_pix = hp.ang2pix(nside, THETA, PHI)
grid_map = m[grid_pix]
# Create a sphere:
r = 0.3
x = r*np.sin(THETA)*np.cos(PHI)
y = r*np.sin(THETA)*np.sin(PHI)
z = r*np.cos(THETA)
# The figure:
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300))
mlab.clf()
mlab.mesh(x, y, z, scalars=grid_map, colormap="jet", vmin=vmin, vmax=vmax)
mlab.draw()
return
def plot_both_mayavi(f, xbounds=(-1, 1), ybounds=(-1, 1), res=401):
""" Plot the real and imaginary parts of
the function 'f', given the bounds and resolution. """
X, Y, vals = get_vals(f, xbounds, ybounds, res)
ml.mesh(X, Y, vals.real)
ml.mesh(X, Y, vals.imag)
ml.show()
def plotOrs(*ptss):
mlab.figure(34); mlab.clf()
r = 1
phi, theta = mgrid[0:pi:101j, 0:2*pi:101j]
x = r*sin(phi)*cos(theta)
y = r*sin(phi)*sin(theta)
z = r*cos(phi)
mlab.mesh(x,y,z, colormap='gray',opacity=.2)
colors = [(1,0,0),(0,1,0),(0,0,1)]
print len(colors)
print len(ptss)
for (pts,col) in izip(ptss,colors):
print col
ors = normr(pts)
# Create a sphere
x,y,z = ors.T
mlab.points3d(x,y,z,color=col)
mlab.plot3d(x,y,z,color=col,tube_radius=.025)
def addDepthMap(figure,
input_atoms,
vmin,
vmax,
scale_factors=None,
colormap='hot',
resolution=32,
):
from mayavi import mlab
if scale_factors is None:
scale_factors=np.ones(len(input_atoms))
numbers = input_atoms.get_atomic_numbers()
collections = set(zip(numbers, scale_factors))
for number, scale_factor in collections:
take1 = numbers == number
take2 = scale_factors == scale_factor
take = np.logical_and(take1, take2)
atoms = input_atoms[take]
points = atoms.positions
radius = my_radii[number]/2.0
radius *= scale_factor
points = points[points[:, 2] > vmin - radius]
for point in points:
x, y, z = sphere(point, radius, resolution=resolution)
mlab.mesh(x,y,z,
scalars=z,
vmin=vmin,
vmax=vmax,
colormap=colormap,
figure=figure,
)
def plot_sphere_func(f, grid='Clenshaw-Curtis', theta=None, phi=None, colormap='jet', fignum=0):
# Note: all grids except Clenshaw-Curtis have holes at the poles
import matplotlib
matplotlib.use('WxAgg')
matplotlib.interactive(True)
from mayavi import mlab
if grid == 'Driscoll-Healy':
b = f.shape[0] / 2
elif grid == 'Clenshaw-Curtis':
b = (f.shape[0] - 2) / 2
elif grid == 'SOFT':
b = f.shape[0] / 2
elif grid == 'Gauss-Legendre':
b = (f.shape[0] - 2) / 2
if theta is None or phi is None:
theta, phi = meshgrid(b=b, convention=grid)
phi = np.r_[phi, phi[0, :][None, :]]
theta = np.r_[theta, theta[0, :][None, :]]
f = np.r_[f, f[0, :][None, :]]
x = np.sin(theta) * np.cos(phi)
y = np.sin(theta) * np.sin(phi)
z = np.cos(theta)
mlab.figure(fignum, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(600, 400))
mlab.clf()
mlab.mesh(x, y, z, scalars=f, colormap=colormap)
# mlab.view(90, 70, 6.2, (-1.3, -2.9, 0.25))
mlab.show()
def plotView(rays,pts=None, **kwargs):
if not pts is None:
x = scipy.zeros((len(rays)+1,pts))
y = scipy.zeros(x.shape)
z = scipy.zeros(x.shape)
for i in rays:
i.norm.s = scipy.linspace(i.norm.s[0],i.norm.s[-1],pts)
else:
x = scipy.zeros((len(rays)+1,len(rays[0].norm.s)))
y = scipy.zeros(x.shape)
z = scipy.zeros(x.shape)
for i in xrange(len(rays)):
if rays[i]._origin.flag:
rays[i] = rays[i].c()
x[i] = rays[i].x()[0]
y[i] = rays[i].x()[1]
z[i] = rays[i].x()[2]
x[-1] = rays[0].x()[0]
y[-1] = rays[0].x()[1]
z[-1] = rays[0].x()[2]
mlab.mesh(x,y,z,**kwargs)
def display(self,bgcolor=None,showAxes=False):
mlab.figure(bgcolor=bgcolor)
for x,y,z,op,col in zip(self.xMesh,self.yMesh,self.zMesh,self.meshOpacity,self.meshColor):
mlab.mesh(x,y,z,opacity=op,color=col)
if showAxes:
mlab.axes()
mlab.show()
def drawstick(p1, p2, radius=1, clr = (1,0,0), alpha = 1, samplenum = 6, ): if isinstance(radius,tuple): clr = radius[1] alpha = radius[2] samplenum = radius[3] radius = radius[0] pi = np.pi cos = np.cos sin = np.sin theta = np.linspace(0,2*pi,samplenum) normv = (p1-p2) / np.linalg.norm(p1-p2) u = np.empty((3,1)) if normv[0]!= 0: u[1] = u[2] = 1 u[0] = -(normv[1]+normv[2])/normv[0] elif normv[1]!= 0: u[0] = u[2] = 1 u[1] = -(normv[0]+normv[2])/normv[1] else:# normv[2]!= 0 u[0] = u[1] = 1 u[2] = -(normv[1]+normv[0])/normv[2] u = u / np.linalg.norm(u) circle = radius * np.multiply(cos(theta),u) + radius * np.multiply( sin(theta), np.cross(normv.T, u.T).T ) p1arr = np.tile(p1,circle.shape[1]) p2arr = np.tile(p2,circle.shape[1]) c1 = circle + p1arr c2 = circle + p2arr x = np.vstack((p1arr[0],c1[0],c2[0],p2arr[0])) y = np.vstack((p1arr[1],c1[1],c2[1],p2arr[1])) z = np.vstack((p1arr[2],c1[2],c2[2],p2arr[2])) mlab.mesh(x,y,z, color=clr, opacity=alpha)
def plot_mayavi(self):
"""Use mayavi to plot a phenotype phase plane in 3D.
The resulting figure will be quick to interact with in real time,
but might be difficult to save as a vector figure.
returns: mlab figure object"""
from mayavi import mlab
figure = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
figure.name = "Phenotype Phase Plane"
max = 10.0
xmax = self.reaction1_fluxes.max()
ymax = self.reaction2_fluxes.max()
zmax = self.growth_rates.max()
xgrid, ygrid = meshgrid(self.reaction1_fluxes, self.reaction2_fluxes)
xgrid = xgrid.transpose()
ygrid = ygrid.transpose()
xscale = max / xmax
yscale = max / ymax
zscale = max / zmax
mlab.surf(xgrid * xscale, ygrid * yscale, self.growth_rates * zscale,
representation="wireframe", color=(0, 0, 0), figure=figure)
mlab.mesh(xgrid * xscale, ygrid * yscale, self.growth_rates * zscale,
scalars=self.shadow_prices1 + self.shadow_prices2,
resolution=1, representation="surface", opacity=0.75,
figure=figure)
# draw axes
mlab.outline(extent=(0, max, 0, max, 0, max))
mlab.axes(opacity=0, ranges=[0, xmax, 0, ymax, 0, zmax])
mlab.xlabel(self.reaction1_name)
mlab.ylabel(self.reaction2_name)
mlab.zlabel("Growth rates")
return figure
def plot_3D_spectrum(self, xmin=None, xmax=None, xN=None, ymin=None,
ymax=None, yN=None, trajectory=False, tube_radius=1e-2,
part='imag'):
"""Plot the Riemann sheet structure around the EP.
Parameters:
-----------
xmin, xmax: float
Dimensions in x-direction.
ymin, ymax: float
Dimensions in y-direction.
xN, yN: int
Number of sampling points in x and y direction.
trajectory: bool
Whether to include a projected trajectory of the eigenbasis
coefficients.
part: str
Which function to apply to the eigenvalues before plotting.
tube_radius: float
Trajectory tube thickness.
"""
from mayavi import mlab
X, Y, Z = self.sample_H(xmin, xmax, xN, ymin, ymax, yN)
Z0, Z1 = [Z[..., n] for n in (0, 1)]
def get_min_and_max(*args):
data = np.concatenate(*args)
return data.min(), data.max()
surf_kwargs = dict(colormap='Spectral', mask=np.diff(Z0.real) > 0.015)
mlab.figure(0)
Z_min, Z_max = get_min_and_max([Z0.real, Z1.real])
mlab.surf(X.real, Y.real, Z0.real, vmin=Z_min, vmax=Z_max, **surf_kwargs)
mlab.surf(X.real, Y.real, Z1.real, vmin=Z_min, vmax=Z_max, **surf_kwargs)
mlab.axes(zlabel="Re(E)")
mlab.figure(1)
Z_min, Z_max = get_min_and_max([Z0.imag, Z1.imag])
mlab.mesh(X.real, Y.real, Z0.imag, vmin=Z_min, vmax=Z_max, **surf_kwargs)
mlab.mesh(X.real, Y.real, Z1.imag, vmin=Z_min, vmax=Z_max, **surf_kwargs)
mlab.axes(zlabel="Im(E)")
if trajectory:
x, y = self.get_cycle_parameters(self.t)
_, c1, c2 = self.solve_ODE()
for i, part in enumerate([np.real, np.imag]):
e1, e2 = [part(self.eVals[:, n]) for n in (0, 1)]
z = map_trajectory(c1, c2, e1, e2)
mlab.figure(i)
mlab.plot3d(x, y, z, tube_radius=tube_radius)
mlab.points3d(x[0], y[0], z[0],
# color=line_color,
scale_factor=1e-1,
mode='sphere')
mlab.show()
def plane(self,n,p,c=None):
d = -np.sum(p*n)
x = np.linspace(-2,2)
y = np.linspace(-2,2)
[xx,yy]=np.meshgrid(x,y);
zz = (-n[0]*xx - n[1]*yy - d)/n[2]
mlab.mesh(xx,yy,zz,opacity=0.15,figure=self.fig)
def makefinegrid():
coor=make_coor(20j)
mlab.figure()
mlab.mesh(coor.x, coor.y, coor.z)
mlab.points3d(coor.x, coor.y, coor.z,
scale_factor=0.1)
savefig('finegrid.png')
def plotElements():
for i in range(25-1):
for j in range(25-1):
xtmp = x[j:j+2,i:i+2]
ytmp = y[j:j+2,i:i+2]
ztmp = z[j:j+2,i:i+2]
ctmp = c[j:j+2,i:i+2]
mlab.mesh(xtmp,ytmp,ztmp,scalars=ctmp,vmin=vmin,vmax=vmax)
mlab.mesh(xtmp,ytmp,ztmp,representation='wireframe',color = (0,0,0))
def plot_alpha_sphere(): x,y,z = make_sphere(50, 1.0) mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300)) mlab.clf() s = np.zeros(x.shape) mlab.mesh(x, y, z, scalars=s, colormap='jet', opacity=0.1) mlab.show(stop=True)
def show(self, using='maya', fit_ellipse=False, show_hull=False):
"""
A simple scatter-plot to represent the aggregate - either using mpl
or mayavi
"""
if fit_ellipse:
(center, radii, rotation) = self.fit_ellipse()
u = np.linspace(0.0, 2.0 * np.pi, 100)
v = np.linspace(0.0, np.pi, 100)
# cartesian coordinates that correspond to the spherical angles:
x = radii[0] * np.outer(np.cos(u), np.sin(v))
y = radii[1] * np.outer(np.sin(u), np.sin(v))
z = radii[2] * np.outer(np.ones_like(u), np.cos(v))
# rotate accordingly
for i in range(len(x)):
for j in range(len(x)):
[x[i,j],y[i,j],z[i,j]] = np.dot([x[i,j],y[i,j],z[i,j]], rotation) + center
if show_hull:
hull = self.chull()
hull_x = hull.points[:,0]
hull_y = hull.points[:,1]
hull_z = hull.points[:,2]
if using=='mpl':
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d'
ax = Axes3D(fig)
# ax.set_aspect('equal')
h = ax.scatter(self.pos[:,0], self.pos[:,1], self.pos[:,2], s=100.)
if fit_ellipse:
ax.plot_wireframe(x, y, z, rstride=4, cstride=4, color='k', alpha=0.2)
plt.show()
elif using=='maya':
import mayavi.mlab as mlab
fig = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
h = mlab.points3d(self.pos[:,0], self.pos[:,1], self.pos[:,2], self.radius, scale_factor=2, resolution=16)
if fit_ellipse:
mlab.mesh(x,y,z, opacity=0.25, color=(1,1,1))
if show_hull:
mlab.triangular_mesh(hull_x, hull_y, hull_z, hull.simplices, representation='wireframe', color=(1,1,1))
else:
print('ERROR: using= must ne either mpl or maya')
return h
def plotvMF(mu,tau,pi,figm,color=(1.,0.0,0.0)): X,Y,Z = S2.mesh(1.) q = np.c_[X.ravel(),Y.ravel(),Z.ravel()].T pdf = pi*np.exp(mu.T.dot(q)*tau)*tau/(4*np.pi*np.sinh(tau)) +1. X*=np.reshape(pdf,X.shape) Y*=np.reshape(pdf,X.shape) Z*=np.reshape(pdf,X.shape) mlab.mesh(X,Y,Z,color=color,opacity=0.3,figure=figm,mode='2dtriangle') mlab.plot3d([0,mu[0]],[0,mu[1]],[0,mu[2]],color=color,opacity=0.9,figure=figm)
def make_exact(self):
if(self.exact!=None):
sol = self.exact(self.X,self.Y,0)
for k in xrange(self.Nt):
mlab.mesh(self.X,self.Y,sol, color=(0.0, 0.3, 0.6))
sol = self.exact(self.X, self.Y, self.t[k])
mlab.savefig("wtmp%04d.png" %k)
mlab.clf()
filename = "exact_dt%2.1f.gif" %self.dt
sci.movie("wtmp*.png",encoder='convert', fps=5, output_file=filename)
def display_depth(z):
"""
Display the computed depth function as a surface using
mayavi mlab.
"""
m, n = z.shape
x, y = np.mgrid[0:m, 0:n]
mlab.mesh(x, y, z, colormap="gray")
mlab.view(azimuth=0, elevation=90)
mlab.show()
def estimate_lebesgue_constant(n, nodes, visualize=False):
"""Estimate the
`Lebesgue constant
<https://en.wikipedia.org/wiki/Lebesgue_constant_(interpolation)>`_
of the *nodes* at polynomial order *n*.
:arg nodes: an array of shape *(dims, nnodes)* as returned by
:func:`modepy.warp_and_blend_nodes`.
:arg visualize: visualize the function that gives rise to the
returned Lebesgue constant. (2D only for now)
:return: the Lebesgue constant, a scalar
.. versionadded:: 2013.2
"""
from modepy.matrices import vandermonde
from modepy.modes import simplex_onb
dims = len(nodes)
basis = simplex_onb(dims, n)
vdm = vandermonde(basis, nodes)
from pytools import generate_nonnegative_integer_tuples_summing_to_at_most \
as gnitstam
huge_n = 30*n
equi_node_tuples = list(gnitstam(huge_n, dims))
tons_of_equi_nodes = (
np.array(equi_node_tuples, dtype=np.float64)
/ huge_n * 2 - 1).T
eq_vdm = vandermonde(basis, tons_of_equi_nodes)
eq_to_out = la.solve(vdm.T, eq_vdm.T).T
lebesgue_worst = np.sum(np.abs(eq_to_out), axis=1)
lebesgue_constant = np.max(lebesgue_worst)
if visualize:
print("Lebesgue constant: %g" % lebesgue_constant)
from modepy.tools import submesh
import mayavi.mlab as mlab
mlab.figure(bgcolor=(1, 1, 1))
mlab.triangular_mesh(
tons_of_equi_nodes[0],
tons_of_equi_nodes[1],
lebesgue_worst / lebesgue_constant,
submesh(equi_node_tuples))
x, y = np.mgrid[-1:1:20j, -1:1:20j]
mlab.mesh(x, y, 0*x, representation="wireframe", color=(0.4, 0.4, 0.4),
line_width=0.6)
mlab.show()
return lebesgue_constant
def plot_data(x, y, z):
if PLOT_BY == 'matplotlib':
fig = pylab.figure()
axes = Axes3D(fig)
#axes.plot_surface(y, x, z)
axes.plot_surface(x, y, z, rstride=4, cstride=4, cmap = cm.jet )
pylab.show()
elif PLOT_BY == 'mayavi':
mlab.mesh(x, y, z, colormap='YlGnBu', )
mlab.view(.0, -5.0, 4)
mlab.show()
def harmonic2(name):
x = np.linspace(0, np.pi)
y = np.linspace(0, np.pi)
x, y = np.meshgrid(x, y, copy=False)
z = np.sin(2 * x) * np.sin(2 * y)
mlab.mesh(x, y, z)
# Zoom in a bit.
mlab.gcf().scene.camera.position = mlab.gcf().scene.camera.position / 1.3
# Save the plot.
mlab.savefig(name)
mlab.clf()
def plot3d(self,type='surface',**kargs):
nmax=kargs.get('nmax',200) # max wireframe lines
if type=='surface':
# mayavi and pylab version<3 needs the environment variable
# QT_API=pyqt, otherwise there will be the error "ValueError: API
# 'QString' has already been set to version 1
# This is because ipython uses pyqt4 using the version 1 of the api
# see: http://mail.scipy.org/pipermail/ipython-user/2012-February/009478.html
import sys
pver=eval(sys.version[:3])
qtapi=os.environ.get('QT_API','')
if pver<3 and qtapi!='pyqt':
print 'warning: environmen variable QT_API may need to be set as pyqt '\
'in python <3 so that mayavi can be used after pylab' \
a=raw_input('Wanna continue ([n],y) ?')
if a!='y': return
from mayavi import mlab
z=-self.h.copy()
z[self.mask==0]=np.nan
rx=self.lon.max()-self.lon.min()
ry=self.lat.max()-self.lat.min()
r=2*(self.h.max()-self.h.min())/(0.5*(rx+ry))
mlab.mesh(self.lon,self.lat,z/r)
else: # wireframe
from mpl_toolkits.mplot3d import axes3d
import pylab as pl
fig=pl.figure()
ax=fig.add_subplot(111, projection='3d')
ny,nx=self.h.shape
dx=int(np.round(nx/float(nmax)))
dy=int(np.round(ny/float(nmax)))
x=self.lon[::dy,::dx]
y=self.lat[::dy,::dx]
h=-self.h[::dy,::dx]
h[self.mask[::dy,::dx]==0]=np.nan
ax.plot_wireframe(x,y,h,lw=0.5,alpha=.5)
ax.contour3D(self.lon,self.lat,self.mask,[.5])
def draw_plane(self, plane, points3d, color=(1, 0.1, 0)):
# a plane is a*x+b*y+c*z+d=0
# [a,b,c] is the normal. Thus, we have to calculate d and we're set
a, b, c, d = plane
#xmin, xmax = int(np.min(points3d[:,0])), int(np.max(points3d[:,0]))
#ymin, ymax = int(np.min(points3d[:,1])), int(np.max(points3d[:,1]))
ymin, ymax = -0.1, 0.1
zmin, zmax = 0, 1.5 * np.max(points3d[:, 2])
#x, z = np.mgrid[xmin:xmax:10, 0:500:10]
#y = (a * x + c * z + d) / -b
y, z = np.mgrid[ymin:ymax:0.09, zmin:zmax:0.09]
x = (b * y + c * z + d) / -a
print x, y, z
mlab.mesh(x, y, z, color=color, opacity=0.5, transparent=True)
x = r * sin(phi) * cos(theta)
y = r * sin(phi) * sin(theta)
z = r * cos(phi)
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300))
mlab.clf()
# Represent spherical harmonics on the surface of the sphere
for n in range(0, 4):
s_tot = np.zeros((201, 201))
for m in range(-n, 1):
s = np.abs(sph_harm(m, n, theta, phi).real)
s_tot = s_tot + s
s = s_tot
# mlab.mesh(x - m, y - n, z, scalars=s, colormap='jet')
mlab.mesh(x - m, y - n, z, scalars=s, colormap='Spectral')
s[s < 0] *= 0.97
s /= s.max()
mlab.mesh(s * x - m,
s * y - n,
s * z + 1.3,
scalars=s,
colormap='Spectral') # scalars=s, colormap='jet')
mlab.view(90, 70, 6.2, (-1.3, -2.9, 0.25))
mlab.show()
for i in range(d.shape[0]):
if d[i] < r:
y[i] = (r - abs(x[i]-u))/r
return y
x = np.linspace(-0.5, 0.5, num=1000)
y = gaussian(x, 0.0, 0.03)
#y = uni(x, 0.0, 0.05)
plt.plot(x,y)
plt.show()
'''
dphi, dtheta = pi / 250.0, pi / 250.0
[phi, theta] = mgrid[0:pi + dphi * 1.5:dphi, 0:2 * pi + dtheta * 1.5:dtheta]
m0 = 4
m1 = 3
m2 = 2
m3 = 3
m4 = 6
m5 = 2
m6 = 6
m7 = 4
r = sin(m0 * phi)**m1 + cos(m2 * phi)**m3 + sin(m4 * theta)**m5 + cos(
m6 * theta)**m7
x = r * sin(phi) * cos(theta)
y = r * cos(phi)
z = r * sin(phi) * sin(theta)
s = mlab.mesh(x, y, z)
mlab.show()
plt.xlabel("Phi [degrees]")
plt.ylabel("Theta [degrees]")
plt.colorbar(label="Realized Gain [dBi]")
# plt.show()
## Plot in 3D (project onto surface of sphere) ##
x2d = np.sin(np.deg2rad(theta2d)) * np.cos(np.deg2rad(phi2d))
y2d = np.sin(np.deg2rad(theta2d)) * np.sin(np.deg2rad(phi2d))
z2d = np.cos(np.deg2rad(theta2d))
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0., 0., 0.))
mlab.mesh(x2d,
y2d,
z2d,
scalars=pattern_db[:, :, fidx],
colormap='jet',
vmax=pltmax,
vmin=pltmin)
mlab.colorbar(orientation='vertical')
mlab.orientation_axes()
# mlab.show()
## Plot in 3D ##
r = pattern_db[:, :, fidx] - pltmin
r[r < 0.0] = 0.0
x2d = r * np.sin(np.deg2rad(theta2d)) * np.cos(np.deg2rad(phi2d))
y2d = r * np.sin(np.deg2rad(theta2d)) * np.sin(np.deg2rad(phi2d))
z2d = r * np.cos(np.deg2rad(theta2d))
idx = np.sqrt(x**2 + y**2)
if x > 0 and y > 0:
V11 = A * (1 - np.exp(-B * idx))
elif x < 0 and y < 0:
V11 = -A * (1 - np.exp(B * idx))
else:
V11 = 0
V22 = -V11
V12 = C * np.exp(-D * (idx**2))
return np.matrix([[V11, V12], [V12, V22]])
allH = np.zeros((x.shape[0], x.shape[1], 2, 2))
allE = np.zeros((x.shape[0], x.shape[1], 2))
allU = np.zeros_like(allH)
for ix in range(x.shape[0]):
for iy in range(x.shape[1]):
H = create_H1(x[ix, iy], y[ix, iy])
E, U = np.linalg.eigh(H)
allH[ix, iy] = H
allE[ix, iy] = E
allU[ix, iy] = U
mlab.mesh(x, y, allE[:, :, 0] * 100, color=(1, 0, 0))
#mlab.points3d(x, y, allE[:, :, 1], color=(0, 0, 1))
mlab.show()
boundary_l.remove()
# Make a fading colormap by changing opacity at ends
lut = np.reshape(np.array([150, 150, 150, 255] * 256),
(256, 4))
fade_value = 125
lut[:fade_value, -1] = np.linspace(0, 255, fade_value)
lut[-fade_value:, -1] = np.linspace(255, 0, fade_value)
# Set up boundary visualisation
boundary_r = mlab.mesh(xix_boundary_r_vals_t,
zgrid_zy,
ygrid_zy,
extent=[
ext_min_r, ext_max_r, 1, nz,
0, (ny - 1) * y_spacing
],
opacity=1.,
representation='wireframe',
line_width=12.,
scalars=zgrid_zy)
boundary_l = mlab.mesh(xix_boundary_l_vals_t,
zgrid_zy,
ygrid_zy,
extent=[
ext_min_l, ext_max_l, 1, nz,
0, (ny - 1) * y_spacing
],
opacity=1.,
representation='wireframe',
line_width=12.,
# Create a sphere r = 0.3 pi = np.pi cos = np.cos sin = np.sin phi, theta = np.mgrid[0:pi:501j, 0:2 * pi:501j] x = r * sin(phi) * cos(theta) y = r * sin(phi) * sin(theta) z = r * cos(phi) mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300)) mlab.clf() # Represent spherical harmonics on the surface of the sphere mlab.mesh(x, y, z, color=(0.5, 0.5, 0.5), opacity=1) # phi, theta = np.mgrid[-.2:.2:10j, 0:2*pi:10j] phi = 0.1 * np.random.randn(100) theta = np.random.randn(100) * 2.0 * pi; theta = theta + pi / 6.0 x = r * sin(phi) * cos(theta) y = r * sin(phi) * sin(theta) z = r * cos(phi) R = rotation_matrix([2, 3, 1], pi / 4) v = np.dot(R, np.array([x, y, z])) mlab.points3d(v[0,], v[1,], v[2,], scale_factor=0.01, color=(1, 0, 0)) phi = 0.1 * np.random.randn(100) theta = np.random.randn(100) * 2.0 * pi;
grid_theta=np.linspace(min_new_theta,max_new_theta,num=500)
grid_phi=np.linspace(min_new_phi,max_new_phi,num=400)
grid_theta,grid_phi=np.meshgrid(grid_theta,grid_phi)
grid_theta2=np.linspace(110/180.0*np.pi,145/180.0*np.pi,num=500)
grid_phi2=np.linspace(-40/180.0*np.pi,60/180.0*np.pi,num=400)
grid_theta2,grid_phi2=np.meshgrid(grid_theta2,grid_phi2)
v_grid=griddata((new_theta,new_phi), new_v, (grid_theta2,grid_phi2), method='linear')
B_grid=griddata((new_theta,new_phi), new_B, (grid_theta,grid_phi), method='linear')
vA_grid=griddata((new_theta,new_phi), new_vA, (grid_theta,grid_phi), method='linear')
X_grid=target_R*np.sin(grid_theta)*np.cos(grid_phi)
Y_grid=target_R*np.sin(grid_theta)*np.sin(grid_phi)
Z_grid=target_R*np.cos(grid_theta)
mlab.figure("|B| field")
sphere_mesh=mlab.mesh(X_grid[::-1,:],Y_grid[::-1,:],Z_grid,scalars=np.log(B_grid[:,:]),colormap='bone')
sphere_mesh.actor.property.backface_culling = True
sphere_mesh.module_manager.scalar_lut_manager.reverse_lut = True
if plot_field_lines:
for idx in range(min(len(R_start),len(theta_start),len(phi_start))):
field_line_start=np.array([R_start[idx],theta_start[idx],phi_start[idx]])
field_line_sph=field_line_flicks(field_line_start,coord_logR,coord_theta,coord_phi,B_flicks,1.0,2.9,nlblks,n1pm1,n2pm1,n3pm1,step_size=1E-2)
field_line_X=field_line_sph[:,0]*np.sin(field_line_sph[:,1])*np.cos(field_line_sph[:,2])
field_line_Y=-field_line_sph[:,0]*np.sin(field_line_sph[:,1])*np.sin(field_line_sph[:,2])
field_line_Z=field_line_sph[:,0]*np.cos(field_line_sph[:,1])
mlab.plot3d(field_line_X,field_line_Y,field_line_Z,line_width=0.01,color=(0,0,0),tube_radius=0.004)
plt.plot(field_line_sph[:,2],field_line_sph[:,1],color="black")
mlab.view(azimuth=0, elevation=110, roll=90, distance=4.0)#, focalpoint=None, roll=None, reset_roll=True, figure=None)
mlab.show()
def _plot_radiation_pattern_mayavi(ned_mt):
"""
Plot the radiation pattern using MayaVi.
This private function uses the mayavi (vtk) library to plot the radiation
pattern to screen. Note that you might have to set the QT_API environmental
variable to e.g. export QT_API=pyqt that mayavi works properly.
:param ned_mt: moment tensor in NED convention
"""
# use mayavi if possible.
try:
from mayavi import mlab
except Exception as err:
print(err)
msg = ("ObsPy failed to import MayaVi. "
"You need to install the mayavi module "
"(e.g. 'conda install mayavi', 'pip install mayavi'). "
"If it is installed and still doesn't work, "
"try setting the environmental variable QT_API to "
"pyqt (e.g. export QT_API=pyqt) before running the "
"code. Another option is to avoid mayavi and "
"directly use kind='vtk' for vtk file output of the "
"radiation pattern that can be used by external "
"software like ParaView")
raise ImportError(msg)
# get mopad moment tensor
mopad_mt = MomentTensor(ned_mt, system='NED')
bb = BeachBall(mopad_mt, npoints=200)
bb._setup_BB(unit_circle=False)
# extract the coordinates of the nodal lines
neg_nodalline = bb._nodalline_negative
pos_nodalline = bb._nodalline_positive
# add the first point to the end to close the nodal line
neg_nodalline = np.hstack((neg_nodalline, neg_nodalline[:, 0][:, None]))
pos_nodalline = np.hstack((pos_nodalline, pos_nodalline[:, 0][:, None]))
# plot radiation pattern and nodal lines
points = _equalarea_spherical_grid(nlat=20)
dispp = farfield(ned_mt, points, type="P")
disps = farfield(ned_mt, points, type="S")
# get vector lengths
normp = np.sum(dispp * points, axis=0)
normp /= np.max(np.abs(normp))
norms = np.sqrt(np.sum(disps * disps, axis=0))
norms /= np.max(np.abs(norms))
# make sphere to block view to the other side of the beachball
rad = 0.8
pi = np.pi
cos = np.cos
sin = np.sin
phi, theta = np.mgrid[0:pi:101j, 0:2 * pi:101j]
x = rad * sin(phi) * cos(theta)
y = rad * sin(phi) * sin(theta)
z = rad * cos(phi)
# p wave radiation pattern
mlab.figure(size=(800, 800), bgcolor=(0, 0, 0))
pts1 = mlab.quiver3d(points[0], points[1], points[2],
dispp[0], dispp[1], dispp[2],
scalars=normp, vmin=-1., vmax=1.)
pts1.glyph.color_mode = 'color_by_scalar'
mlab.plot3d(*neg_nodalline, color=(0, 0.5, 0), tube_radius=0.01)
mlab.plot3d(*pos_nodalline, color=(0, 0.5, 0), tube_radius=0.01)
mlab.mesh(x, y, z, color=(0, 0, 0))
# s wave radiation pattern
mlab.figure(size=(800, 800), bgcolor=(0, 0, 0))
pts2 = mlab.quiver3d(points[0], points[1], points[2],
disps[0], disps[1], disps[2], scalars=norms,
vmin=-0., vmax=1.)
pts2.glyph.color_mode = 'color_by_scalar'
mlab.plot3d(*neg_nodalline, color=(0, 0.5, 0), tube_radius=0.01)
mlab.plot3d(*pos_nodalline, color=(0, 0.5, 0), tube_radius=0.01)
mlab.mesh(x, y, z, color=(0, 0, 0))
mlab.show()
import numpy as np
from scipy.special import sph_harm
# Create a sphere
r = 0.3
pi = np.pi
cos = np.cos
sin = np.sin
phi, theta = np.mgrid[0:pi:101j, 0:2 * pi:101j]
x = r * sin(phi) * cos(theta)
y = r * sin(phi) * sin(theta)
z = r * cos(phi)
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300))
mlab.clf()
# Represent spherical harmonics on the surface of the sphere
for n in range(3, 4):
for m in range(n):
s = sph_harm(m, n, theta, phi).real
mlab.mesh(x - m, y - n, z, scalars=s, colormap='jet')
s[s < 0] *= 0.97
s /= s.max()
#mlab.mesh(s*x-m, s*y-n, s*z+1.3, scalars=s, colormap='Spectral')
mlab.view(90, 70, 6.2, (-1.3, -2.9, 0.25))
mlab.show()
g2r = 'rect(eta+tau[i,j], te)[0]'
g1i = 'rect(eta, te)[1]'
g2i = 'rect(eta+tau[i,j], te)[1]'
mu = 1
g1r = 'LFM(eta, mu, te)[0]'
g2r = 'LFM(eta+tau[i,j], mu, te)[0]'
g1i = 'LFM(eta, mu, te)[1]'
g2i = 'LFM(eta+tau[i,j], mu, te)[1]'
ambig = np.zeros(wd.shape, dtype='complex')
for i in range(wd.shape[0]):
for j in range(wd.shape[1]):
gg_real = lambda eta: (eval(g1r) * eval(g2r) + eval(g1i) * eval(
g2i)) * np.cos(wd[i, j] * eta) - (eval(g1r) * eval(g2i) - eval(
g1i) * eval(g2r)) * np.sin(wd[i, j] * eta)
tmp_real = integrate.quad(gg_real, -np.inf, np.inf)[0]
gg_imag = lambda eta: (eval(g1r) * eval(g2r) + eval(g1i) * eval(
g2i)) * np.sin(wd[i, j] * eta) + (eval(g1r) * eval(g2i) - eval(
g1i) * eval(g2r)) * np.cos(wd[i, j] * eta)
tmp_imag = integrate.quad(gg_imag, -np.inf, np.inf)[0]
ambig[i, j] = tmp_real + 1j * tmp_imag
mlab.mesh(tau, wd, abs(ambig))
mlab.show()
else:
f = open("V.txt", "w+")
for i in range(numbers):
for j in range(numbers):
f.write("%9.6f %9.6f %9.6f %9.3f\n" %
(xyz[0, i, j], xyz[1, i, j], xyz[2, i, j], V[i, j]))
f.close()
#plot
mlab.options.backend = 'envisage'
if caltype == "E":
s = mlab.mesh(xyz[0],
xyz[1],
xyz[2],
representation="wireframe",
line_width=3.0,
scalars=E)
mlab.title("Young's modulus")
elif caltype == "B":
s = mlab.mesh(xyz[0],
xyz[1],
xyz[2],
representation="wireframe",
line_width=3.0,
scalars=B)
mlab.title("linear compressibility")
elif caltype == "G":
s = mlab.mesh(xyz[0],
xyz[1],
xyz[2],
def generate_cartesian_volume(fig, length=250, spacing=10):
'''
Generates a meshed cartesian volume that encapsulates the ROI.
INPUTS:
- fig : Scene/figure to populate.
- length : Length of axes ( generates a KxKxK sized cube ).
- spacing: Spacing between consecutive lines.
OUTPUT:
- No return; generates a mesh on the render window.
'''
print("Constructing cartesian volume ..."),
K = length + 1 # Number of lines to plot
N = spacing # Subdivisions (aka K/N lines are drawn)
# Draw horizontal lines on the xy-, yz-, and xz-planes
lvlC_H = np.arange(0, K, N) # Horizontal level curve
lvlC_V = np.zeros_like(lvlC_H) # Vertical level curve
H, V = np.meshgrid(lvlC_H, lvlC_V) # Mesh both arrays to a matrix (a grid)
lvlC_0 = np.zeros_like(
H) # Force everything into a 2D plane by setting the 3rd plane to 0
for i in range(0, K, N):
mlab.mesh(H,
V + i,
lvlC_0,
figure=fig,
representation='mesh',
tube_radius=0.25,
color=(1., 1., 1.))
mlab.mesh(lvlC_0,
H,
V + i,
figure=fig,
representation='mesh',
tube_radius=0.25,
color=(1., 1., 1.))
mlab.mesh(H,
lvlC_0,
V + i,
figure=fig,
representation='mesh',
tube_radius=0.25,
color=(1., 1., 1.))
# Draw vertical lines on the xy-, yz-, and xz-planes
lvlC_V = np.arange(0, K, N) # Vertical level curve
lvlC_H = np.zeros_like(lvlC_V) # Horizontal level curve
H, V = np.meshgrid(lvlC_H,
lvlC_V) # Mesh both arrays to form a matrix (a grid)
lvlC_0 = np.zeros_like(
H) # Force everything into a 2D plane by setting the 3rd plane to 0
for i in range(0, K, N):
mlab.mesh(H + i,
V,
lvlC_0,
figure=fig,
representation='mesh',
tube_radius=0.25,
color=(1., 1., 1.))
mlab.mesh(lvlC_0,
H + i,
V,
figure=fig,
representation='mesh',
tube_radius=0.25,
color=(1., 1., 1.))
mlab.mesh(H + i,
lvlC_0,
V,
figure=fig,
representation='mesh',
tube_radius=0.25,
color=(1., 1., 1.))
# Generate outline and label axes
mlab.outline(extent=[0, K - 1, 0, K - 1, 0, K - 1], figure=fig)
mlab.axes(extent=[0, K - 1, 0, K - 1, 0, K - 1],
figure=fig,
line_width=1.0,
x_axis_visibility=True,
y_axis_visibility=True,
z_axis_visibility=True)
print("SUCCESS!")
#!/usr/bin/env python # -*- coding: utf-8 -*- import numpy as np import mayavi.mlab as mlab mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1)) #更改背景色 s = np.random.normal(size=(21, 21)) #mlab.barchart(s) #x,y为矩阵p的坐标,可以省略 #mlab.imshow(s) # the colormap x, y = np.mgrid[-10:10:21j, -10:10:21j] mlab.mesh(x, y, s) mlab.vectorbar() #颜色bar mlab.show()
# -*- coding: utf-8 *-*
# Author: Gael Varoquaux <*****@*****.**>
# Copyright (c) 2007, Enthought, Inc.
# License: BSD Style.
from numpy import sin, cos, mgrid, pi, sqrt
from mayavi import mlab
import os
os.environ['QT_API'] = 'pyside'
mlab.figure(fgcolor=(0, 0, 0), bgcolor=(1, 1, 1))
u, v = mgrid[-0.035:pi:0.01, -0.035:pi:0.01]
X = 2 / 3. * (cos(u) * cos(2 * v) + sqrt(2) * sin(u) * cos(v)) * cos(u) / (
sqrt(2) - sin(2 * u) * sin(3 * v))
Y = 2 / 3. * (cos(u) * sin(2 * v) - sqrt(2) * sin(u) * sin(v)) * cos(u) / (
sqrt(2) - sin(2 * u) * sin(3 * v))
Z = -sqrt(2) * cos(u) * cos(u) / (sqrt(2) - sin(2 * u) * sin(3 * v))
S = sin(u)
mlab.mesh(
X,
Y,
Z,
scalars=S,
colormap='YlGnBu',
)
# Nice view from the front
mlab.view(.0, -5.0, 4)
mlab.show()
import numpy as np
from mayavi import mlab
from scipy.special import sph_harm
# Mit diesem Werten kann gespielt werden:
l = 3
m = 0
# --------------------------------------
theta_ld = np.linspace(0, np.pi, 91)
phi_ld = np.linspace(0, 2 * np.pi, 181)
theta_2d, phi_2d = np.meshgrid(theta_ld, phi_ld)
xyz_2d = np.array([
np.sin(theta_2d) * np.sin(phi_2d),
np.sin(theta_2d) * np.cos(phi_2d),
np.cos(theta_2d)
])
Y_lm = sph_harm(m, l, phi_2d, theta_2d)
r = abs(Y_lm.real) * xyz_2d
mlab.figure(size=(700, 830))
mlab.mesh(r[0], r[1], r[2], scalars=Y_lm.real, colormap="cool")
mlab.view(azimuth=0, elevation=75, distance=2.4, roll=-50)
mlab.savefig("images/Y_%i_%i.jpg" % (l, m))
mlab.show()
print("I did it, Babe!")
""" From "COMPUTATIONAL PHYSICS" & "COMPUTER PROBLEMS in PHYSICS"
by RH Landau, MJ Paez, and CC Bordeianu (deceased)
Copyright R Landau, Oregon State Unv, MJ Paez, Univ Antioquia,
C Bordeianu, Univ Bucharest, 2017.
Please respect copyright & acknowledge our work."""
# YlmMayavi: Surface plot of spherical harmonic
from numpy import pi, sin, cos, mgrid
dphi, dtheta = pi / 250.0, pi / 250.0
[phi, theta] = mgrid[0:pi + dphi * 1.5:dphi,
0:2 * pi + dtheta * 1.5:dtheta] # Create data
m0 = 4
m1 = 3
m2 = 2
m3 = 3
m4 = 6
m5 = 2
m6 = 6
m7 = 4
r = sin(m0 * phi)**m1 + cos(m2 * phi)**m3 + sin(m4 * theta)**m5 + cos(
m6 * theta)**m7
x = r * sin(phi) * cos(theta)
y = r * cos(phi)
z = r * sin(phi) * sin(theta)
from mayavi import mlab
s = mlab.mesh(x, y, z) # View data
mlab.show()
def plot_surface(r):
x = r[:, :, 0]
y = r[:, :, 1]
z = r[:, :, 2]
p = mlab.mesh(x, y, z, color=(0.8, 0.0, 0.0))
return p
# (use 'extent' for auto-scaling)
plt.plot3d(curve_x,
curve_y,
curve_z,
tube_radius=0.2,
extent=(0, 1, 0, 1, 0, 1))
plt.figure(3, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
# Use 'warp_scale' for vertical scaling
plt.surf(xv, yv, hv, warp_scale=0.01, color=(.5, .5, .5))
plt.plot3d(curve_x, curve_y, 0.01 * curve_z, tube_radius=0.2)
# endsimpleplots
# Create one figure with three subplots
plt.figure(4, fgcolor=(.0, .0, .0), bgcolor=(1.0, 1.0, 1.0))
plt.mesh(xv, yv, hv, extent=(0, 0.25, 0, 0.25, 0, 0.25), colormap='cool')
plt.outline(
plt.mesh(xv,
yv,
hv,
extent=(0.375, 0.625, 0, 0.25, 0, 0.25),
colormap='Accent'))
plt.outline(plt.mesh(xv,
yv,
hv,
extent=(0.75, 1, 0, 0.25, 0, 0.25),
colormap='prism'),
color=(.5, .5, .5))
# endsubplot
hv = h0 / (1 + (xv**2 + yv**2) / (R**2))
#field_line_Z=field_line_sph[:,0]*np.cos(field_line_sph[:,1])
#plt.figure()
#print(np.shape(Q_grid1),np.shape(phi_grid1),np.shape(theta_grid1))
#plt.plot(phi_grid1[:,0])
#plt.plot(theta_grid1[0,:])
fig=plt.figure("",figsize=(10,7))
color_plot=plt.pcolormesh(phi_grid1,theta_grid1,Q_grid1,cmap='RdBu_r',vmin=-10,vmax=10)
#mlab.figure(bgcolor=(1,0.8,0.55))
mlab.figure(bgcolor=(1,1,1))
sphere_mesh=mlab.mesh(X_grid1[::-1,:],Y_grid1[::-1,:],Z_grid1[:,:],scalars=Q_grid1[:,:],colormap='RdBu',vmin=-10,vmax=10)
sphere_mesh.actor.property.backface_culling = True
sphere_mesh.module_manager.scalar_lut_manager.reverse_lut = True
#mlab.colorbar(orientation="vertical")
if plot_R3:
sphere_mesh3=mlab.mesh(X_grid3[::-1,:],Y_grid3[::-1,:],Z_grid3[:,:],scalars=Q_grid3[:,:],colormap='RdBu',vmin=-10,vmax=10,opacity=0.4)
sphere_mesh3.actor.property.backface_culling = True
sphere_mesh3.module_manager.scalar_lut_manager.reverse_lut = True
for idx in range(min(len(R_start),len(theta_start),len(phi_start))):
field_line_start=np.array([R_start[idx],theta_start[idx],phi_start[idx]])
field_line_sph=field_line_spherical(field_line_start,R,theta,phi,B,1.0,2.9,step_size=1E-2)
field_line_X=field_line_sph[:,0]*np.sin(field_line_sph[:,1])*np.cos(field_line_sph[:,2])
field_line_Y=-field_line_sph[:,0]*np.sin(field_line_sph[:,1])*np.sin(field_line_sph[:,2])
# * $y=x^2+\delta$
# * $z=x^2-\delta$
# * $y=z^2+\delta$
# In[ ]:
delta = 0.5
# In[ ]:
res = 200j
x, z = np.mgrid[-3:3:res, -3:3:res]
y = x**2 + delta
mlab.mesh(x, y, z)
if 1:
y, x = np.mgrid[-3:3:res, -3:3:res]
z = x**2 - delta
mlab.mesh(x, y, z)
if 1:
x, z = np.mgrid[-3:3:res, -3:3:res]
y = z**2 + delta
mlab.mesh(x, y, z)
# In[ ]:
x_size = 318
y_size = 159
theta = np.linspace(np.pi, 0, y_size)
phi = np.linspace(-np.pi, np.pi, x_size)
longitude = np.radians(np.linspace(-180, 180, x_size))
latitude = np.radians(np.linspace(-90, 90, y_size))
# project the map to a rectangular matrix xsize x ysize
PHI, THETA = np.meshgrid(phi, theta)
# Create a sphere
r = 0.3
x = r*np.sin(THETA)*np.cos(PHI)
y = r*np.sin(THETA)*np.sin(PHI)
z = r*np.cos(THETA)
# Plot it!
mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(400, 300))
mlab.clf()
mlab.mesh(x, y, z, scalars=img, colormap="jet", vmin=vmin, vmax=vmax)
cv2.imshow('Image', img)
mlab.show()
while True:
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Create forcing function
ix = grid[:, 2] >= surf_plane
chi[ix] = image[xi[ix], yi[ix]]
# Make sure forcing is a cp extension
chi = E * chi
# Re-threshold
chi = (chi > 0.9).astype(np.int)
xp, yp, zp = s.parametric_grid(256)
Eplot = build_interp_matrix(
int_grid, np.column_stack((xp.ravel(), yp.ravel(), zp.ravel())), dx, p, ll,
virtual_grid_shape)
# Plot forcing function
mlab.figure(1, fgcolor=(1.0, 1.0, 1.0))
mlab.mesh(xp, yp, zp, scalars=(Eplot * (1 - chi)).reshape(xp.shape))
mlab.view(azimuth=158, elevation=25, distance=7)
mlab.title('forcing function')
# Parameters and functions for Gray--Scott
F = 0.054
kk = 0.063
nuu = 1 / (3 / dx.min())**2
nuv = nuu / 2.
f = lambda u, v: -u * v**2 + F * (1 - u)
g = lambda u, v: u * v**2 - (F + kk) * v
# Initial conditions - small perturbation from steady state
pert = 0.5 * np.exp(-(10 * (grid[:, 2] - 1))**2) + 0.5 * np.random.randn(
grid.shape[0])
u0 = 1 - pert
def plot_potential(self, fig=None, r=None, theta=None, phi=None, lowering=True):
if not self.USE_MAYAVI:
return None
if fig is None:
fig = mlab.figure('Potential')#, bgcolor=(0, 0, 0))
else:
mlab.figure(fig, bgcolor=fig.scene.background)
if r is None:
r = np.linspace(1e-9, 20e-9, num=50, endpoint=True)
if theta is None:
theta = np.linspace(0, np.pi, num=50, endpoint=True)
if phi is None:
phi = np.linspace(0, 2 * np.pi, num=50, endpoint=True)
r_grid, theta_grid, phi_grid = np.meshgrid(r, theta, phi)
volumetric_data = self.potential(r, theta, phi)
energy_scale = np.max(np.abs(r)) / np.max(np.abs(volumetric_data[:, :, 0]))
dim_idx = 2
for i, dim in enumerate(r_grid.shape):
if dim == 1:
dim_idx = i
break
if dim_idx == 2:
print 'here'
surf = mlab.mesh(r_grid[:, :, 0] * np.cos(theta_grid[:, :, 0]),
r_grid[:, :, 0] * np.sin(theta_grid[:, :, 0]),
volumetric_data[:, :, 0] * energy_scale,
#extent=(0, 1, 0, 1, 0, 1),
representation='wireframe', colormap='RdBu')
elif dim_idx == 1:
surf = mlab.mesh(r_grid[:, 0, :] * np.sin(theta_grid[0, 0, 0]) * np.cos(phi_grid[:, 0, :]),
r_grid[:, 0, :] * np.sin(theta_grid[0, 0, 0]) * np.sin(phi_grid[:, 0, :]),
volumetric_data[:, 0, :],
extent=(0, 1, 0, 1, 0, 1),
representation='wireframe', colormap='RdBu')
elif dim_idx == 0:
surf = mlab.mesh(theta_grid[0, :, :],
phi_grid[0, :, :],
volumetric_data[0, :, :],
extent=(0, 1, 0, 1, 0, 1),
representation='wireframe', colormap='RdBu')
mlab.outline()
if lowering:
theta2 = np.linspace(0, 2*np.pi, num=50)
barrier_lowering = np.array([self.barrier_lowering(theta_i) for theta_i in theta2])
idx = np.where(barrier_lowering[:, 1] > 0)
print barrier_lowering[idx]
mlab.plot3d(barrier_lowering[idx][:, 1] * np.cos(theta2[idx]),
barrier_lowering[idx][:, 1] * np.sin(theta2[idx]),
barrier_lowering[idx][:, 0] * energy_scale,
tube_radius=energy_scale * 5e-3,
color=(1, 0, 0))
#mlab.outline()
'''
mlab.points3d(barrier_lowering[idx][:, 1] * np.cos(theta2[idx]),
barrier_lowering[idx][:, 1] * np.sin(theta2[idx]),
barrier_lowering[idx][:, 0] * energy_scale)
mlab.outline()
'''
return fig
#==============================================================================
# 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)
# s1 = mlab.mesh(x, y, z1,colormap="gray", transparent=True)
# mlab.quiver3d(x, y, z1, u1, v1, w1,colormap="gray",transparent=True, scale_factor=1)
#==============================================================================
# Show the scene
#==============================================================================
def show_sph_harm(l, m, real=True, N=50, use_sphere=True, plot='mpl'):
'''
Show the spherical harmonics on a unit sphere
'''
assert plot.lower() in ['mpl', 'mayavi', 'plotly']
theta = np.linspace(0, np.pi, N)
phi = np.linspace(0, 2 * np.pi, N)
theta, phi = np.meshgrid(theta, phi)
# The Cartesian coordinates of the unit sphere
x = np.sin(theta) * np.cos(phi)
y = np.sin(theta) * np.sin(phi)
z = np.cos(theta)
xyz = np.c_[x.ravel(), y.ravel(), z.ravel()]
# from time import time
# t0 = time()
if real:
ylm = sph_r(xyz, l, m).reshape(N, N)
else:
ylm = sph_c(xyz, l, m).reshape(N, N).real
# t1 = time()
# print(t1 - t0)
# Calculate the spherical harmonic Y(l,m) and normalize to [0,1]
fcolors = ylm
fmax, fmin = fcolors.max(), fcolors.min()
fcolors = (fcolors - fmin) / (fmax - fmin)
if not use_sphere:
r0 = np.abs(ylm)
if plot.lower() == 'mpl':
import matplotlib.pyplot as plt
from matplotlib import cm, colors
from mpl_toolkits.mplot3d import Axes3D
# Set the aspect ratio to 1 so our sphere looks spherical
fig = plt.figure(figsize=plt.figaspect(1.))
ax = fig.add_subplot(111, projection='3d')
if use_sphere:
ax.plot_surface(x,
y,
z,
rstride=1,
cstride=1,
facecolors=cm.seismic(fcolors))
xmax = ymax = zmax = np.max([x, y, z])
xmin = ymin = zmin = np.min([x, y, z])
else:
ax.plot_surface(x * r0,
y * r0,
z * r0,
rstride=1,
cstride=1,
facecolors=cm.seismic(fcolors))
xmax = ymax = zmax = np.max([r0 * x, r0 * y, r0 * z])
xmin = ymin = zmin = np.min([r0 * x, r0 * y, r0 * z])
# Turn off the axis planes
# ax.set_axis_off()
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
ax.set_zlim(zmin, zmax)
ax.set_xticks([])
ax.set_yticks([])
ax.set_zticks([])
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
elif plot == 'mayavi':
from mayavi import mlab
fig = mlab.figure(size=(800, 800), bgcolor=(1, 1, 1))
if use_sphere:
mlab.mesh(x, y, z, colormap='seismic', scalars=fcolors)
else:
mlab.mesh(x * r0,
y * r0,
z * r0,
colormap='seismic',
scalars=fcolors)
mlab.orientation_axes()
mlab.show()
else:
import plotly.graph_objects as go
if use_sphere:
fig = go.Figure(data=[
go.Surface(z=z,
x=x,
y=y,
surfacecolor=fcolors,
colorscale='balance',
showscale=False,
opacity=1.0,
hoverinfo='none')
], )
else:
fig = go.Figure(data=[
go.Surface(z=r0 * z,
x=r0 * x,
y=r0 * y,
surfacecolor=fcolors,
colorscale='balance',
showscale=False,
opacity=1.0,
hoverinfo='none')
], )
fig.update_layout(
width=800,
height=800,
)
fig.show()
gnitstam(p, dims),
simplex_onb(dims, p),
):
all_nodes.append(plot_nodes + [stretch_factor * i, stretch_factor * j])
all_triangles.append(tri_subtriangles + node_count)
all_values.append(basis_func(eval_nodes))
node_count += len(plot_nodes)
all_nodes = np.vstack(all_nodes)
all_triangles = np.vstack(all_triangles)
all_values = np.hstack(all_values)
# plot
import mayavi.mlab as mlab
fig = mlab.figure(bgcolor=(1, 1, 1))
mlab.triangular_mesh(all_nodes[:, 0], all_nodes[:, 1], 0.2 * all_values,
all_triangles)
x, y = np.mgrid[-1:p * stretch_factor + 1:20j, -1:p * stretch_factor + 1:20j]
mlab.mesh(x,
y,
0 * x,
representation="wireframe",
color=(0.4, 0.4, 0.4),
line_width=0.6)
mlab.view(-153, 58, 10, np.array([1.61, 2.49, -0.59]))
mlab.show()
def explore(prefix, faults):
"""
CFMX: Community Fault Model Explorer
A simple tool for exploring the CFM
Keyboard Controls:
Fault selection [ ]
Fault selection and view { }
Clear fault selection \\
Rotate the view Arrows
Pan the view Shift-Arrows
Zoom the view - =
Reset view 0
Toggle stereo view 3
Save a screen-shot S
Help h ?
"""
doc = explore.__doc__ # must use hardcode function name to inspect doc
if not faults:
print('No faults found')
return
import pyproj
from mayavi import mlab
fault_names = json.load(open(home + 'data/CFM-Fault-Names.json'))
# parameters
extent = (-122.0, -114.0), (31.5, 37.5)
resolution = 'high'
view_azimuth = -90
view_elevation = 45
view_angle = 15
color_bg = 1.0, 1.0, 0.0
color_hl = 1.0, 0.0, 0.0
single_fault = isinstance(faults, str)
# projection
proj = pyproj.Proj(**projection)
# setup figure
s = 'SCEC Community Fault Model'
if prefix:
s = [s] + [fault_names[i][k] for i, k in enumerate(prefix[:3])]
if single_fault:
s += [prefix[3].replace('_', ' ')]
s = ', '.join(s)
print('\n%s\n' % s)
fig = mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(1280, 720))
fig.name = s
fig.scene.disable_render = True
# DEM
f = os.path.join(repository, 'CFM', 'dem.npy')
if os.path.exists(f):
x, y, z = numpy.load(f)
else:
x, y, z = data.dem(extent, mesh=True)
extent = (x.min(), x.max()), (y.min(), y.max())
x, y = proj(x, y)
numpy.save(f, [x, y, z])
mlab.mesh(x, y, z, color=(1, 1, 1), opacity=0.3)
# base map
f = os.path.join(repository, 'CFM', 'mapdata.npy')
if os.path.exists(f):
x, y, z = numpy.load(f)
else:
ddeg = 0.5 / 60.0
# FIXME
x, y = numpy.c_[
data.gshhg('coastlines', resolution, extent, 10.0, delta=ddeg),
[float('nan'), float('nan')],
data.gshhg('borders', resolution, extent, delta=ddeg), ]
x -= 360.0
z = interp.interp2(extent, z, (x, y))
x, y = proj(x, y)
i = numpy.isnan(z)
x[i] = float('nan')
y[i] = float('nan')
numpy.save(f, [x, y, z])
mlab.plot3d(x, y, z, color=(0, 0, 0), line_width=1, tube_radius=None)
mlab.view(view_azimuth, view_elevation)
fig.scene.camera.view_angle = view_angle
fig.scene.disable_render = False
fig.scene.disable_render = True
# read fault surfaces
tsurfs = []
if single_fault:
f, s = (faults + ':').split(':')[:2]
x, t = read(f)[:2]
if s:
for i in s.split(','):
tsurfs.append(('%s:%s' % (f, i), x.T, t[int(i)].T))
else:
for i, j in enumerate(t):
tsurfs.append(('%s:%s' % (f, i), x.T, j.T))
else:
for f in faults:
if isinstance(f, str):
x, t = tsurf_merge(read(f))
else:
f, s = f
x, t = tsurf_merge(read(i) for i in s)
tsurfs.append((f, x, t))
# plot fault surfaces
surfs = []
for isurf, f in enumerate(tsurfs):
name, vtx, tri = f
print(name)
m = geometry(vtx, tri)
x, y, z = vtx
s = [
'Mean Strike: %10.5f deg' % m['strike'],
'Mean Dip: %10.5f deg' % m['dip'],
'Centroid Lon: %10.5f deg' % m['centroid_lon'],
'Centroid Lat: %10.5f deg' % m['centroid_lat'],
'Centroid Elev: %10d m' % m['centroid_z'],
'Min Elevation: %10d m' % z.min(),
'Max Elevation: %10d m' % z.max(),
'Surface Area: %10d km^2' % (m['area'] * 0.000001),
]
k = name.split('-', 3)
s += [fault_names[i][a] for i, a in enumerate(k[:3])]
if k[3:]:
s += [k[3].replace('_', ' ')]
s += [name]
p = mlab.triangular_mesh(
x,
y,
z,
tri,
representation='surface',
color=color_bg,
).actor.actor.property
u = m['centroid_x'], m['centroid_y'], m['centroid_z']
if single_fault:
surfs.append((isurf, u, s, p))
else:
i = m['centroid_lon']
surfs.append((i, u, s, p))
surfs = [i[1:] for i in sorted(surfs)]
# handle key press
def on_key_press(obj, event, save=[0]):
k = obj.GetKeyCode()
isurf = save[0]
fig.scene.disable_render = True
if k in '[]{}':
c, s, p = surfs[isurf]
if p.color == color_bg:
p.color = color_hl
else:
p.color = color_bg
d = {'[': -1, ']': 1, '{': -1, '}': 1}[k]
isurf = (isurf + d) % len(surfs)
c, s, p = surfs[isurf]
p.color = color_hl
print('\n' + '\n'.join(s))
if k in '{}':
mlab.view(focalpoint=c)
elif k == '\\':
surfs[isurf][-1].color = color_bg
elif k == '0':
mlab.view(view_azimuth, view_elevation)
fig.scene.camera.view_angle = view_angle
elif k in '/?h':
print(doc)
fig.scene.disable_render = False
save[0] = isurf
return
# finish up
fig.scene.interactor.add_observer('KeyPressEvent', on_key_press)
mlab.view(view_azimuth, view_elevation)
fig.scene.camera.view_angle = view_angle
fig.scene.disable_render = False
print('\nPress H in the figure window for help.')
mlab.show()
return
from mayavi import mlab import numpy as np r = 0.5 R = 1.0 u, v = np.mgrid[0:2 * np.pi:25j, 0:2 * np.pi:50j] x = (R + r * np.cos(v)) * np.cos(u) y = (R + r * np.cos(v)) * np.sin(u) z = r * np.sin(v) mlab.figure(1, bgcolor=(1, 1, 1), fgcolor=(0, 0, 0), size=(800, 800)) mlab.clf() # https://docs.enthought.com/mayavi/mayavi/auto/mlab_helper_functions.html#mesh mlab.mesh(x, y, z, opacity=0.8) # , color=(0.1, 0.1, 0.6) mlab.mesh(x, y, z, opacity=0.8, color=(0, 0, 0), representation='wireframe') mlab.points3d(1.5, 0, 0, color=(1.0, 0.0, 0.0), scale_factor=0.1) mlab.orientation_axes() mlab.show()
def main(thetamax=None,
link_in_ra=True,
dec_refine_factor=1,
ra_refine_factor=1):
from math import radians
if not thetamax:
thetamax = 30
ra_limits = [0.0, 360.0]
dec_limits = [-90.0, 90.0]
sphere, num_ra = gridlink_sphere(thetamax,
link_in_ra=link_in_ra,
dec_refine_factor=dec_refine_factor,
ra_refine_factor=ra_refine_factor,
ra_limits=ra_limits,
dec_limits=dec_limits,
return_num_ra_cells=True)
ra_limits = [radians(a) for a in ra_limits]
dec_limits = [radians(a) for a in dec_limits]
ndec = num_ra.size
ncells = sphere.size
dec, ra, dec_bands, scalars = spherical_meshgrid(sphere, num_ra)
x = cos(dec) * cos(ra)
y = cos(dec) * sin(ra)
z = sin(dec)
volume = mlab.mesh(x,
y,
z,
scalars=scalars,
colormap='viridis',
opacity=0.95)
volume.actor.property.specular = 0.45
volume.actor.property.specular_power = 5
# Backface culling is necessary for more a beautiful transparent
# rendering.
volume.actor.property.backface_culling = True
# Mark the declination bands
phi = np.linspace(ra_limits[0], ra_limits[1], 100)
for angle in dec_bands:
# Lower limits of the dec-bands, except for the two poles
if angle == -0.5 * np.pi or angle == 0.5 * np.pi:
continue
# Draw the declination band
xx = np.cos(phi) * np.cos(angle)
yy = np.sin(phi) * np.cos(angle)
zz = np.ones_like(phi) * np.sin(angle)
mlab.plot3d(xx, yy, zz, color=(1, 1, 1), opacity=0.5, tube_radius=None)
# Now draw the ra cells.
from itertools import izip, count
off = 0
for idec, dec_low, dec_hi in izip(count(), dec_bands[0:-1], dec_bands[1:]):
dodgerblue = (0.1167315175, 0.5625, 1.0)
white = (1.0, 1.0, 1.0)
gray = (0.5, 0.5, 0.5)
for ira in xrange(num_ra[idec]):
phi = sphere['ra_limit'][off]
color, tube_radius = (dodgerblue, 0.01) if ira == 0 else (white,
None)
theta = np.linspace(dec_low, dec_hi, 100)
xx = np.cos(theta) * np.cos(phi[0])
yy = np.cos(theta) * np.sin(phi[0])
zz = np.sin(theta)
mlab.plot3d(xx,
yy,
zz,
color=color,
opacity=0.5,
tube_radius=tube_radius)
off += 1
theta = np.linspace(dec_low, dec_hi, 100)
xx = np.cos(theta) * np.cos(phi[1])
yy = np.cos(theta) * np.sin(phi[1])
zz = np.sin(theta)
mlab.plot3d(xx,
yy,
zz,
color=dodgerblue,
opacity=0.5,
tube_radius=0.01)
mlab.show()
from numpy import pi, sin, cos, mgrid
from mayavi import mlab
#建立数据
dphi, dtheta = pi / 250.0, pi / 250.0
[phi, theta] = mgrid[0:pi + dphi * 1.5:dphi, 0:2 * pi + dtheta * 1.5:dtheta]
m0 = 4
m1 = 3
m2 = 2
m3 = 3
m4 = 6
m5 = 2
m6 = 6
m7 = 4
r = sin(m0 * phi)**m1 + cos(m2 * phi)**m3 + sin(m4 * theta)**m5 + cos(
m6 * theta)**m7
x = r * sin(phi) * cos(theta)
y = r * cos(phi)
z = r * sin(phi) * sin(theta)
#对该数据进行三维可视化
s = mlab.mesh(x, y, z, representation='wireframe', line_width=3.0)
mlab.show()
