"""Some examples for quick testing/demonstrations
All function accept `show` and `draw` arguments
* If `draw` is `True` it will return the widgets (Scatter, Volume, Mesh)
* If `draw` is `False`, it will return the data
* if `show` is `False`, `ipv.show()` will not be called.
"""
import numpy as np
import ipyvolume
from numpy import cos, sin, sqrt, pi
try:
import scipy.ndimage
import scipy.special
except:
pass # it's ok, it's not crucial
# __all__ = ["example_ylm"]
[docs]def xyz(shape=128, limits=[-3, 3], spherical=False, sparse=True, centers=False):
dim = 3
try:
shape[0]
except:
shape = [shape] * dim
try:
limits[0][0]
except:
limits = [limits] * dim
if centers:
#print([( vmin+(vmax-vmin)/float(N)/2, vmax-(vmax-vmin)/float(N)/4, (vmax-vmin)/float(N)) for (vmin, vmax), N in zip(limits, shape)])
v = [slice(vmin+(vmax-vmin)/float(N)/2, vmax-(vmax-vmin)/float(N)/4, (vmax-vmin)/float(N)) for (vmin, vmax), N in zip(limits, shape)]
else:
v = [slice(vmin, vmax+(vmax-vmin)/float(N)/2, (vmax-vmin)/float(N-1)) for (vmin, vmax), N in zip(limits, shape)]
if sparse:
x, y, z = np.ogrid.__getitem__(v)
else:
x, y, z = np.mgrid.__getitem__(v)
if spherical:
r = np.linalg.norm([x, y, z])
theta = np.arctan2(y, x)
phi = np.arccos(z / r)
return x, y, z, r, theta, phi
else:
return x, y, z
[docs]def example_ylm(m=0, n=2, shape=128, limits=[-4, 4], draw=True, show=True, **kwargs):
"""Show a spherical harmonic"""
import ipyvolume.pylab as p3
__, __, __, r, theta, phi = xyz(shape=shape, limits=limits, spherical=True)
radial = np.exp(-(r - 2) ** 2)
data = np.abs(scipy.special.sph_harm(m, n, theta, phi) ** 2) * radial
if draw:
vol = p3.volshow(data=data, **kwargs)
if show:
p3.show()
return vol
else:
return data
#return ipyvolume.volshow(data=data.T, **kwargs)
[docs]def ball(rmax=3, rmin=0, shape=128, limits=[-4, 4], draw=True, show=True, **kwargs):
"""Show a ball"""
import ipyvolume.pylab as p3
__, __, __, r, theta, phi = xyz(shape=shape, limits=limits, spherical=True)
data = r * 0
data[(r < rmax) & (r >= rmin)] = 0.5
if "data_min" not in kwargs:
kwargs["data_min"] = 0
if "data_max" not in kwargs:
kwargs["data_max"] = 1
data = data.T
if draw:
vol = p3.volshow(data=data, **kwargs)
if show:
p3.show()
return vol
else:
return data
# http://graphics.stanford.edu/data/voldata/
[docs]def klein_bottle(draw=True, show=True, figure8=False, endpoint=True, uv=True, wireframe=False, texture=None, both=False, interval=1000):
"""Show one or two Klein bottles"""
import ipyvolume.pylab as p3
# http://paulbourke.net/geometry/klein/
u = np.linspace(0, 2 * pi, num=40, endpoint=endpoint)
v = np.linspace(0, 2 * pi, num=40, endpoint=endpoint)
u, v = np.meshgrid(u, v)
if both:
x1, y1, z1, u1, v1 = klein_bottle(endpoint=endpoint, draw=False, show=False)
x2, y2, z2, u2, v2 = klein_bottle(endpoint=endpoint, draw=False, show=False, figure8=True)
x = [x1, x2]
y = [y1, y2]
z = [z1, z2]
else:
if figure8:
#u -= np.pi
#v -= np.pi
a = 2
s = 5
x = s * (a + cos(u / 2) * sin(v) - sin(u / 2) * sin(2 * v)/2) * cos(u)
y = s * (a + cos(u / 2) * sin(v) - sin(u / 2) * sin(2 * v)/2) * sin(u)
z = s * (sin(u / 2) * sin(v) + cos(u / 2) * sin(2 * v)/2)
else:
r = 4 * (1 - cos(u) / 2)
x = 6 * cos(u) * (1 + sin(u)) \
+ r * cos(u) * cos(v) * (u < pi) \
+ r * cos(v + pi) * (u >= pi)
y = 16 * sin(u) + r * sin(u) * cos(v) * (u < pi)
z = r * sin(v)
if draw:
if texture:
uv = True
if uv:
mesh = p3.plot_mesh(x, y, z, wrapx=not endpoint, wrapy=not endpoint, u=u/(2*np.pi), v=v/(2*np.pi), wireframe=wireframe, texture=texture)
else:
mesh = p3.plot_mesh(x, y, z, wrapx=not endpoint, wrapy=not endpoint, wireframe=wireframe, texture=texture)
if show:
if both:
p3.animation_control(mesh, interval=interval)
p3.squarelim()
p3.show()
return mesh
else:
return x, y, z, u, v
import warnings
[docs]def brain(draw=True, show=True, fiducial=True, flat=True, inflated=True, subject='S1', interval=1000, uv=True, color=None):
"""Show a human brain model
Requirement:
$ pip install https://github.com/gallantlab/pycortex
"""
import ipyvolume as ipv
try:
import cortex
except:
warnings.warn("it seems pycortex is not installed, which is needed for this example")
raise
xlist, ylist, zlist = [], [], []
polys_list = []
def add(pts, polys):
xlist.append(pts[:,0])
ylist.append(pts[:,1])
zlist.append(pts[:,2])
polys_list.append(polys)
def n(x):
return (x - x.min()) / x.ptp()
if fiducial or color is True:
pts, polys = cortex.db.get_surf('S1', 'fiducial', merge=True)
x, y, z = pts.T
r = n(x)
g = n(y)
b = n(z)
if color is True:
color = np.array([r,g,b]).T.copy()
else:
color = None
if fiducial:
add(pts, polys)
else:
if color is False:
color = None
if inflated:
add(*cortex.db.get_surf('S1', 'inflated', merge=True, nudge=True))
u = v = None
if flat or uv:
pts, polys = cortex.db.get_surf('S1', 'flat', merge=True, nudge=True)
x, y, z = pts.T
u = n(x)
v = n(y)
if flat:
add(pts, polys)
polys_list.sort(key=lambda x: len(x))
polys = polys_list[0]
if draw:
if color is None:
mesh = ipv.plot_trisurf(xlist, ylist, zlist, polys, u=u, v=v)
else:
mesh = ipv.plot_trisurf(xlist, ylist, zlist, polys, color=color, u=u, v=v)
if show:
if len(x) > 1:
ipv.animation_control(mesh, interval=interval)
ipv.squarelim()
ipv.show()
return mesh
else:
return xlist, ylist, zlist, polys
[docs]def head(draw=True, show=True, max_shape=256):
"""Show a volumetric rendering of a human male head"""
# inspired by http://graphicsrunner.blogspot.com/2009/01/volume-rendering-102-transfer-functions.html
import ipyvolume as ipv
from scipy.interpolate import splrep, splev, interp1d
# First part is a simpler version of setting up the transfer function. Interpolation with higher order
# splines does not work well, the original must do sth different
colors = [[.91, .7, .61, 0.],
[.91, .7, .61, 80.],
[1.0, 1.0, .85, 82.],
[1.0, 1.0, .85, 256]]
x = np.array([k[-1] for k in colors])
rgb = np.array([k[:3] for k in colors])
N = 256
xnew = np.linspace(0, 256, N)
tf_data = np.zeros((N, 4))
k = 1
s = 1
# kind = 'quadratic'
kind = 'linear'
for channel in range(3):
# spline_data = splrep(x, rgb[:,channel], k=k, s=s)
# ynew = splev(xnew, spline_data)
f = interp1d(x, rgb[:,channel], kind=kind)
ynew = f(xnew)
tf_data[:,channel] = ynew
alphas = [[0, 0], [0, 40], [0.2, 60], [0.05, 63], [0, 80], [0.9, 82], [1., 256]]
x = np.array([k[1]*1. for k in alphas])
y = np.array([k[0]*1. for k in alphas])
# spline_data = splrep(x, y, k=k, s=s)
# ynew = splev(xnew, spline_data)
f = interp1d(x, y, kind=kind)
ynew = f(xnew)
tf_data[:,3] = ynew
tf = ipv.TransferFunction(rgba=tf_data.astype(np.float32))
head_data = ipv.datasets.head.fetch().data
if draw:
vol = ipv.volshow(head_data, tf=tf, max_shape=max_shape)
if show:
ipv.show()
return vol
else:
return head_data
[docs]def gaussian(N=1000, draw=True, show=True, seed=42, color=None, marker='sphere'):
"""Show N random gaussian distributed points using a scatter plot"""
import ipyvolume as ipv
rng = np.random.RandomState(seed)
x, y, z = rng.normal(size=(3, N))
if draw:
if color:
mesh = ipv.scatter(x, y, z, marker=marker, color=color)
else:
mesh = ipv.scatter(x, y, z, marker=marker)
if show:
#ipv.squarelim()
ipv.show()
return mesh
else:
return x, y, z