"""Metrics of quality for
the cells of a triangular mesh
"""
from vtkplotter import *
from vtkplotter.pyplot import histogram
mesh = load(datadir + "bunny.obj").scale(100).computeNormals()
mesh.lineWidth(0.1)
# generate an array for mesh quality
arr = mesh.quality(cmap='RdYlBu')
#printHistogram(arr, title='mesh quality', c='w')
hist = histogram(arr, xtitle='mesh quality', bc='w')
hist.scale(.2).pos(-8, 4, -9) # make it bigger and place it
# add a scalar bar for the active scalars
mesh.addScalarBar()
# create numeric labels of active scalar on top of cells
labs = mesh.labels(cells=True, precision=3).color('k')
show(mesh, labs, hist, __doc__, bg='bb', zoom=2)
data.append(vals)
data = np.array(data)
print("Print first 5 rows:")
print(names)
print(data[:5])
print("Number of rows:", len(data))
##################################################
# extract the columns into separate vectors:
genes = data.T
g0, g1, g2, g3, g4 = genes # unpack
n0, n1, n2, n3, n4 = names
# now create and show histograms of the gene expressions
h0 = histogram(g0, xtitle=n0, c=0)
h1 = histogram(g1, xtitle=n1, c=1)
h2 = histogram(g2, xtitle=n2, c=2)
h3 = histogram(g3, xtitle=n3, c=3, logscale=True)
show(h0, h1, h2, h3, shape=(1,4))
# this is where you choose what variables to show as 3D points
pts = np.c_[g4,g2,g3] # form an array of 3d points from the columns
axs = dict(xtitle='gene4', ytitle='gene2', ztitle='gene3')
pts_1 = pts[g0>0] # select only points that have g0>0
print("after selection nr. of points is", len(pts_1))
p_1 = Points(pts_1, r=4, c='red') # create the vtkplotter object
pts_2 = pts[(g0<0) & (g1>.5)] # select excluded points that have g1>0.5
p_2 = Points(pts_2, r=8, c='green') # create the vtkplotter object
"""Fit a plane to regions of a surface defined by
N points that are closest to a given point of the surface.
Green histogram is the distribution of residuals from the fitting.
"""
from vtkplotter import *
from vtkplotter.pyplot import histogram
plt = Plotter()
apple = load(datadir + "apple.ply").subdivide().pointGaussNoise(1)
plt += apple.alpha(0.1)
variances = []
for i, p in enumerate(apple.points()):
pts = apple.closestPoint(p, N=12) # find the N closest points to p
plane = fitPlane(pts) # find the fitting plane
variances.append(plane.variance)
if i % 400: continue
plt += plane
plt += Points(pts)
plt += Arrow(plane.center, plane.center + plane.normal / 5)
plt += histogram(variances, xtitle='variance').scale(6).pos(1.2, .2, -1)
plt += __doc__ + "\nNr. of fits performed: " + str(len(variances))
plt.show()
"""Create an animated logo"""
from vtkplotter import *
from vtkplotter.pyplot import histogram
exa = Polygon().scale(4.1).pos(5.25, 4.8, 0).off()
his = histogram([-1, 1], [-1, 1], mode='hex').unpack()
exah, cmh = [], []
for h in his:
cm = h.centerOfMass()
if exa.isInside(cm):
h.c('green').shrink(0.9).addShadow(z=-.4)
exah.append(h)
cmh.append(cm)
v1 = vector(9.4, 5.2, 0)
v2 = vector(9.4, 2.7, 0)
t1 = Text("EMBL", v1, c="k", s=1.5, depth=0)
t2 = Text("European Molecular\nBiology Laboratory", v2, c="dg", s=0.6, depth=0)
show(exa, exah, t1, t2, axes=0, interactive=0, elevation=-50)
for ti in reversed(range(100)):
t = ti / 100.
for j, h in enumerate(exah):
cx, cy, _ = cmh[j] - [4,5,0]
x = t*-4+(1-t)*6
g = exp(-(cx-x)**2/.5)*2
h.z(g)
t1.pos([sin(t)*-10, 0, -0.41] + v1).alpha((1-t)**2)
t2.pos([sin(t)*-15, 0, -0.41] + v2).alpha((1-t)**4)
exah[13].c('red')
"""Fit a plane to regions of a surface defined by
N points that are closest to a given point of the surface.
For some of these point we show the fitting plane.
Black points are the N points used for fitting.
Green histogram is the distribution of residuals from the fitting.
"""
from vtkplotter import *
from vtkplotter.pyplot import histogram
vp = Plotter()
vp += __doc__
s = load(datadir + "cow.vtk").subdivide().normalize().alpha(0.3)
vp += s
variances = []
for i, p in enumerate(s.points()):
if i % 100:
continue # skip most points
pts = s.closestPoint(p, N=12) # find the N closest points to p
plane = fitPlane(pts) # find the fitting plane
vp += plane
vp += Points(pts) # blue points
vp += Arrow(plane.center, plane.center + plane.normal / 10, c="g")
variances.append(plane.variance)
vp += histogram(variances).scale(25).pos(.6, -.3, -.9)
vp.show(viewup="z")
from vtkplotter import *
from vtkplotter.pyplot import histogram
import numpy as np
N = 2000
x = np.random.randn(N) * 1.0
y = np.random.randn(N) * 1.5
# hexagonal binned histogram:
histo = histogram(
x,
y,
bins=10,
mode='hexbin',
xtitle="x gaussian, s=1.0",
ytitle="y gaussian, s=1.5",
fill=True,
cmap='terrain',
)
# add a formula:
f = r'f(x, y)=A \exp \left(-\left(\frac{\left(x-x_{o}\right)^{2}}'
f += r'{2 \sigma_{x}^{2}}+\frac{\left(y-y_{o}\right)^{2}}'
f += r'{2 \sigma_{y}^{2}}\right)\right)'
formula = Latex(f, c='k', s=1.5).rotateX(90).rotateZ(90).pos(1.5, -2, 1)
show(histo, formula, axes=1, viewup='z')
"""Fit a plane to regions of a surface defined by
N points that are closest to a given point of the surface.
Green histogram is the distribution of residuals from the fitting.
"""
from vtkplotter import *
from vtkplotter.pyplot import histogram
plt = Plotter()
apple = load(datadir+"apple.ply").subdivide().addGaussNoise(1)
plt += apple.alpha(0.1)
variances = []
for i, p in enumerate(apple.points()):
pts = apple.closestPoint(p, N=12) # find the N closest points to p
plane = fitPlane(pts) # find the fitting plane
variances.append(plane.variance)
if i % 400: continue
plt += plane
plt += Points(pts)
plt += Arrow(plane.center, plane.center+plane.normal/5)
plt += histogram(variances).scale(6).pos(1.2,.2,-1)
plt += __doc__ + "\nNr. of fits performed: "+str(len(variances))
plt.show()
"""A uniform distribution on a plane is not uniform on a sphere""" import numpy as np from vtkplotter.pyplot import histogram phi = np.random.rand(1000)*6.28 the = np.random.rand(1000)*3.14 h = histogram(the, phi, mode='spheric') h.show(axes=12, viewup='z')
from vtkplotter import *
from vtkplotter.pyplot import histogram
vp = Plotter()
s = vp.load(datadir+"cow.vtk", alpha=0.3)
pts1, pts2, vals, cols = [], [], [], []
for i in range(0, s.N(), 10):
p = s.points(i)
pts = s.closestPoint(p, N=12) # find the N closest points to p
sph = fitSphere(pts) # find the fitting sphere
if sph is None:
continue
value = sph.radius * 10
color = colorMap(value, "jet", 0, 1) # map value to a RGB color
n = versor(p - sph.center) # unit vector from sphere center to p
vals.append(value)
cols.append(color)
pts1.append(p)
pts2.append(p + n / 8)
vp += Points(pts1, c=cols)
vp += Lines(pts1, pts2, c="black")
vp += histogram(vals, xtitle='radius', xlim=[0,2]).pos(-1,0.5,-1)
vp += Text2D(__doc__, pos=1)
vp.show()
"""Probe a voxel dataset at specified points and plot a histogram of the values """ from vtkplotter import * from vtkplotter.pyplot import histogram import numpy as np vol = load(datadir + 'embryo.slc') pts = np.random.rand(4000, 3) * 256 mpts = probePoints(vol, pts).pointSize(3).printInfo() scals = mpts.getPointArray() h = histogram(scals, xlim=(5, 120), xtitle='voxel value') h.scale(2.2) show(vol, mpts, h, __doc__)
import numpy as np
from vtkplotter.pyplot import histogram
from vtkplotter import *
np.random.seed(3)
data1 = np.random.randn(250)
data2 = (np.random.rand(250) - 0.5) * 12
hst1 = histogram(
data1,
bins=30,
errors=True,
aspect=4 / 3,
title='My distributions',
c='red',
marker='o',
)
# pick the 16th bin and color it violet
hst1.unpack(15).c('violet')
hst1 += Text('Highlight a\nspecial bin', pos=(0.5, 20), c='v')
# A second histogram:
# make it in same format as hst1 so it can be superimposed
hst2 = histogram(data2, format=hst1, alpha=0.5)
# Show both:
show(hst1, hst2)
from vtkplotter import Hyperboloid, show
from vtkplotter.pyplot import histogram
import numpy as np
np.random.seed(1)
##################################################################
radhisto = histogram(
np.random.rand(200) * 6.28,
mode='polar',
title="random orientations",
bins=10,
c=range(10), #'orange', #uniform color
alpha=0.8,
labels=["label" + str(i) for i in range(10)],
)
show(radhisto, at=0, N=2, axes=0, sharecam=False)
##################################################################
hyp = Hyperboloid(res=20).cutWithPlane().rotateY(-90)
hyp.color('grey').alpha(0.3)
# select 10 random indices of points on the surface
idx = np.random.randint(0, hyp.NPoints(), size=10)
radhistos = []
for i in idx:
#generate a random histogram
rh = histogram(
np.random.randn(100),
mode='polar',
"""Histogram of 2 variables"""
from vtkplotter import *
from vtkplotter.pyplot import histogram
import numpy as np
n = 10000
x = np.random.normal(2, 1, n) * 2 + 3
y = np.random.normal(1, 1, n) * 1 + 7
xm, ym = np.mean(x), np.mean(y)
h = histogram(
x,
y,
bins=50,
aspect=4 / 3,
# cmap='Blues',
cmap='PuBu',
title='2D Gauss histo',
)
# add some object to the plot
h += Marker('*', s=0.3, c='r').pos(xm, ym, 0.1)
show(h)
