"""Histogram of 2 variables"""
from vedo import *
from vedo.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',
title='2D Gauss histo',
scalarbar=True,
)
# add some object to the plot
h += Marker('*', s=0.3, c='r').pos(xm, ym, 0.1)
show(h, interactorStyle="Image").close()
"""Generate a polygonal Mesh
from a contour line."""
from vedo import *
from vedo.pyplot import histogram
shapes = load(dataurl + 'timecourse1d.npy') # list of lines
shape = shapes[56].mirror().rotateZ(-90)
cmap = "RdYlBu"
msh = shape.tomesh() # Generate the Mesh from the line
msh.smoothLaplacian() # Make the triangles more uniform
msh.addQuality() # Measure triangle quality
msh.cmap(cmap, on="cells").addScalarBar3D()
contour = Line(shape).c('red4').lw(5)
histo = histogram(msh.getCellArray('Quality'),
aspect=3 / 4,
c=cmap,
xtitle='triangle mesh quality')
show(
[
[contour, contour.labels('id'), msh, __doc__],
[histo],
],
N=2,
sharecam=False,
)
"""A uniform distribution on a plane is not uniform on a sphere""" import numpy as np from vedo.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').close()
from vedo import *
from vedo.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()
)
plt += DashedLine(x, y)
# Fit points and evaluate, with a boostrap and Monte-Carlo technique,
# the correct errors and error bands. Return a Line object:
pfit = fit([x, y+noise],
deg=deg, # degree of the polynomial
niter=500, # nr. of MC iterations to compute error bands
nstd=2, # nr. of std deviations to display
xerrors=xerrs, # optional array of errors on x
yerrors=yerrs, # optional array of errors on y
vrange=(-3,15), # specify the domain of fit
)
plt += [pfit, pfit.errorBand, *pfit.errorLines] # add these objects to Plot
txt = "fit coefficients:\n " + precision(pfit.coefficients, 2) \
+ "\n\pm" + precision(pfit.coefficientErrors, 2) \
+ "\n\Chi^2_\nu = " + precision(pfit.reducedChi2, 3)
plt += Text3D(txt, s=0.42, font='VictorMono').pos(2,-2).c('k')
# Create an histo to show the correlation of fit parameters
h = histogram(pfit.MonteCarloCoefficients[:,0],
pfit.MonteCarloCoefficients[:,1],
title="parameters correlation",
xtitle='coeff_0', ytitle='coeff_1',
cmap='bone_r', scalarbar=True)
h.scale(150).shift(-1,11) # make it a lot bigger and move it
show(plt, h, zoom=1.3, mode="image").close()
"""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 vedo import *
from vedo.pyplot import histogram
plt = Plotter()
apple = load(dataurl + "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()
def main():
all_loaded_images = []
x_scale = 1
y_scale = 1
z_scale = 1
res = 10 #read every nth pixel in x,y,z
#set path
#all_images = "C:/Users/sgat/OneDrive - KADK/03_CITA/59_DataViz/03_data/all/"
#all_images = "C:/Users/sgat/OneDrive - KADK/03_CITA/59_DataViz/03_data/zebicon-7a12a3/31853_2020-09-18_Wood_1_Top/"
#all_images = "C:/Users/sgat/OneDrive - KADK/Sebastian_after_Sync/06_bones_to_flesh/browsing male/(VKH) CT Images (494 X 281)/"
#all_images = "C:/Users/sgat/OneDrive - KADK/03_CITA/59_DataViz/03_data/31853_2020-09-18_Cellulose_2_Top/"
#all_images = "C:/Users/sgat/OneDrive - KADK/03_CITA/59_DataViz/03_data/31853_2020-09-18_Cellulose_1_Top/"
#all_images = "C:/Users/sgat/OneDrive - KADK/03_CITA/59_DataViz/03_data/zebicon-ccc195/31853_2020-10_02_Fungar_1_Top/"
all_images = "C:/Users/sgat/OneDrive - KADK/03_CITA/59_DataViz/03_data/zebicon-ccc195/31853_2020-10_02_Fungar_2_Top/"
#all_images = "C:/Users/sgat/OneDrive - KADK/03_CITA/59_DataViz/03_data/zebicon-ccc195/cropped_jpeg/"
os.chdir(all_images)
#load all images
types = ('*.gif', '*.png', '*.jpg', "*bmp", "*tif")
files = []
for f in types:
files.extend(glob.glob(f))
print (len(files))
files = natsorted(files, key=lambda y: y.lower())
#create image stack
img_stack = 0
for i in range(0,len(files),res):
if i % 100 == 0:
print ("img loaded: ", i)
img = cv2.imread(files[i],0)
if res > 1:
img = cv2.resize(img, (0,0), fx=1/res, fy=1/res)
if i == 0:
img.fill(0)
img_stack = img
else:
img_stack = np.dstack((img_stack,img))
img = cv2.imread(files[0],0)
img = cv2.resize(img, (0,0), fx=1/res, fy=1/res)
img.fill(0)
img_stack = np.dstack((img_stack,img))
print ("image stack created. shape:", img_stack.shape)
#prepare data for historgam
data = img_stack.copy()
data = data.flatten()
data = data [data > 5] #only data highe than 5
#create historgam
hist = histogram(data,
bins=25,
errors=False,
c = "white",
bc = "white",
ytitle= "Voxel Counts",
xtitle= "Density"
)
#create volume
vol = Volume(img_stack, spacing=(x_scale,y_scale,z_scale), mapper="smart", mode=1, alpha= [0.0, 0.9, 1.0], c= "coolwarm")
#render and show
vp = Plotter(N=2, axes=False, bg="black", size="fullscreen", sharecam=False) #(1000,1000))
vp.show(vol, "Voxel Render", axes=0, at=0)
vp.show(hist,"Density Historgam", axes=0, at=1)
vp.show(interactive=True)
"""Metrics of quality for
the cells of a triangular mesh
"""
from vedo import *
from vedo.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)
"""Superimpose histograms and curves"""
import numpy as np
from vedo.pyplot import histogram, plot, settings
settings.defaultFont = "Bongas"
mu, sigma, n, bins = 100.0, 15.0, 600, 50
samples = np.random.normal(loc=mu, scale=sigma, size=n)
x = np.linspace(min(samples), max(samples), num=50)
y = 1/(sigma*np.sqrt(2*np.pi)) * np.exp( -(x-mu)**2 /(2*sigma**2))
dy = 1/np.sqrt(n)
plt = histogram(samples, title=__doc__,
bins=bins, density=True, c='cyan3', aspect=8/7)
plt += plot(x, y, "-", lc='orange5')
plt += plot(x, y*(1+dy), "--", lc='orange5', lw=2)
plt += plot(x, y*(1-dy), "--", lc='orange5', lw=2)
plt.show(size=(800,700), zoom=1.2, interactorStyle="Image").close()
"""Metrics of quality for
the cells of a triangular mesh
(zoom to see cell label values)"""
from vedo import *
from vedo.pyplot import histogram
mesh = Mesh(dataurl + "panther.stl").computeNormals().lineWidth(0.1).flat()
# generate a numpy array for mesh quality
mesh.addQuality(measure=6).cmap('RdYlBu', on='cells').print()
hist = histogram(mesh.celldata["Quality"], xtitle='mesh quality', bc='w')
# make it smaller and position it, useBounds makes the cam
# ignore the object when resetting the 3d qscene
hist.scale(0.6).pos(40, -53, 0).useBounds(False)
# add a scalar bar for the active scalars
mesh.addScalarBar3D(c='w', title='triangle quality by min(\alpha_i )')
# create numeric labels of active scalar on top of cells
labs = mesh.labels(
cells=True,
precision=3,
scale=0.4,
font='Quikhand',
c='black',
)
cam = dict(pos=(59.8, -191, 78.9),
focalPoint=(27.9, -2.94, 3.33),
viewup=(-0.0170, 0.370, 0.929),
data_g = np.zeros(msh.N())
data_r = np.zeros(msh.N())
data_w = np.zeros(msh.N())
data_r[ids_r] = 1.0
data_g[ids_g] = 1.0
data_w[ids_w] = 1.0
ngreen = len(ids_g[0])
total = len(rgb) - len(ids_r[0]) - len(ids_w[0])
gvalue = int(ngreen / total * 100 + 0.5)
show(
[
[pic, pic.box().lw(3), "Original image. How much grass is there?"],
histogram(ratio_g, logscale=True, xtitle='ratio of green'),
[
msh.clone().cmap('Greens', data_g),
f'Ratio of green is \approx {gvalue}%'
],
[msh.clone().cmap('Reds', data_r), 'Masking the vase region'],
[msh.clone().cmap('Greys', data_w), 'Masking bright areas'],
],
shape="2|3",
size=(1370, 1130),
sharecam=False,
bg='aliceblue',
mode='image',
zoom=1.5,
interactive=True,
)
from vedo import *
from vedo.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')
ln = lns.split(delimiter)
vals = [float(x) for x in ln]
data.append(vals)
data = np.array(data)
print("Print first 5 rows:\n", names)
print(data[:5])
print("Number of rows:", len(data))
##################################################
# extract the columns into separate vectors:
g0, g1, g2, g3, g4 = data.T # unpack genes
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)
h4 = histogram(g4, xtitle=n4, c=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
pts_1 = pts[g0 > 0] # select only points that have g0>0
p1 = Points(pts_1, r=4, c='red') # create the vedo object
print("after selection nr. of points is", len(pts_1))
pts_2 = pts[(g0 < 0) & (g1 > .5)] # select excluded points that have g1>0.5
p2 = Points(pts_2, r=8, c='green') # create the vedo object
"""Probe a voxel dataset at specified points and plot a histogram of the values """ from vedo import * from vedo.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__)
from vedo import *
from vedo.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="\sigma_x =1.0",
ytitle="\sigma_y =1.5",
ztitle="counts",
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')
"""Generate a polygonal Mesh
from a contour line"""
from vedo import *
from vedo.pyplot import histogram
shapes = load(dataurl + 'timecourse1d.npy') # list of lines
shape = shapes[56].mirror().rotateZ(-90)
cmap = "RdYlBu"
msh = shape.tomesh() # Generate the Mesh from the line
msh.smooth() # make the triangles more uniform
msh.addQuality() # add a measure of triangle quality
msh.cmap(cmap, on="cells").addScalarBar3D()
contour = Line(shape).c('red4').lw(5)
histo = histogram(msh.celldata['Quality'],
aspect=3 / 4,
c=cmap,
xtitle='triangle mesh quality')
show(
[
(contour, contour.labels('id'), msh, __doc__),
histo,
],
N=2,
sharecam=False,
).close()
import numpy as np
from vedo import *
from vedo.pyplot import histogram, plot
cmap = 'nipy_spectral'
alpha = np.array([0, 0, 0.05, 0.2, 0.8, 1])
vol = Volume(dataurl + "embryo.slc")
vol.cmap(cmap).alpha(alpha).addScalarBar3D(c='w')
xvals = np.linspace(*vol.scalarRange(), len(alpha))
p = histogram(vol, logscale=True, c=cmap, bc='white')
p += plot(xvals, alpha * p.ybounds()[1], '--ow').z(1)
show(
[(vol, Axes(vol, c='w'), f"Original Volume\ncolor map: {cmap}"),
(p, "Voxel scalar histogram\nand opacity transfer function")],
N=2,
sharecam=False,
bg=(82, 87, 110),
).close()
import numpy as np
from vedo.pyplot import histogram
from vedo 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',
xtitle='some stochastic x_\mu^0',
c='red',
marker='o',
)
# pick the 16th bin and color it violet
hst1.unpack(15).c('violet')
hst1 += Text3D('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, mode="image").close()
"""Probe a voxel dataset at specified points and plot a histogram of the values""" from vedo import * from vedo.pyplot import histogram import numpy as np vol = Volume(dataurl + 'embryo.slc') pts = np.random.rand(5000, 3) * 256 mpts = probePoints(vol, pts).pointSize(3) mpts.print() # valid = mpts.pointdata['vtkValidPointMask'] scals = mpts.pointdata['SLCImage'] his = histogram(scals, xtitle='probed voxel value', xlim=(5, 100)) show([(vol, Axes(vol), mpts, __doc__), his], N=2, sharecam=False).close()
"""Create an animated logo"""
from vedo import *
from vedo.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')
"""Metrics of quality for
the cells of a triangular mesh
(zoom to see cell label values)"""
from vedo import *
from vedo.pyplot import histogram
mesh = load(dataurl + "panther.stl").computeNormals().lineWidth(0.1)
# generate a numpy array for mesh quality
mesh.addQuality(measure=6).printInfo().cmap('RdYlBu', on='cells')
hist = histogram(mesh.getCellArray("Quality"), xtitle='mesh quality', bc='w')
# make it smaller and position it, useBounds makes the cam
# ignore the object when resetting the 3d qscene
hist.scale(0.6).pos(40, -53, 0).useBounds(False)
# add a scalar bar for the active scalars
mesh.addScalarBar3D(c='w', title='triangle quality by min(\alpha_i )')
# create numeric labels of active scalar on top of cells
labs = mesh.labels(
cells=True,
precision=3,
scale=0.4,
font='Quikhand',
c='black',
)
cam = dict(pos=(59.8, -191, 78.9),
focalPoint=(27.9, -2.94, 3.33),
viewup=(-0.0170, 0.370, 0.929),
from vedo import Hyperboloid, show
from vedo.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),
"""Histogram of 2 variables"""
from vedo import *
from vedo.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)