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A python module for scientific analysis and visualization of эd objects
Quick Start
To visualize a file from a web URL (or from your Dropbox!),
simply type in your terminal:
pip install vedo
vedo https://vedo.embl.es/examples/data/panther.stl.gz
To visualize a full 3D interactive scene, type:
vedo https://vedo.embl.es/examples/geo_scene.npz
Let's create an interactive 3D scene with a simple cone,
in 3 lines of python code:
from vedo import Cone
# Create a simple cone
c = Cone()
# Show it (with axes)
c.show(axes=1)
# Now you can interact with the 3D scene,
# Press "h" and explore the possibilities!
Let's write a simple python script that loads a polygonal Mesh and generates
a cool rendering by adding some custom light sources to the scene:
from vedo import *
# Load a polygonal mesh, make it white and glossy:
man = Mesh('https://vedo.embl.es/examples/data/man.vtk')
man.c('white').lighting('glossy')
# Create two points:
p1 = Point([ 1,0,1], c='yellow')
p2 = Point([1,0,2], c='red')
# Add colored light sources at the point positions:
l1 = Light(p1, c='yellow')
l2 = Light(p2, c='red')
# Show everything in one go:
show("Hello World", man, l1, l2, p1, p2, axes=True)
Let's create a Volume, a volumetric dataset, from a
numpy array:
import numpy as np
from vedo import *
# Create a scalar field: the distance from point (15,15,15)
X, Y, Z = np.mgrid[:30, :30, :30]
scalar_field = ((X15)**2 + (Y15)**2 + (Z15)**2)/225
# Create the Volume from the numpy object
vol = Volume(scalar_field)
# Generate the surface that contains all voxels in range [1,2]
lego = vol.legosurface(1,2).cmap("afmhot_r").add_scalarbar()
show(lego, axes=True)
References
Scientific publications leveraging
vedo
:

X. Diego et al.,
"Key features of Turing systems are determined purely by network topology",
Phys. Rev. X 8, 021071, 20 June 2018,
DOI.

M. Musy, K. Flaherty et al.,
"A Quantitative Method for Staging Mouse Limb Embryos based on Limb Morphometry",
Development, (2018) 145 (7): dev154856,
DOI.

F. Claudi, A. L. Tyson, T. Branco,
"Brainrender. A python based software for visualisation of neuroanatomical and morphological data.",
eLife 2021;10:e65751,
DOI.
 J. S. Bennett, D. Sijacki,
"Resolving shocks and filaments in galaxy formation simulations: effects on gas properties and star formation in the circumgalactic medium",
Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 1, November 2020,
DOI.

A. Pollack et al.,
"Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study"
Geothermics Volume 95, September 2021,
DOI.
 X. Lu et al.,
"3D electromagnetic modeling of graphitic faults in the Athabasca Basin using a finitevolume timedomain approach with unstructured grids",
GeophysicsJune 2021,
DOI.

M. Deepa Maheshvare et al.,
"A GraphBased Framework for Multiscale Modeling of Physiological Transport",
Front. Netw. Physiol. 1:802881,
DOI.

F. Claudi, T. Branco,
"Differential geometry methods for constructing manifoldtargeted recurrent neural networks",
bioRxiv 2021.10.07.463479,
DOI.

G. Dalmasso et al.,
"4D reconstruction of developmental trajectories using spherical harmonics",
bioRxiv 2021.12.16.472948,
DOI.

J. Klatzow, G. Dalmasso, N. MartínezAbadías, J. Sharpe, V. Uhlmann,
"µMatch: 3D shape correspondence for microscopy data",
Front. Comput. Sci., 15 February 2022,
DOI.

D.J.E Waibel et al.,
"Capturing Shape Information with Multiscale Topological Loss Terms for 3D Reconstruction".
Lecture Notes in Computer Science, vol 13434. Springer, Cham.
DOI.
Presentations at Conferences:

M. Musy, G. Dalmasso, J. Sharpe and N. Sime,
"Plotting in FEniCS with python",
Poster
at FEniCS'2019,
SDTM, Washington DC, June 2019.

G. Dalmasso,
"Evolution in space and time of 3D volumetric images".
Talk at Imagebased Modeling and Simulation of Morphogenesis.
Max Planck Institute for the Physics of Complex Systems, Dresden, Germany, March 2019.

G. Dalmasso,
"A fourdimensional growing mouse limb bud reconstruction".
Talk at SEBD, Spain, November 2020.

M. Musy,
"vedo. A python module for scientific analysis and visualization of 3D data".
Seminar at MOIA
(Microscopy Optics and Image Analysis), Heidelberg, November 2021.
Cite
vedo
as:

M. Musy et al.,
"
vedo
, a python module for scientific analysis and visualization of 3D objects and point clouds",
Zenodo, 10.5281/zenodo.2561401.