Jupyter widgets enable interactive data visualization in the Jupyter notebooks.
Notebooks come alive when interactive widgets are used. Users can visualize and control changes in the data. Learning becomes an immersive, plus fun, experience. Researchers can easily see how changing inputs to a model impacts the results.
A library for creating simple interactive maps with panning and zooming, ipyleaflet supports annotations such as polygons, markers, and more generally any geojson-encoded geographical data structure.
from ipyleaflet import Map
Map(center=[34.6252978589571, -77.34580993652344], zoom=10)
conda install -c conda-forge ipyleaflet
pip install ipyleaflet
jupyter nbextension enable --py --sys-prefix ipyleaflet
jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-leaflet
A Jupyter widget to interactively view molecular structures and trajectories.
import pytraj as pt
import nglview as nv
traj = pt.load('sim.nc', top='sim.prmtop')
traj.strip(":TIP3")
view = nv.show_pytraj(traj)
view.clear()
view.add_cartoon('protein', color_scheme='residueindex')
view.add_ball_and_stick('not protein', opacity=0.5)
view
conda install -c bioconda nglview
pip install nglview
jupyter nbextension enable --py --sys-prefix nglview
K3D lets you create 3D plots backed by WebGL with high-level API (surfaces, isosurfaces, voxels, mesh, cloud points, vtk objects, volume renderer, colormaps, etc). The primary aim of K3D-jupyter is to be easy for use as stand alone package like matplotlib, but also to allow interoperation with existing libraries as VTK. The power of ipywidgets makes it also a fast and performant visualisation tool for HPC computing e.g. fluid dynamics.
Showcase gallery: https://k3d-jupyter.org/gallery/index.html.
import k3d
import numpy as np
lines = np.load('vertices.npy')
lines_attributes = np.load('attributes.npy')
plot = k3d.plot()
for l, a in zip(lines, lines_attributes):
plot += k3d.line(l, attribute=a, width=0.0001,
color_map=k3d.matplotlib_color_maps.Inferno, color_range=[0,0.5], shader='mesh',
compression_level=9)
plot.display()
pip install k3d
jupyter nbextension enable --py --sys-prefix k3d
jupyter labextension install @jupyter-widgets/jupyterlab-manager k3d
A 2-D interactive data visualization library implementing the constructs of the grammar of graphics, bqplot provides a simple API for creating custom user interactions.
import numpy as np
import bqplot.pyplot as plt
size = 100
plt.figure(title='Scatter plot with colors')
plt.scatter(np.random.randn(size), np.random.randn(size), color=np.random.randn(size))
plt.show()
conda install -c conda-forge bqplot
pip install bqplot
jupyter nbextension enable --py --sys-prefix bqplot
jupyter labextension install @jupyter-widgets/jupyterlab-manager bqplot
A 3-D visualization library enabling GPU-accelerated computer graphics in Jupyter.
from pythreejs import *
f = """
function f(origu,origv) {
// scale u and v to the ranges I want: [0, 2*pi]
var u = 2*Math.PI*origu;
var v = 2*Math.PI*origv;
var x = Math.sin(u);
var y = Math.cos(v);
var z = Math.cos(u+v);
return new THREE.Vector3(x,y,z);
}
"""
surf_g = ParametricGeometry(func=f, slices=16, stacks=16)
surf = Mesh(geometry=surf_g, material=MeshLambertMaterial(color='green', side='FrontSide'))
surf2 = Mesh(geometry=surf_g, material=MeshLambertMaterial(color='yellow', side='BackSide'))
c = PerspectiveCamera(position=[5, 5, 3], up=[0, 0, 1],
children=[DirectionalLight(color='white',
position=[3, 5, 1],
intensity=0.6)])
scene = Scene(children=[surf, surf2, c, AmbientLight(intensity=0.5)])
Renderer(camera=c, scene=scene, controls=[OrbitControls(controlling=c)], width=400, height=400)
conda install -c conda-forge pythreejs
pip install pythreejs
jupyter nbextension enable --py --sys-prefix pythreejs
jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-threejs
3-D plotting for Python in the Jupyter notebook based on IPython widgets using WebGL.
import ipyvolume.pylab as p3
import numpy as np
fig = p3.figure()
q = p3.quiver(*stream.data[:,0:50,:200], color="red", size=7)
p3.style.use("dark") # looks better
p3.animation_control(q, interval=200)
p3.show()