Title: Contours Element
Dependencies: Matplotlib
Backends: Matplotlib; Bokeh

In [1]:

import numpy as np
import holoviews as hv
hv.extension('matplotlib')

A Contours object is similar to Path object except it may be associated with a numeric value (the level ), which can be used to apply colormapping the Contours . To see the effect of this we can create a number of Contours with varying shapes and level values. In this case we will create a number of concentric rings with increasing radii and level values and colormap the Contours with the viridis colormap:

In [2]:

%%opts Contours (cmap='viridis')

def circle(radius, x=0, y=0):
    angles = np.linspace(0, 2*np.pi, 100)
    return np.array(list(zip(x+radius*np.sin(angles), y+radius*np.cos(angles))))

hv.Overlay([hv.Contours([circle(i+0.05)], level=i) for i in np.linspace(0, 1, 10)])

Out[2]:

Often Contours will be directly computed from an underlying Image , which is made easy using the contours operation. The operation accepts an Image type as input and will compute an NdOverlay containing a Contours Element for each of the specified levels . We will declare an Image of sine rings and then compute Contours at 5 levels spaced linearly over the range of values in the Image:

In [3]:

%%opts Contours [show_legend=False colorbar=True width=325] (cmap='fire')
x,y = np.mgrid[-50:51, -50:51] * 0.05
img = hv.Image(np.sin(x**2+y**3))

z0, z1 = img.range('z')
img + hv.operation.contours(img, levels=np.linspace(z0, z1, 5), overlaid=False)

Out[3]:

Contours ¶