Title
Dependencies
Matplotlib
Backends
Matplotlib
Bokeh
In [1]:
import numpy as np
import holoviews as hv
hv.extension('matplotlib')


 Spread  elements have the same data format as the  ErrorBars  element, namely x- and y-values with associated symmetric or assymetric errors, but are interpreted as samples from a continuous distribution (just as  Curve  is the continuous version of  Scatter  ). These are often paired with an overlaid  Curve  to show an average trend along with a corresponding spread of values; see the Columnar Data tutorial for examples.

Note that as the  Spread  element is used to add information to a plot (typically a  Curve  ) the default alpha value is less that one, making it partially transparent.

##### Symmetric ¶

Given two value dimensions corresponding to the position on the y-axis and the error,  Spread  will visualize itself assuming symmetric errors:

In [2]:
np.random.seed(42)
xs = np.linspace(0, np.pi*2, 20)
err = 0.2+np.random.rand(len(xs))

Given three value dimensions corresponding to the position on the y-axis, the negative error and the positive error,  Spread  can be used to visualize assymmetric errors:
%%opts Spread (facecolor='indianred' alpha=1)