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 asymmetric 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 Tabular Datasets user guide 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 than one, making it partially transparent.
Given two value dimensions corresponding to the position on the y-axis and the error,
Spread will visualize itself assuming symmetric errors:
np.random.seed(42) xs = np.linspace(0, np.pi*2, 20) err = 0.2+np.random.rand(len(xs)) hv.Spread((xs, np.sin(xs), err))
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 asymmetric errors:
xs = np.linspace(0, np.pi*2, 20) spread = hv.Spread((xs, np.sin(xs), 0.1+np.random.rand(len(xs)), 0.1+np.random.rand(len(xs))), vdims=['y', 'yerrneg', 'yerrpos']) spread.opts(alpha=1, facecolor='indianred')
For full documentation and the available style and plot options, use