Spread#
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- Title
- Spread Element
- Dependencies
- Bokeh
- Backends
- Bokeh
- Matplotlib
- Plotly
import numpy as np
import holoviews as hv
hv.extension('bokeh')
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.
Symmetric#
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))
Asymmetric#
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. In the next example, we also disable the default alpha value by setting fill_alpha to unity:
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(fill_alpha=1, fill_color='indianred')
For full documentation and the available style and plot options, use hv.help(hv.Spread).
Download this notebook from GitHub (right-click to download).