lets_plot.scale_stroke_identity#

lets_plot.scale_stroke_identity(name=None, breaks=None, labels=None, lablim=None, limits=None, na_value=None, guide='none', format=None)#

Use this scale when your data has already been scaled. I.e. it already represents aesthetic values that can be handled directly. This will not produce a legend unless you also supply the breaks and labels.

Parameters:
namestr

The name of the scale - used as the axis label or the legend title.

breakslist or dict

A list of data values specifying the positions of ticks, or a dictionary which maps the tick labels to the breaks values.

labelslist of str or dict

A list of labels on ticks, or a dictionary which maps the breaks values to the tick labels.

lablimint, default=None

The maximum label length (in characters) before trimming is applied.

limitslist

Continuous scale: a numeric vector of length two providing limits of the scale. Discrete scale: a vector specifying the data range for the scale and the default order of their display in guides.

guide, default=’none’

Guide to use for this scale.

formatstr

Define the format for labels on the scale. The syntax resembles Python’s:

  • ‘.2f’ -> ‘12.45’

  • ‘Num {}’ -> ‘Num 12.456789’

  • ‘TTL: {.2f}$’ -> ‘TTL: 12.45$’

For more info see https://lets-plot.org/python/pages/formats.html.

Returns:
FeatureSpec

Scale specification.

Notes

Input data expected: positive numeric values.

Examples

1from lets_plot import *
2LetsPlot.setup_html()
3data = {
4    'x': [0, 1, 2],
5    'y': [1, 2, 1],
6    's': [1, 3, 2],
7}
8ggplot(data, aes('x', 'y')) + geom_lollipop(aes(stroke='s')) + \
9    scale_stroke_identity()