lets_plot.scale_alpha_identity#
- lets_plot.scale_alpha_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 the library can handle 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: numeric values in range [0, 1].
Examples
1import numpy as np 2from lets_plot import * 3LetsPlot.setup_html() 4n = 100 5np.random.seed(42) 6x = np.random.normal(size=n) 7y = np.random.normal(size=n) 8a = np.random.uniform(0, .5, size=n) 9ggplot({'x': x, 'y': y, 'a': a}, aes('x', 'y')) + \ 10 geom_point(aes(alpha='a'), shape=21, size=10) + \ 11 scale_alpha_identity(limits=[.2, .5], breaks=[.2, .3, .4, .5])