lets_plot.geom_raster

lets_plot.geom_raster(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, **other_args)

Display rectangles with x, y values mapped to the center of the tile. This is a high performance special function for same-sized tiles. Much faster than geom_tile() but doesn’t support width/height and color.

Parameters
mappingFeatureSpec

Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or DataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

statstr, default=’identity’

The statistical transformation to use on the data for this layer, as a string.

positionstr or FeatureSpec

Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

show_legendbool, default=True

False - do not show legend for this layer.

samplingFeatureSpec

Result of the call to the sampling_xxx() function. Value None (or ‘none’) will disable sampling for this layer.

other_args

Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns
LayerSpec

Geom object specification.

Notes

Understands the following aesthetics mappings:

  • x : x-axis coordinates of the center of rectangles.

  • y : y-axis coordinates of the center of rectangles.

  • alpha : transparency level of a layer. Understands numbers between 0 and 1.

  • fill : color of geometry filling.

Examples

 1import numpy as np
 2from scipy.stats import multivariate_normal
 3from lets_plot import *
 4LetsPlot.setup_html()
 5np.random.seed(42)
 6n = 25
 7x = np.linspace(-1, 1, n)
 8y = np.linspace(-1, 1, n)
 9X, Y = np.meshgrid(x, y)
10mean = np.zeros(2)
11cov = [[1, -.5],
12       [-.5, 1]]
13rv = multivariate_normal(mean, cov)
14Z = rv.pdf(np.dstack((X, Y)))
15data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
16ggplot(data) + \
17    geom_raster(aes(x='x', y='y', fill='z')) + \
18    scale_fill_gradient(low='#54278f', high='#f2f0f7')