lets_plot.geom_raster#

lets_plot.geom_raster(mapping=None, *, data=None, stat=None, position=None, show_legend=None, manual_key=None, sampling=None, fill_by=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 Pandas or Polars 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, default=’identity’

Position adjustment. Either a position adjustment name: ‘dodge’, ‘dodgev’, ‘jitter’, ‘nudge’, ‘jitterdodge’, ‘fill’, ‘stack’ or ‘identity’, or the result of calling a position adjustment function (e.g., position_dodge() etc.).

show_legendbool, default=True

False - do not show legend for this layer.

manual_keystr or layer_key

The key to show in the manual legend. Specify text for the legend label or advanced settings using the layer_key() function.

samplingFeatureSpec

Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).

fill_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’fill’

Define the fill aesthetic for the geometry.

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

geom_raster() understands the following aesthetics mappings:

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')