lets_plot.geom_tile#

lets_plot.geom_tile(mapping=None, *, data=None, stat=None, position=None, show_legend=None, manual_key=None, sampling=None, tooltips=None, color_by=None, fill_by=None, **other_args)#

Display rectangles with x, y values mapped to the center of the tile.

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”).

tooltipslayer_tooltips

Result of the call to the layer_tooltips() function. Specify appearance, style and content.

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

Define the color aesthetic for the geometry.

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_tile() 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. Accept values between 0 and 1.

  • color (colour) : color of the geometry lines. For more info see https://lets-plot.org/python/pages/aesthetics.html#color-and-fill.

  • fill : fill color. For more info see https://lets-plot.org/python/pages/aesthetics.html#color-and-fill.

  • size : line width, default=0 (i.e. tiles outline initially is not visible).

  • width : width of a tile. Typically range between 0 and 1. Values that are greater than 1 lead to overlapping of the tiles.

  • height : height of a tile. Typically range between 0 and 1. Values that are greater than 1 lead to overlapping of the tiles.

  • linetype : type of the line of tile’s border. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’. For more info see https://lets-plot.org/python/pages/aesthetics.html#line-types.

Examples

 1import numpy as np
 2from scipy.stats import multivariate_normal
 3from lets_plot import *
 4LetsPlot.setup_html()
 5n = 100
 6a, b = -1, 0
 7x = np.linspace(-3, 3, n)
 8y = np.linspace(-3, 3, n)
 9X, Y = np.meshgrid(x, y)
10Z = np.exp(-5 * np.abs(Y ** 2 - X ** 3 - a * X - b))
11data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
12ggplot(data, aes(x='x', y='y', color='z', fill='z')) + geom_tile(size=.5)


 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, aes(x='x', y='y')) + \
17    geom_tile(aes(fill='z'), width=.8, height=.8, color='black', size=.5) + \
18    scale_fill_gradient(low='yellow', high='darkgreen')


 1import numpy as np
 2import geopandas as gpd
 3from lets_plot import *
 4from lets_plot.geo_data import *
 5LetsPlot.setup_html()
 6nlon, nlat = 30, 20
 7geometry = geocode_countries("Kazakhstan").get_boundaries().iloc[0].geometry
 8bbox = geometry.bounds
 9lonspace = np.linspace(bbox[0], bbox[2], nlon)
10latspace = np.linspace(bbox[1], bbox[3], nlat)
11longrid, latgrid = np.meshgrid(lonspace, latspace)
12lon, lat = longrid.flatten(), latgrid.flatten()
13within = gpd.points_from_xy(lon, lat).within(geometry)
14ggplot() + geom_livemap() + \
15    geom_tile(aes(x=lon, y=lat, fill=within), alpha=.5, show_legend=False)

The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).
On this page