lets_plot.geom_contour

lets_plot.geom_contour(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, bins=None, binwidth=None, color_by=None, **other_args)

Display contours of a 3d surface in 2d.

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 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=’contour’

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

positionstr or FeatureSpec, default=’identity’

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. 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.

binsint

Number of levels.

binwidthfloat

Distance between levels.

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

Define the color 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_contour() displays contours of a 3d surface in 2d.

Computed variables:

  • ..level.. : height of a contour.

geom_contour() understands the following aesthetics mappings:

  • x : x-axis coordinates of the center of rectangles, forming a tessellation.

  • y : y-axis coordinates of the center of rectangles, forming a tessellation.

  • z : value at point (x, y).

  • alpha : transparency level of a layer. Accept values between 0 and 1.

  • color (colour) : color of the geometry lines. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”).

  • size : lines width.

  • linetype : type of the line. Codes and names: 0 = ‘blank’, 1 = ‘solid’, 2 = ‘dashed’, 3 = ‘dotted’, 4 = ‘dotdash’, 5 = ‘longdash’, 6 = ‘twodash’.

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, aes(x='x', y='y', z='z')) + geom_contour()


 1import numpy as np
 2from lets_plot import *
 3LetsPlot.setup_html()
 4n = 100
 5a, b = -1, 0
 6x = np.linspace(-3, 3, n)
 7y = np.linspace(-3, 3, n)
 8X, Y = np.meshgrid(x, y)
 9Z = np.exp(-5 * np.abs(Y ** 2 - X ** 3 - a * X - b))
10data = {'x': X.flatten(), 'y': Y.flatten(), 'z': Z.flatten()}
11ggplot(data, aes(x='x', y='y', z='z')) + \
12    geom_contour(aes(color='..level..'), bins=3, size=1) + \
13    scale_color_gradient(low='#dadaeb', high='#3f007d')


1import numpy as np
2from lets_plot import *
3LetsPlot.setup_html()
4n = 1000
5np.random.seed(42)
6data = {'x': 10 * np.random.normal(size=n) - 100, \
7        'y': 3 * np.random.normal(size=n) + 40}
8ggplot(data, aes('x', 'y')) + geom_livemap() + \
9    geom_contour(stat='density2d')