lets_plot.geom_density2df

lets_plot.geom_density2df(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, kernel=None, adjust=None, bw=None, n=None, bins=None, binwidth=None, color_by=None, fill_by=None, **other_args)

Fill density function contour.

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=’density2df’

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.

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.

kernelstr, default=’gaussian’

The kernel we use to calculate the density function. Choose among ‘gaussian’, ‘cosine’, ‘optcosine’, ‘rectangular’ (or ‘uniform’), ‘triangular’, ‘biweight’ (or ‘quartic’), ‘epanechikov’ (or ‘parabolic’).

bwstr or list of float

The method (or exact value) of bandwidth. Either a string (choose among ‘nrd0’ and ‘nrd’), or a float array of length 2.

adjustfloat

Adjust the value of bandwidth by multiplying it. Change how smooth the frequency curve is.

nlist of int

The number of sampled points for plotting the function (on x and y direction correspondingly).

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.

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_density2df() fills density contours.

Computed variables:

• ..group.. : number of density estimate contour band.

• ..level.. : calculated value of the density estimate for given contour band.

geom_density2df() understands the following aesthetics mappings:

• x : x-axis coordinates.

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

• fill : fill color. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

‘density2df’ statistical transformation combined with parameter value contour=False could be used to draw heatmaps (see the example below).

Examples

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    geom_density2df()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
    geom_density2df(aes(fill='..group..'), show_legend=False) + \
    scale_fill_brewer(type='seq', palette='GnBu', direction=-1)

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
p = ggplot({'x': x, 'y': y}, aes(x='x', y='y'))
bunch = GGBunch()
for i, bw in enumerate([.2, .4]):
    for j, n in enumerate([16, 256]):
        bunch.add_plot(p + geom_density2df(kernel='epanechikov', bw=bw, n=n, \
                                           size=.5, color='white') + \
                           ggtitle('bw={0}, n={1}'.format(bw, n)),
                       j * 400, i * 400, 400, 400)
bunch.show()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
p = ggplot({'x': x, 'y': y}, aes(x='x', y='y'))
bunch = GGBunch()
for i, adjust in enumerate([1.5, 2.5]):
    for j, bins in enumerate([5, 15]):
        bunch.add_plot(p + geom_density2df(kernel='cosine', \
                                           size=.5, color='white', \
                                           adjust=adjust, bins=bins) + \
                           ggtitle('adjust={0}, bins={1}'.format(adjust, bins)),
                       j * 400, i * 400, 400, 400)
bunch.show()

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
x = np.random.normal(size=n)
y = np.random.normal(size=n)
ggplot({'x': x, 'y': y}, aes('x', 'y')) + \
    geom_tile(aes(fill='..density..'), color='black', \
               stat='density2df', contour=False, n=50) + \
    scale_fill_gradient(low='#49006a', high='#fff7f3')

import numpy as np
from lets_plot import *
LetsPlot.setup_html()
n = 1000
np.random.seed(42)
data = {'x': 10 * np.random.normal(size=n) - 100, \
        'y': 3 * np.random.normal(size=n) + 40}
ggplot(data, aes('x', 'y')) + geom_livemap() + \
    geom_density2df(aes(fill='..group..'), \
                    alpha=.5, show_legend=False)