heatmap

# MakieCore.heatmap — Function.

julia

heatmap(x, y, matrix)
heatmap(x, y, func)
heatmap(matrix)
heatmap(xvector, yvector, zvector)

Plots a heatmap as a collection of rectangles. x and y can either be of length i and j where (i, j) is size(matrix), in this case the rectangles will be placed around these grid points like voronoi cells. Note that for irregularly spaced x and y, the points specified by them are not centered within the resulting rectangles.

x and y can also be of length i+1 and j+1, in this case they are interpreted as the edges of the rectangles.

Colors of the rectangles are derived from matrix[i, j]. The third argument may also be a Function (i, j) -> v which is then evaluated over the grid spanned by x and y.

Another allowed form is using three vectors xvector, yvector and zvector. In this case it is assumed that no pair of elements x and y exists twice. Pairs that are missing from the resulting grid will be treated as if zvector had a NaN element at that position.

If x and y are omitted with a matrix argument, they default to x, y = axes(matrix).

Note that heatmap is slower to render than image so image should be preferred for large, regularly spaced grids.

Plot type

The plot type alias for the heatmap function is Heatmap.

source

Examples

Two vectors and a matrix

In this example, x and y specify the points around which the heatmap cells are placed.

julia

using CairoMakie
f = Figure()
ax = Axis(f[1, 1])

centers_x = 1:5
centers_y = 6:10
data = reshape(1:25, 5, 5)

heatmap!(ax, centers_x, centers_y, data)

scatter!(ax, [(x, y) for x in centers_x for y in centers_y], color=:white, strokecolor=:black, strokewidth=1)

f

The same approach works for irregularly spaced cells. Note how the rectangles are not centered around the points, because the boundaries are between adjacent points like voronoi cells.

julia

using CairoMakie
f = Figure()
ax = Axis(f[1, 1])

centers_x = [1, 2, 4, 7, 11]
centers_y = [6, 7, 9, 12, 16]
data = reshape(1:25, 5, 5)

heatmap!(ax, centers_x, centers_y, data)

scatter!(ax, [(x, y) for x in centers_x for y in centers_y], color=:white, strokecolor=:black, strokewidth=1)
f

If we add one more element to x and y, they now specify the edges of the rectangular cells. Here's a regular grid:

julia

using CairoMakie
f = Figure()
ax = Axis(f[1, 1])

edges_x = 1:6
edges_y = 7:12
data = reshape(1:25, 5, 5)

heatmap!(ax, edges_x, edges_y, data)

scatter!(ax, [(x, y) for x in edges_x for y in edges_y], color=:white, strokecolor=:black, strokewidth=1)
f

We can do the same with an irregular grid as well:

julia

using CairoMakie
f = Figure()
ax = Axis(f[1, 1])

borders_x = [1, 2, 4, 7, 11, 16]
borders_y = [6, 7, 9, 12, 16, 21]
data = reshape(1:25, 5, 5)

heatmap!(ax, borders_x, borders_y, data)
scatter!(ax, [(x, y) for x in borders_x for y in borders_y], color=:white, strokecolor=:black, strokewidth=1)
f

Using a `Function` instead of a `Matrix`

When using a Function of the form (i, j) -> v as the values argument, it is evaluated over the grid spanned by x and y.

julia

using CairoMakie
function mandelbrot(x, y)
    z = c = x + y*im
    for i in 1:30.0; abs(z) > 2 && return i; z = z^2 + c; end; 0
end

heatmap(-2:0.001:1, -1.1:0.001:1.1, mandelbrot,
    colormap = Reverse(:deep))

Three vectors

There must be no duplicate combinations of x and y, but it is allowed to leave out values.

julia

using CairoMakie
xs = [1, 2, 3, 1, 2, 3, 1, 2, 3]
ys = [1, 1, 1, 2, 2, 2, 3, 3, 3]
zs = [1, 2, 3, 4, 5, 6, 7, 8, NaN]

heatmap(xs, ys, zs)

Colorbar for single heatmap

To get a scale for what the colors represent, add a colorbar. The colorbar is placed within the figure in the first argument, and the scale and colormap can be conveniently set by passing the relevant heatmap to it.

julia

using CairoMakie
xs = range(0, 2π, length=100)
ys = range(0, 2π, length=100)
zs = [sin(x*y) for x in xs, y in ys]

fig, ax, hm = heatmap(xs, ys, zs)
Colorbar(fig[:, end+1], hm)

fig

Colorbar for multiple heatmaps

When there are several heatmaps in a single figure, it can be useful to have a single colorbar represent all of them. It is important to then have synchronized scales and colormaps for the heatmaps and colorbar. This is done by setting the colorrange explicitly, so that it is independent of the data shown by that particular heatmap.

Since the heatmaps in the example below have the same colorrange and colormap, any of them can be passed to Colorbar to give the colorbar the same attributes. Alternativly, the colorbar attributes can be set explicitly.

julia

using CairoMakie
xs = range(0, 2π, length=100)
ys = range(0, 2π, length=100)
zs1 = [sin(x*y) for x in xs, y in ys]
zs2 = [2sin(x*y) for x in xs, y in ys]

joint_limits = (-2, 2)  # here we pick the limits manually for simplicity instead of computing them

fig, ax1, hm1 = heatmap(xs, ys, zs1,  colorrange = joint_limits)
ax2, hm2 = heatmap(fig[1, end+1], xs, ys, zs2, colorrange = joint_limits)

Colorbar(fig[:, end+1], hm1)                     # These three
Colorbar(fig[:, end+1], hm2)                     # colorbars are
Colorbar(fig[:, end+1], colorrange = joint_limits)  # equivalent

fig

Using a custom colorscale

One can define a custom (color)scale using the ReversibleScale type. When the transformation is simple enough (log, sqrt, ...), the inverse transform is automatically deduced.

julia

using CairoMakie
x = 10.0.^(1:0.1:4)
y = 1.0:0.1:5.0
z = broadcast((x, y) -> x - 10, x, y')

scale = ReversibleScale(x -> asinh(x / 2) / log(10), x -> 2sinh(log(10) * x))
fig, ax, hm = heatmap(x, y, z; colorscale = scale, axis = (; xscale = scale))
Colorbar(fig[1, 2], hm)

fig

Adjusts how the plot deals with transparency. In GLMakie transparency = true results in using Order Independent Transparency.

visible

Defaults to true

Controls whether the plot will be rendered or not.

heatmap ​

Examples ​

Two vectors and a matrix ​

Using a Function instead of a Matrix ​

Three vectors ​

Colorbar for single heatmap ​

Colorbar for multiple heatmaps ​

Using a custom colorscale ​

Attributes ​

alpha ​

colormap ​

colorrange ​

colorscale ​

depth_shift ​

fxaa ​

highclip ​

inspectable ​

inspector_clear ​

inspector_hover ​

inspector_label ​

interpolate ​

lowclip ​

model ​

nan_color ​

overdraw ​

space ​

ssao ​

transformation ​

transparency ​

visible ​

heatmap

Examples

Two vectors and a matrix

Using a `Function` instead of a `Matrix`

Three vectors

Colorbar for single heatmap

Colorbar for multiple heatmaps

Using a custom colorscale

Attributes

alpha

colormap

colorrange

colorscale

depth_shift

fxaa

highclip

inspectable

inspector_clear

inspector_hover

inspector_label

interpolate

lowclip

model

nan_color

overdraw

space

ssao

transformation

transparency

visible