Colormaps

Figure objects such as CurtainFigure accept matplotlib.colors.Colormap objects or a name string, which then retrieves the appropriate colormap from matplotlib, plotly, cmcrameri, and from a list of pre-defined colormaps for earthcarekit:

Advanced

In case you want to get an matplotlib.Colormap object and call by name, use the get_cmap function.

All pre-defined colour tables for earthcarekit are also listed in earthcarekit.cmaps.

Categorical colormaps

For classification data (e.g., ATLID target classification) categorical colormaps can be created using the Cmap.to_categorical method:

cmap = eck.get_cmap("viridis")
values_to_labels = {
    0: "class 1",
    1: "class 2",
    100: "class 3",
    -1: "missing data",
}
cmap_categorical = cmap.to_categorical(values_to_labels)

# Example plot
eck.CurtainFigure().plot(
    values=[[-1,  0,  1, 100],
            [ 1, -1,  1,   0],
            [ 0,  1, -1,   1]],
    height=[5e3,15e3, 25e3, 35e3],
    time=["20250101", "20250201", "20250301"],
    cmap=cmap_categorical,
)

Shifting the midpoint

See shift_cmap.

def plot_cmap(c):
    import matplotlib.pyplot as plt
    _, ax = plt.subplots(figsize=(6, 0.5))
    plt.colorbar(plt.cm.ScalarMappable(cmap=c), cax=ax, orientation="horizontal", label=c.name)
    plt.show()

cmap = eck.get_cmap("RdBu")
plot_cmap(cmap)

cmap_shifted = eck.shift_cmap(cmap, midpoint=0.2, name="RdBu_shifted")
plot_cmap(cmap_shifted)