🔴 Data Visualization

Contents:

eomaps.eomaps

Quick overview

To visualize a dataset, first assign the dataset to the :pyMaps object, then select how you want to visualize the data and finally call :pyMaps.plot_map.

Assign the data <assign_the_data> to a :pyMaps object via :pyMaps.set_data
(optional) Set the shape <set_the_shape> used to represent the data via :pyMaps.set_shape
(optional) Classify the data <classify_the_data> via :pyMaps.set_classify
Plot the data <plot_the_data> by calling :pyMaps.plot_map

from eomaps import Maps
m = Maps()
m.add_feature.preset("ocean", "land")

m_data = m.new_layer()                         # Create a new layer for the data
m_data.set_data(                               # Assign the data
    data=[1, 2, 3, 4, 5],                      #    Data values
    x=[-10, 20, 30, 40, 50],                   #    x coordinate values
    y=[-50, -20, 0, 20, 40],                   #    y coordinate values
    crs=4326                                   #    Coordinate system of (x, y)
    )

m_data.set_shape.geod_circles(radius=1e6)      # Draw geodesic circles
m_data.set_classify.EqualInterval(k=3)         # Classify into 3 equal intervals
m_data.plot_map(vmin=0, ec="k", cmap="magma")  # Plot the data
m_data.add_colorbar(hist_bins="bins")          # Add a colorbar

Note

A :pyMaps object can only manage a single dataset!

To plot multiple datasets on the same map, use :pyMaps.new_layer to get a unique :pyMaps object for each dataset! To quickly create a new layer that uses the same dataset, cassification and shape as the parent, use:

m_1 = m.new_layer(inherit_data=True,            # m_1 inherits the data from m
                  inherit_classification=True,  # m_1 inherits the classification and colormap from m
                  inherit_shape=True            # m_1 inherits the plot-shape from m
                  )

1) Assign the data

To assign a dataset to a :pyMaps object, use :pyMaps.set_data.

Maps.set_data

A dataset is fully specified by setting the following properties:

data : The data-values
x, y: The coordinates of the provided data
crs: The coordinate-reference-system of the provided coordinates
parameter (optional): The parameter name
encoding (optional): The encoding of the data
cpos, cpos_radius (optional): the pixel offset

The following data-types are currently accepted as input:

pandas.DataFrames

Assignments

data: pandas.DataFrame
x, y: The column-names to use as coordinates (string)
parameter: The column-name to use as data-values (string)

basic DataFrame

from eomaps import Maps
import pandas as pd

df = pd.DataFrame(dict(lon=[1,2,3], lat=[2,5,4], data=[12, 43, 2]))
m = Maps()
m.set_data(df, x="lon", y="lat", crs=4326, parameter="data")
m.plot_map()

multiindex DataFrame

from eomaps import Maps
import pandas as pd

data = dict(param=[10, 29, 39])
index = pd.MultiIndex.from_arrays([[10,20,30], [10,20,30]], names=("lon", "lat"))
df = pd.DataFrame(data=data, index=index)

m = Maps()
m.set_data(df, x="lon", y="lat", crs=4326, parameter="param")
m.plot_map()

numpy.Array | pandas.Series | list

Assignments

data, x, y: numpy.array, pandas.Series or list
- either data and coordinates have the same 1D/2D shape or
- 2D data=(m, n) and 1D coordinates x=(m,), y=(n,)
parameter: (optional) parameter name (string)

1D list

from eomaps import Maps

x, y, data = [1,2,3], [2, 5, 4], [12, 43, 2]
m = Maps()
m.set_data(data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()

1D numpy

from eomaps import Maps
import numpy as np

x, y, data = np.array([1,2,3]), np.array([5, 7, 9]), np.array([1, 2, 3])
m = Maps()
m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()

2D numpy

from eomaps import Maps
import numpy as np

x, y = np.meshgrid(np.array([1,2,3]), np.array([5, 7, 9]))
data = np.random.randint(0, 10, x.shape)
m = Maps()
m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()

2D + 1D numpy

from eomaps import Maps
import numpy as np

x, y = np.linspace(-20, 20, 100), np.linspace(15, 34, 50)
data = np.random.randint(0, 100, size=(100, 50)
m = Maps()
m.set_data(data=data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()

pandas Series

from eomaps import Maps
import pandas as pd

x, y, data = pd.Series([1,2,3]), pd.Series([2, 5, 4]), pd.Series([12, 43, 2])
m = Maps()
m.set_data(data, x=x, y=y, crs=4326, parameter="param_name")
m.plot_map()

xarray.Dataset

Assignments

data: xarray.Dataset
x, y: The variables to use as coordinates (string)
parameter: The variable to use as data-values (string)

2D data and coords

from eomaps import Maps
import xarray as xar
import numpy as np

param = np.random.randint(0, 10, (2,2,3))
lon = [[-20, 20], [23, 54]]
lat = [[-10, 20], [-10, 20]]
time = [1,2,3]

ds = xar.Dataset(
    data_vars=dict(my_param=(["x", "y", "time"], param)),
    coords=dict(lon=(["x", "y"], lon), lat=(["x", "y"], lat), time=time),
)

m = Maps()
m.set_data(data=ds.sel(time=1), x="lon", y="lat", parameter="my_param", crs=4326)
m.plot_map()

1D coords 2D data

from eomaps import Maps
import xarray as xar
import numpy as np

param = np.random.randint(0, 10, (2,2,3))
lon = [-20, 20]
lat = [30, 60]
time = [1,2,3]

ds = xar.Dataset(
    data_vars=dict(my_param=(["lon", "lat", "time"], param)),
    coords=dict(lon=lon, lat=lat, time=time),
)

m = Maps()
m.set_data(data=ds.sel(time=1), x="lon", y="lat", parameter="my_param", crs=4326)
m.plot_map()

Note

Make sure to use a individual :pyMaps object (e.g. with m2 = m.new_layer()) for each dataset! Calling :pyMaps.plot_map multiple times on the same :pyMaps object will remove and override the previously plotted dataset!

A note on data-reprojection...

EOmaps handles the reprojection of the data from the input-crs to the plot-crs.

Plotting data in its native crs will omit the reprojection step and is therefore a lot faster!
If your dataset is 2D (e.g. a raster), it is best (for speed and memory) to provide the coordinates as 1D vectors!
- 1D coordinate vectors will be broadcasted using matrix-indexing! (e.g. x[nx], y[ny] -> data[nx, ny])
- Note that reprojecting 1D coordinate vectors to a different crs will result in (possibly very large) 2D coordinate arrays!

2) Plot shapes

To specify how a dataset is visualized on the map, you have to set the "plot-shape" via :pyMaps.set_shape.

eomaps.eomaps

Maps.set_shape

Available shapes (see bleow for details on each plot-shape!):

A note on speed and performance

Some ways to visualize the data require more computational effort than others! Make sure to select an appropriate shape based on the size of the dataset you want to plot!

eomaps.eomaps

EOmaps dynamically pre-selects the data with respect to the current plot-extent before the actual plot is created! If you do not need to see the whole extent of the data, make sure to set the desired plot-extent via :pyMaps.set_extent or :pyMaps.set_extent_to_location BEFORE calling :pyMaps.plot_map to get a (possibly huge) speedup!

The suggested "suitable datasizes" mentioned below always refer to the number of datapoints that are visible in the desired plot-extent.

eomaps.eomaps

For very large datasets, make sure to have a look at the :pyraster <Maps.set_shape.raster>, :pyshade_raster <Maps.set_shape.shade_raster>, and :pyshade_points <Maps.set_shape.shade_points> shapes which use fast aggregation techniques to resample the data prior to plotting. This way datasets with billions of datapoints can be visualized fast.

Optional dependencies

:pyshade_raster <Maps.set_shape.shade_raster>, and :pyshade_points <Maps.set_shape.shade_points> require the datashader package! You can install it via:

mamba install -c conda-forge datashader

What's used by default?

By default, the plot-shape is assigned based on the associated dataset.

For datasets with less than 500 000 pixels, :pyellipses <Maps.set_shape.ellipses> is used.
For larger 2D datasets :pyraster <m.set_shape.raster> is used
... and :pyshade_points <Maps.set_shape.shade_points is attempted to be used for the rest.

Ellipses

Maps.set_shape.ellipses

Suitable data size	Supported data structures
up to ~500k datapoints	1D, 2D or mixed

m.set_shape.ellipses(radius=(2, 5),   # ellipse dimensions [rx , ry]
                     radius_crs=4326, # projection in which the ellipse is defined
                     n=50             # number of calculated points on the ellipse
                     )

Rectangles

Maps.set_shape.rectangles

Suitable data size	Supported data structures
up to ~500k datapoints	1D, 2D or mixed

m.set_shape.rectangles(radius=(2, 5),   # rectangle dimensions [rx , ry]
                       radius_crs=4326, # projection in which the rectangle is defined
                       n=50             # number of calculated points on the ellipse
                       )

Geodesic Circles

Maps.set_shape.geod_circles

Suitable data size	Supported data structures
up to ~500k	1D, 2D or mixed

m.set_shape.geod_circles(radius=(2, 5),  # radius in meters
                         n=50             # number of calculated points on the circle
                         )

Voronoi Diagram

Maps.set_shape.voronoi_diagram

Suitable data size	Supported data structures
up to ~500k datapoints	1D, 2D or mixed

m.set_shape.voronoi_diagram(masked=True,      # mask too large polygons
                            mask_radius=10,   # min. size for masked polygons
                            )

Delaunay Triangulation

Maps.set_shape.delaunay_triangulation

Suitable data size	Supported data structures
up to ~500k datapoints	1D, 2D or mixed

m.set_shape.delaunay_triangulation(flat=False,       # color=mean of triplet (True) or interpolated values (False)
                                   masked=True,      # mask too large polygons
                                   mask_radius=10,   # min. size for masked polygons
                                   mask_crs="in",     # projection of the mask dimension
                                  )

Contour plots

Maps.set_shape.contour

Suitable data size	Supported data structures
up to a few million datapoints	1D, 2D or mixed

m.set_shape.contour(filled=True)   # filled contour polygons (True) or contour lines (False)

Hexbin plots

Maps.set_shape.hexbin

Suitable data size	Supported data structures
up to a few million datapoints	1D, 2D or mixed

m.set_shape.hexbin(size=(40, 20),      # number of hexagons in x- and y-direction
                   aggregator="mean",  # the aggregation method to use
                   )

Scatter Points

Maps.set_shape.scatter_points

Suitable data size	Supported data structures
~500k datapoints	1D, 2D or mixed

m.set_shape.scatter_points(size=[1, 2, 3],   # the marker size in points**2
                           marker="*",       # the marker shape to use
                           )

Raster

Maps.set_shape.raster

Suitable data size	Supported data structures
billions of datapoints (large datasets are pre-aggregated)	2D or 1D coordinates + 2D data

m.set_shape.raster(maxsize=5e5,         # data size at which aggregation kicks in
                   aggregator='mean',   # aggregation method to use
                   valid_fraction=0.5,  # % of masked values in aggregation bin for masked result
                   interp_order=0,      # spline interpolation order for "spline" aggregator

Shade Raster

Maps.set_shape.shade_raster Maps.set_shade_dpi

Suitable data size	Supported data structures	Optional dependencies
billions of datapoints (large datasets are pre-aggregated)	2D or 1D coordinates + 2D data	datashader

m.set_shape.shade_raster(aggregator='mean',    # aggregation method
                         shade_hook=None,      # datashader shade hook callback
                         agg_hook=None,        # datashader aggregation hook callback
                         )

Shade Points

Maps.set_shape.shade_raster Maps.set_shade_dpi

Suitable data size	Supported data structures	Optional dependencies
no limit (large datasets are pre-aggregated)	1D, 2D or mixed	datashader

m.set_shape.shade_raster(aggregator='mean',    # aggregation method
                         shade_hook=None,      # datashader shade hook callback
                         agg_hook=None,        # datashader aggregation hook callback
                         )

3) Classify the data

eomaps.eomaps

EOmaps provides an interface for mapclassify to classify datasets prior to plotting.

To assign a classification scheme to a :pyMaps object, use m.set_classify.< SCHEME >(...).

Available classifier names are accessible via Maps.CLASSIFIERS.

Maps.set_classify

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from eomaps import Maps
import numpy as np

data = np.random.normal(0, 1, (50, 50))
x = np.linspace(-45, 45, 50)
y = np.linspace(-45, 45, 50)

m = Maps(figsize=(4, 5))
m.add_feature.preset.coastline(lw=2)
m.add_feature.preset.ocean(zorder=99, alpha=0.5)
m.set_data(data, x, y)
m.set_shape.ellipses()
m.set_classify.StdMean(multiples=[-1.5, -.5, .5, 1.5])
m.plot_map(vmin=-3, vmax=3)

cb = m.add_colorbar(pos=0.2, label="StdMean classification")
cb.tick_params(labelsize=7)

Currently available classification-schemes are (see mapclassify for details):

BoxPlot(hinge)
EqualInterval(k)
FisherJenks(k)
FisherJenksSampled(k, pct, truncate)
HeadTailBreaks()
JenksCaspall(k)
JenksCaspallForced(k)
JenksCaspallSampled(k, pct)
MaxP(k, initial)
MaximumBreaks(k, mindiff)
NaturalBreaks(k, initial)
Quantiles(k)
Percentiles(pct)
StdMean(multiples)
UserDefined(bins)

4) Plot the data

Now that the data is assigned to a :pyMaps object, you can trigger plotting the data via :pyMaps.plot_map.

Any additional keyword-arguments passed to :pyMaps.plot_map are forwarded to the actual matplotlib plot-command for the selected shape.

Useful arguments that are supported by all shapes are:

"cmap" : the colormap to use

"vmin", "vmax" : the range of values used when assigning the colors

"alpha" : the color transparency

"zorder" : the "stacking-order" of the feature

Arguments that are supported by all shapes except shade shapes are:

"fc" or "facecolor" : set the face color for the whole dataset
"ec" or "edgecolor" : set the edge color for the whole dataset
"lw" or "linewidth" : the line width of the shapes

By default, the plot-extent of the axis is adjusted to the extent of the data if the extent has not been set explicitly before. To always keep the extent as-is, use m.plot_map(set_extent=False).

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from eomaps import Maps
m = Maps()
m.add_feature.preset.ocean()

m.set_data(
    data=[1, 2, 3, 4, 5],
    x = [-10, 20, 40, 60, 70],
    y = [-10, 20, 50, 70, 30])
m.set_shape.geod_circles(radius=7e5)
m.plot_map(cmap="viridis", set_extent=False)

You can then continue to add a colorbar or create zoomed_in_views_on_datasets.

eomaps.eomaps

Maps.plot_map Maps.savefig

Customize the plot

eomaps.eomaps

All arguments to customize the appearance of a dataset are passed to :pyMaps.plot_map.

In general, the colors assigned to the shapes are specified by

selecting a colormap (cmap)
- either a name of a pre-defined matplotlib colormap (e.g. "viridis", "RdYlBu" etc.)
- or a general matplotlib colormap object (see matplotlib-docs for more details)
(optionally) setting appropriate data-limits via vmin and vmax.
- vmin and vmax set the range of data-values that are mapped to the colorbar-colors
- Any values outside this range will get the colormaps over and under colors assigned.

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from eomaps import Maps
m = Maps()
m.set_frame(rounded=0.2)
m.add_feature.preset.ocean()
m.add_feature.preset.land(fc="k")

m.set_data(
    data=[1, 2, 3, 4, 5],
    x = [-10, 20, 40, 60, 70],
    y = [-10, 20, 50, 70, 30])
m.set_shape.geod_circles(radius=1e6)
m.plot_map(
    cmap="tab10", # the colormap to use
    vmin=2,       # min value for cmap
    vmax=4,       # max value for cmap
    alpha=0.8,    # transparency
    ec="w",       # edgecolor
    lw=1.5,       # linewidth
    ls="--"       # linestyle
    )

Colors can also be set manually by providing one of the following arguments to :pyMaps.plot_map:

to set both facecolor AND edgecolor use color=...
to set the facecolor use fc=... or facecolor=...
to set the edgecolor use ec=... or edgecolor=...

Note

Manual color specifications do not work with the shade_raster and shade_points shapes!
Providing manual colors will override the colors assigned by the cmap!
The colorbar does not represent manually defined colors!

Uniform colors

To apply a uniform color to all datapoints, you can use matpltolib's named colors or pass an RGB or RGBA tuple.

m.plot_map(fc="orange")
m.plot_map(fc=(0.4, 0.3, 0.2))
m.plot_map(fc=(1, 0, 0.2, 0.5))

1 1 1 2

from eomaps import Maps

m = Maps()
m.add_feature.preset("coastline", "ocean")
m.set_data(data=None, x=[10,20,30], y=[10,20,30])

# Use any of matplotlibs "named colors"
m1 = m.new_layer(inherit_data=True)
m1.set_shape.ellipses(radius=10)
m1.plot_map(fc="r", zorder=0)

m2 = m.new_layer(inherit_data=True)
m2.set_shape.ellipses(radius=8)
m2.plot_map(fc="orange", zorder=1)

# Use RGB tuples (r, g, b)
m3 = m.new_layer(inherit_data=True)
m3.set_shape.ellipses(radius=6)
m3.plot_map(fc=(1, 0, 0.5), zorder=2)

# Use RGBA tuples (r, g, b, alpha)
m4 = m.new_layer(inherit_data=True)
m4.set_shape.ellipses(radius=4)
m4.plot_map(fc=(1, 1, 1, .75), zorder=3)

# For grayscale use a string of a number between 0 and 1
m5 = m.new_layer(inherit_data=True)
m5.set_shape.ellipses(radius=2)
m5.plot_map(fc="0.3", zorder=4)

Explicit colors

To explicitly color each datapoint with a pre-defined color, simply provide a list or array of the aforementioned types.

1 1 1 2

from eomaps import Maps

m = Maps()
m.add_feature.preset("coastline", "ocean")
m.set_data(data=None, x=[10, 20, 30], y=[10, 20, 30])

# Use any of matplotlibs "named colors"
# (https://matplotlib.org/stable/gallery/color/named_colors.html)
m1 = m.new_layer(inherit_data=True)
m1.set_shape.ellipses(radius=10)
m1.plot_map(fc=["indigo", "g", "orange"], zorder=1)

# Use RGB tuples
m2 = m.new_layer(inherit_data=True)
m2.set_shape.ellipses(radius=6)
m2.plot_map(fc=[(1, 0, 0.5),
                (0.3, 0.4, 0.5),
                (1, 1, 0)], zorder=2)

# Use RGBA tuples
m3 = m.new_layer(inherit_data=True)
m3.set_shape.ellipses(radius=8)
m3.plot_map(fc=[(1, 0, 0.5, 0.25),
                (1, 0, 0.5, 0.75),
                (0.1, 0.2, 0.5, 0.5)], zorder=3)

# For grayscale use a string of a number between 0 and 1
m4 = m.new_layer(inherit_data=True)
m4.set_shape.ellipses(radius=4)
m4.plot_map(fc=[".1", ".2", "0.3"], zorder=4)

RGB/RGBA composites

To create an RGB or RGBA composite from 3 (or 4) datasets, pass the datasets as tuple:

the datasets must have the same size as the coordinate arrays!
the datasets must be scaled between 0 and 1

If you pass a tuple of 3 or 4 arrays, they will be used to set the RGB (or RGBA) colors of the shapes, e.g.:

m.plot_map(fc=(<R-array>, <G-array>, <B-array>))
m.plot_map(fc=(<R-array>, <G-array>, <B-array>, <A-array>))

You can fix individual color channels by passing a list with 1 element, e.g.:

m.plot_map(fc=(<R-array>, [0.12345], <B-array>, <A-array>))

1 1 1 2

from eomaps import Maps
import numpy as np

x, y = np.meshgrid(np.linspace(-30, 30, 50),
                   np.linspace(30, 30, 50))

# values must be between 0 and 1
r = np.random.randint(0, 100, x.shape) / 100
g = np.random.randint(0, 100, x.shape) / 100
b = [0.4]
a = np.random.randint(0, 100, x.shape) / 100

m = Maps()
m.add_feature.preset.ocean()
m.set_data(data=None, x=x, y=y)
m.plot_map(fc=(r, g, b, a))

🌈 Colorbars (with a histogram)

eomaps.eomaps

Before adding a colorbar, you must plot the data using m.plot_map(vmin=..., vmax=...).

vmin and vmax hereby specify the value-range used for assigning colors (e.g. the limits of the colorbar).
If no explicit limits are provided, the min/max values of the data are used.
For more details, see visualize_data.

Once a dataset has been plotted, a colorbar with a colored histogram on top can be added to the map by calling :pyMaps.add_colorbar.

Note

| The colorbar always represents the dataset that was used in the last call to :pyMaps.plot_map. | If you need multiple colorbars, use an individual :pyMaps object for each dataset! (e.g. via m2 = m.new_layer())

Note

Colorbars are only visible if the layer at which the data was plotted is visible!

1 1 1 2

from eomaps import Maps
import numpy as np

data = np.random.normal(0, 1, (50, 50))
x = np.linspace(-45, 45, 50)
y = np.linspace(-45, 45, 50)

m = Maps(layer="all")
m.add_feature.preset.coastline()
m.add_feature.preset.ocean(zorder=99, alpha=0.5)
m.util.layer_selector(loc="upper left")

mA = m.new_layer("A")
mA.set_data(data, x, y)
mA.set_classify.Quantiles(k=5)
mA.plot_map(vmin=-3, vmax=3)
cbA = mA.add_colorbar(label="Quantile classification")
cbA.tick_params(rotation=45)

mB = m.new_layer("B")
mB.set_data(data, x, y)
mB.set_classify.EqualInterval(k=5)
mB.plot_map(vmin=-3, vmax=3)
cbB = mB.add_colorbar(label="EqualInterval classification")
cbB.tick_params(labelcolor="darkblue", labelsize=9)

m.subplots_adjust(bottom=0.1)
m.show_layer(mA.layer)

Maps.add_colorbar

The returned ColorBar-object has the following useful methods defined:

eomaps.colorbar

ColorBar.set_position ColorBar.set_labels ColorBar.set_hist_size ColorBar.tick_params ColorBar.set_visible

Set colorbar tick labels based on bins

eomaps.colorbar

To label the colorbar with custom names for a given set of bins, use :pyColorBar.set_bin_labels:

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import numpy as np
from eomaps import Maps
# specify some random data
lon, lat = np.mgrid[-45:45, -45:45]
data = np.random.normal(0, 50, lon.shape)

# use a custom set of bins to classify the data
bins = np.array([-50, -30, -20, 20, 30, 40, 50])
names = np.array(["below -50", "A", "B", "C", "D", "E", "F", "above 50"])

m = Maps()
m.add_feature.preset.coastline()
m.set_data(data, lon, lat)
m.set_classify.UserDefined(bins=bins)
m.plot_map(cmap="tab10")
m.add_colorbar()

# set custom colorbar-ticks based on the bins
m.colorbar.set_bin_labels(bins, names)

ColorBar.set_bin_labels

Using the colorbar as a "dynamic shade indicator"

Note

This will only work if you use m.set_shape.shade_raster() or m.set_shape.shade_points() as plot-shape!

For shade shapes, the colorbar can be used to indicate the distribution of the shaded pixels within the current field of view by setting dynamic_shade_indicator=True.

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from eomaps import Maps
import numpy as np
x, y = np.mgrid[-45:45, 20:60]

m = Maps()
m.add_feature.preset.coastline()
m.set_data(data=x+y, x=x, y=y, crs=4326)
m.set_shape.shade_raster()
m.plot_map()
m.add_colorbar(dynamic_shade_indicator=True, hist_bins=20)

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api_data_visualization.rst

api_data_visualization.rst

🔴 Data Visualization

Quick overview

1) Assign the data

2) Plot shapes

Ellipses

Rectangles

Geodesic Circles

Voronoi Diagram

Delaunay Triangulation

Contour plots

Hexbin plots

Scatter Points

Raster

Shade Raster

Shade Points

3) Classify the data

4) Plot the data

Customize the plot

Uniform colors

Explicit colors

RGB/RGBA composites

🌈 Colorbars (with a histogram)

Set colorbar tick labels based on bins

Using the colorbar as a "dynamic shade indicator"

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🔴 Data Visualization

Quick overview

1) Assign the data

2) Plot shapes

Ellipses

Rectangles

Geodesic Circles

Voronoi Diagram

Delaunay Triangulation

Contour plots

Hexbin plots

Scatter Points

Raster

Shade Raster

Shade Points

3) Classify the data

4) Plot the data

Customize the plot

Uniform colors

Explicit colors

RGB/RGBA composites

🌈 Colorbars (with a histogram)

Set colorbar tick labels based on bins

Using the colorbar as a "dynamic shade indicator"