Scatter plots#

Summary

On this page, you will see how to

  • use .plot.facet_grid.scatter to plot 2-dimensional scatter plots, plotting further data dimensions on the hue and markersize properties

  • use .plot.facet_grid.scatter3d to plot 3-dimensional scatter plots, plotting further data dimensions on the hue and markersize properties

  • adjust the colormap used in the scatter plot

  • create facet grids of both 2- and 3-dimensional scatter plots by adding data dimensions to the row and/or col .

2-Dimensional Scatter Plot#

You can create two-dimensional scatter plots using the .plot.facet_grid.scatter base function, which invokes the xarray.plot.scatter() plot, which in turn wraps matplotlib.pyplot.scatter().

For example, here we are plotting a phase diagram of the SEIRD model showing the number of susceptible and infected agents:

phase_diagram_SI:
  based_on:
    - .creator.universe
    - .plot.facet_grid.scatter

  select:
    kind:
      path: densities

  transform:
    - .sel: [!dag_tag kind, { kind: susceptible }]
      kwargs: {drop: true}
      tag: susceptible
    - .sel: [!dag_tag kind, { kind: infected }]
      kwargs: {drop: true}
      tag: infected

    # Combine into a Dataset
    - xr.Dataset:
        data_vars:
          susceptible: !dag_tag susceptible
          infected: !dag_tag infected
      tag: data

  # Tell the scatter plot what to plot on x- and y-axes
  x: susceptible
  y: infected

Hint

kwargs: {drop: true} is necessary here to drop coordinates variables: see xarray.DataArray.sel().

This will output the following plot:

A simple 2d scatter plot

You can change the color of the dots using the color keyword.

This would be more useful if we knew which dot corresponded to which time step. We can use the hue and the markersize to encode additional variables; for example, we can encode the time as the hue and the number of recovered patients as the markersize:

x: susceptible
y: infected
hue: time
markersize: recovered

That will produce something like this:

2d scatter plot with hue and markersize set

Note that this requires you to have first also included the kind: recovered in the dataset above.

Warning

You can only use the markersize for actual data variables. If you only want to change the actual size of the markers, use the s key.

x: susceptible
y: infected
s: 20

This will set the size of the markers to 20. Naturally you cannot provide both markersize and s keys! The .plot.facet_grid.scatter function eventually calls xarray.plot.scatter(), whence the syntax originates.

You can set the colormap via the cmap key, for instance by passing the name of a matplotlib or seaborn colormap. You can also create your own colormap from a custom color palette:

phase_diagram_SI:

  # Everything as above ...

  cmap:
    continuous: true
    from_values:
      # Add your own colors here:
      0: crimson
      0.5: darkblue
      1: gold

The keys are the positions of the colors on the colormap, and must be floats between 0 and 1. You can pass as many keys as you like. See the styles section for more details on colormaps.

3-Dimensional Scatter Plot#

For 3-dimensional scatter plots, use the .plot.facet_grid.scatter3d base function, which calls the corresponding matplotlib 3d scatter function for 3-dimensional axes.

Let’s plot a 3-dimensional phase diagram, showing susceptible, infected, and recovered agents all in a single plot. Additionally, let’s encode the time dimension as the hue:

phase_diagram_SIR:
  based_on:
    - .creator.universe
    - .plot.facet_grid.scatter3d

  select:
    kind:
      path: densities

  transform:
    - .sel: [!dag_tag kind, { kind: susceptible }]
      kwargs: {drop: true}
      tag: susceptible
    - .sel: [!dag_tag kind, { kind: infected }]
      kwargs: {drop: true}
      tag: infected
    - .sel: [!dag_tag kind, { kind: recovered }]
      kwargs: {drop: true}
      tag: recovered

    - xr.Dataset:
        data_vars:
          susceptible: !dag_tag susceptible
          infected: !dag_tag infected
          recovered: !dag_tag recovered
      tag: data

  x: susceptible
  y: infected
  z: recovered
  hue: time

Notice the addition of the z key. This outputs a plot like this:

simple 3d scatter plot

Adjusting the colormap works just as in the 2-dimensional case.

If you want to change the view of the axis, use the PlotHelper to change the elevation and azimuthal angle of the view:

phase_diagram_SIR:

  # Everything as above ...

  helpers:
    setup_figure:
      subplot_kw:
        elev: 20
        azim: 45

Trying to debug errors in your DAG?

Have a look at Debugging DAG computations for approaches to do that.

Facet grid scatter plots#

You can plot both types of scatter plot in a facet grid, using rows and columns as additional plot dimensions for variables. For more details on facet grids in general, take a look at the separate article on facet grids.

A facet grid of two dimensional scatter plots might look something like this:

2d facet grid scatter plot

Here, we have performed a sweep over the transmission rate and immunity rate, which we are now plotting on the columns and rows respectively. As with all facet grid plots, all this requires is encoding the row and col variable:

phase_diagram_SI:

  # Everything as above ...

  x: susceptible
  y: infected
  hue: time
  col: transmission rate
  row: immunity rate

And of course, the same works for three-dimensional plots (take care to change the base plot to .plots.facet_grid.scatter3d!):

phase_diagram_SIR:

  # Base your plot on facet_grid.scatter3d
  based_on:
    - .creator.universe
    - .plot.facet_grid.scatter3d

  # Select and transform your data, as before
  select_and_combine:
    fields:
      kind:
        path: densities

  transform:
    - .sel: [!dag_tag kind, { kind: susceptible }]
      kwargs: { drop: true }
      tag: susceptible
    - .sel: [!dag_tag kind, { kind: infected }]
      kwargs: { drop: true }
      tag: infected
    - .sel: [!dag_tag kind, { kind: recovered }]
      kwargs: { drop: true }
      tag: recovered

    - xr.Dataset:
        data_vars:
          susceptible: !dag_tag susceptible
          infected: !dag_tag infected
          recovered: !dag_tag recovered
      tag: data

  # Distribute your variables:
  x: susceptible
  y: infected
  z: recovered
  col: transmission rate
  hue: time

  # Set a colormap, if you like
  cmap:
    continuous: true
    from_values:
      0: gold
      1: skyblue
3d facet grid scatter plot

You might need to adjust the figure size and the margins a little. The figsize keyword (handled by xarray.plot.FacetGrid) as well as various features of the PlotHelper might be useful here:

phase_diagram_SIR:

  # Everything as above ...

  # Control the figure size
  figsize: [8, 4]

  # Use the plot helper to set various additional features
  helpers:
    # Adjust the right border of the plot
    subplots_adjust:
      right: 0.75

    # Set the ticks.
    set_tick_formatters:
      x: &tick_format
        major:
          name: StrMethodFormatter
          args: ['{x: 0.1f}']
      z:
        <<: *tick_format
    set_ticks:
      y:
        major: [0, 0.05, 0.1]

Observe the use of YAML anchors to avoid having to type things multiple times: these are described in more detail in the style article. The PlotHelper gives you a variety of options to format the ticks and use specific labels.

Note

If you want to change the view of the axis in the case of the faceting 3-dimensional scatter plot, the parameters need to be passed somewhat differently than in the non-faceting case:

phase_diagram_SIR:
  # Everything as above ...
  subplot_kws:  # sic, with trailing s unlike within setup_figure
    elev: 20
    azim: 45

This is because in the faceting case, the xarray.plot.FacetGrid class takes care of setting up the figure, not the PlotHelper. We are working on a better solution that avoids needing to specify the parameters in multiple places.