Making subplots

When you’re preparing a figure for a paper, there will often be times when you’ll need to put many individual plots into one large figure, and label them ‘abcd’. These individual plots are called subplots.

There are two main ways to create subplots in GMT:

The first method is easier to use and should handle simple cases involving a couple of subplots. For more advanced subplot layouts, however, we recommend the use of pygmt.Figure.subplot which offers finer grained control, and this is what the tutorial below will cover.

import pygmt

Let’s start by initializing a pygmt.Figure instance.

Define subplot layout

The pygmt.Figure.subplot function is used to set up the layout, size, and other attributes of the figure. It divides the whole canvas into regular grid areas with n rows and m columns. Each grid area can contain an individual subplot. For example:

with fig.subplot(nrows=2, ncols=3, figsize=("15c", "6c"), frame="lrtb"):
    ...

will define our figure to have a 2 row and 3 column grid layout. figsize=("15c", "6c") defines the overall size of the figure to be 15 cm wide by 6 cm high. Using frame="lrtb" allows us to customize the map frame for all subplots instead of setting them individually. The figure layout will look like the following:

with fig.subplot(nrows=2, ncols=3, figsize=("15c", "6c"), frame="lrtb"):
    for i in range(2):  # row number starting from 0
        for j in range(3):  # column number starting from 0
            index = i * 3 + j  # index number starting from 0
            with fig.set_panel(panel=index):  # sets the current panel
                fig.text(
                    position="MC",
                    text=f"index: {index}; row: {i}, col: {j}",
                    region=[0, 1, 0, 1],
                )
fig.show()
subplots

Out:

<IPython.core.display.Image object>

The pygmt.Figure.set_panel function activates a specified subplot, and all subsequent plotting functions will take place in that subplot panel. This is similar to matplotlib’s plt.sca method. In order to specify a subplot, you will need to provide the identifier for that subplot via the panel parameter. Pass in either the index number, or a tuple/list like (row, col) to panel.

Note

The row and column numbering starts from 0. So for a subplot layout with N rows and M columns, row numbers will go from 0 to N-1, and column numbers will go from 0 to M-1.

For example, to activate the subplot on the top right corner (index: 2) at row=0 and col=2, so that all subsequent plotting commands happen there, you can use the following command:

with fig.set_panel(panel=[0, 2]):
    ...

Making your first subplot

Next, let’s use what we learned above to make a 2 row by 2 column subplot figure. We’ll also pick up on some new parameters to configure our subplot.

fig = pygmt.Figure()
with fig.subplot(
    nrows=2,
    ncols=2,
    figsize=("15c", "6c"),
    autolabel=True,
    frame=["af", "WSne"],
    margins=["0.1c", "0.2c"],
    title="My Subplot Heading",
):
    fig.basemap(region=[0, 10, 0, 10], projection="X?", panel=[0, 0])
    fig.basemap(region=[0, 20, 0, 10], projection="X?", panel=[0, 1])
    fig.basemap(region=[0, 10, 0, 20], projection="X?", panel=[1, 0])
    fig.basemap(region=[0, 20, 0, 20], projection="X?", panel=[1, 1])
fig.show()
subplots

Out:

<IPython.core.display.Image object>

In this example, we define a 2-row, 2-column (2x2) subplot layout using pygmt.Figure.subplot. The overall figure dimensions is set to be 15 cm wide and 6 cm high (figsize=["15c", "6c"]). In addition, we use some optional parameters to fine-tune some details of the figure creation:

  • autolabel=True: Each subplot is automatically labelled abcd

  • margins=["0.1c", "0.2c"]: adjusts the space between adjacent subplots. In this case, it is set as 0.1 cm in the X direction and 0.2 cm in the Y direction.

  • title="My Subplot Heading": adds a title on top of the whole figure.

Notice that each subplot was set to use a linear projection "X?". Usually, we need to specify the width and height of the map frame, but it is also possible to use a question mark "?" to let GMT decide automatically on what is the most appropriate width/height for the each subplot’s map frame.

Tip

In the above example, we used the following commands to activate the four subplots explicitly one after another:

fig.basemap(..., panel=[0, 0])
fig.basemap(..., panel=[0, 1])
fig.basemap(..., panel=[1, 0])
fig.basemap(..., panel=[1, 1])

In fact, we can just use fig.basemap(..., panel=True) without specifying any subplot index number, and GMT will automatically activate the next subplot panel.

Note

All plotting functions (e.g. pygmt.Figure.coast, pygmt.Figure.text, etc) are able to use panel parameter when in subplot mode. Once a panel is activated using panel or pygmt.Figure.set_panel, subsequent plotting commands that don’t set a panel will have their elements added to the same panel as before.

Shared X and Y axis labels

In the example above with the four subplots, the two subplots for each row have the same Y-axis range, and the two subplots for each column have the same X-axis range. You can use the sharex/sharey parameters to set a common X and/or Y axis between subplots.

fig = pygmt.Figure()
with fig.subplot(
    nrows=2,
    ncols=2,
    figsize=("15c", "6c"),  # width of 15 cm, height of 6 cm
    autolabel=True,
    margins=["0.3c", "0.2c"],  # horizontal 0.3 cm and vertical 0.2 cm margins
    title="My Subplot Heading",
    sharex="b",  # shared x-axis on the bottom side
    sharey="l",  # shared y-axis on the left side
    frame="WSrt",
):
    fig.basemap(region=[0, 10, 0, 10], projection="X?", panel=True)
    fig.basemap(region=[0, 20, 0, 10], projection="X?", panel=True)
    fig.basemap(region=[0, 10, 0, 20], projection="X?", panel=True)
    fig.basemap(region=[0, 20, 0, 20], projection="X?", panel=True)
fig.show()
subplots

Out:

<IPython.core.display.Image object>

sharex="b" indicates that subplots in a column will share the x-axis, and only the bottom axis is displayed. sharey="l" indicates that subplots within a row will share the y-axis, and only the left axis is displayed.

Of course, instead of using the sharex/sharey option, you can also set a different frame for each subplot to control the axis properties individually for each subplot.

Advanced subplot layouts

Nested subplot are currently not supported. If you want to create more complex subplot layouts, some manual adjustments are needed.

The following example draws three subplots in a 2-row, 2-column layout, with the first subplot occupying the first row.

fig = pygmt.Figure()
# Bottom row, two subplots
with fig.subplot(nrows=1, ncols=2, figsize=("15c", "3c"), autolabel="b)"):
    fig.basemap(
        region=[0, 5, 0, 5], projection="X?", frame=["af", "WSne"], panel=[0, 0]
    )
    fig.basemap(
        region=[0, 5, 0, 5], projection="X?", frame=["af", "WSne"], panel=[0, 1]
    )
# Move plot origin by 1 cm above the height of the entire figure
fig.shift_origin(yshift="h+1c")
# Top row, one subplot
with fig.subplot(nrows=1, ncols=1, figsize=("15c", "3c"), autolabel="a)"):
    fig.basemap(
        region=[0, 10, 0, 10], projection="X?", frame=["af", "WSne"], panel=[0, 0]
    )
    fig.text(text="TEXT", x=5, y=5)

fig.show()
subplots

Out:

<IPython.core.display.Image object>

We start by drawing the bottom two subplots, setting autolabel="b)" so that the subplots are labelled ‘b)’ and ‘c)’. Next, we use pygmt.Figure.shift_origin to move the plot origin 1 cm above the height of the entire figure that is currently plotted (i.e. the bottom row subplots). A single subplot is then plotted on the top row. You may need to adjust the yshift parameter to make your plot look nice. This top row uses autolabel="a)", and we also plotted some text inside. Note that projection="X?" was used to let GMT automatically determine the size of the subplot according to the size of the subplot area.

You can also manually override the autolabel for each subplot using for example, fig.set_panel(..., fixedlabel="b) Panel 2") which would allow you to manually label a single subplot as you wish. This can be useful for adding a more descriptive subtitle to individual subplots.

Total running time of the script: ( 0 minutes 7.954 seconds)

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