Formatting and Cleaning Up Plots

Note

This tutorial is a work in progress and will be gradually updated.

In this tutorial we will illustrate some straightfoward formatting for your plots which will allow for greater customisation as needed. As pyrolite heavily uses and exposes the API of matplotlib for the visualisation components (and also mpltern for ternary diagrams), you should also check out their documentation pages for more in-depth guides, examples and API documentation.

First let’s pull in a simple dataset to use throughout these examples:

from pyrolite.util.synthetic import normal_frame


df = normal_frame(columns=["SiO2", "CaO", "MgO", "Al2O3", "TiO2", "27Al", "d11B"])

Basic Figure and Axes Settings

matplotlib makes it relatively straightfoward to customise most settings for your figures and axes. These settings can be defined at creation (e.g. in a call to subplots()), or they can be defined after you’ve created an axis (with the methods ax.set_<parameter>()). For example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(1)

ax.set_xlabel("My X Axis Label")
ax.set_title("My Axis Title", fontsize=12)
ax.set_yscale("log")
ax.set_xlim((0.5, 10))

fig.suptitle("My Figure Title", fontsize=15)

plt.show()

You can use a single method to set most of these things: set(). For example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(1)
ax.set(yscale="log", xlim=(0, 1), ylabel="YAxis", xlabel="XAxis")
plt.show()

Labels and Text

matplotlib enables you to use \(\TeX\) within all text elements, including labels and annotations. This can be leveraged for more complex formatting, incorporating math and symbols into your plots. Check out the mod:matplotlib tutorial, and for more on working with text generally in matplotlib, check out the relevant tutorials gallery.

The ability to use TeX syntax in matplotlib text objects can also be used for typsetting, like for subscripts and superscripts. This is particularly relevant for geochemical oxides labels (e.g. Al2O3, which would ideally be rendered as \(Al_2O_3\)) and isotopes (e.g. d11B, which should be \(\delta^{11}B\)). At the moment, pyrolite won’t do this for you, so you may want to adjust the labelling after you’ve made them. For example:

import pyrolite.plot
import matplotlib.pyplot as plt

fig, ax = plt.subplots(2, 1)
df[["Al2O3", "TiO2"]].pyroplot.scatter(ax=ax[0])
ax[0].set_xlabel("Al$_2$O$_3$")
ax[0].set_ylabel("TiO$_2$")

df[["27Al", "d11B"]].pyroplot.scatter(ax=ax[1])
ax[1].set_xlabel("$^{27}$Al")
ax[1].set_ylabel("$\delta^{11}$B")

plt.tight_layout()  # rearrange the plots to fit nicely together
plt.show()

Sharing Axes

If you’re building figures which have variables which are re-used, you’ll typically want to ‘share’ them between your axes. The matplotlib.pyplot API makes this easy for when you want to share among all the axes as your create them:

import matplotlib.pyplot as plt

fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
plt.show()

However, if you want to share axes in a way which is less standard, it can be difficult to set up using this function. pyrolite has a utility function which can be used to share axes after they’re created in slightly more arbitrary ways. For example, imagine we wanted to share the first and third x-axes, and the first three y-axes, you could use:

import matplotlib.pyplot as plt
from pyrolite.util.plot.axes import share_axes

fig, ax = plt.subplots(2, 2)
ax = ax.flat  # turn the (2,2) array of axes into one flat axes with shape (4,)
share_axes([ax[0], ax[2]], which="x")  # share x-axes for 0, 2
share_axes(ax[0:3], which="y")  # share y-axes for 0, 1, 2

ax[0].set_xlim((0, 10))
ax[1].set_ylim((-5, 5))
plt.show()

Legends

While it’s simple to set up basic legends in maplotlib (see the docs for matplotlib.axes.Axes.legend()), often you’ll want to customise your legends to fit nicely within your figures. Here we’ll create a few synthetic datasets, add them to a figure and create the default legend:

import pyrolite.plot
import matplotlib.pyplot as plt

fig, ax = plt.subplots(1)
for i in range(3):
    sample_data = normal_frame(columns=["CaO", "MgO", "FeO"])  # a new random sample
    sample_data[["CaO", "MgO"]].pyroplot.scatter(ax=ax, label="Sample {:d}".format(i))
ax.legend()
plt.show()

On many of the pyrolite examples, you’ll find legends formatted along the lines of the following to clean them up a little (these are the default styles):

ax.legend(
    facecolor=None,  # have a transparent legend background
    frameon=False,  # remove the legend frame
    bbox_to_anchor=(1, 1),  # anchor legend's corner to the axes' top-right
    loc="upper left",  # use the upper left corner for the anchor
)
plt.show()

Check out the matplotlib legend guide for more.

Ternary Plots

The ternary plots in pyrolite are generated using mpltern, and while the syntax is very similar to the matplotlib API, as we have three axes to deal with sometimes things are little different. Here we demonstrate how to complete some common tasks, but you should check out the mpltern documentation if you want to dig deeper into customising your ternary diagrams (e.g. see the example gallery), which these examples were developed from.

One of the key things to note in mpltern is that you have top, left and right axes.

Ternary Plot Axes Labels

Labelling ternary axes is done similarly to in matplotlib, but using the axes prefixes t, l and r for top, left and right axes, respectively:

import pyrolite.plot
import matplotlib.pyplot as plt

ax = df[["CaO", "MgO", "Al2O3"]].pyroplot.scatter()
ax.set_tlabel("Top")
ax.set_llabel("Left")
ax.set_rlabel("Right")
plt.show()

Ternary Plot Grids

To add a simple grid to your ternary plot, you can use grid():

import pyrolite.plot
import matplotlib.pyplot as plt

ax = df[["CaO", "MgO", "Al2O3"]].pyroplot.scatter()
ax.grid()
plt.show()

With this method, you can also specify an axis, which tickmarks you want to use for the grid (‘major’, ‘minor’ or ‘both’) and a linestyle:

import pyrolite.plot
import matplotlib.pyplot as plt

ax = df[["CaO", "MgO", "Al2O3"]].pyroplot.scatter()
ax.grid(axis="r", which="both", linestyle="--")
plt.show()

Ternary Plot Limits

To focus on a specific area, you can reset the limits of your ternary axes with set_ternary_lim().

Also check out the mpltern inset axes example if you’re after ways to focus on specific regions.

import pyrolite.plot
import matplotlib.pyplot as plt

ax = df[["CaO", "MgO", "Al2O3"]].pyroplot.scatter()
ax.set_ternary_lim(
    0.1, 0.5, 0.2, 0.6, 0.3, 0.7  # tmin  # tmax  # lmin  # lmax  # rmin  # rmax
)
plt.show()