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================ by Jawad Haider

Chpt 4 - Visualization with Matplotlib

11 - Customizing Ticks

Customizing Ticks

Matplotlib’s default tick locators and formatters are designed to be generally sufficient in many common situations, but are in no way optimal for every plot.

Major and Minor Ticks

Within each axis, there is the concept of a major tick mark and a minor tick mark. As the names would imply, major ticks are usually bigger or more pronounced, while minor ticks are usually smaller. By default, Matplotlib rarely makes use of minor ticks, but one place you can see them is within logarithmic plots

%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np

ax = plt.axes(xscale='log', yscale='log')


print(ax.xaxis.get_major_locator())
print(ax.xaxis.get_minor_locator())

<matplotlib.ticker.LogLocator object at 0x7faa7bb8f640>
<matplotlib.ticker.LogLocator object at 0x7faa7be3baf0>

print(ax.xaxis.get_major_formatter())
print(ax.xaxis.get_minor_formatter())

<matplotlib.ticker.LogFormatterSciNotation object at 0x7faa7bb7ca60>
<matplotlib.ticker.LogFormatterSciNotation object at 0x7faab054b6d0>

Hiding Ticks or Labels

Perhaps the most common tick/label formatting operation is the act of hiding ticks or labels. We can do this using plt.NullLocator() and plt.NullFormatter(), as shown here

ax=plt.axes()
ax.plot(np.random.rand(50))

ax.yaxis.set_major_locator(plt.NullLocator())
ax.yaxis.set_major_formatter(plt.NullFormatter())


ax=plt.axes()
ax.plot(np.random.rand(50))


fig, ax = plt.subplots(5,5,figsize=(5,5))
fig.subplots_adjust(hspace=0,wspace=0)

#Get some face images from scikit-learn
from sklearn.datasets import fetch_olivetti_faces
faces = fetch_olivetti_faces().images

for i in range(5):
    for j in range(5):
        ax[i,j].xaxis.set_major_locator(plt.NullLocator())
        ax[i,j].yaxis.set_major_locator(plt.NullLocator())
        ax[i,j].imshow(faces[10*i+j], cmap='bone')

Reducing or Increasing the Number of Ticks

One common problem with the default settings is that smaller subplots can end up with crowded labels.

fig, ac =plt.subplots(4,4,sharex=True, sharey=True)

Particularly for the x ticks, the numbers nearly overlap, making them quite difficult to decipher. We can fix this with the plt.MaxNLocator(), which allows us to specify the maximum number of ticks that will be displayed. Given this maximum number, Mat‐ plotlib will use internal logic to choose the particular tick locations

for axi in ax.flat:
    axi.xaxis.set_major_locator(plt.MaxNLocator(3))
    axi.yaxis.set_major_locator(plt.MaxNLocator(3))

fig

Fancy Tick Formats

Matplotlib’s default tick formatting can leave a lot to be desired; it works well as a broad default, but sometimes you’d like to do something more

# Plot a sine and cosine curve
fig, ax = plt.subplots()
x = np.linspace(0, 3 * np.pi, 1000)
ax.plot(x, np.sin(x), lw=3, label='Sine')
ax.plot(x, np.cos(x), lw=3, label='Cosine')

# Set up grid, legend, and limits
ax.grid(True)
ax.legend(frameon=False)
ax.axis('equal')
ax.set_xlim(0, 3 * np.pi);


ax.xaxis.set_major_locator(plt.MultipleLocator(np.pi / 2))
ax.xaxis.set_minor_locator(plt.MultipleLocator(np.pi / 4))
fig


def format_func(value, tick_number):
    # find number of multiples of pi/2
    N = int(np.round(2 * value / np.pi))
    if N == 0:
        return "0"
    elif N == 1:
        return r"$\pi/2$"
    elif N == 2:
        return r"$\pi$"
    elif N % 2 > 0:
        return r"${0}\pi/2$".format(N)
    else:
        return r"${0}\pi$".format(N // 2)


ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
fig

This is much better! Notice that we’ve made use of Matplotlib’s LaTeX support, speci‐ fied by enclosing the string within dollar signs. This is very convenient for display of mathematical symbols and formulae; in this case, “\(\pi\)” is rendered as the Greek character π. The plt.FuncFormatter() offers extremely fine-grained control over the appearance of your plot ticks, and comes in very handy when you’re preparing plots for presenta‐ tion or publication.