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"""
=========================
Multilevel (nested) ticks
=========================
Sometimes we want another level of tick labels on an axis, perhaps to indicate
a grouping of the ticks.
Matplotlib does not provide an automated way to do this, but it is relatively
straightforward to annotate below the main axis.
These examples use `.Axes.secondary_xaxis`, which is one approach. It has the
advantage that we can use Matplotlib Locators and Formatters on the axis that
does the grouping if we want.
This first example creates a secondary xaxis and manually adds the ticks and
labels using `.Axes.set_xticks`. Note that the tick labels have a newline
(e.g. ``"\nOughts"``) at the beginning of them to put the second-level tick
labels below the main tick labels.
"""
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
rng = np.random.default_rng(19680801)
fig, ax = plt.subplots(layout='constrained', figsize=(4, 4))
ax.plot(np.arange(30))
sec = ax.secondary_xaxis(location=0)
sec.set_xticks([5, 15, 25], labels=['\nOughts', '\nTeens', '\nTwenties'])
# %%
# This second example adds a second level of annotation to a categorical axis.
# Here we need to note that each animal (category) is assigned an integer, so
# ``cats`` is at x=0, ``dogs`` at x=1 etc. Then we place the ticks on the
# second level on an x that is at the middle of the animal class we are trying
# to delineate.
#
# This example also adds tick marks between the classes by adding a second
# secondary xaxis, and placing long, wide ticks at the boundaries between the
# animal classes.
fig, ax = plt.subplots(layout='constrained', figsize=(7, 4))
ax.plot(['cats', 'dogs', 'pigs', 'snakes', 'lizards', 'chickens',
'eagles', 'herons', 'buzzards'],
rng.normal(size=9), 'o')
# label the classes:
sec = ax.secondary_xaxis(location=0)
sec.set_xticks([1, 3.5, 6.5], labels=['\n\nMammals', '\n\nReptiles', '\n\nBirds'])
sec.tick_params('x', length=0)
# lines between the classes:
sec2 = ax.secondary_xaxis(location=0)
sec2.set_xticks([-0.5, 2.5, 4.5, 8.5], labels=[])
sec2.tick_params('x', length=40, width=1.5)
ax.set_xlim(-0.6, 8.6)
# %%
# Dates are another common place where we may want to have a second level of
# tick labels. In this last example, we take advantage of the ability to add
# an automatic locator and formatter to the secondary xaxis, which means we do
# not need to set the ticks manually.
#
# This example also differs from the above, in that we placed it at a location
# below the main axes ``location=-0.075`` and then we hide the spine by setting
# the line width to zero. That means that our formatter no longer needs the
# carriage returns of the previous two examples.
fig, ax = plt.subplots(layout='constrained', figsize=(7, 4))
time = np.arange(np.datetime64('2020-01-01'), np.datetime64('2020-03-31'),
np.timedelta64(1, 'D'))
ax.plot(time, rng.random(size=len(time)))
# just format the days:
ax.xaxis.set_major_formatter(mdates.DateFormatter('%d'))
# label the months:
sec = ax.secondary_xaxis(location=-0.075)
sec.xaxis.set_major_locator(mdates.MonthLocator(bymonthday=1))
# note the extra spaces in the label to align the month label inside the month.
# Note that this could have been done by changing ``bymonthday`` above as well:
sec.xaxis.set_major_formatter(mdates.DateFormatter(' %b'))
sec.tick_params('x', length=0)
sec.spines['bottom'].set_linewidth(0)
# label the xaxis, but note for this to look good, it needs to be on the
# secondary xaxis.
sec.set_xlabel('Dates (2020)')
plt.show()
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