Dec 2, 2023 · Dec 2, 2023 · Dec 2, 2023 · Dec 2, 2023 · Dec 2, 2023 · Dec 2, 2023
diff --git a/galleries/examples/ticks/multilevel_ticks.py b/galleries/examples/ticks/multilevel_ticks.py
 """
 =========================
 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()
Original file line number	Diff line number	Diff line change
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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
jklymak marked this conversation as resolved. OutdatedShow resolvedHide resolved
		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'))
Copy link Member rcomerDec 2, 2023 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. I assume you add the spaces before the`%b` to improve the alignment with the tick. I just spent longer than I care to admit trying to figure out a way to set the horizontal alignment of the labels, and now I think I understand why you want to overhaul`Axis`! For the record, this works, but I'm not sure it would be an improvement. fig.draw_without_rendering()# Otherwise only one label exists to loop through. Is there a cheaper way to force tick generation?forlabelinsec.xaxis.get_ticklabels():label.set_horizontalalignment('left') Is it worth a comment to explain the spaces though? jklymak reacted with thumbs up emoji Copy link MemberAuthor jklymakDec 2, 2023 Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. Yes, it's all pretty annoying. And of course that alignment only holds for ticks that presently exist - if you pan off to right or left the new ticks won't get the alignment. rcomer reacted with thumbs up emoji Copy link Member story645Dec 2, 2023• edited Loading Choose a reason for hiding this comment The reason will be displayed to describe this comment to others.Learn more. aI think lighter lift to improve this would be to add alignment to`tick_params`#20644 rcomer reacted with thumbs up emoji
		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()