Dec 21, 2024 · Dec 21, 2024
diff --git a/galleries/examples/scales/symlog_demo.py b/galleries/examples/scales/symlog_demo.py
 """
 ===========
 Symlog Demo
 ===========
 ============
 Symlog scale
 ============

 The symmetric logarithmic scale is an extension of the logarithmic scale that
 also covers negative values. As with the logarithmic scale, it is particularly
 useful for numerical data that spans a broad range of values, especially when there
 are significant differences between the magnitudes of the numbers involved.

 Example use of symlog (symmetric log) axis scaling.
 """
 plt.show()

 # %%
 # It should be noted that the coordinate transform used by ``symlog``
 # has a discontinuous gradient at the transition between its linear
 # and logarithmic regions. The ``asinh`` axis scale is an alternative
 # technique that may avoid visual artifacts caused by these discontinuities.
 # Linear threshold
 # ----------------
 # Since each decade on a logarithmic scale covers the same amount of visual space
 # and there are infinitely many decades between a given number and zero, the symlog
 # scale must deviate from logarithmic mapping in a small range
 # *(-linthresh, linthresh)*, so that the range is mapped to a finite visual space.


 def format_axes(ax, title=None):
    """A helper function to better visualize properties of the symlog scale."""
    ax.xaxis.get_minor_locator().set_params(subs=[2, 3, 4, 5, 6, 7, 8, 9])
    ax.grid()
    ax.xaxis.grid(which='minor')  # minor grid on too
    linthresh = ax.xaxis.get_transform().linthresh
    linscale = ax.xaxis.get_transform().linscale
    ax.axvspan(-linthresh, linthresh, color='0.9')
    if title:
        ax.set_title(title.format(linthresh=linthresh, linscale=linscale))


 x = np.linspace(-60, 60, 201)
 y = np.linspace(0, 100.0, 201)

 fig, (ax1, ax2) = plt.subplots(nrows=2, layout="constrained")

 ax1.plot(x, y)
 ax1.set_xscale('symlog', linthresh=1)
 format_axes(ax1, title='Linear region: linthresh={linthresh}')

 ax2.plot(x, y)
 ax2.set_xscale('symlog', linthresh=5)
 format_axes(ax2, title='Linear region: linthresh={linthresh}')

 # %%
 # Generally, *linthresh* should be chosen so that no or only a few
 # data points are in the linear region. As a rule of thumb,
 # :math:`linthresh \approx \mathrm{min} |x|`.
 #
 #
 # Linear scale
 # ------------
 # Additionally, the *linscale* parameter determines how much visual space should be
 # used for the linear range. More precisely, it defines the ratio of visual space
 # of the region (0, linthresh) relative to one decade.

 fig, (ax1, ax2) = plt.subplots(nrows=2, layout="constrained")

 ax1.plot(x, y)
 ax1.set_xscale('symlog', linthresh=1)
 format_axes(ax1, title='Linear region: linthresh={linthresh}, linscale={linscale}')

 ax2.plot(x, y)
 ax2.set_xscale('symlog', linthresh=1, linscale=0.1)
 format_axes(ax2, title='Linear region: linthresh={linthresh}, linscale={linscale}')

 # %%
 # The suitable value for linscale depends on the dynamic range of data. As most data
 # will be outside the linear region, you typically the linear region only to cover
 # a small fraction of the visual area.
 #
 # Limitations and alternatives
 # ----------------------------
 # The coordinate transform used by ``symlog`` has a discontinuous gradient at the
 # transition between its linear and logarithmic regions. Depending on data and
 # scaling, this will be more or less obvious in the plot.

 fig, ax = plt.subplots()
 ax.plot(x, y)
 ax.set_xscale('symlog', linscale=0.05)
 format_axes(ax, title="Discontinuous gradient at linear/log transition")

 # %%
 # The ``asinh`` axis scale is an alternative transformation that supports a wide
 # dynamic range with a smooth gradient and thus may avoid such visual artifacts.
 # See :doc:`/gallery/scales/asinh_demo`.
 #
 #
 # .. admonition:: References
 #
Original file line number	Diff line number	Diff line change
		@@ -1,7 +1,12 @@
		"""
		===========
		Symlog Demo
		===========
		============
		Symlog scale
		============

		The symmetric logarithmic scale is an extension of the logarithmic scale that
		also covers negative values. As with the logarithmic scale, it is particularly
		useful for numerical data that spans a broad range of values, especially when there
		are significant differences between the magnitudes of the numbers involved.

		Example use of symlog (symmetric log) axis scaling.
		"""
Expand DownExpand Up		@@ -34,12 +39,82 @@
		plt.show()

		# %%
		# It should be noted that the coordinate transform used by ``symlog``
		# has a discontinuous gradient at the transition between its linear
		# and logarithmic regions. The ``asinh`` axis scale is an alternative
		# technique that may avoid visual artifacts caused by these discontinuities.
		# Linear threshold
		# ----------------
		# Since each decade on a logarithmic scale covers the same amount of visual space
		# and there are infinitely many decades between a given number and zero, the symlog
		# scale must deviate from logarithmic mapping in a small range
		# (-linthresh, linthresh), so that the range is mapped to a finite visual space.


		def format_axes(ax, title=None):
		"""A helper function to better visualize properties of the symlog scale."""
		ax.xaxis.get_minor_locator().set_params(subs=[2, 3, 4, 5, 6, 7, 8, 9])
		ax.grid()
		ax.xaxis.grid(which='minor') # minor grid on too
		linthresh = ax.xaxis.get_transform().linthresh
		linscale = ax.xaxis.get_transform().linscale
		ax.axvspan(-linthresh, linthresh, color='0.9')
		if title:
		ax.set_title(title.format(linthresh=linthresh, linscale=linscale))


		x = np.linspace(-60, 60, 201)
		y = np.linspace(0, 100.0, 201)

		fig, (ax1, ax2) = plt.subplots(nrows=2, layout="constrained")

		ax1.plot(x, y)
		ax1.set_xscale('symlog', linthresh=1)
		format_axes(ax1, title='Linear region: linthresh={linthresh}')

		ax2.plot(x, y)
		ax2.set_xscale('symlog', linthresh=5)
		format_axes(ax2, title='Linear region: linthresh={linthresh}')

		# %%
		# Generally, linthresh should be chosen so that no or only a few
		# data points are in the linear region. As a rule of thumb,
		# :math:`linthresh \approx \mathrm{min} \|x\|`.
		#
		#
		# Linear scale
		# ------------
		# Additionally, the linscale parameter determines how much visual space should be
		# used for the linear range. More precisely, it defines the ratio of visual space
		# of the region (0, linthresh) relative to one decade.

		fig, (ax1, ax2) = plt.subplots(nrows=2, layout="constrained")

		ax1.plot(x, y)
		ax1.set_xscale('symlog', linthresh=1)
		format_axes(ax1, title='Linear region: linthresh={linthresh}, linscale={linscale}')

		ax2.plot(x, y)
		ax2.set_xscale('symlog', linthresh=1, linscale=0.1)
		format_axes(ax2, title='Linear region: linthresh={linthresh}, linscale={linscale}')

		# %%
		# The suitable value for linscale depends on the dynamic range of data. As most data
		# will be outside the linear region, you typically the linear region only to cover
		# a small fraction of the visual area.
		#
		# Limitations and alternatives
		# ----------------------------
		# The coordinate transform used by ``symlog`` has a discontinuous gradient at the
		# transition between its linear and logarithmic regions. Depending on data and
		# scaling, this will be more or less obvious in the plot.

		fig, ax = plt.subplots()
		ax.plot(x, y)
		ax.set_xscale('symlog', linscale=0.05)
		format_axes(ax, title="Discontinuous gradient at linear/log transition")

		# %%
		# The ``asinh`` axis scale is an alternative transformation that supports a wide
		# dynamic range with a smooth gradient and thus may avoid such visual artifacts.
		# See :doc:`/gallery/scales/asinh_demo`.
		#
		#
		# .. admonition:: References
		#
Expand Down