Jan 10, 2023 · Jan 10, 2023
diff --git a/tutorials/colors/colormapnorms.py b/tutorials/colors/colormapnorms.py
 Artists that map data to color pass the arguments *vmin* and *vmax* to
 construct a :func:`matplotlib.colors.Normalize` instance, then call it:

 ..ipython::
 ..code-block:: pycon

   In [1]: import matplotlib as mpl

   In [2]: norm = mpl.colors.Normalize(vmin=-1, vmax=1)

   In [3]: norm(0)
   Out[3]: 0.5
   >>> import matplotlib as mpl
   >>> norm = mpl.colors.Normalize(vmin=-1, vmax=1)
   >>> norm(0)
   0.5

 However, there are sometimes cases where it is useful to map data to
 colormaps in a non-linear fashion.
 # lower out-of-bounds values to the range over which the colors are
 # distributed. For instance:
 #
 # .. ipython::
 #
 #   In [2]: import matplotlib.colors as colors
 #
 #   In [3]: bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
 #
 #   In [4]: norm = colors.BoundaryNorm(boundaries=bounds, ncolors=4)
 # .. code-block:: pycon
 #
 #   In [5]: print(norm([-0.2, -0.15, -0.02, 0.3, 0.8, 0.99]))
 #   >>> import matplotlib.colors as colors
 #   >>> bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
 #   >>> norm = colors.BoundaryNorm(boundaries=bounds, ncolors=4)
 #   >>> print(norm([-0.2, -0.15, -0.02, 0.3, 0.8, 0.99]))
 #   [0 0 1 2 3 3]
 #
 # Note: Unlike the other norms, this norm returns values from 0 to *ncolors*-1.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -20,14 +20,12 @@
		Artists that map data to color pass the arguments vmin and vmax to
		construct a :func:`matplotlib.colors.Normalize` instance, then call it:

		..ipython::
		..code-block:: pycon

		In [1]: import matplotlib as mpl

		In [2]: norm = mpl.colors.Normalize(vmin=-1, vmax=1)

		In [3]: norm(0)
		Out[3]: 0.5
		>>> import matplotlib as mpl
		>>> norm = mpl.colors.Normalize(vmin=-1, vmax=1)
		>>> norm(0)
		0.5

		However, there are sometimes cases where it is useful to map data to
		colormaps in a non-linear fashion.
Expand DownExpand Up		@@ -192,15 +190,12 @@
		# lower out-of-bounds values to the range over which the colors are
		# distributed. For instance:
		#
		# .. ipython::
		#
		# In [2]: import matplotlib.colors as colors
		#
		# In [3]: bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
		#
		# In [4]: norm = colors.BoundaryNorm(boundaries=bounds, ncolors=4)
		# .. code-block:: pycon
		#
		# In [5]: print(norm([-0.2, -0.15, -0.02, 0.3, 0.8, 0.99]))
		# >>> import matplotlib.colors as colors
		# >>> bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
		# >>> norm = colors.BoundaryNorm(boundaries=bounds, ncolors=4)
		# >>> print(norm([-0.2, -0.15, -0.02, 0.3, 0.8, 0.99]))
		# [0 0 1 2 3 3]
		#
		# Note: Unlike the other norms, this norm returns values from 0 to ncolors-1.
Expand Down