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Backport PR #24934 on branch v3.7.x (Swap ipython directives for code-block directives) by meeseeksmachine · Pull Request #24935 · matplotlib/matplotlib · GitHub

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Backport PR #24934 on branch v3.7.x (Swap ipython directives for code-block directives) #24935

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Merged
QuLogic merged 1 commit into from
Jan 11, 2023
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Backport PR #24934 on branch v3.7.x (Swap ipython directives for code-block directives) #24935

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25 changes: 10 additions & 15 deletions tutorials/colors/colormapnorms.py
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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 Down Expand 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.
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