Thanks to visit codestin.com
Credit goes to github.com

Skip to content

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

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jan 11, 2023
Merged

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

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
25 changes: 10 additions & 15 deletions tutorials/colors/colormapnorms.py
View file
Open in desktop
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.
Expand Down