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Jul 19, 2021
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Improve tutorial figures in the new theme #20546

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72f88ae
Move colormaps to their respective tutorial sections.
QuLogic Jun 26, 2021
0154443
Cleanup sample plots tutorial in new theme.
QuLogic Jun 26, 2021
f1bf5be
Fix minor typos in colormap manipulation tutorial.
QuLogic Jun 26, 2021
fa2f2cf
Re-arrange mplot3d tutorial.
QuLogic Jun 26, 2021
e6f4ee2
Clean up figures in annotations tutorials.
QuLogic Jun 28, 2021
785a9a5
Revert joining of figures in Axes Divider example.
QuLogic Jul 9, 2021
b9ea55e
Remove 50% figure scale in remaining tutorials.
QuLogic Jun 29, 2021
0e5f9eb
Improve xkcd vs CSS colour comparison chart.
QuLogic Jun 29, 2021
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35 changes: 19 additions & 16 deletions examples/axes_grid1/simple_axes_divider1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,48 +18,51 @@ def label_axes(ax, text):
left=False, labelleft=False)


fig = plt.figure(figsize=(12, 6))
sfs = fig.subfigures(1, 2)
##############################################################################
# Fixed axes sizes; fixed paddings.

fig = plt.figure(figsize=(6, 6))
fig.suptitle("Fixed axes sizes, fixed paddings")

sfs[0].suptitle("Fixed axes sizes, fixed paddings")
# Sizes are in inches.
horiz = [Size.Fixed(1.), Size.Fixed(.5), Size.Fixed(1.5), Size.Fixed(.5)]
vert = [Size.Fixed(1.5), Size.Fixed(.5), Size.Fixed(1.)]

rect = (0.1, 0.1, 0.8, 0.8)
# Divide the axes rectangle into a grid with sizes specified by horiz * vert.
div = Divider(sfs[0], rect, horiz, vert, aspect=False)
div = Divider(fig, rect, horiz, vert, aspect=False)

# The rect parameter will actually be ignored and overridden by axes_locator.
ax1 = sfs[0].add_axes(rect, axes_locator=div.new_locator(nx=0, ny=0))
ax1 = fig.add_axes(rect, axes_locator=div.new_locator(nx=0, ny=0))
label_axes(ax1, "nx=0, ny=0")
ax2 = sfs[0].add_axes(rect, axes_locator=div.new_locator(nx=0, ny=2))
ax2 = fig.add_axes(rect, axes_locator=div.new_locator(nx=0, ny=2))
label_axes(ax2, "nx=0, ny=2")
ax3 = sfs[0].add_axes(rect, axes_locator=div.new_locator(nx=2, ny=2))
ax3 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, ny=2))
label_axes(ax3, "nx=2, ny=2")
ax4 = sfs[0].add_axes(rect, axes_locator=div.new_locator(nx=2, nx1=4, ny=0))
ax4 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, nx1=4, ny=0))
label_axes(ax4, "nx=2, nx1=4, ny=0")

##############################################################################
# Axes sizes that scale with the figure size; fixed paddings.

fig = plt.figure(figsize=(6, 6))
fig.suptitle("Scalable axes sizes, fixed paddings")

sfs[1].suptitle("Scalable axes sizes, fixed paddings")
# Fixed sizes are in inches, scaled sizes are relative.
horiz = [Size.Scaled(1.5), Size.Fixed(.5), Size.Scaled(1.), Size.Scaled(.5)]
vert = [Size.Scaled(1.), Size.Fixed(.5), Size.Scaled(1.5)]

rect = (0.1, 0.1, 0.8, 0.8)
# Divide the axes rectangle into a grid with sizes specified by horiz * vert.
div = Divider(sfs[1], rect, horiz, vert, aspect=False)
div = Divider(fig, rect, horiz, vert, aspect=False)

# The rect parameter will actually be ignored and overridden by axes_locator.
ax1 = sfs[1].add_axes(rect, axes_locator=div.new_locator(nx=0, ny=0))
ax1 = fig.add_axes(rect, axes_locator=div.new_locator(nx=0, ny=0))
label_axes(ax1, "nx=0, ny=0")
ax2 = sfs[1].add_axes(rect, axes_locator=div.new_locator(nx=0, ny=2))
ax2 = fig.add_axes(rect, axes_locator=div.new_locator(nx=0, ny=2))
label_axes(ax2, "nx=0, ny=2")
ax3 = sfs[1].add_axes(rect, axes_locator=div.new_locator(nx=2, ny=2))
ax3 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, ny=2))
label_axes(ax3, "nx=2, ny=2")
ax4 = sfs[1].add_axes(rect, axes_locator=div.new_locator(nx=2, nx1=4, ny=0))
ax4 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, nx1=4, ny=0))
label_axes(ax4, "nx=2, nx1=4, ny=0")


plt.show()
6 changes: 3 additions & 3 deletions examples/userdemo/connectionstyle_demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@ def demo_con_style(ax, connectionstyle):
transform=ax.transAxes, ha="left", va="top")


fig, axs = plt.subplots(3, 5, figsize=(8, 4.8))
fig, axs = plt.subplots(3, 5, figsize=(7, 6.3), constrained_layout=True)
demo_con_style(axs[0, 0], "angle3,angleA=90,angleB=0")
demo_con_style(axs[1, 0], "angle3,angleA=0,angleB=90")
demo_con_style(axs[0, 1], "arc3,rad=0.")
Expand All @@ -47,8 +47,8 @@ def demo_con_style(ax, connectionstyle):
demo_con_style(axs[2, 4], "bar,angle=180,fraction=-0.2")

for ax in axs.flat:
ax.set(xlim=(0, 1), ylim=(0, 1), xticks=[], yticks=[], aspect=1)
fig.tight_layout(pad=0.2)
ax.set(xlim=(0, 1), ylim=(0, 1.25), xticks=[], yticks=[], aspect=1.25)
fig.set_constrained_layout_pads(wspace=0, hspace=0, w_pad=0, h_pad=0)

plt.show()

Expand Down
23 changes: 12 additions & 11 deletions tutorials/colors/colormap-manipulation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@
# ListedColormap
# --------------
#
# `.ListedColormap` s store their color values in a ``.colors`` attribute.
# `.ListedColormap`\s store their color values in a ``.colors`` attribute.
# The list of colors that comprise the colormap can be directly accessed using
# the ``colors`` property,
# or it can be accessed indirectly by calling ``viridis`` with an array
Expand All @@ -68,7 +68,7 @@
##############################################################################
# LinearSegmentedColormap
# -----------------------
# `.LinearSegmentedColormap` s do not have a ``.colors`` attribute.
# `.LinearSegmentedColormap`\s do not have a ``.colors`` attribute.
# However, one may still call the colormap with an integer array, or with a
# float array between 0 and 1.

Expand Down Expand Up @@ -114,7 +114,7 @@ def plot_examples(colormaps):

##############################################################################
# In fact, that list may contain any valid
# :doc:`matplotlib color specification </tutorials/colors/colors>`.
# :doc:`Matplotlib color specification </tutorials/colors/colors>`.
# Particularly useful for creating custom colormaps are Nx4 numpy arrays.
# Because with the variety of numpy operations that we can do on a such an
# array, carpentry of new colormaps from existing colormaps become quite
Expand Down Expand Up @@ -168,7 +168,7 @@ def plot_examples(colormaps):
# Creating linear segmented colormaps
# ===================================
#
# `.LinearSegmentedColormap` class specifies colormaps using anchor points
# The `.LinearSegmentedColormap` class specifies colormaps using anchor points
# between which RGB(A) values are interpolated.
#
# The format to specify these colormaps allows discontinuities at the anchor
Expand All @@ -177,7 +177,7 @@ def plot_examples(colormaps):
# ``yleft[i]`` and ``yright[i]`` are the values of the color on either
# side of the anchor point.
#
# If there are no discontinuities, then ``yleft[i]=yright[i]``:
# If there are no discontinuities, then ``yleft[i] == yright[i]``:

cdict = {'red': [[0.0, 0.0, 0.0],
[0.5, 1.0, 1.0],
Expand Down Expand Up @@ -221,9 +221,10 @@ def plot_linearmap(cdict):
#
# In the example below there is a discontinuity in red at 0.5. The
# interpolation between 0 and 0.5 goes from 0.3 to 1, and between 0.5 and 1
# it goes from 0.9 to 1. Note that red[0, 1], and red[2, 2] are both
# superfluous to the interpolation because red[0, 1] is the value to the
# left of 0, and red[2, 2] is the value to the right of 1.0.
# it goes from 0.9 to 1. Note that ``red[0, 1]``, and ``red[2, 2]`` are both
# superfluous to the interpolation because ``red[0, 1]`` (i.e., ``yleft[0]``)
# is the value to the left of 0, and ``red[2, 2]`` (i.e., ``yright[2]``) is the
# value to the right of 1, which are outside the color mapping domain.

cdict['red'] = [[0.0, 0.0, 0.3],
[0.5, 1.0, 0.9],
Expand All @@ -234,7 +235,7 @@ def plot_linearmap(cdict):
# Directly creating a segmented colormap from a list
# --------------------------------------------------
#
# The above described is a very versatile approach, but admittedly a bit
# The approach described above is very versatile, but admittedly a bit
# cumbersome to implement. For some basic cases, the use of
# `.LinearSegmentedColormap.from_list` may be easier. This creates a segmented
# colormap with equal spacings from a supplied list of colors.
Expand All @@ -243,8 +244,8 @@ def plot_linearmap(cdict):
cmap1 = LinearSegmentedColormap.from_list("mycmap", colors)

#############################################################################
# If desired, the nodes of the colormap can be given as numbers
# between 0 and 1. E.g. one could have the reddish part take more space in the
# If desired, the nodes of the colormap can be given as numbers between 0 and
# 1. For example, one could have the reddish part take more space in the
# colormap.

nodes = [0.0, 0.4, 0.8, 1.0]
Expand Down
111 changes: 58 additions & 53 deletions tutorials/colors/colormaps.py
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Expand Up @@ -47,6 +47,8 @@
is from [IBM]_.


.. _color-colormaps_reference:

Classes of colormaps
====================

Expand Down Expand Up @@ -82,8 +84,39 @@
from colorspacious import cspace_converter


###############################################################################
#
# First, we'll show the range of each colormap. Note that some seem
# to change more "quickly" than others.

cmaps = {}

gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))


def plot_color_gradients(category, cmap_list):
# Create figure and adjust figure height to number of colormaps
nrows = len(cmap_list)
figh = 0.35 + 0.15 + (nrows + (nrows - 1) * 0.1) * 0.22
fig, axs = plt.subplots(nrows=nrows + 1, figsize=(6.4, figh))
fig.subplots_adjust(top=1 - 0.35 / figh, bottom=0.15 / figh,
left=0.2, right=0.99)
axs[0].set_title(f'{category} colormaps', fontsize=14)

for ax, name in zip(axs, cmap_list):
ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
ax.text(-0.01, 0.5, name, va='center', ha='right', fontsize=10,
transform=ax.transAxes)

# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axs:
ax.set_axis_off()

# Save colormap list for later.
cmaps[category] = cmap_list


###############################################################################
# Sequential
# ----------
Expand All @@ -96,13 +129,15 @@
# amongst the colormaps: some are approximately linear in :math:`L^*` and others
# are more curved.

cmaps['Perceptually Uniform Sequential'] = [
'viridis', 'plasma', 'inferno', 'magma', 'cividis']
plot_color_gradients('Perceptually Uniform Sequential',
['viridis', 'plasma', 'inferno', 'magma', 'cividis'])

###############################################################################

cmaps['Sequential'] = [
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']
plot_color_gradients('Sequential',
['Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'])

###############################################################################
# Sequential2
Expand All @@ -116,10 +151,10 @@
# banding of the data in those values in the colormap (see [mycarta-banding]_ for
# an excellent example of this).

cmaps['Sequential (2)'] = [
'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
'hot', 'afmhot', 'gist_heat', 'copper']
plot_color_gradients('Sequential (2)',
['binary', 'gist_yarg', 'gist_gray', 'gray', 'bone',
'pink', 'spring', 'summer', 'autumn', 'winter', 'cool',
'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper'])

###############################################################################
# Diverging
Expand All @@ -132,9 +167,9 @@
# measures, BrBG and RdBu are good options. coolwarm is a good option, but it
# doesn't span a wide range of :math:`L^*` values (see grayscale section below).

cmaps['Diverging'] = [
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']
plot_color_gradients('Diverging',
['PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu',
'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic'])

###############################################################################
# Cyclic
Expand All @@ -154,7 +189,7 @@
# for viewers to see perceptually. See an extension on this idea at
# [mycarta-jet]_.

cmaps['Cyclic'] = ['twilight', 'twilight_shifted', 'hsv']
plot_color_gradients('Cyclic', ['twilight', 'twilight_shifted', 'hsv'])

###############################################################################
# Qualitative
Expand All @@ -165,9 +200,10 @@
# the place throughout the colormap, and are clearly not monotonically increasing.
# These would not be good options for use as perceptual colormaps.

cmaps['Qualitative'] = ['Pastel1', 'Pastel2', 'Paired', 'Accent',
'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c']
plot_color_gradients('Qualitative',
['Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2',
'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b',
'tab20c'])

###############################################################################
# Miscellaneous
Expand All @@ -189,43 +225,12 @@
# poor choice for representing data for viewers to see perceptually. See an
# extension on this idea at [mycarta-jet]_ and [turbo]_.

cmaps['Miscellaneous'] = [
'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg',
'gist_rainbow', 'rainbow', 'jet', 'turbo', 'nipy_spectral',
'gist_ncar']

###############################################################################
# .. _color-colormaps_reference:
#
# First, we'll show the range of each colormap. Note that some seem
# to change more "quickly" than others.

gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))


def plot_color_gradients(cmap_category, cmap_list):
# Create figure and adjust figure height to number of colormaps
nrows = len(cmap_list)
figh = 0.35 + 0.15 + (nrows + (nrows - 1) * 0.1) * 0.22
fig, axs = plt.subplots(nrows=nrows + 1, figsize=(6.4, figh))
fig.subplots_adjust(top=1 - 0.35 / figh, bottom=0.15 / figh,
left=0.2, right=0.99)
axs[0].set_title(cmap_category + ' colormaps', fontsize=14)

for ax, name in zip(axs, cmap_list):
ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
ax.text(-0.01, 0.5, name, va='center', ha='right', fontsize=10,
transform=ax.transAxes)

# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axs:
ax.set_axis_off()


for cmap_category, cmap_list in cmaps.items():
plot_color_gradients(cmap_category, cmap_list)
plot_color_gradients('Miscellaneous',
['flag', 'prism', 'ocean', 'gist_earth', 'terrain',
'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap',
'cubehelix', 'brg', 'gist_rainbow', 'rainbow', 'jet',
'turbo', 'nipy_spectral', 'gist_ncar'])

plt.show()

Expand Down
31 changes: 19 additions & 12 deletions tutorials/colors/colors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,11 +160,11 @@ def demo(sty):
# The visual below shows name collisions. Color names where color values agree
# are in bold.

import matplotlib._color_data as mcd
import matplotlib.colors as mcolors
import matplotlib.patches as mpatch

overlap = {name for name in mcd.CSS4_COLORS
if "xkcd:" + name in mcd.XKCD_COLORS}
overlap = {name for name in mcolors.CSS4_COLORS
if f'xkcd:{name}' in mcolors.XKCD_COLORS}

fig = plt.figure(figsize=[9, 5])
ax = fig.add_axes([0, 0, 1, 1])
Expand All @@ -173,23 +173,30 @@ def demo(sty):
n_rows = len(overlap) // n_groups + 1

for j, color_name in enumerate(sorted(overlap)):
css4 = mcd.CSS4_COLORS[color_name]
xkcd = mcd.XKCD_COLORS["xkcd:" + color_name].upper()
css4 = mcolors.CSS4_COLORS[color_name]
xkcd = mcolors.XKCD_COLORS[f'xkcd:{color_name}'].upper()

# Pick text colour based on perceived luminance.
rgba = mcolors.to_rgba_array([css4, xkcd])
luma = 0.299 * rgba[:, 0] + 0.587 * rgba[:, 1] + 0.114 * rgba[:, 2]
css4_text_color = 'k' if luma[0] > 0.5 else 'w'
xkcd_text_color = 'k' if luma[1] > 0.5 else 'w'

col_shift = (j // n_rows) * 3
y_pos = j % n_rows
text_args = dict(va='center', fontsize=10,
weight='bold' if css4 == xkcd else None)
text_args = dict(fontsize=10, weight='bold' if css4 == xkcd else None)
ax.add_patch(mpatch.Rectangle((0 + col_shift, y_pos), 1, 1, color=css4))
ax.add_patch(mpatch.Rectangle((1 + col_shift, y_pos), 1, 1, color=xkcd))
ax.text(0 + col_shift, y_pos + .5, ' ' + css4, alpha=0.5, **text_args)
ax.text(1 + col_shift, y_pos + .5, ' ' + xkcd, alpha=0.5, **text_args)
ax.text(2 + col_shift, y_pos + .5, ' ' + color_name, **text_args)
ax.text(0.5 + col_shift, y_pos + .7, css4,
color=css4_text_color, ha='center', **text_args)
ax.text(1.5 + col_shift, y_pos + .7, xkcd,
color=xkcd_text_color, ha='center', **text_args)
ax.text(2 + col_shift, y_pos + .7, f' {color_name}', **text_args)

for g in range(n_groups):
ax.hlines(range(n_rows), 3*g, 3*g + 2.8, color='0.7', linewidth=1)
ax.text(0.5 + 3*g, -0.5, 'X11', ha='center', va='center')
ax.text(1.5 + 3*g, -0.5, 'xkcd', ha='center', va='center')
ax.text(0.5 + 3*g, -0.3, 'X11/CSS4', ha='center')
ax.text(1.5 + 3*g, -0.3, 'xkcd', ha='center')

ax.set_xlim(0, 3 * n_groups)
ax.set_ylim(n_rows, -1)
Expand Down
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