8000 DOC: Change colormaps for examples (showcase different types, discourage 'jet') by endolith · Pull Request #921 · matplotlib/matplotlib · GitHub
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DOC: Change colormaps for examples (showcase different types, discourage 'jet') #921

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Merged
merged 19 commits into from
Jun 24, 2012
Merged

DOC: Change colormaps for examples (showcase different types, discourage 'jet') #921

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5adee64
use sequential YlOrRd colormap for unipolar data, discourage using je…
endolith May 31, 2012
9614b11
Jet is discouraged for MRI images. Google Image Search shows the vast…
endolith May 31, 2012
5564284
use HSV for cyclic data
endolith Jun 3, 2012
c6fac36
fix typos
endolith Jun 3, 2012
fccaa18
ran a spell-checker on some files
endolith Jun 4, 2012
a3ed3ab
more examples of using sequential colormaps for non-negative data, di…
endolith Jun 4, 2012
b2c6723
more typo and spelling fixes
endolith Jun 5, 2012
d147008
coolwarm is specifically designed for diverging data on shaded 3D sur…
endolith Jun 5, 2012
fb4a980
sequential map for non-negative surface
endolith Jun 5, 2012
f16b15f
rainbow instead of jet for diverging data, summer for sequential data
endolith Jun 5, 2012
2df6609
diverging colormaps with centered norms
endolith Jun 5, 2012
f3ba478
better sequential map for showing contours
endolith Jun 5, 2012
80008a2
more complicated example of centered norm diverging colormap
endolith Jun 5, 2012
04409c8
tricontour example rainbow instead of jet, center norm
endolith Jun 5, 2012
e1b17ec
add imports where needed
endolith Jun 8, 2012
2ac91ec
add imports where needed
endolith Jun 8, 2012
9231c4c
remove note to self
endolith Jun 8, 2012
8a810fa
style fixes
endolith Jun 18, 2012
8b80779
style fixes
endolith Jun 18, 2012
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diverging colormaps with centered norms
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@endolith
endolith committed Jun 5, 2012
commit 2df6609940882d914b5476311db3e0999408dfa1
2 changes: 1 addition & 1 deletion examples/pylab_examples/griddata_demo.py
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Expand Up @@ -16,7 +16,7 @@
zi = griddata(x,y,z,xi,yi,interp='linear')
# contour the gridded data, plotting dots at the nonuniform data points.
CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')
CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.jet)
CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow, vmax=abs(zi).max(), vmin=-abs(zi).max())
plt.colorbar() # draw colorbar
# plot data points.
plt.scatter(x,y,marker='o',c='b',s=5,zorder=10)
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4 changes: 2 additions & 2 deletions examples/pylab_examples/hexbin_demo2.py
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Expand Up @@ -39,14 +39,14 @@
gridsize=30

plt.subplot(211)
plt.hexbin(x,y, C=z, gridsize=gridsize, marginals=True)
plt.hexbin(x,y, C=z, gridsize=gridsize, marginals=True, cmap=cm.RdBu, vmax=abs(z).max(), vmin=-abs(z).max()) # NEEDS NORM
plt.axis([xmin, xmax, ymin, ymax])
cb = plt.colorbar()
cb.set_label('mean value')


plt.subplot(212)
plt.hexbin(x,y, gridsize=gridsize)
plt.hexbin(x,y, gridsize=gridsize, cmap=cm.Blues_r)
plt.axis([xmin, xmax, ymin, ymax])
cb = plt.colorbar()
cb.set_label('N observations')
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5 changes: 3 additions & 2 deletions examples/pylab_examples/image_demo.py
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Expand Up @@ -11,8 +11,9 @@
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2-Z1 # difference of Gaussians

im = plt.imshow(Z, interpolation='bilinear', cmap=cm.gray,
origin='lower', extent=[-3,3,-3,3])
im = plt.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
origin='lower', extent=[-3,3,-3,3],
vmax=abs(Z).max(), vmin=-abs(Z).max())

plt.show()

2 changes: 1 addition & 1 deletion examples/pylab_examples/pcolor_demo.py
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Expand Up @@ -19,7 +19,7 @@ def func3(x,y):
X,Y = meshgrid(x, y)

Z = func3(X, Y)
pcolor(X, Y, Z)
pcolor(X, Y, Z, cmap=cm.RdBu, vmax=abs(Z).max(), vmin=-abs(Z).max())
colorbar()
axis([-3,3,-3,3])

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2 changes: 1 addition & 1 deletion examples/pylab_examples/pcolor_demo2.py
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Expand Up @@ -20,7 +20,7 @@ def func3(x,y):


ax = subplot(111)
im = imshow(Z, cmap=cm.jet)
im = imshow(Z, cmap=cm.RdBu, vmax=abs(Z).max(), vmin=-abs(Z).max())
#im.set_interpolation('nearest')
#im.set_interpolation('bicubic')
im.set_interpolation('bilinear')
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6 changes: 3 additions & 3 deletions examples/pylab_examples/poormans_contour.py
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Expand Up @@ -18,9 +18,9 @@
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2 - Z1 # difference of Gaussians

cmap = cm.get_cmap('jet', 10) # 10 discrete colors
51F5
im = imshow(Z, cmap=cmap, interpolation='bilinear')
cmap = cm.get_cmap('PiYG', 11) # 11 discrete colors
#NORM
im = imshow(Z, cmap=cmap, interpolation='bilinear', vmax=abs(Z).max(), vmin=-abs(Z).max())
axis('off')
colorbar()

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