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I'm plotting maps using contourf and I'd usually go with the default (rainbow) colorscheme with levels = 50.
#Various imports
#LOTS OF OTHER CODE BEFORE
plot = plt.contourf(to_plot, 50)
plt.show()
#LOTS OF OTHER CODE AFTER
The output is below. I do various other stuff to get the coastlines etc. It's done using iris and cartopy, if anyone's interested.
Now I've decided that I don't want to use a rainbow scheme so I'm using some Cyntia Brewer colours:
brewer_cmap = mpl.cm.get_cmap('brewer_Reds_09')
plot = iplt.contourf(to_plot, 50, cmap=brewer_cmap) # expect 50 levels
However the output is:
You can see Here that this palette only has 9 colours. So my question is, are the contourf levels limited by the amount of available colours in the colormap? I quite like this map and I wonder if it'd be possible to generate a new one like it but with more levels of red?
I'm interested in being able to capture the variability of the data so more contour levels seems like a good idea but I'm keen on losing the rainbow scheme and just going with one based on a single colour.
Cheers!
565
A contour of the function z(x,y) is a set of points in the (x,y) plane, such that z(x,y) is fixed at some constant value. That constant value is the contour "level". That set of points in the (x,y) plane is often called a level set.
The contourf() function in pyplot module of matplotlib library is used to plot contours. But contourf draw filled contours, while contourf draws contour lines.
contour() and contourf() draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions. contourf() differs from the MATLAB version in that it does not draw the polygon edges. To draw edges, add line contours with calls to contour() .
The default color scheme of Matplotlib contour and filled contour plots can be modified. A general way to modify the color scheme is to call Matplotlib's plt. get_cmap() function that outputs a color map object. There are many different colormaps available to apply to contour plots.
Yes, it is a discrete colormap, and if you want to have a continuos one you need to make a customized colormap.
#the colormap data can be found here: https://github.com/SciTools/iris/blob/master/lib/iris/etc/palette/sequential/Reds_09.txt
In [22]:
%%file temp.txt
1.000000 0.960784 0.941176
0.996078 0.878431 0.823529
0.988235 0.733333 0.631373
0.988235 0.572549 0.447059
0.984314 0.415686 0.290196
0.937255 0.231373 0.172549
0.796078 0.094118 0.113725
0.647059 0.058824 0.082353
0.403922 0.000000 0.050980
Overwriting temp.txt
In [23]:
c_array = np.genfromtxt('temp.txt')
from matplotlib.colors import LinearSegmentedColormap
plt.register_cmap(name='Test', data={key: tuple(zip(np.linspace(0,1,c_array.shape[0]), c_array[:,i], c_array[:,i]))
for key, i in zip(['red','green','blue'], (0,1,2))})
In [24]:
plt.contourf(X, Y, Z, 50, cmap=plt.get_cmap('Test'))
plt.colorbar()
Out[24]:
<matplotlib.colorbar.Colorbar instance at 0x108948320>
157
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