>>>>> "Francesco" == Francesco Davide Calabrese <[EMAIL PROTECTED]> writes:
Francesco> Hi, the following simple code (in Matlab syntax):
Francesco> x1 = [5, 6] y1 = [3, 2] bar([1,2], [x1; y1])
Francesco> produces a bar plot where two of the bars have a color
Francesco> (let's say red) and the two other bars have another
Francesco> color (let's say blue). I'd like to know whether it's
Francesco> possible in matplotlib to plot the bars not simply with
Francesco> a different color but with a different filling
Francesco> pattern. So, for example, the blue bars filled with
Francesco> vertical lines (or something else) and the red bars
Francesco> filled with horizontal lines (or again something else).
Francesco> Please, note that I'm not talking about the borders of
Francesco> the bars. The reason why I need this feature is that I
Francesco> have to clearly distinguish the bars also when they are
Francesco> plotted in black and white, because many publications
Francesco> can't be done in colors.
The postscript backend supports this -- we call it "hatching".
Unfortunately, the others do not yet. See the example at
http://matplotlib.sf.net/examples/hatch_demo.py
One could be clever with "imshow" and create arrays that have the
hatching. In the example I wrote below I created a custom bar chart
using gradient images. By adding horizontal, vertical, or diangonal
lines to the arrays, or dots, one could support various hatching
schemes. Here is the sample code for gradients that i wrote in
response to an earlier post:
from pylab import figure, show, nx, cm
def gbar(ax, x, y, width=0.5, bottom=0):
X = [[.6, .6],[.7,.7]]
for left,top in zip(x, y):
right = left+width
ax.imshow(X, interpolation='bicubic', cmap=cm.Blues,
extent=(left, right, bottom, top), alpha=1)
fig = figure()
xmin, xmax = xlim = 0,10
ymin, ymax = ylim = 0,1
ax = fig.add_subplot(111, xlim=xlim, ylim=ylim,
autoscale_on=False)
X = [[.6, .6],[.7,.7]]
ax.imshow(X, interpolation='bicubic', cmap=cm.copper,
extent=(xmin, xmax, ymin, ymax), alpha=1)
N = 10
x = nx.arange(N)+0.25
y = nx.mlab.rand(N)
gbar(ax, x, y, width=0.7)
ax.set_aspect('normal')
show()
Ideally, we could do the work in Agg to support these hatched
images....
JDH
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