Hi everybody,
I have a problem with LinearSegmentedColormap.
In the example below (see PS), I make a colormap, and use it to plot an
EllipseCollection. My plot is parameterized by a quantity that I have named
"large_value". For large_value equal to 257, a blue point is obtained at
(x=0.3, y=0.4). But for large_value equal to 258, it becomes black.
This is because of the way LinearSegmentedColormap is working. It has a
parameter N which allows to set the "number of colors":
http://matplotlib.org/api/colors_api.html#matplotlib.colors.LinearSegmentedColormap
It is 256 by default, so if I increase N to a greater value, the point remains
blue for large_value equal to 258.
Now, my real plot (not this dummy example) is such that I need N to be very
large so as to obtain the right colors on my plot, although very few colors
are used at the end.
However, when N is too large, the plot becomes very slow, and a lot of memory
is used; I think because an array is probably built with this size, although
in theory there is no need to construct such a complete array.
Is there an easy workaround, or have I to study and modify the matplotlib code
myself?
Thanks,
TP
PS: Here is the test code:
···
##################
from pylab import *
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.collections import CircleCollection
ioff()
large_value = 257 # blue below this value
#large_value = 258 # black above this value
N = 1e5 # 256 by default
cdict = { 'blue': [(0.0, 0.0, 0.0),
(2*1/large_value, 1, 1)
, (1.0, 1.0, 1.0)]
, 'green': [(0.0, 0.0, 0.0),
(2*1/large_value, 0, 0)
, (1.0, 1.0, 1.0)]
, 'red': [(0.0, 0.0, 0.0),
(2*1/large_value, 0, 0),
(1.0, 1.0, 1.0)] }
measures= array([[ 0.2, 0.3, 1],
[ 0.3, 0.4, 2],
[ 0.5, 0.6, large_value]])
cmap = LinearSegmentedColormap( "cmap foobar"
, cdict
# , N= N )
)
fig = figure()
axes = fig.add_subplot(111)
ec = CircleCollection( [80]
, offsets = measures[:,:2]
, transOffset = axes.transData
)
ec.set_array( measures[:,2] )
ec.set_cmap( cmap )
axes.add_collection( ec )
show()
##################