You want this?
----
import matplotlib as mpl
mpl.rcParams['axes.color_cycle'] = ['#0000FF', '#00FF00', '#FF0000', '#00FFFF',
'FF00FF', 'FFFF00', '000000']
# test it
from pylab import *
import matplotlib.cm as cm
x = linspace(0, 2*pi, num=100, endpoint=True)
for i in range(1, 10):
plot(x, sin(x + pi*i/10.0))
show()
----
I still think, that this is not a sensible default choice. Btw you can also
easily generate your color scheme from colormaps:
mpl.rcParams['axes.color_cycle'] = [cm.winter(i/10.) for i in range(10)].
There also was an example in the Mailing list for how to do this on a
per-plot-basis: https://gist.github.com/3150091
Am 21.07.2012 um 21:00 schrieb klo uo:
> On Sat, Jul 21, 2012 at 7:37 PM, Felix Patzelt wrote:
>> Have you ever been in a talk where someone uses 100% green on a slide? The
>> result is usually that no one can see what is shown unless it is a really
>> large green area.
>
> No, but I would have expected in that case appropriate bg. I've seen a
> talk by MPL developer that he dislikes systems that help too much,
> like I want it to do what I tell it to do ;)
> Like cyan is not teal, and yellow is not 75% yellow etc.
>
> I really would have expected that MPL uses some nifty CMYK scheme (as
> in CorelDraw approxiamtion i.e.) instead the one usually defined as in
> html color names: http://www.w3schools.com/html/html_colornames.asp
> which are commonly used (gnuplot i.e.), but MPL doesn't even use that
>
> Can this colors be defined (i.e. in matplotlibrc) w/o changing MPL source?
>
>
>> My guess is that this happens because rod cells which are the most light
>> sensitive ones have very similar wavelength responsiveness to the cone cells
>> (the ones for color vision) which react to green light. Hence, you just
>> don't have as much contrast for green text on a white background as you have
>> for example for blue text. Also, green on black is much easier to read than
>> blue or red on black by the same argument.
>
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