(Sorry for sending this twice, Pythonified, but I forgot to copy the list)
On Wed, May 4, 2011 at 9:51 PM, Pythonified <netdriverem...@gmail.com>wrote:
> I have been trying to assign different colors for each line I plot, where
> the colors are incrementally darkened (or lightened), or selected from a
> colorbar (e.g. rainbow). Any ideas?
>
I posted some code awhile back to do what you're looking for (see:
http://old.nabble.com/Where-to-post-examples-%28specifically,-one-that-may-be-useful-for-time-evolution-plots%29-td23901837.html
).
I'm copying the code below again because it's evolved a bit (I really need
to start posting code to github). Anyway, if you copy the attached code to a
module, you should be able to call the "cycle_cmap" function to change the
cmap globally, or the "cycle_cmap_axes" function to create an axes with the
specified cmap.
Best,
-Tony
#---- start of code
import matplotlib.pyplot as plt
import numpy as np
# reverse these colormaps so that it goes from light to dark
REVERSE_CMAP = ['summer', 'autumn', 'winter', 'spring', 'copper']
# clip some colormaps so the colors aren't too light
CMAP_RANGE = dict(gray={'start':200, 'stop':0},
Blues={'start':60, 'stop':255},
Oranges={'start':100, 'stop':255},
OrRd={'start':60, 'stop':255},
BuGn={'start':60, 'stop':255},
PuRd={'start':60, 'stop':255},
YlGn={'start':60, 'stop':255},
YlGnBu={'start':60, 'stop':255},
YlOrBr={'start':60, 'stop':255},
YlOrRd={'start':60, 'stop':255},
hot={'start':230, 'stop':0},
bone={'start':200, 'stop':0},
pink={'start':160, 'stop':0})
def cmap_intervals(length=50, cmap='YlOrBr', start=None, stop=None):
"""Return evenly spaced intervals of a given colormap `cmap`.
Colormaps listed in REVERSE_CMAP will be cycled in reverse order.
Certain
colormaps have pre-specified color ranges in CMAP_RANGE. These module
variables ensure that colors cycle from light to dark and light colors
are
not too close to white.
Parameters
----------
length : int
the number of colors used before cycling back to first color. When
`length` is large (> ~10), it is difficult to distinguish between
successive lines because successive colors are very similar.
cmap : str
name of a matplotlib colormap (see matplotlib.pyplot.cm)
"""
cm = getattr(plt.cm, cmap)
crange = CMAP_RANGE.get(cmap, dict(start=0, stop=255))
if cmap in REVERSE_CMAP:
crange = dict(start=crange['stop'], stop=crange['start'])
if start is not None:
crange['start'] = start
if stop is not None:
crange['stop'] = stop
if length > abs(crange['start'] - crange['stop']):
print ('Warning: the input length is greater than the number of ' +
'colors in the colormap; some colors will be repeated')
idx = np.linspace(crange['start'], crange['stop'], length).astype(np.int
)
return cm(idx)
def cycle_cmap(length=50, cmap='YlOrBr', start=None, stop=None):
"""Set default color cycle of matplotlib to a given colormap `cmap`.
The default color cycle of matplotlib is set to evenly distribute colors
in
color cycle over specified colormap.
Note: this function must be called before *any* plot commands because it
changes the default color cycle.
See ``cmap_intervals`` for input details.
"""
color_cycle = cmap_intervals(length, cmap, start, stop)
# set_default_color_cycle doesn't play nice with numpy arrays
plt.rc('axes', color_cycle=color_cycle.tolist())
def cycle_cmap_axes(length=50, cmap='YlOrBr', start=None, stop=None):
"""Return axes with color cycle set to a given colormap `cmap`.
The color cycle of the axes is set to evenly distribute colors in color
cycle over specified colormap.
See ``cmap_intervals`` for input details.
"""
color_cycle = cmap_intervals(length, cmap, start, stop)
# set_default_color_cycle doesn't play nice with numpy arrays
ax = plt.gca()
ax.set_color_cycle(color_cycle)
return ax
if __name__ == '__main__':
n_lines = 10
x = np.linspace(0, n_lines)
# change the global cmap
cycle_cmap(n_lines, 'Oranges')
for shift in np.linspace(0, np.pi, n_lines):
plt.plot(x, np.sin(x - shift))
plt.figure()
# create an axes that is set to desired cmap
ax = cycle_cmap_axes(n_lines, 'Blues')
for shift in np.linspace(0, np.pi, n_lines):
ax.plot(x, np.sin(x - shift))
plt.show()
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