Hey everyone,
Over on IRC (#scipy channel on freenode), Baribal asked this:
<Baribal> I'm computing a dendrite on a continuous surface ([0,
1[, [0, 1[) and want to visualize it. What module would you
recommend? In practice, I need to draw lines from x_1/y_1 to
x_2/y_2 with a color gradient applied.
And further specified that the this is just for straight lines,
and each point also has an RGB value associated with it.
I coded up a solution (plot_gradient_rbg_pairs), and thought I'd
share it here. I've also posted it to a gist, in case I end up
updating it later. https://gist.github.com/5439438
#!/usr/bin/env python
"""A quick hack to draw gradient lines using a colormap.
This was written in response to <Baribal>'s question on IRC.
There are two functions provided here:
`plot_gradient_hack` takes two arguments, p0 and p1, which are both (x,y)
pairs, and plots a gradient between them that spans the full colormap.
`plot_gradient_rbg_pairs` does the same thing, but also takes rgb0 and rgb1
arguments, makes a new colormap that spans between those two values, and uses
that colormap for the plot.
There's an alternative solution over here [1], but that uses many more points.
1. http://matplotlib.1069221.n5.nabble.com/Gradient-color-on-a-line-plot-td17643.html
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.colors import LinearSegmentedColormap
def plot_gradient_hack( p0, p1, npts=20, cmap=None, **kw):
"""
Draw a gradient between p0 and p1 using a colormap
The **kw dictionary gets passed to plt.plot, so things like linestyle,
linewidth, labels, etc can be modified directly.
"""
x_1, y_1 = p0
x_2, y_2 = p1
X = np.linspace(x_1, x_2, npts)
Xs = X[:-1]
Xf = X[1:]
Xpairs = zip(Xs, Xf)
Y = np.linspace(y_1, y_2, npts)
Ys = Y[:-1]
Yf = Y[1:]
Ypairs = zip(Ys, Yf)
C = np.linspace(0,1, npts)
cmap = plt.get_cmap(cmap)
# the simplest way of doing this is to just do the following:
for x, y, c in zip(Xpairs, Ypairs, C):
plt.plot(x, y, '-', c=cmap(c), **kw)
# But for cases when that will be too slow, you can make this go faster,
# follow along with this example:
# http://matplotlib.org/examples/pylab_examples/line_collection2.html
def plot_gradient_rbg_pairs(p0, p1, rgb0, rgb1, **kw):
"""Form the gradient from RGB values at each point
The **kw dictionary gets passed to plt.plot, so things like linestyle,
linewidth, labels, etc can be modified directly.
"""
cmap = LinearSegmentedColormap.from_list('tmp', (rgb0, rgb1))
plot_gradient_hack(p0, p1, cmap=cmap, **kw)
# plot gradient that just spans the full colormap
plot_gradient_hack( (1,2), (5,6) )
# we can specify the colormap, and set some properties for the plot
plot_gradient_hack( (2,5), (5,3), cmap='bwr', linewidth=3.)
# We also have a simple wrapper to specify the two rgb points to interpolate
# the gradient between
plot_gradient_rbg_pairs( (1.1,2), (5.1,6), (0,0,0), (1,1,1) ) # black to white
plot_gradient_rbg_pairs( (1.2,2), (5.2,6), (0,0,0), (0,0,1), # black to blue
linestyle='--', linewidth=9)
plot_gradient_rbg_pairs( (1.3,2), (5.3,6), (1,0,0), (0,1,0), # red to green
linewidth=4 )
plt.show()
# we can use this gradient plot to display all colormaps on one plot easily
plt.figure()
with matplotlib.rc_context({'lines.solid_capstyle':'butt'}):
# the default projecting capstyle looks kind of ugly. rc_context was
# introduced in matpltolib 1.2.0, if you are running a version older than
# that, you can ignore this line and remove one level of indentation from
# the for loop
for i, map_name in enumerate(plt.cm.cmap_d):
plot_gradient_hack((0, i), (1, i), cmap = map_name, linewidth=4)
plt.text(1,i, map_name, va='center')
# comment out this last line to plot all ~140 colormaps
if i==25: break
plt.show()
best,
···
--
Paul Ivanov
314 address only used for lists, off-list direct email at:
http://pirsquared.org | GPG/PGP key id: 0x0F3E28F7