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