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 ------------------------------------------------------------------------------ Precog is a next-generation analytics platform capable of advanced analytics on semi-structured data. The platform includes APIs for building apps and a phenomenal toolset for data science. Developers can use our toolset for easy data analysis & visualization. Get a free account! http://www2.precog.com/precogplatform/slashdotnewsletter _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
