Matplotlib - add colorbar to a sequence of line plots

Question

I have a sequence of line plots for two variables (x,y) for a number of different values of a variable z. I would normally add the line plots with legends like this:

import matplotlib.pyplot as plt  fig = plt.figure() ax  = fig.add_subplot(111) # suppose mydata is a list of tuples containing (xs, ys, z)  # where xs and ys are lists of x's and y's and z is a number.  legns = [] for(xs,ys,z) in mydata:    pl = ax.plot(xs,ys,color = (z,0,0))    legns.append("z = %f"%(z)) ax.legends(legns)  plt.show() 

But I have too many graphs and the legends will cover the graph. I'd rather have a colorbar indicating the value of z corresponding to the color. I can't find anything like that in the galery and all my attempts do deal with the colorbar failed. Apparently I must create a collection of plots before trying to add a colorbar.

Is there an easy way to do this? Thanks.

EDIT (clarification):

I wanted to do something like this:

import matplotlib.pyplot as plt import matplotlib.cm     as cm  fig = plt.figure() ax  = fig.add_subplot(111) mycmap = cm.hot # suppose mydata is a list of tuples containing (xs, ys, z)  # where xs and ys are lists of x's and y's and z is a number between 0 and 1 plots = [] for(xs,ys,z) in mydata:    pl = ax.plot(xs,ys,color = mycmap(z))    plots.append(pl) fig.colorbar(plots) plt.show() 

But this won't work according to the Matplotlib reference because a list of plots is not a "mappable", whatever this means.

I've created an alternative plot function using LineCollection:

def myplot(ax,xs,ys,zs, cmap):     plot = lc([zip(x,y) for (x,y) in zip(xs,ys)], cmap = cmap)     plot.set_array(array(zs))     x0,x1 = amin(xs),amax(xs)     y0,y1 = amin(ys),amax(ys)     ax.add_collection(plot)     ax.set_xlim(x0,x1)     ax.set_ylim(y0,y1)     return plot 

xs and ys are lists of lists of x and y coordinates and zs is a list of the different conditions to colorize each line. It feels a bit like a cludge though... I thought that there would be a more neat way to do this. I like the flexibility of the plt.plot() function.

Answer 1

(I know this is an old question but...) Colorbars require a matplotlib.cm.ScalarMappable, plt.plot produces lines which are not scalar mappable, therefore, in order to make a colorbar, we are going to need to make a scalar mappable.

Ok. So the constructor of a ScalarMappable takes a cmap and a norm instance. (norms scale data to the range 0-1, cmaps you have already worked with and take a number between 0-1 and returns a color). So in your case:

import matplotlib.pyplot as plt sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.normalize(min=0, max=1)) plt.colorbar(sm) 

Because your data is in the range 0-1 already, you can simplify the sm creation to:

sm = plt.cm.ScalarMappable(cmap=my_cmap) 

Hope that helps somebody.

EDIT: For matplotlib v1.2 or greater the code becomes:

import matplotlib.pyplot as plt sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.normalize(vmin=0, vmax=1)) # fake up the array of the scalar mappable. Urgh... sm._A = [] plt.colorbar(sm) 

EDIT: For matplotlib v1.3 or greater the code becomes:

import matplotlib.pyplot as plt sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.Normalize(vmin=0, vmax=1)) # fake up the array of the scalar mappable. Urgh... sm._A = [] plt.colorbar(sm) 

EDIT: For matplotlib v3.1 or greater simplifies to:

import matplotlib.pyplot as plt sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.Normalize(vmin=0, vmax=1)) plt.colorbar(sm) 

Answer 2

Here's one way to do it while still using plt.plot(). Basically, you make a throw-away plot and get the colorbar from there.

import matplotlib as mpl import matplotlib.pyplot as plt  min, max = (-40, 30) step = 10  # Setting up a colormap that's a simple transtion mymap = mpl.colors.LinearSegmentedColormap.from_list('mycolors',['blue','red'])  # Using contourf to provide my colorbar info, then clearing the figure Z = [[0,0],[0,0]] levels = range(min,max+step,step) CS3 = plt.contourf(Z, levels, cmap=mymap) plt.clf()  # Plotting what I actually want X=[[1,2],[1,2],[1,2],[1,2]] Y=[[1,2],[1,3],[1,4],[1,5]] Z=[-40,-20,0,30] for x,y,z in zip(X,Y,Z):     # setting rgb color based on z normalized to my range     r = (float(z)-min)/(max-min)     g = 0     b = 1-r     plt.plot(x,y,color=(r,g,b)) plt.colorbar(CS3) # using the colorbar info I got from contourf plt.show() 

It's a little wasteful, but convenient. It's also not very wasteful if you make multiple plots as you can call plt.colorbar() without regenerating the info for it.