How to build multiple x or y axes in matplotlib?

Question

I am currently using matplotlib to plot a dimension against 2 or 3 other dimensions (sometimes categorical) along the x axis. I am currently grouping the x-axis data into tuples and sorting them before plotting ... the result looks something like the left image below. What I would like to do is build data with several x axes, as you see in the correct image. The grouping of the “processing” marks along the X axis will be icing on the cake.

Answer 1

First of all, the tough question! This is definitely possible with matplotlib> = 1.0.0. (New vertebral functionality allows this)

This requires honest voodoo, though ... My example is far from perfect, but hopefully this makes sense:

import numpy as np import matplotlib.pyplot as plt import matplotlib as mpl def main(): #-- Generate some data ---------------------------------------------------- nx = 10 x = np.linspace(0, 2*np.pi, 10) y = 2 * np.sin(x) groups = [('GroupA', (x[0], x[nx//3])), ('GroupB', (x[-2*nx//3], x[2*nx//3])), ('GroupC', (x[-nx//3], x[-1]))] #-- Plot the results ------------------------------------------------------ fig = plt.figure() ax = fig.add_subplot(111) # Give ourselves a bit more room at the bottom plt.subplots_adjust(bottom=0.2) ax.plot(x,y, 'k^') # Drop the bottom spine by 40 pts ax.spines['bottom'].set_position(('outward', 40)) # Make a second bottom spine in the position of the original bottom spine make_second_bottom_spine(label='Treatment') # Annotate the groups for name, xspan in groups: annotate_group(name, xspan) plt.xlabel('Dose') plt.ylabel('Response') plt.title('Experimental Data') plt.show() def annotate_group(name, xspan, ax=None): """Annotates a span of the x-axis""" def annotate(ax, name, left, right, y, pad): arrow = ax.annotate(name, xy=(left, y), xycoords='data', xytext=(right, y-pad), textcoords='data', annotation_clip=False, verticalalignment='top', horizontalalignment='center', linespacing=2.0, arrowprops=dict(arrowstyle='-', shrinkA=0, shrinkB=0, connectionstyle='angle,angleB=90,angleA=0,rad=5') ) return arrow if ax is None: ax = plt.gca() ymin = ax.get_ylim()[0] ypad = 0.01 * np.ptp(ax.get_ylim()) xcenter = np.mean(xspan) left_arrow = annotate(ax, name, xspan[0], xcenter, ymin, ypad) right_arrow = annotate(ax, name, xspan[1], xcenter, ymin, ypad) return left_arrow, right_arrow def make_second_bottom_spine(ax=None, label=None, offset=0, labeloffset=20): """Makes a second bottom spine""" if ax is None: ax = plt.gca() second_bottom = mpl.spines.Spine(ax, 'bottom', ax.spines['bottom']._path) second_bottom.set_position(('outward', offset)) ax.spines['second_bottom'] = second_bottom if label is not None: # Make a new xlabel ax.annotate(label, xy=(0.5, 0), xycoords='axes fraction', xytext=(0, -labeloffset), textcoords='offset points', verticalalignment='top', horizontalalignment='center') if __name__ == '__main__': main() 

Answer 2

Joe's example is good. I'll throw it away too. I worked on it a few hours ago, but then I had to run away to meet. He goes over here .

 import matplotlib.pyplot as plt import matplotlib.ticker as ticker ## the following two functions override the default behavior or twiny() def make_patch_spines_invisible(ax): ax.set_frame_on(True) ax.patch.set_visible(False) for sp in ax.spines.itervalues(): sp.set_visible(False) def make_spine_invisible(ax, direction): if direction in ["right", "left"]: ax.yaxis.set_ticks_position(direction) ax.yaxis.set_label_position(direction) elif direction in ["top", "bottom"]: ax.xaxis.set_ticks_position(direction) ax.xaxis.set_label_position(direction) else: raise ValueError("Unknown Direction : %s" % (direction,)) ax.spines[direction].set_visible(True) data = (('A',0.01),('A',0.02),('B',0.10),('B',0.20)) # fake data fig = plt.figure(1) sb = fig.add_subplot(111) sb.xaxis.set_major_locator(ticker.FixedLocator([0,1,2,3])) sb.plot([i[1] for i in data],"*",markersize=10) sb.set_xlabel("dose") plt.subplots_adjust(bottom=0.17) # make room on bottom par2 = sb.twiny() # create a second axes par2.spines["bottom"].set_position(("axes", -.1)) # move it down ## override the default behavior for a twiny axis make_patch_spines_invisible(par2) make_spine_invisible(par2, "bottom") par2.set_xlabel("treatment") par2.plot([i[1] for i in data],"*",markersize=10) #redraw to put twiny on same scale par2.xaxis.set_major_locator(ticker.FixedLocator([0,1,2,3])) par2.xaxis.set_ticklabels([i[0] for i in data]) plt.show() 

It produces: