From: Dr. Phillip M. Feldman <pfeldman@ve...>  20091028 02:09:29

Here's my code (now nicely organized and documented, but still buggy): # multiple_yaxes_with_spines.py # This is a template Python program for creating plots (line graphs) with 2, 3, # or 4 yaxes. (A template program is one that you can readily modify to meet # your needs). Almost all usermodifiable code is in Section 2. For most # purposes, it should not be necessary to modify anything else. # Dr. Phillip M. Feldman, 27 Oct, 2009 # Acknowledgment: This program is based on code written by JaeJoon Lee, # URL= http://matplotlib.svn.sourceforge.net/viewvc/matplotlib/trunk/matplotlib/ # examples/pylab_examples/multiple_yaxis_with_spines.py?revision=7908&view=markup # Section 1: Import modules, define functions, and allocate storage. import matplotlib.pyplot as plt from numpy import * 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) # Create list to store dependent variable data: y= [0, 0, 0, 0, 0] # Section 2: Define names of variables and the data to be plotted. # `labels` stores the names of the independent and dependent variables). The # first (zeroth) item in the list is the xaxis label; remaining labels are the # first yaxis label, second yaxis label, and so on. There must be at least # two dependent variables and not more than four. labels= ['Indep. Variable', 'Dep. Variable #1', 'Dep. Variable #2', 'Dep. Variable #3', 'Dep. Variable #4'] # Plug in your data here, or code equations to generate the data if you wish to # plot mathematical functions. x stores values of the independent variable; # y[1], y[2], ... store values of the dependent variable. (y[0] is not used). # All of these objects should be NumPy arrays. # If you are plotting mathematical functions, you will probably want an array of # uniformly spaced values of x; such an array can be created using the # `linspace` function. For example, to define x as an array of 51 values # uniformly spaced between 0 and 2, use the following command: # x= linspace(0., 2., 51) # Here is an example of 6 experimentally measured y1values: # y[1]= array( [3, 2.5, 7.3e4, 4, 8, 3] ) # Note that the above statement requires both parentheses and square brackets. # With a bit of work, one could make this program read the data from a text file # or Excel worksheet. # Independent variable: x = linspace(0., 2., 51) # First dependent variable: y[1]= sqrt(x) # Second dependent variable: y[2]= 0.2 + x**0.3 y[3]= 30.*sin(1.5*x) y[4]= 30.*abs(cos(1.5*x)) # Set line colors here; each color can be specified using a singleletter color # identifier ('b'= blue, 'r'= red, 'g'= green, 'k'= black, 'y'= yellow, # 'm'= magenta, 'y'= yellow), an RGB tuple, or almost any standard English color # name written without spaces, e.g., 'darkred'. The first element of this list # is not used. colors= [' ', 'b', 'darkred', 'g', 'magenta'] # Set the line width here. linewidth=2 is recommended. linewidth= 2 # Section 3: Generate the plot. N_dependents= len(labels)  1 if N_dependents > 4: raise Exception, \ 'This code currently handles a maximum of four independent variables.' # Open a new figure window, setting the size to 10by7 inches and the facecolor # to white: fig= plt.figure(figsize=(10,7), dpi=120, facecolor=[1,1,1]) host= fig.add_subplot(111) host.set_xlabel(labels[0]) # Use twinx() to create extra axes for all dependent variables except the first # (we get the first as part of the host axes). The first element of y_axis is # not used. y_axis= (N_dependents+2) * [0] y_axis[1]= host for i in range(2,len(labels)+1): y_axis[i]= host.twinx() if N_dependents >= 3: # The following statement positions the third yaxis to the right of the # frame, with the space between the frame and the axis controlled by the # numerical argument to set_position; this value should be between 1.10 and # 1.2. y_axis[3].spines["right"].set_position(("axes", 1.15)) make_patch_spines_invisible(y_axis[3]) make_spine_invisible(y_axis[3], "right") plt.subplots_adjust(left=0.0, right=0.8) if N_dependents >= 4: # The following statement positions the fourth yaxis to the left of the # frame, with the space between the frame and the axis controlled by the # numerical argument to set_position; this value should be between 1.10 and # 1.2. y_axis[4].spines["left"].set_position(("axes", 0.15)) make_patch_spines_invisible(y_axis[4]) make_spine_invisible(y_axis[4], "left") plt.subplots_adjust(left=0.2, right=0.8) p= (N_dependents+1) * [0] # Plot the curves: for i in range(1,N_dependents+1): p[i], = y_axis[i].plot(x, y[i], colors[i], linewidth=linewidth, label=labels[i]) # Set axis limits. Use ceil() to force upper yaxis limits to be round numbers. host.set_xlim(x.min(), x.max()) host.set_xlabel(labels[0], size=16) for i in range(1,N_dependents+1): y_axis[i].set_ylim(0.0, ceil(y[i].max())) y_axis[i].set_ylabel(labels[i], size=16) y_axis[i].yaxis.label.set_color(colors[i]) for obj in y_axis[i].yaxis.get_ticklines(): # `obj` is a matplotlib.lines.Line2D instance obj.set_color(colors[i]) obj.set_markeredgewidth(3) for obj in y_axis[i].yaxis.get_ticklabels(): obj.set_color(colors[i]) obj.set_size(12) obj.set_weight(600) # To get rid of the legend, comment out the following two lines: lines= p[1:] host.legend(lines, [l.get_label() for l in lines]) plt.draw(); plt.show()  View this message in context: http://www.nabble.com/Possibletogetfouryaxesonasingleplottp26041500p26088240.html Sent from the matplotlib  users mailing list archive at Nabble.com. 