I very much appreciate the help, but I still haven't been able to figure
out how to make this work.
If I get one y-axis with the 'host', and each invocation of twinx adds
another y-axis, then it seems that I must invoke twinx three times to
get four y-axes. Does twinx add more than one y-axis per invocation?
(The documentation that I've been able to find is ambiguous about this).
Also, I've experimented with selectively setting colors of specific
spines, but have not been able to figure out which ones I should be
changing.
My current code is attached.
Phillip
P.S. As per your suggestion, I've rewritten the code to follow the
Python list index convention.
Jae-Joon Lee wrote:
On Tue, Oct 27, 2009 at 11:12 PM, Dr. Phillip M. Feldman
<pfeld...@verizon.net> wrote:
(1) Not only is the y-axis for dependent variable #1 blue (as it should be),
but the entire frame around the plot is blue.
at line 158, you're changing the color of all spines. Change the color
of spine that you only want to change.
(2) The y-axis for dependent variable #2 has two sets of tick labels. The
set in black contains the correct values in the correct positions, but has
the wrong color. The other set of tick labels has the correct color (dark
red), but the values and locations are wrong. (In fact, these are same
values and positions as for dependent variable #1).
At line 113, you're creating 4 twinx axes, instead of 3, i.e, the
figure has total of 5 axes.
Also, I recommend you to use the pythonic convention that list index
starts from 0.
Regards,
-JJ
# multiple_yaxes_with_spines.py
# This is a template Python program for creating plots (line graphs) with 2, 3,
# or 4 y-axes. (A template program is one that you can readily modify to meet
# your needs). Almost all user-modifiable 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 Jae-Joon 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 set_spine_direction(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]
# 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 x-axis label; remaining labels are the
# first y-axis label, second y-axis 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[0], y[1], ... store values of the dependent variables. Each of these should
# be a NumPy array.
# 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 values for the first dependent
# variable:
# y[0]= 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[0]= sqrt(x)
# Second dependent variable:
y[1]= 0.2 + x**0.3 - 0.1*x**2
y[2]= 30.*sin(1.5*x)
y[3]= 30.*abs(cos(1.5*x))
# Set line colors here; each color can be specified using a single-letter 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'.
colors= ['b', 'darkred', 'g', 'magenta']
# Set the line width here. linewidth=2 is recommended.
linewidth= 2
# Set the axis label size in points here. 16 is recommended.
axis_label_size= 16
# 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 10-by-7 inches and the facecolor
# to white:
fig= plt.figure(figsize=(10,7), dpi=120, facecolor=[1,1,1])
host= fig.add_subplot(111)
# Use twinx() to create extra axes for all dependent variables except the first
# (we get the first as part of the host axes).
y_axis= N_dependents * [0]
y_axis[0]= host
for i in range(1,len(labels)): y_axis[i-1]= host.twinx()
if N_dependents >= 3:
# The following statement positions the third y-axis 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[2].spines["right"].set_position(("axes", 1.15))
make_patch_spines_invisible(y_axis[2])
set_spine_direction(y_axis[2], "right")
plt.subplots_adjust(left=0.0, right=0.8)
if N_dependents >= 4:
# The following statement positions the fourth y-axis 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[3].spines["left"].set_position(("axes", -0.15))
make_patch_spines_invisible(y_axis[3])
set_spine_direction(y_axis[3], "left")
plt.subplots_adjust(left=0.2, right=0.8)
p= N_dependents * [0]
# Plot the curves:
for i in range(N_dependents):
p[i], = y_axis[i].plot(x, y[i], colors[i],
linewidth=linewidth, label=labels[i])
# Set axis limits. Use ceil() to force upper y-axis limits to be round numbers.
host.set_xlim(x.min(), x.max())
# Label the x-axis:
host.set_xlabel(labels[0], size=axis_label_size)
for i in range(N_dependents):
# Label the y-axis and set text color:
y_axis[i].set_ylabel(labels[i+1], size=axis_label_size)
y_axis[i].yaxis.label.set_color(colors[i])
# If you want to override the default axis limits, uncomment the following
# line of code and adjust arguments appropriately:
# y_axis[i].set_ylim(0.0, ceil(y[i].max()))
if i== 1: y_axis[i].set_ylim(0.0, 1.5)
j= 0
for sp in y_axis[i].spines.itervalues():
if j==i: sp.set_color(colors[i])
j+= 1
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 enable the legend, uncomment the following two lines:
# lines= p[1:]
# host.legend(lines, [l.get_label() for l in lines])
plt.draw(); plt.show()
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