Hi,
Not directly answering your questions but the code below produces what you
are trying to achieve:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.parasite_axes import SubplotHost
plt.close('all')
fig1 = plt.figure(figsize=(11, 8.5))
ax1 = SubplotHost(fig1, 111)
fig1.add_subplot(ax1)
## define data
x = np.arange(1,11)
y = np.random.random(len(x))
# First X-axis
ax1.plot(x, y)
xmin, xmax = ax1.xaxis.get_view_interval()
ax1.xaxis.set_label_text("First X-axis")
ax1.yaxis.set_label_text("Sample data")
# Second X-axis
parx = ax1.twiny()
offset = 0, -60
new_axisline = parx.get_grid_helper().new_fixed_axis
parx.axis["bottom"] = new_axisline(loc="bottom", axes=parx, offset=offset)
parx.axis["bottom"].label.set_text("Second X-axis")
line1, = parx.plot((1./x), np.ones(len(x)))
line1.set_visible(0)
parx.set_xlim(xmin=xmax,xmax=xmin)
parx.axis["top"].set_visible(False)
ax1.grid(1)
plt.tight_layout()
plt.show()
A few notes:
1-) axes is plural for axis. I always get confused with these words. Tricky
English :)
2-) This code uses the older AxisGrid toolkit functions, and not super
efficient since it makes an empty plot call with a different x-units.
However, using the same ideas and with some dedication you can create
multi-axes like the one shown here -
http://imageshack.us/photo/my-images/820/plot1r.png/
I had tried the newer AxisGrid approach as JJ suggested but I failed making
the plot look pretty. Plus sometimes using the AffineTransforms are not very
practical.
3-) I said "empty plot call" but it is actually where you convert your unit.
(np.ones call is another dummy call since the line is made invisible after
its call) To me it is more explicit than converting units via transforms. In
this example I just assume x as wavelength and convert it to wavenumber with
1/x. You need to adjust that call according to your unit conversion as well
as adjusting the limits of your newly scaled unit.
As usual JJ can give a more elegant solution for your question, but
sometimes practicality beats purity and you engineer your own solution.
On Wed, Oct 12, 2011 at 11:36 PM, List Comprehension <
listcomprehens...@gmail.com> wrote:
>
> Hello, I am interested in plotting two (x) axes for the same figure (one of
> is just showing linearly transformed values of the other); one of them
> offset from the plot if possible. For instance, I have the following code:
>
> import numpy as np
> import matplotlib.pyplot as plt
> import mpl_toolkits.axisartist as AA
>
> ## define data
> x = np.array(range(11))
> xlab = x*2
> y = np.random.random(len(x))
>
> fig = plt.figure()
> ## first axes which is only for drawing the axis
> axsecond = AA.Subplot(fig, 1,1,1)
> fig.add_subplot(axsecond)
> axsecond.set_xlim([min(xlab),max(xlab)])
> axsecond.set_xticks(np.linspace(min(xlab),max(xlab),5))
> axsecond.axis["bottom", "top", "left", "right"].set_visible(False)
> axsecond.axis["newx"] = axsecond.new_floating_axis(nth_coord=0, value=1)
> axsecond.axis["newx"].set_axis_direction("top")
> axsecond.set_clip_on(False)
> axsecond.axis["newx"].toggle(all=True)
> axsecond.axis["newx"].set_visible(True)
> plt.draw()
> ## the main set of axes containing the data
> axmain = fig.add_subplot(1,1,1)
> plt.plot(x,y)
>
>
> In this example I wonder
>
> 1) How to turn off the top axis of axmain so the ticks from axsecond are
> visible? Something like axmain["top"].set_visible(False) (which does not
> work of course).
>
> 2) If I wanted to include an offset (below the main axis), I feel like
> something like the following should work but doesn't:
>
> ax.axis["newx"] = ax.new_floating_axis(nth_coord=0, value=-0.2) ## outside
> of (0,1)
> ax.axis["newx"].set_axis_direction("bottom")
> ax.set_clip_on(False)
>
> Thanks in advance!
>
--
Gökhan
--
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