I very much miss the 'l' shortcut for toggling log/lin y-scale in the
trunk! I use it a lot.
I suggest restoring it with something like
if self.get_yscale() is ("log" or "linear"):
self.toggle_log_lineary()
else: pass
I think most of time most people use log or linear scales.
This seems reasonable, but when I tried to implement it it looked like
the nan mask for the simple_plot.py example was sticky, eg when I
toggled back to linear the negative values were still masked. I tried
a simpler example still (all positive y data) and got something very
strange: the plotted y values appear to change on a toggle from log
and back to linear:
In [18]: import matplotlib.pyplot as plt
In [19]: plt.close('all')
In [20]: ax = plt.subplot(111)
In [21]: ax.plot(np.random.rand(20))
Out[21]: [<matplotlib.lines.Line2D object at 0x123082f0>]
In [22]: ax.set_yscale('linear'); ax.figure.canvas.draw()
In [23]: ax.set_yscale('log'); ax.figure.canvas.draw()
In [24]: ax.set_yscale('linear'); ax.figure.canvas.draw() # the y
data are now plotted differently
I am not sure what is going on yet, but I'm sure Michael will chime in
since I think we are seeing some funkiness in the new transforms and
path infrastructure.
The new hist() function looks really good, I especially welcome the "step"
mode. A couple of comments:
The latest svn incarnation doesn't allow for log scale in step-mode
(unless you set it manually).
Also, I think the step-mode should have fill=False as default, otherwise
it does not look that much different from bar-mode. The nice thing about
step histograms is that you can put several of them in the same plot while
keeping it intelligible!
Manuel is the developer behind these nice new changes to hist --
hopefully he can help you here.
JDH
···
On Sat, May 24, 2008 at 6:02 PM, Olle Engdegård <olle@...599...> wrote: