On Sat, May 24, 2008 at 6:02 PM, Olle Engdegård <[EMAIL PROTECTED]> wrote:
> 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
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