matplotlib-users Mailing List for matplotlib

2012年7月30日 Benjamin Root <ben...@ou...>:
>
>
> On Sunday, July 29, 2012, Vlastimil Brom wrote:
>>
>> Hi all,
>> I'd like to ask about the possibility to eventually reuse the gui of
>> pylab for showing and manipulating the plots in the custom application
>> (written in wxpython).
>> [...]
>> Thanks in advance for any hints
>> regards
>> Vlastimil Brom
>
>
> Absolutely! It is one of the things mpl does best. Do a search in the online
> documentation for "embedding codex".
>
> I hope that helps!
>
> Ben Root
Thanks for the quick response and a useful hint!
I apparently didn't search for the right terms before...
I turns out, that
http://matplotlib.sourceforge.net/examples/user_interfaces/embedding_in_wx2.html
shows exactly, what I need.
By the way is there a place to propose a small correction of the example code?
namely
 self.SetBackgroundColour(wx.NamedColor("WHITE"))
causes an error in wxpython 2.9, which removed the "...Color" aliases
and only knows "...Colour"
hence:
 self.SetBackgroundColour(wx.NamedColour("WHITE"))
works in wxpython 2.9 and should be backwards compatible too.
Thanks again,
 vbr

On Sunday, July 29, 2012, Vlastimil Brom wrote:
> Hi all,
> I'd like to ask about the possibility to eventually reuse the gui of
> pylab for showing and manipulating the plots in the custom application
> (written in wxpython).
> Basically, I'd like to add some application specific controls to the
> toolbar, or to place the existing gui to a custom frame, which would
> add such additional controls (slider, buttons, etc.)
> I looked at ...
> Python27\Lib\site-packages\matplotlib\backends\backend_wx.py , but
> couldn't find any way to access this windows in custom code.
> Would it be possible at all, or are there other options? Is there
> maybe some equivalent class/module etc. which could be reused or
> adapted?
> Or is the only way to reimplement the functionality writing my own
> plot gui (or to have the additionally needed controls in a separate
> window, which doesn't look very elegant, but could probably work).
>
> Thanks in advance for any hints
> regards
> Vlastimil Brom
Absolutely! It is one of the things mpl does best. Do a search in the
online documentation for "embedding codex".
I hope that helps!
Ben Root

Hi all,
I'd like to ask about the possibility to eventually reuse the gui of
pylab for showing and manipulating the plots in the custom application
(written in wxpython).
Basically, I'd like to add some application specific controls to the
toolbar, or to place the existing gui to a custom frame, which would
add such additional controls (slider, buttons, etc.)
I looked at ...
Python27\Lib\site-packages\matplotlib\backends\backend_wx.py , but
couldn't find any way to access this windows in custom code.
Would it be possible at all, or are there other options? Is there
maybe some equivalent class/module etc. which could be reused or
adapted?
Or is the only way to reimplement the functionality writing my own
plot gui (or to have the additionally needed controls in a separate
window, which doesn't look very elegant, but could probably work).
Thanks in advance for any hints
 regards
 Vlastimil Brom

On 2012年07月29日 5:13 AM, Jeffrey Spencer wrote:
> Eric,
>
> Normalize appears to be working correctly and as you stated above but
> when passed into contourf appears to have inconsistent results not
> following the docstring by allowing the value to change.
>
> Quick examples:
>
> X, Y = meshgrid(arange(20),arange(20))
> Z = arange(20*20)
> Z = Z.reshape(20,20)
> norm = colors.Normalize(vmin=200,vmax=None)
> print norm.vmin
> fig = figure(10)
> ax = fig.add_subplot(111)
> surf = ax.contourf(X,Y,Z, 100, cmap=matplotlib.cm.jet, norm = norm)
>
> This vmin has no effect where if you pass in:
>
> X, Y = meshgrid(arange(20),arange(20))
> Z = arange(20*20)
> Z = Z.reshape(20,20)
> norm = colors.Normalize(vmin=200,vmax=Z.max())
> print norm.vmin
> fig = figure(10)
> ax = fig.add_subplot(111)
> surf = ax.contourf(X,Y,Z, 100, cmap=matplotlib.cm.jet, norm = norm)
>
> it has the desired effect.
>
> Let me know if this is correct or I'm missing something here.
>
> Cheers,
> Jeff
You are correct. Contour and contourf are respecting vmin, vmax that 
have been set in the norm only if both have been set, so that it is 
fully scaled. I think that changing this to respect a single preset 
vmin or vmax would be reasonable.
Eric

Phil Elson <pelson.pub@...> writes:
> 
> I don't have a good answer to this and have had to implement such a
> thing in an outstanding pull request (in my case, I have a figure
> which has been un-pickled and needs re-attaching to pyplot).
> 
> My proposed mechanism goes something like:
> 
> figure = matplotlib.figure.Figure(...)
> 
> import matplotlib.pyplot as plt
> 
> mgr = plt._backend_mod.new_figure_manager_given_figure(num, self)
> 
> Obviously this doesn't even exist on master yet, so if you need this
> functionality from a version before this you will have to reverse
> engineer the pyplot.figure function.
Thank you Phil,
This would be acceptable, although I cannot say that I'm very happy with the way 
matplotlib is organized now. Can you point me to a code example where anything 
like this reverse engineering is done?
Thanks,
Anton

I don't have a good answer to this and have had to implement such a
thing in an outstanding pull request (in my case, I have a figure
which has been un-pickled and needs re-attaching to pyplot).
My proposed mechanism goes something like:
figure = matplotlib.figure.Figure(...)
import matplotlib.pyplot as plt
mgr = plt._backend_mod.new_figure_manager_given_figure(num, self)
Obviously this doesn't even exist on master yet, so if you need this
functionality from a version before this you will have to reverse
engineer the pyplot.figure function.
HTH,
On 29 July 2012 13:12, Anton Akhmerov <ant...@gm...> wrote:
> Hi,
>
> I am writing a module which should, among other things, output images, and it
> should work both in interactive mode and when imported as a script.
>
> In order for the plotting behavior to be clean, I would like to prepare the
> figure separate from pyplot. The reason for this, is that the figure is drawn in
> a non-interactive loop, and I don't want to show all the steps as they go.
> Moreover, for some reason with ipython inline backend the figure is rendered
> twice for no obvious reason. So the preferred behavior of my plotting function
> (when pyplot output is requested) should be similar to:
>
> fig = matplotlib.figure.Figure()
> ax = fig.add_subplot(111)
> ax.plot([1,2,3])
> matplotlib.pyplot.<ADD_FIGURE>(fig)
>
> The best approximation to this that I was able to find so far is to use the
> following dirty hack (although none of the attributes I use are _private!):
>
> f = plt.figure()
> fig.canvas = f.canvas
> f.canvas.figure = fig
>
> However this is still problematic: if the interactive mode is on, then rendering
> of the figure will still be called after these two attribute. Moreover in
> IPython notebook without inline plotting it will result in an opened GUI window,
> but GUI mainloop not running.
>
> So here's my question: what's the best way to achieve the behavior that I want?
>
> Thank you,
> Anton
>
>
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Eric,
Normalize appears to be working correctly and as you stated above but when
passed into contourf appears to have inconsistent results not following the
docstring by allowing the value to change.
Quick examples:
X, Y = meshgrid(arange(20),arange(20))
Z = arange(20*20)
Z = Z.reshape(20,20)
norm = colors.Normalize(vmin=200,vmax=None)
print norm.vmin
fig = figure(10)
ax = fig.add_subplot(111)
surf = ax.contourf(X,Y,Z, 100, cmap=matplotlib.cm.jet, norm = norm)
This vmin has no effect where if you pass in:
X, Y = meshgrid(arange(20),arange(20))
Z = arange(20*20)
Z = Z.reshape(20,20)
norm = colors.Normalize(vmin=200,vmax=Z.max())
print norm.vmin
fig = figure(10)
ax = fig.add_subplot(111)
surf = ax.contourf(X,Y,Z, 100, cmap=matplotlib.cm.jet, norm = norm)
it has the desired effect.
Let me know if this is correct or I'm missing something here.
Cheers,
Jeff
On Mon, Jul 30, 2012 at 12:32 AM, Eric Firing <ef...@ha...> wrote:
> On 2012年07月28日 10:17 PM, Jeffrey Spencer wrote:
>
>> Think I figured out an actual bug in the function: colors.Normalize().
>>
>> The behavior states that if vmin or vmax is passed in as None it should
>> take the minimum or maximum value respectively.
>>
>> If only one value is passed into the function, both values are
>> overwritten to the min and max instead of just the one that is not
>> passed in. So the only case where it sets the limits is if both vmin and
>> vmax are passed to the function. Is this the desired behavior because
>> from the docstring it seems this is incorrect.
>>
>> Cheers,
>> Jeff
>>
>>
>>
> Jeff,
>
> I don't see it, so you need to provide an actual example. Here is what
> happens (in ipython --pylab) when I try to reproduce what it sounds like
> you are describing:
>
> In [2]: norm = Normalize(vmax=10)
>
> In [3]: norm([-10, 10, 20])
> Out[3]:
> masked_array(data = [ 0. 1. 1.5],
> mask = False,
> fill_value = 1e+20)
>
>
> In [4]: norm.vmin
> Out[4]: -10.0
>
> In [5]: norm.vmax
> Out[5]: 10
>
> This behavior is as described in the docstring.
>
> The one potentially confusing aspect that I see at the moment is that the
> Normalize class does not distinguish between explicit vmin, vmax, and the
> values that it calculates via its autoscale_None method when called. As
> soon as a None is replaced by an actual value, whether by being set
> explicitly or by autoscaling, the replacement is permanent. Perhaps this
> should be made explicit in the docstring. The docstrings could be
> re-arranged to clarify the roles of the class initializer and its __call__
> method.
>
> Eric
>
>

On 2012年07月28日 10:17 PM, Jeffrey Spencer wrote:
> Think I figured out an actual bug in the function: colors.Normalize().
>
> The behavior states that if vmin or vmax is passed in as None it should
> take the minimum or maximum value respectively.
>
> If only one value is passed into the function, both values are
> overwritten to the min and max instead of just the one that is not
> passed in. So the only case where it sets the limits is if both vmin and
> vmax are passed to the function. Is this the desired behavior because
> from the docstring it seems this is incorrect.
>
> Cheers,
> Jeff
>
>
Jeff,
I don't see it, so you need to provide an actual example. Here is what 
happens (in ipython --pylab) when I try to reproduce what it sounds like 
you are describing:
In [2]: norm = Normalize(vmax=10)
In [3]: norm([-10, 10, 20])
Out[3]:
masked_array(data = [ 0. 1. 1.5],
 mask = False,
 fill_value = 1e+20)
In [4]: norm.vmin
Out[4]: -10.0
In [5]: norm.vmax
Out[5]: 10
This behavior is as described in the docstring.
The one potentially confusing aspect that I see at the moment is that 
the Normalize class does not distinguish between explicit vmin, vmax, 
and the values that it calculates via its autoscale_None method when 
called. As soon as a None is replaced by an actual value, whether by 
being set explicitly or by autoscaling, the replacement is permanent. 
Perhaps this should be made explicit in the docstring. The docstrings 
could be re-arranged to clarify the roles of the class initializer and 
its __call__ method.
Eric

Hi,
I am writing a module which should, among other things, output images, and it 
should work both in interactive mode and when imported as a script.
In order for the plotting behavior to be clean, I would like to prepare the 
figure separate from pyplot. The reason for this, is that the figure is drawn in 
a non-interactive loop, and I don't want to show all the steps as they go. 
Moreover, for some reason with ipython inline backend the figure is rendered 
twice for no obvious reason. So the preferred behavior of my plotting function 
(when pyplot output is requested) should be similar to:
fig = matplotlib.figure.Figure()
ax = fig.add_subplot(111)
ax.plot([1,2,3])
matplotlib.pyplot.<ADD_FIGURE>(fig)
The best approximation to this that I was able to find so far is to use the 
following dirty hack (although none of the attributes I use are _private!):
f = plt.figure()
fig.canvas = f.canvas
f.canvas.figure = fig
However this is still problematic: if the interactive mode is on, then rendering 
of the figure will still be called after these two attribute. Moreover in 
IPython notebook without inline plotting it will result in an opened GUI window, 
but GUI mainloop not running.
So here's my question: what's the best way to achieve the behavior that I want?
Thank you,
Anton

Think I figured out an actual bug in the function: colors.Normalize().
The behavior states that if vmin or vmax is passed in as None it should
take the minimum or maximum value respectively.
If only one value is passed into the function, both values are overwritten
to the min and max instead of just the one that is not passed in. So the
only case where it sets the limits is if both vmin and vmax are passed to
the function. Is this the desired behavior because from the docstring it
seems this is incorrect.
Cheers,
Jeff
On Sat, Jul 28, 2012 at 2:15 PM, Jeffrey Spencer <jef...@gm...>wrote:
> I figured out you can pass in the rasterized keyword to all of those to
> change the rasterization in the output.
>
> Also the docs say for pcolormesh it defaults to the backend if not set.
> Therefore, in the case of a vector based it would output vectors if not set
> to rasterize.
>
> Haven't tested but curious. Lets say I want to output at 600dpi but I
> display images interactively at 100dpi. Does it always rasterize the image
> to the higher dpi? I had noticed this didn't seem to occur in specgram but
> figured because the specgram is a relatively low resolution image that
> outputing at 600dpi doesn't do anything because original image is already a
> low resolution. I would expect the other modes do do this where the image
> isn't already output and have to rasterize the image when saving like
> pcolormesh and contour plots.
>
> Cheers,
> Jeff
>
>
> On Sat, Jul 28, 2012 at 1:43 PM, Jeffrey Spencer <jef...@gm...>wrote:
>
>> Yes, specgram rasterizes and contourf is definately a vector
>> specification which isn't optimal for 100 levels.
>>
>> I would switch to pcolormesh but the output file can't be rasterizing the
>> image. It outputs a huge file in .pdf (40X bigger than equivalent .png) and
>> it looks like it is vector based not rasterized.
>>
>>
>> Basically, If you output specgram or imshow in .pdf or .png the file
>> sizes are relatively comparable with .pdf, .eps, .svg being slightly larger
>> due to embedding the picture.
>>
>> If I output in pcolor, pcolormesh, contourf (with more than 100 levels)
>> the file sizes are huge for .pdf, .eps, .svg which I'm assuming because
>> vector based output. It also looks like vector based output because can see
>> the lines it draws for contours. Could this possibly be a selection for
>> these outputs to force raster based processing or is that not easy.
>>
>>
>> On Sat, Jul 28, 2012 at 3:26 AM, Eric Firing <ef...@ha...> wrote:
>>
>>> On 2012年07月26日 10:26 PM, Jeffrey Spencer wrote:
>>>
>>>> Thanks, that is all good info to know. I change my data to log and
>>>> normalize it so the logNorm is just linear actually so specifying only
>>>> levels is fine. I'll let you know if that doesn't work properly for some
>>>> reason.
>>>>
>>>> Ok, yeah I looked at pcolormesh quickly and can't remember why I chose
>>>> originally when I wrote this to go with contourf but I use to only do
>>>> like 10 levels. I think it might be because use a log yaxis and think it
>>>> used to be a bit funky or couldn't get it working properly but seemed
>>>> fine now.
>>>>
>>>> No, I don't want to modify the ticks but the black lines around that
>>>> like how they are removed on the major axis in this example:
>>>> https://dl.dropbox.com/u/**13534143/example1.png<https://dl.dropbox.com/u/13534143/example1.png>
>>>> I want to remove the black lines also around the colorbar. Not the tick
>>>> marks. Does that make sense?
>>>>
>>>
>>> cbar.outline.set_color('none')
>>> or
>>> cbar.outline.set_visible(**False)
>>>
>>>
>>>
>>>> One more quick question out of curiosity noticing from saving plots to
>>>> .pdf from contourf and pcolormesh vs specgram. Specgram seems to output
>>>> the lines and text as vector graphics. Then imbeds the image. When
>>>> outputting from pcolormesh or contourf this isn't the case. It tries to
>>>> write the lines or something else weird happens. Can you output to .pdf
>>>> from these and make the lines and text be vectors. Then the image output
>>>> as an image in the pdf like in specgram. Or is there a setting to do
>>>> this and specify the .dpi of the image in the .pdf.
>>>>
>>>
>>> Lines and text are output to pdf exactly the same by specgram,
>>> pcolormesh, and contourf. The difference should be only in the image part
>>> of the plot, which is rasterized for a specgram image and for the
>>> "quadmesh" produced by pcolormesh, but is a set of patches (vector
>>> specification, not rasterized) for contourf. Are you seeing results that
>>> are inconsistent with this expectation?
>>>
>>> Eric
>>>
>>>
>>>> Thanks a lot,
>>>> Jeff
>>>>
>>>> On Fri, Jul 27, 2012 at 5:51 PM, Eric Firing <ef...@ha...
>>>> <mailto:ef...@ha...>> wrote:
>>>>
>>>> On 2012年07月26日 9:20 PM, Jeffrey Spencer wrote:
>>>>
>>>> import numpy as np
>>>> import matplotlib as mpl
>>>> X, Y = np.meshgrid(arange(20),arange(**__20))
>>>>
>>>> Z = np.arange(20*20)
>>>> Z = Z.reshape(20,20)
>>>> logNorm = mpl.colors.Normalize(vmin=0,__**vmax=200)
>>>>
>>>> fig = mpl.pyplot.figure(10)
>>>> ax = fig.add_subplot(111)
>>>> surf = ax.contourf(X,Y,Z, 100, cmap=matplotlib.cm.jet, norm =
>>>> logNorm)
>>>> cbar = fig.colorbar(surf, shrink=0.70, norm=logNorm)
>>>> show()
>>>>
>>>>
>>>>
>>>> OK, the basic problem here is that you are specifying 100 levels,
>>>> which are being auto-selected to cover the actual data range; and
>>>> the colorbar is doing what it is supposed to do, which is show the
>>>> levels you actually have. Try leaving out the norm, and just
>>>> specify the levels to cover what you want, more like this:
>>>>
>>>> surf = ax.contourf(X, Y, Z, np.arange(0, 200.1, 2), cmap=mpl.cm.jet,
>>>> extend='both')
>>>> cbar = fig.colorbar(surf, shrink=0.7)
>>>>
>>>> If you actually do want a log norm, you can pass that in to contourf
>>>> and it will be passed on to colorbar; but most likely you should
>>>> still specify the levels you want as an array, and not specify vmin
>>>> and vmax in the norm. If you want log scaling, it may work better
>>>> to simply plot the log of Z, and use the colorbar label to indicate
>>>> that this is what you are doing.
>>>>
>>>> Note that with a recent change, you can use the set_under and
>>>> set_over methods of the cmap to specify arbitrary colors, or no
>>>> color, for the extended regions; or you can leave out the "extend"
>>>> kwarg and not color the regions outside the range of your contour
>>>> levels.
>>>>
>>>> In general, contourf is most appropriate when there is a moderate
>>>> number of levels, well under 100; if you want that many gradations,
>>>> then you might do better with pcolormesh or ax.pcolorfast or imshow.
>>>> For those image-like methods, it is appropriate to use vmin and
>>>> vmax, either directly, or in a norm.
>>>>
>>>> Eric
>>>>
>>>>
>>>>
>>>
>>
>