matplotlib-users Mailing List for matplotlib

On 2014/01/08 11:40 AM, Skip Montanaro wrote:
> Apologies. Gmail (or my fingers) were acting up...
>
> On Wed, Jan 8, 2014 at 3:39 PM, Skip Montanaro <sk...@po...> wrote:
>> I'm happy with the draggable legends, but I have a problem. It seems
>> there are three pointer modes, the initial mode (updates x, y as you
>> move the mouse), zoom rectangle mode, and pan/zoom mode. Once I enter
>> either of those modes, I can't see how to get back to the original
>> mode. I found this navigation documentation:
>
> http://matplotlib.org/users/navigation_toolbar.html
>
> but I didn't see any way to get back to the starting mode (what's that
> mode called?), and the legend can only be moved as far as I can tell
> when that is the current mode.

If you are in pan/zoom or rectangle mode, just unselect it by clicking 
its select button again.

Ideally we would have an obvious radio button setup for this.

Eric

>
> Thx,
>
> Skip

Apologies. Gmail (or my fingers) were acting up...

On Wed, Jan 8, 2014 at 3:39 PM, Skip Montanaro <sk...@po...> wrote:
> I'm happy with the draggable legends, but I have a problem. It seems
> there are three pointer modes, the initial mode (updates x, y as you
> move the mouse), zoom rectangle mode, and pan/zoom mode. Once I enter
> either of those modes, I can't see how to get back to the original
> mode. I found this navigation documentation:

http://matplotlib.org/users/navigation_toolbar.html

but I didn't see any way to get back to the starting mode (what's that
mode called?), and the legend can only be moved as far as I can tell
when that is the current mode.

Thx,

Skip

I'm happy with the draggable legends, but I have a problem. It seems
there are three pointer modes, the initial mode (updates x, y as you
move the mouse), zoom rectangle mode, and pan/zoom mode. Once I enter
either of those modes, I can't see how to get back to the original
mode. I found this navigation documentation:

I am trying to update a figure in a loop:

    import matplotlib.pyplot as plt
    plt.ion()
    plt.figure (1)
    def c2r (z):
        return z.real, z.imag
    
    plt.hexbin (*c2r (run_ofdm (xconst_pred)[:opt.used]), mincnt=1)
    plt.draw()

But no figure appears on the screen.  What am I doing wrong?

This is using using Qt4Agg (I think, that's what's in my matplotlibrc)

Le 07/01/2014 17:51, Paul Hobson a écrit :
> I believe (as of v1.3.1) that after you create the legend you call
> leg.draggable(True)
I had never heard of that nice possibility!

Would it make sense to add a few lines to the Legend Guide/Legend location ?
http://matplotlib.org/users/legend_guide.html#legend-location

and possibly to the legend demo ?
http://matplotlib.org/examples/api/legend_demo.html

(and remove 
http://matplotlib.org/examples/old_animation/draggable_legend.html ?)

best,
Pierre

Sorry, had forgot to reply all:

Thanks Joe, that's perfect.  I appreciate the tip, as I would not have
realized I needed a PathCollection for lines and curves.  PS, do you know
if it is possible to have a background image behind a plot of patches?  I
know it's doable for scatter, but hadn't seen an example for patch plots in
general.

Paul, thanks for you help as well.  I'm actually pretty confident in the
primitives I've chosen.  I was inspired by scikit-image.draw:

http://scikit-image.org/docs/dev/api/skimage.draw.html

Which returns the indicies of an array, such that anytime one ones to draw
the array, they merely pass by index.  For example:

image = np.zeroes( (256, 256) )
rr, cc = draw.circle( center=(128,128), radius=5)
image[rr, cc] = (1, 0, 0)

The code above would generate a red circle.  My library creates primitive
classes that have an rr, cc attribute, with enough metadata to hide
effectively bury this representation.  This can be generated a number of
ways, but the classes take care of all of this, as well as other aspects.
 By keeping only these indicies as primitives, the shapes can be
manipulated and managed outside of any representation (ie the image).  What
I'd like to do is build wrappers to return PatchCollections from my already
storred rr, cc data (and other metadata that is stored).  In this way, I'll
be able to add the very nice patches from matplotlib, but retain the same
api.

The project is called "pyparty" and I'll share it pretty soon with the
scikit image mailing list.  If I am able to get the patches built it, would
anyone mind if I share it with the matplotlib list as well?

Thanks


On Tue, Jan 7, 2014 at 4:10 PM, Joe Kington <jof...@gm...> wrote:

>
>
>
> On Tue, Jan 7, 2014 at 2:29 PM, Adam Hughes <hug...@gm...>wrote:
>
>> Sorry, quick followup.  I did find the gallery example to plot multiple
>> patches together:
>>
>> http://matplotlib.org/examples/api/patch_collection.html
>>
>> That's excellent.  Now I guess my question is how best to generalize the
>> process of turning my objects into patches.  I think I will just try to
>> keep the geometry (ie line --> mpatch.Line) unless anyone has any better
>> suggestions.
>>
>
> As you've already found out, it sounds like you want a PatchCollection.
>
> There is one catch, though.  Your lines/curves will need to be converted
> to PathPatches (which is trivial), which can then have a facecolor.
> Because all items in a collection will have the same facecolor by default,
> this means that your lines will become "filled" polygons, unless you
> specify otherwise.
>
> Therefore, you'll need to do something like this:
>
> import matplotlib.pyplot as plt
> from matplotlib.path import Path
> import matplotlib.patches as mpatches
> from matplotlib.collections import PatchCollection
>
> # Just a simple line, but it could be a bezier curve, etc.
> line = Path([(-20, -20), (-10, 10), (20, 20)])
>
> # We'll need to convert the line to a PathPatch, and we'll throw in a
> circle, too
> line = mpatches.PathPatch(line)
> circle = mpatches.Circle([0, 0])
>
> # If we don't specify facecolor='none' for the line, it will be filled!
> col = PatchCollection([line, circle], facecolors=['none', 'red'])
>
> fig, ax = plt.subplots()
> ax.add_collection(col)
> ax.autoscale()
> plt.show()
>
> Alternatively, you can just put the lines/curves in a PathCollection and
> the patches/polygons/etc in a PatchCollection.
>
> Hope that helps!
> -Joe
>
>
>>
>> Thanks!
>>
>>
>> On Tue, Jan 7, 2014 at 3:08 PM, Adam Hughes <hug...@gm...>wrote:
>>
>>> Hi,
>>>
>>> I am working on a library for image analysis which stores particles as
>>> indexed numpy arrays and provides functionality for managing the particles
>>> beyond merely image masking or altering the arrays directly.  I've already
>>> designed classes for many common shapes including Lines/Curves,
>>> Circles/Ellipses, Polygons, Multi-shapes (eg 4 circles with variable
>>> overlap).
>>>
>>> What I'd really LOVE to do would be able to generate a
>>> matplotlib.Collection instance from these objects as generally as possible.
>>>  Then, I'd be able to show data as a masked image, but also get a really
>>> nice looking plot from the objects in their Collection representation.
>>>
>>> So my question really is in the implementation.  First, is there a
>>> general collection object that could work with ANY shape, or am I better
>>> off matching my shape to that collection?  For example:
>>>
>>>     line --> LineCollection       *vs.*    line --> GeneralCollection
>>>     circle --> CircleCollection           circle ---> GeneralCollection
>>>
>>> And then, is the Collections plotting API flexible enough to mix all of
>>> these types together?  Or would I have to settle for only being able to
>>> plot a collection of any 1 shape type at at time?
>>>
>>> I will delve into the API further, but ascertaining this information
>>> would really help me get started.
>>>
>>> Thanks
>>>
>>
>>
>>
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