Ben,
Sorry. I probably should have just dropped that entirely. In my code
sample, it is actually commented out because it breaks the animation with
the nbagg backend. It was in Tom's example, so I left it in because I
wanted to find out what it was doing.
Ryan
On Thu, Apr 16, 2015 at 9:30 AM, Benjamin Root <ben.r...@ou.edu> wrote:
> I just noticed your use of "animated=True". I have had trouble using that
> in the past with the animation module. It is a leftover from the days
> before the animation module and isn't actually used by it, IIRC. Try not
> supplying that argument.
>
> On Thu, Apr 16, 2015 at 8:18 AM, Ryan Nelson <rnelsonc...@gmail.com>
> wrote:
>
>> Tom,
>>
>> Thanks for the code. As it was given, I had to change `blit=True` in the
>> `FuncAnimation` call in order to get this to work in a regular Qt backend.
>> It did not work with the nbagg backend; however, if I used this code it
>> works fine:
>> ####
>> %matplotlib nbagg
>>
>> import numpy as np
>> import matplotlib.pyplot as plt
>> import matplotlib.animation as animate
>>
>> class Testing(object):
>> def __init__(self, ):
>> self.fig = plt.figure()
>> array = np.random.rand(4,5)
>> array = np.zeros((4,5))
>> self.pc = plt.pcolor(array, edgecolor='k', linewidth=1.)#,
>> animated=True)
>> self.pc.set_clim([0, 1])
>> self.points = [plt.scatter(np.random.rand(), np.random.rand())]#,
>> animated=True)]
>>
>> def update(self, iter_num):
>> array = np.random.rand(4*5)
>> self.pc.set_array(array)
>> for point in self.points:
>> point.set_offsets([np.random.rand(), np.random.rand()])
>> #return (self.pc, ) + tuple(self.points)
>>
>>
>> test = Testing()
>> ani = animate.FuncAnimation(test.fig, test.update, interval=250,
>> blit=False, frames=50)
>> plt.show()
>> ####
>> Also this code solves the problem I was having with several scatter
>> points being displayed upon multiple runs of the same code cell.
>>
>> I wasn't familiar with the "animated" keyword, and it is not well
>> documented yet. Can you give me a quick explanation of what it is doing?
>>
>> Ben: thanks for the hint about the _stop() method. I might look into that
>> for my example.
>>
>> Thank you all for your assistance. Things are working pretty much as I
>> need now!
>>
>> Ryan
>>
>> On Sun, Apr 12, 2015 at 9:24 AM, Thomas Caswell <tcasw...@gmail.com>
>> wrote:
>>
>>> You can
>>>
>>>
>>> ```
>>>
>>> #import matplotlib
>>>
>>> #matplotlib.use('nbagg')
>>>
>>> #%matplotlib nbagg
>>>
>>> import numpy as np
>>>
>>> import matplotlib.pyplot as plt
>>>
>>> import matplotlib.animation as animate
>>>
>>>
>>> class Testing(object):
>>>
>>> def __init__(self, ):
>>>
>>> self.fig = plt.figure()
>>>
>>> array = np.random.rand(4,5)
>>>
>>> array = np.zeros((4,5))
>>>
>>> self.pc = plt.pcolor(array, edgecolor='k', linewidth=1.,
>>> animated=True)
>>>
>>> self.pc.set_clim([0, 1])
>>>
>>> self.points = [plt.scatter(np.random.rand(), np.random.rand(),
>>> animated=True)]
>>>
>>>
>>> def update(self, iter_num):
>>>
>>> array = np.random.rand(4*5)
>>>
>>> self.pc.set_array(array)
>>>
>>> for point in self.points:
>>>
>>> point.set_offsets([np.random.rand(), np.random.rand()])
>>>
>>>
>>> return (self.pc, ) + tuple(self.points)
>>>
>>>
>>>
>>> test = Testing()
>>>
>>> ani = animate.FuncAnimation(test.fig, test.update, interval=10,
>>> blit=False, frames=50)
>>>
>>> plt.show()
>>>
>>> ```
>>>
>>> note the addition of the `set_clim` line in the `__init__` method.
>>>
>>>
>>> You can also update the scatter artist in-place. The other changes will
>>> make it a bit for performant if you use bliting (which does not work with
>>> nbagg currently)
>>>
>>> Sorry I missed that part of the question first time through.
>>>
>>> Tom
>>>
>>> On Sun, Apr 12, 2015, 08:31 Ryan Nelson <rnelsonc...@gmail.com> wrote:
>>>
>>>> Tom,
>>>>
>>>> Thanks for the links. It does seem like fragments of my problem are
>>>> addressed in each of those comments, so I guess I'll have to wait for a bit
>>>> until those things get resolved. For now, I can just tell my students to
>>>> restart the IPython kernel each time they run the animation, which isn't
>>>> that hard. It's too bad that there isn't a 'stop' method now, but it's good
>>>> to hear that it isn't a completely terrible idea.
>>>>
>>>> I do still need help with Question #3 from my original email, though,
>>>> because it affects both the Qt and nbagg backends, and it is a bit of a
>>>> show stopper. I can't quite understand why initializing a pcolor(mesh) with
>>>> random numbers makes it possible to update the array in an animation, but
>>>> if you use all zeros or ones, it seems to be immutable.
>>>>
>>>> Ryan
>>>>
>>>> On Sat, Apr 11, 2015 at 8:35 PM, Thomas Caswell <tcasw...@gmail.com>
>>>> wrote:
>>>>
>>>>> Ryan,
>>>>>
>>>>> I have not looked at your exact issue yet, but there seems to be some
>>>>> underlying issues with animation and nbagg which we have not tracked down
>>>>> yet. See:
>>>>>
>>>>> https://github.com/matplotlib/matplotlib/pull/4290
>>>>> https://github.com/matplotlib/matplotlib/issues/4287
>>>>> https://github.com/matplotlib/matplotlib/issues/4288
>>>>>
>>>>> Running until a given condition is an interesting idea, but I think
>>>>> that means the animation objects needs to have a public 'stop' method
>>>>> first!
>>>>>
>>>>> Tom
>>>>>
>>>>> On Fri, Apr 10, 2015 at 3:00 PM Ryan Nelson <rnelsonc...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Good afternoon, all!
>>>>>>
>>>>>> I'm really digging the nbagg backend, and I'm trying to use it to
>>>>>> make an animation. As the subject suggests, though, I'm having some
>>>>>> issues
>>>>>> with these features. I'm using Python 3.4, Matplotlib 1.4.3, and IPython
>>>>>> 3.1. Below is a small code sample that emulates my system. The pcolor
>>>>>> call
>>>>>> can be substituted for pcolormesh, and I see the same behavior. (Sorry
>>>>>> this
>>>>>> is a bit long. I tried to break it up as best as possible.)
>>>>>>
>>>>>> #############
>>>>>> #import matplotlib
>>>>>> #matplotlib.use('nbagg')
>>>>>> #%matplotlib nbagg
>>>>>> import numpy as np
>>>>>> import matplotlib.pyplot as plt
>>>>>> import matplotlib.animation as animate
>>>>>>
>>>>>> class Testing(object):
>>>>>> def __init__(self, ):
>>>>>> self.fig = plt.figure()
>>>>>> array = np.random.rand(4,5)
>>>>>> #array = np.zeros((4,5))
>>>>>> self.pc = plt.pcolor(array, edgecolor='k', linewidth=1.)
>>>>>> self.points = [plt.scatter(np.random.rand(),
>>>>>> np.random.rand())]
>>>>>>
>>>>>> def update(self, iter_num):
>>>>>> array = np.random.rand(4*5)
>>>>>> self.pc.set_array(array)
>>>>>> for point in self.points:
>>>>>> point.remove()
>>>>>> self.points = [plt.scatter(np.random.rand(),
>>>>>> np.random.rand())]
>>>>>>
>>>>>> test = Testing()
>>>>>> animate.FuncAnimation(test.fig, test.update, interval=1000,
>>>>>> blit=False)
>>>>>> plt.show()
>>>>>> ###############
>>>>>>
>>>>>> 1. As is, this code runs fine with a Qt backend. It also runs fine as
>>>>>> a first call in a notebook if the `show` call is commented out and the
>>>>>> `%matplotlib` line is uncommented. However, if the `show` call is left in
>>>>>> and the `matplotlib.use` call is uncommented, then the pcolor array
>>>>>> changes, but the scatterpoint only shows on the first update and then
>>>>>> disappears forever. What is the difference between these two invocations?
>>>>>>
>>>>>> 2. With the `%matplotlib` magic uncommented and `show` removed, the
>>>>>> first invocation of this as a cell works fine. Closing the figure (with
>>>>>> the
>>>>>> red X) and running the cell again shows two scatter plot points. Running
>>>>>> it
>>>>>> a third time shows three scatter plot points. If you call `plt.clf` in
>>>>>> the
>>>>>> next cell, I get a series of errors as follows:
>>>>>> _____
>>>>>> ERROR:tornado.application:Exception in callback <bound method
>>>>>> TimerTornado._on_timer of <matplotlib.backends.backend_nbagg.TimerTornado
>>>>>> object at 0x7f894cb10f98>>
>>>>>> Traceback (most recent call last):
>>>>>> File "/usr/lib64/python3.4/site-packages/tornado/ioloop.py", line
>>>>>> 976, in _run
>>>>>> return self.callback()
>>>>>> File
>>>>>> "/usr/lib64/python3.4/site-packages/matplotlib/backend_bases.py", line
>>>>>> 1290, in _on_timer
>>>>>> ret = func(*args, **kwargs)
>>>>>> File "/usr/lib64/python3.4/site-packages/matplotlib/animation.py",
>>>>>> line 925, in _step
>>>>>> still_going = Animation._step(self, *args)
>>>>>> File "/usr/lib64/python3.4/site-packages/matplotlib/animation.py",
>>>>>> line 784, in _step
>>>>>> self._draw_next_frame(framedata, self._blit)
>>>>>> File "/usr/lib64/python3.4/site-packages/matplotlib/animation.py",
>>>>>> line 803, in _draw_next_frame
>>>>>> self._draw_frame(framedata)
>>>>>> File "/usr/lib64/python3.4/site-packages/matplotlib/animation.py",
>>>>>> line 1106, in _draw_frame
>>>>>> self._drawn_artists = self._func(framedata, *self._args)
>>>>>> File "<ipython-input-2-f9290d8f6154>", line 22, in update
>>>>>> point.remove()
>>>>>> File "/usr/lib64/python3.4/site-packages/matplotlib/artist.py",
>>>>>> line 139, in remove
>>>>>> self._remove_method(self)
>>>>>> File "/usr/lib64/python3.4/site-packages/matplotlib/axes/_base.py",
>>>>>> line 1479, in <lambda>
>>>>>> collection._remove_method = lambda h: self.collections.remove(h)
>>>>>> ValueError: list.remove(x): x not in list
>>>>>> ______
>>>>>> Why does this happen? Is there a way to close the animation cleanly?
>>>>>>
>>>>>> 3. If I uncomment the `np.zeros` call, the pcolor array never updates
>>>>>> irrespective of the backend. I see the same behavior with `np.ones` as
>>>>>> well, even if the dtype is set to `float`. Is there are a way to start
>>>>>> with
>>>>>> a all-zero pcolor that allow dynamic updates?
>>>>>>
>>>>>> 4. I'd like to be able to have the animation run until a certain
>>>>>> condition is met. Is there a way to code a clean break for the animation?
>>>>>>
>>>>>>
>>>>>> As always, any help is most appreciated!
>>>>>>
>>>>>> Ryan
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> ------------------------------------------------------------
>>>>>> ------------------
>>>>>> BPM Camp - Free Virtual Workshop May 6th at 10am PDT/1PM EDT
>>>>>> Develop your own process in accordance with the BPMN 2 standard
>>>>>> Learn Process modeling best practices with Bonita BPM through live
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>>>>>>
>>>>>
>>>>
>>
>>
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>>
>
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