matplotlib-users Mailing List for matplotlib (Page 119)

On Jul 1, 2013, at 1:42 PM, psb1967 <ps...@ho...> wrote:

> Hi Ben,
> 
> 
> Benjamin Root-2 wrote
>> On Sun, Jun 23, 2013 at 6:29 AM, psb1967 &lt;
> 
>> psb67@
> 
>> &gt; wrote:
>> 
>> 
>>> 
>> Since nobody has replied, I would suggest asking your question on the
>> scipy
>> users list, which is more geared towards implementation of various
>> algorithms. The matplotlib mailing list mostly deals with plotting
>> questions.  When you need help with plotting your results, let us know!
>> 
>> Cheers!
>> Ben Root
> 
> May be it was my mistake posting a wrong request, without properly
> understanding the nature of this elite site, nevertheless, I am grateful for
> your taking time to reply and guide me in the proper direction.
> 
> I will give it a try with scipy user list.
> 
> Regards
> 
> Suresh
> 
> 

Suresh,  I think the most likely reason no one has answered is that the intersection of the set of matplotlib experts and the set of people who know what Bollinger bands are is too close to zero.  I'm a beginner at matplotlib, but I do know what Bollinger bands are, and I'd suggest your best approach is to take a look at the matplotlib demo gallery here:  

http://matplotlib.org/examples

in particular, study the code for the fifth example from the top here:  http://matplotlib.org/examples/pylab_examples/plotfile_demo.html

which implements a plot of Open, High. Low, and Closing prices.  I'm assuming you know enough math to implement a Bollinger band version of a moving average, and can just substitute it for the code that plots the extra lines on the example.

Good luck,
Bill

Hi Ben,


Benjamin Root-2 wrote
> On Sun, Jun 23, 2013 at 6:29 AM, psb1967 <

> psb67@

> > wrote:
> 
> 
>>
> Since nobody has replied, I would suggest asking your question on the
> scipy
> users list, which is more geared towards implementation of various
> algorithms. The matplotlib mailing list mostly deals with plotting
> questions.  When you need help with plotting your results, let us know!
> 
> Cheers!
> Ben Root

May be it was my mistake posting a wrong request, without properly
understanding the nature of this elite site, nevertheless, I am grateful for
your taking time to reply and guide me in the proper direction.

I will give it a try with scipy user list.

Regards

Suresh




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On Sun, Jun 23, 2013 at 6:29 AM, psb1967 <ps...@ho...> wrote:

> Hi,
>
> I am new to programming and this is my first major work.
>
> I wish to add Bollinger Bands and Hull Moving Average(HMA) in
> finance_work2.py. Need your guidance.
>
> Installation details:
>
> Windows 7
> Python 3.3.2
> Matplotlib 1.2.1
> Numpy 1.7.1
> Ta-Lib
>
> As it is I don't have any issues running the script however, adding BB and
> HMA will complete my task.
>
> Thanks in advance.
>
>
>
Since nobody has replied, I would suggest asking your question on the scipy
users list, which is more geared towards implementation of various
algorithms. The matplotlib mailing list mostly deals with plotting
questions.  When you need help with plotting your results, let us know!

Cheers!
Ben Root

SciPy 2013 was a great success.  I didn't get good headcount at the 
matplotlib BOF, but it was a good number, and we had 15 participants at 
various points during the sprints.  It was nice to see the diversity of 
experience with matplotlib at the sprints, and I hope we oldtimers were 
helpful to the newtimers getting started so they can continue to 
contribute in the future.  It was also great to put some faces to many 
of the talented names I've been seeing on github and the mailing list 
lately.

I've summarized the matplotlib BOF as well as the sprint.  I've also 
gone ahead and created MEP placeholders and issues for the major themes 
discussed.

https://github.com/matplotlib/matplotlib/wiki/Scipy-2013-BOF-Notes

https://github.com/matplotlib/matplotlib/wiki/Scipy-2013-sprinting-notes

Both of these documents are based on the realtime etherpad notes we made 
during the sessions, and on some of my own recollection, which is known 
to be incomplete or incorrect on occasion.  If I missed something, or 
you feel something isn't expressed in the right light, feel free to edit 
the wiki, but we should keep further discussion on the mailing list, 
github issues or other channels.  It would also be great to have 
volunteers to help write a MEP or two based on the BOF discussion.

Thanks again.  It was really nice to meet all of you!

Mike

Hi,

I have set of points in a plane and make triplot:

    subplot(121)
    plot(points[:,0], points[:,1], 'o')
    title('Set of points')
    subplot(122)
    triplot(points[:,0], points[:,1])
    title('Triangulation')

result: http://i.imgur.com/1LG4fxC.png

Does anyone know how to extract just the lines describing each circumscribed circle in this example?
Perhaps by using Delaunay from scipy.spatial?

Just to inform you, I want to do this through triangulation and above example is trivial that can be solved differently, while real problem doesn't contain circles...


Thanks

Yes and no. My understanding of stacked bars is the bar stacked on top is
always larger than the one below. I am trying to show how much the second
bar explains of the first bar. In my case though it is possible the second
bar is larger than the first. As a consequence you wouldn't see both bars
and this is why I want to draw a line to show where the other bar extends
to.the 



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Thanks Paul, that solved the problem.

    matplotlib.rcParams['axes.linewidth'] = 0.5
    matplotlib.rcParams['patch.linewidth'] = 0.5

is what I wanted and I'll put that in matplotlibrc.

Previously I thought that this is related to some anti-aliasing
setting that would affect all lines drawn. But as it is possible to
set this preference to custom plot elements, this is what I would
always prefer. Default setting looks too blurry for horizontal and
vertical lines.


Cheers

On Sat, Jun 29, 2013 at 11:49 PM, Paul Hobson <pmh...@gm...> wrote:
> You need to thin out the default line widths in your matplotlibrc file.
>
> Something like:
> import matplotlib
> matplotlib.rcParams['axes.linewidth'] = 0.5
> matplotlib.rcParams['lines.linewidth'] = 0.5
>
> ...if you don't want to edit your matplotlibrc file.
>
> Hope that helps,
> -paul

You need to thin out the default line widths in your matplotlibrc file.

Something like:
import matplotlib
matplotlib.rcParams['axes.linewidth'] = 0.5
matplotlib.rcParams['lines.linewidth'] = 0.5

...if you don't want to edit your matplotlibrc file.

Hope that helps,
-paul


On Sat, Jun 29, 2013 at 10:48 AM, klo uo <kl...@gm...> wrote:

> This code:
>
> ========================================
> import matplotlib.pyplot as plt
>
> plt.plot([1,2,5,3,4], label='line')
> plt.legend()
> plt.show()
> ========================================
>
> shows this image: http://i.imgur.com/KMPywSp.png
>
> I want axis and legend box with 1px line, like this:
> http://i.imgur.com/Qmb4e6C.png but can't find any pointers except
> turning anti-aliasing off for all plot lines, which is not what I
> want.
>
> Is this anyhow possible?
>
>
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This kind of sounds like stack plots, but I am not quite sure.

Cheers!
Ben Root
On Jun 29, 2013 1:45 AM, "mdekauwe" <mde...@gm...> wrote:

> Hi,
>
> I am plotting overlapping bars in a bar chart. In some places one bar
> overlaps the other so I would like to draw a horizontal line to show where
> the underlying bar extends to. My plots are postscripts so I can't use
> alpha. The only way I could come up with involves me figuring out by trial
> and error where each bar starts, for example:
>
> bar_width = 0.068
> xmin_line = 0.198
> xmax_line = xmin_line+bar_width
> plt.axhline(y=data_biomass[2], xmin=xmin_line, xmax=xmax_line, linewidth=2,
> color="b")
>
> Is there a way I can work out where each bar starts and how wide it is? I
> tried ax.get_xticks, but these don't match with the above function which
> only seems to be a fraction of 0 - 1.
>
> thanks,
>
> Martin
>
>
>
>
> --
> View this message in context:
> http://matplotlib.1069221.n5.nabble.com/drawing-horizontal-line-on-bar-chart-tp41333.html
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>
>
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>

This code:

========================================
import matplotlib.pyplot as plt

plt.plot([1,2,5,3,4], label='line')
plt.legend()
plt.show()
========================================

shows this image: http://i.imgur.com/KMPywSp.png

I want axis and legend box with 1px line, like this:
http://i.imgur.com/Qmb4e6C.png but can't find any pointers except
turning anti-aliasing off for all plot lines, which is not what I
want.

Is this anyhow possible?

Hi,

I am plotting overlapping bars in a bar chart. In some places one bar
overlaps the other so I would like to draw a horizontal line to show where
the underlying bar extends to. My plots are postscripts so I can't use
alpha. The only way I could come up with involves me figuring out by trial
and error where each bar starts, for example:

bar_width = 0.068
xmin_line = 0.198
xmax_line = xmin_line+bar_width
plt.axhline(y=data_biomass[2], xmin=xmin_line, xmax=xmax_line, linewidth=2,
color="b")

Is there a way I can work out where each bar starts and how wide it is? I
tried ax.get_xticks, but these don't match with the above function which
only seems to be a fraction of 0 - 1.

thanks,

Martin
        



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For those not in Austin who are interested in following along with the 
matplotlib sprint at Scipy, feel free to visit here:

https://etherpad.mozilla.org/MatplotlibSprint

Mike

 Dear experts,

                     I have been making some scripts to do some routine plotting 
 of model results with ipython qtconsole --colors=linux --pylab=inline mode for 
 fine tuning and then used the same script for running from command line  ipython 
 alt_hyc_trak.py, the results in plot gets messed up 
 It appears the setting I did by using below commands are different for inline 
 mode and ipython.
 
 plt.subplots_adjust(hspace = -.15,wspace=.1)  
 
 
Is there a way to set the properties of plot acceptable for both.
I am attaching both figures here.
https://www.dropbox.com/s/heeadhkf9u2czns/ipython_cmdline.png
https://www.dropbox.com/s/5hsm6yver152h11/inline_mode.png

 with best regards,
 Sudheer
 
 ***************************************************************
 Sudheer Joseph 
 Indian National Centre for Ocean Information Services
 Ministry of Earth Sciences, Govt. of India
 POST BOX NO: 21, IDA Jeedeemetla P.O.
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 *************************************************************** 

Neal,

I like to use partially transparent rectangles, such as axhspan and 
axvspan. Some people are not so great with colors, so I try to minimize 
line color changes.
If you were going to change line colors, I would do something to the 
line style to highlight the difference as well. Below is a little test.

Ryan

### Code ###
import numpy as np

import matplotlib.pyplot as plt


x = np.linspace(0, 2*np.pi, 1000)

y = np.sin(x)


plt.plot(x, y, 'k-', lw=0.5)


plt.axvspan(0.2, 0.4, color='k', alpha=0.2)


mask = (x > 2.5) & (x < 3.0)

plt.plot(x[mask], y[mask], 'r-', lw=2.5)


plt.show()
###########


On 6/27/2013 9:50 AM, Neal Becker wrote:
> I have a line plot and I'd like to highlight certain segments of it.  I think
> maybe drawing some segments in a different color might be a good visual clue.
> Any suggestion how to approach this?
>
>
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I have a line plot and I'd like to highlight certain segments of it.  I think 
maybe drawing some segments in a different color might be a good visual clue.  
Any suggestion how to approach this?

I have done something slightly different that works more generally. I am
trying to recall it from memory, so some details may be wrong.

Essentially, do the interpolation you want on a filled version of the
masked array (filling with a value that makes sense for your data). Then,
do the same interpolation on the mask, and cast the mask back as booleans.
Finally, build the output masked array by combining the two together with
one of the constructors.

Cheers!
Ben Root
On Jun 26, 2013 7:22 AM, "Jeff Whitaker" <jef...@no...>
wrote:

>   Goodman, Alexander (398J-Affiliate) <ale...@jp...>
>  June 24, 2013 6:27 PM
> Hi Jeff,
>
> Thanks for the quick reply. However I am a little lost with your suggested
> solution. The issue here is that if datain is a masked array, dataout will
> not be a masked array when order=3 is used unless masked=True is set, but
> this just results in none of the elements (besides those outside the domain
> of xin and yin) being masked. So are you saying to try setting the mask to
> what it would be after running interp() with order=0? I would appreciate a
> more concrete example.
>
> Thanks,
> Alex
>
>
> Alex:  After pondering this a bit more, I don't think you can use order=3
> with masked arrays.  Any reason why you can't using a combo of bilinear and
> nearest neighbor as I described in my previous email?
>
> -Jeff
>
>
>
>
>   Goodman, Alexander (398J-Affiliate) <ale...@jp...>
>  June 24, 2013 4:30 PM
> Just something I thought I should add to this, but would the following
> code seem like a reasonable workaround? Basically, since map_coordinates()
> cannot handle mask arrays, I thought that perhaps passing in the actual
> mask itself to interp() with order=3 may produce close to reasonable
> results, eg:
>
> dataout = interp(datain, xin, yin, xout, yout, masked=True, order=3)
> maskin = datain.mask.astype(int)
> maskout = interp(datain.mask, xin, yin, xout, yout, order=3)
> dataout.mask = dataout.mask | maskout
>
> Thanks,
> Alex
>
>
> ------------------------------
>
>
>
>
> ------------------------------------------------------------------------------
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>
>

> Goodman, Alexander (398J-Affiliate) 
> <mailto:ale...@jp...>
> June 24, 2013 6:27 PM
> Hi Jeff,
>
> Thanks for the quick reply. However I am a little lost with your 
> suggested solution. The issue here is that if datain is a masked 
> array, dataout will not be a masked array when order=3 is used unless 
> masked=True is set, but this just results in none of the elements 
> (besides those outside the domain of xin and yin) being masked. So are 
> you saying to try setting the mask to what it would be after running 
> interp() with order=0? I would appreciate a more concrete example.
>
> Thanks,
> Alex

Alex:  After pondering this a bit more, I don't think you can use 
order=3 with masked arrays.  Any reason why you can't using a combo of 
bilinear and nearest neighbor as I described in my previous email?

-Jeff
>
>
>
> Goodman, Alexander (398J-Affiliate) 
> <mailto:ale...@jp...>
> June 24, 2013 4:30 PM
> Just something I thought I should add to this, but would the following 
> code seem like a reasonable workaround? Basically, since 
> map_coordinates() cannot handle mask arrays, I thought that perhaps 
> passing in the actual mask itself to interp() with order=3 may produce 
> close to reasonable results, eg:
>
> dataout = interp(datain, xin, yin, xout, yout, masked=True, order=3)
> maskin = datain.mask.astype(int)
> maskout = interp(datain.mask, xin, yin, xout, yout, order=3)
> dataout.mask = dataout.mask | maskout
>
> Thanks,
> Alex
>
>
> ------------------------------------------------------------------------

Hi Thomas,

As explained in this issue:
https://github.com/matplotlib/matplotlib/issues/166
draw_artist does not work because it is being called from outside the event loop (or, to be exact, because draw_artist will only work if it is called from inside the drawing callback function that is called by the event loop). On some backends, you may get away with calling draw_artist from outside the event loop. However, this suggests a problem in the code design (for comparison, see this issue
https://github.com/matplotlib/matplotlib/issues/531
on the animation code).
As far as I know, the matplotlib documentation does not advertise the use of draw_artist, and there is no guarantee that it will work on all backends. If you can redesign your code such that draw_artist is called from inside the event loop, it should work OK on all backends. (though my first bet would also be to look at draw_idle).

Best,
-Michiel.




________________________________
 From: Thomas Robitaille <tho...@gm...>
To: Michael Droettboom <md...@st...> 
Cc: mat...@li... 
Sent: Tuesday, June 25, 2013 9:45 PM
Subject: Re: [Matplotlib-users] Making an interactive plot faster
 

Hi Mike,

Thanks for the suggestion - however, this doesn't change the FPS. From
my experiments so far, it seems using draw_artist would be the best
bet, but any ideas why it doesn't work with the MacOS X backend?

https://github.com/matplotlib/matplotlib/issues/166

Cheers,
Tom


On 24 June 2013 16:13, Michael Droettboom <md...@st...> wrote:
> Have you tried using "draw_idle"?  That will schedule the draw for the
> next time the event loop is idle.
>
> Mike
>
> On 06/24/2013 07:39 AM, Thomas Robitaille wrote:
>> Hi everyone,
>>
>> The following shows an example of a simple data viewer which includes
>> a slider, a bitmap, and a scatter plot:
>>
>> """
>> import numpy as np
>> from matplotlib import pyplot as plt
>> from matplotlib.widgets import Slider
>>
>> fig = plt.figure()
>>
>> ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.7])
>> image = ax1.imshow(np.random.random((512, 512)), vmin=0., vmax=1.)
>>
>> ax2 = fig.add_axes([0.1, 0.9, 0.85, 0.05])
>> ax2.set_xticklabels("")
>> ax2.set_yticklabels("")
>> slider = Slider(ax2, "", 0., 1.)
>>
>> def update_vmax(value):
>>      image.set_clim(0., value)
>>      fig.canvas.draw()
>>
>> slider.on_changed(update_vmax)
>>
>> ax3 = fig.add_axes([0.55, 0.1, 0.4, 0.7])
>> x = np.random.random(10000)
>> y = np.random.random(10000)
>> ax3.scatter(x, y)
>>
>> plt.show()
>> """
>>
>> When moving the slider, the vmax of the image changes, but I get very
>> slow frame rates (~2/sec) on my computer with the MacOS X backend. I
>> was wondering whether people here have any tips on speeding things up?
>>
>> As far as I can figure out, this is slow because both the slider and
>> the callback function call ``fig.canvas.draw``. Slider calls it before
>> the callback function, so I definitely need to draw things in that
>> function, but I can get a factor of 2x speedup by doing
>>
>> slider.drawon = False
>>
>> which prevents ``canvas.draw()`` being called twice. However, this is
>> still much too slow, so I started to look into using ``draw_artist``
>> to only update elements that need updating, but this requires
>> partially re-implementing the Slider class.
>>
>> As a side note, using ``draw_artist`` also does not work on the MacOS X backend:
>>
>> https://github.com/matplotlib/matplotlib/issues/166
>>
>> but I'm willing to have a solution that wouldn't work on this backend
>> if it was the only way.
>>
>> Does anyone have a clean solution to increasing the frame rate of this example?
>>
>> Thanks!
>> Tom
>>
>> ------------------------------------------------------------------------------
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>>
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Hi Mike,

Thanks for the suggestion - however, this doesn't change the FPS. From
my experiments so far, it seems using draw_artist would be the best
bet, but any ideas why it doesn't work with the MacOS X backend?

https://github.com/matplotlib/matplotlib/issues/166

Cheers,
Tom


On 24 June 2013 16:13, Michael Droettboom <md...@st...> wrote:
> Have you tried using "draw_idle"?  That will schedule the draw for the
> next time the event loop is idle.
>
> Mike
>
> On 06/24/2013 07:39 AM, Thomas Robitaille wrote:
>> Hi everyone,
>>
>> The following shows an example of a simple data viewer which includes
>> a slider, a bitmap, and a scatter plot:
>>
>> """
>> import numpy as np
>> from matplotlib import pyplot as plt
>> from matplotlib.widgets import Slider
>>
>> fig = plt.figure()
>>
>> ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.7])
>> image = ax1.imshow(np.random.random((512, 512)), vmin=0., vmax=1.)
>>
>> ax2 = fig.add_axes([0.1, 0.9, 0.85, 0.05])
>> ax2.set_xticklabels("")
>> ax2.set_yticklabels("")
>> slider = Slider(ax2, "", 0., 1.)
>>
>> def update_vmax(value):
>>      image.set_clim(0., value)
>>      fig.canvas.draw()
>>
>> slider.on_changed(update_vmax)
>>
>> ax3 = fig.add_axes([0.55, 0.1, 0.4, 0.7])
>> x = np.random.random(10000)
>> y = np.random.random(10000)
>> ax3.scatter(x, y)
>>
>> plt.show()
>> """
>>
>> When moving the slider, the vmax of the image changes, but I get very
>> slow frame rates (~2/sec) on my computer with the MacOS X backend. I
>> was wondering whether people here have any tips on speeding things up?
>>
>> As far as I can figure out, this is slow because both the slider and
>> the callback function call ``fig.canvas.draw``. Slider calls it before
>> the callback function, so I definitely need to draw things in that
>> function, but I can get a factor of 2x speedup by doing
>>
>> slider.drawon = False
>>
>> which prevents ``canvas.draw()`` being called twice. However, this is
>> still much too slow, so I started to look into using ``draw_artist``
>> to only update elements that need updating, but this requires
>> partially re-implementing the Slider class.
>>
>> As a side note, using ``draw_artist`` also does not work on the MacOS X backend:
>>
>> https://github.com/matplotlib/matplotlib/issues/166
>>
>> but I'm willing to have a solution that wouldn't work on this backend
>> if it was the only way.
>>
>> Does anyone have a clean solution to increasing the frame rate of this example?
>>
>> Thanks!
>> Tom
>>
>> ------------------------------------------------------------------------------
>> This SF.net email is sponsored by Windows:
>>
>> Build for Windows Store.
>>
>> http://p.sf.net/sfu/windows-dev2dev
>> _______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>
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> Goodman, Alexander (398J-Affiliate) 
> <mailto:ale...@jp...>
> June 24, 2013 4:30 PM
> Just something I thought I should add to this, but would the following 
> code seem like a reasonable workaround? Basically, since 
> map_coordinates() cannot handle mask arrays, I thought that perhaps 
> passing in the actual mask itself to interp() with order=3 may produce 
> close to reasonable results, eg:
>
> dataout = interp(datain, xin, yin, xout, yout, masked=True, order=3)
> maskin = datain.mask.astype(int)
> maskout = interp(datain.mask, xin, yin, xout, yout, order=3)
> dataout.mask = dataout.mask | maskout
>
> Thanks,
> Alex
>
>
> ------------------------------------------------------------------------
Alex:  The basemap.interp docstring includes a note describing a trick I 
often use with masked arrays:

Note

If datain is a masked array and order=1 (bilinear interpolation) is 
used, elements of dataout will be masked if any of the four surrounding 
points in datain are masked. To avoid this, do the interpolation in two 
passes, first with order=1 (producing dataout1), then with order=0 
(producing dataout2). Then replace all the masked values in dataout1 
with the corresponding elements in dataout2 (using numpy.where). This 
effectively uses nearest neighbor interpolation if any of the four 
surrounding points in datain are masked, and bilinear interpolation 
otherwise.


I suppose the same trick might work with order=3, but I have never tried it.

-Jeff

Have you tried using "draw_idle"?  That will schedule the draw for the 
next time the event loop is idle.

Mike

On 06/24/2013 07:39 AM, Thomas Robitaille wrote:
> Hi everyone,
>
> The following shows an example of a simple data viewer which includes
> a slider, a bitmap, and a scatter plot:
>
> """
> import numpy as np
> from matplotlib import pyplot as plt
> from matplotlib.widgets import Slider
>
> fig = plt.figure()
>
> ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.7])
> image = ax1.imshow(np.random.random((512, 512)), vmin=0., vmax=1.)
>
> ax2 = fig.add_axes([0.1, 0.9, 0.85, 0.05])
> ax2.set_xticklabels("")
> ax2.set_yticklabels("")
> slider = Slider(ax2, "", 0., 1.)
>
> def update_vmax(value):
>      image.set_clim(0., value)
>      fig.canvas.draw()
>
> slider.on_changed(update_vmax)
>
> ax3 = fig.add_axes([0.55, 0.1, 0.4, 0.7])
> x = np.random.random(10000)
> y = np.random.random(10000)
> ax3.scatter(x, y)
>
> plt.show()
> """
>
> When moving the slider, the vmax of the image changes, but I get very
> slow frame rates (~2/sec) on my computer with the MacOS X backend. I
> was wondering whether people here have any tips on speeding things up?
>
> As far as I can figure out, this is slow because both the slider and
> the callback function call ``fig.canvas.draw``. Slider calls it before
> the callback function, so I definitely need to draw things in that
> function, but I can get a factor of 2x speedup by doing
>
> slider.drawon = False
>
> which prevents ``canvas.draw()`` being called twice. However, this is
> still much too slow, so I started to look into using ``draw_artist``
> to only update elements that need updating, but this requires
> partially re-implementing the Slider class.
>
> As a side note, using ``draw_artist`` also does not work on the MacOS X backend:
>
> https://github.com/matplotlib/matplotlib/issues/166
>
> but I'm willing to have a solution that wouldn't work on this backend
> if it was the only way.
>
> Does anyone have a clean solution to increasing the frame rate of this example?
>
> Thanks!
> Tom
>
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Hi everyone,

The following shows an example of a simple data viewer which includes
a slider, a bitmap, and a scatter plot:

"""
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.widgets import Slider

fig = plt.figure()

ax1 = fig.add_axes([0.1, 0.1, 0.4, 0.7])
image = ax1.imshow(np.random.random((512, 512)), vmin=0., vmax=1.)

ax2 = fig.add_axes([0.1, 0.9, 0.85, 0.05])
ax2.set_xticklabels("")
ax2.set_yticklabels("")
slider = Slider(ax2, "", 0., 1.)

def update_vmax(value):
    image.set_clim(0., value)
    fig.canvas.draw()

slider.on_changed(update_vmax)

ax3 = fig.add_axes([0.55, 0.1, 0.4, 0.7])
x = np.random.random(10000)
y = np.random.random(10000)
ax3.scatter(x, y)

plt.show()
"""

When moving the slider, the vmax of the image changes, but I get very
slow frame rates (~2/sec) on my computer with the MacOS X backend. I
was wondering whether people here have any tips on speeding things up?

As far as I can figure out, this is slow because both the slider and
the callback function call ``fig.canvas.draw``. Slider calls it before
the callback function, so I definitely need to draw things in that
function, but I can get a factor of 2x speedup by doing

slider.drawon = False

which prevents ``canvas.draw()`` being called twice. However, this is
still much too slow, so I started to look into using ``draw_artist``
to only update elements that need updating, but this requires
partially re-implementing the Slider class.

As a side note, using ``draw_artist`` also does not work on the MacOS X backend:

https://github.com/matplotlib/matplotlib/issues/166

but I'm willing to have a solution that wouldn't work on this backend
if it was the only way.

Does anyone have a clean solution to increasing the frame rate of this example?

Thanks!
Tom

Hi,

I am new to programming and this is my first major work.

I wish to add Bollinger Bands and Hull Moving Average(HMA) in
finance_work2.py. Need your guidance.

Installation details:

Windows 7
Python 3.3.2
Matplotlib 1.2.1
Numpy 1.7.1
Ta-Lib

As it is I don't have any issues running the script however, adding BB and
HMA will complete my task.

Thanks in advance.



--
View this message in context: http://matplotlib.1069221.n5.nabble.com/To-add-Bollinger-Bands-in-finance-work2-py-tp41310.html
Sent from the matplotlib - users mailing list archive at Nabble.com.

Hi,

I am trying to use basemap's interp function for regridding purposes, and I
need to be able to handle masked arrays. Using bilinear or nearest-neighbor
interpolation (order=1, order=0), it seems to work fine for masked arrays.
However, none of the elements get masked after using cubic spline
interpolation (order=3). I am assuming that the reason for this is due to
the limitations of scipy.ndimage.map_coordinates(), which gets called when
order=3. Are there currently any workarounds for this?

Thanks,
Alex
-- 
Alex Goodman

Looks like you found a bug.  It works properly with ax.plot(), but not for
scatter().  Strange...  As for contour(), it doesn't get a legend entry in
the 2D case, so I wouldn't expect it to get a legend entry in the 3D case
either.

Could you file a bug report on github for this, please?

Cheers!
Ben Root