matplotlib-users Mailing List for matplotlib

Hi,
I found when usetex is set to be True, \mathbb cannot return blackboard bold
font. A minimum reproducing code of mine is:
import matplotlib.pyplot as plt
from matplotlib import rcParams
params = {
 'backend':'ps',
 'ps.usedistiller':'xpdf',
 'axes.labelsize':22,
 'text.fontsize':20,
 'legend.fontsize':20,
 'xtick.labelsize':20,
 'ytick.labelsize':20,
 'text.usetex':True,
 'latex.preamble':r'\usepackage{amsmath,amsfonts,amssymb}',
 'figure.figsize':[8,6],
 'font.family':'serif',
 'font.serif':'Times New Roman'
}
rcParams.update(params)
plt.plot([0,1],[0,1],'r-',lw=3,label=r"Using $\mathbb{D}^p$")
plt.legend()
plt.show()
I want to keep my rcParams settings as unchanged as possible.
Ning
--
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2012年11月28日 Angus McMorland <am...@gm...>
> I think your problem is with clipping. Try the no_clip function in
> this thread [1] and see if that helps.
>
> [1]
> http://matplotlib.1069221.n5.nabble.com/How-to-turn-off-all-clipping-td1813.html#a1814
>
Hi Angus,
I tried the no_clip function but it didn't worked:
https://gist.github.com/4171341
Any idea?
Thank you.
-- 
Vilson Vieira
vi...@vo...
((( http://automata.cc )))
((( http://musa.cc )))
((( http://labmacambira.sourceforge.net )))

Hi all,
AFAIK it is currently not possible to modify the ticks of a plot
within the "Figure options" window. Is it planned to add this feature
to the "Figure options" window ?
How can I modify the default path in the Navigation Toolbar if I launch the
file save dialog by clicking the save button ?
http://matplotlib.org/users/navigation_toolbar.html
Nils

Sterling,
Thanks - I ended up adding an extra y-tick with minor=True. Something 
along these lines:
# Add Fermi line
y = self._bandstructure.fermiLevel()
self._axes.axhline(y=y, linestyle=':')
# Add Fermi tick
tick = self._axes.set_yticks([y], minor=True)[0]
tick.label1On = False
tick.label2On = True
self._axes.set_yticklabels([r'$\epsilon_\mathrm{F}$'], minor=True,
 fontsize=font_size*1.19, 
verticalalignment='center')
Evil?
On 28/11/2012 17:35, Sterling Smith wrote:
> Mads,
>
> I recommend trying a text object[1], with a transform which is a blended transform from a transform factory[2]. Also, you probably want the x coordinate in axes coordinates, with a left horizontal alignment.
>
> -Sterling
>
> [1] http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.text
> [2] http://matplotlib.org/users/transforms_tutorial.html#blended-transformations
>
>
> On Nov 28, 2012, at 4:27AM, Mads Ipsen wrote:
>
>> Hi,
>>
>> I would like to add a label or tick label for an axhline(), in such a way that the labels follows the location of the hline.
>>
>> The x-coordinate of the label should be in screen coordinates (a little to the right of the plot) - but the y-coordinate should be in data coordinates.
>>
>> Any good suggestions?
>>
>> Best regards,
>>
>> Mads
>> 
>> -- 
>> +-----------------------------------------------------+
>> | Mads Ipsen |
>> +----------------------+------------------------------+
>> | Gåsebæksvej 7, 4. tv | |
>> | DK-2500 Valby | phone: +45-29716388 |
>> | Denmark | email:
>> mad...@gm...
>> |
>> +----------------------+------------------------------+
>>
>>
>> ------------------------------------------------------------------------------
>> Keep yourself connected to Go Parallel:
>> INSIGHTS What's next for parallel hardware, programming and related areas?
>> Interviews and blogs by thought leaders keep you ahead of the curve.
>> http://goparallel.sourceforge.net_______________________________________________
>> Matplotlib-users mailing list
>> Mat...@li...
>> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
-- 
+-----------------------------------------------------+
| Mads Ipsen |
+----------------------+------------------------------+
| Gåsebæksvej 7, 4. tv | |
| DK-2500 Valby | phone: +45-29716388 |
| Denmark | email: mad...@gm... |
+----------------------+------------------------------+

On 28 November 2012 15:28, Vilson Vieira <vi...@vo...> wrote:
> Hi,
>
> I'm using matplotlib to draw some generative art works. I'm trying to hide
> the axis but when I do that the lines that are over the axis are hidden as
> well, like this example: http://i.imgur.com/hKqMi.png
>
> The code I'm using to generate those kind of random walk plots is here:
> https://gist.github.com/4164025
>
> I already tried subplots_adjust, and set_alpha/set_visible on axis and
> spines but anything worked.
I think your problem is with clipping. Try the no_clip function in
this thread [1] and see if that helps.
[1] http://matplotlib.1069221.n5.nabble.com/How-to-turn-off-all-clipping-td1813.html#a1814
Angus
--
AJC McMorland
Post-doctoral research fellow
Neurobiology, University of Pittsburgh

Hi,
I'm using matplotlib to draw some generative art works. I'm trying to hide
the axis but when I do that the lines that are over the axis are hidden as
well, like this example: http://i.imgur.com/hKqMi.png
The code I'm using to generate those kind of random walk plots is here:
https://gist.github.com/4164025
I already tried subplots_adjust, and set_alpha/set_visible on axis and
spines but anything worked.
Any help?
Thank you so much.
-- 
Vilson Vieira
vi...@vo...
((( http://automata.cc )))
((( http://musa.cc )))
((( http://labmacambira.sourceforge.net )))

Mads,
I recommend trying a text object[1], with a transform which is a blended transform from a transform factory[2]. Also, you probably want the x coordinate in axes coordinates, with a left horizontal alignment.
-Sterling
[1] http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.text
[2] http://matplotlib.org/users/transforms_tutorial.html#blended-transformations
On Nov 28, 2012, at 4:27AM, Mads Ipsen wrote:
> Hi,
> 
> I would like to add a label or tick label for an axhline(), in such a way that the labels follows the location of the hline.
> 
> The x-coordinate of the label should be in screen coordinates (a little to the right of the plot) - but the y-coordinate should be in data coordinates.
> 
> Any good suggestions?
> 
> Best regards,
> 
> Mads
> 
> -- 
> +-----------------------------------------------------+
> | Mads Ipsen |
> +----------------------+------------------------------+
> | Gåsebæksvej 7, 4. tv | |
> | DK-2500 Valby | phone: +45-29716388 |
> | Denmark | email: 
> mad...@gm...
> |
> +----------------------+------------------------------+
> 
> 
> ------------------------------------------------------------------------------
> Keep yourself connected to Go Parallel: 
> INSIGHTS What's next for parallel hardware, programming and related areas?
> Interviews and blogs by thought leaders keep you ahead of the curve.
> http://goparallel.sourceforge.net_______________________________________________
> Matplotlib-users mailing list
> Mat...@li...
> https://lists.sourceforge.net/lists/listinfo/matplotlib-users

Hi Folks-
I'm working on a plot using an Aitoff projection, and I've noticed a couple
of problems:
1. There seems a problem with the positioning of contour labels in some of
the projections. In the Aitoff, Lambert, and Mollweide projections, the
some of the contour labels appear to be shifted. In the Hammer projection
of my example one of them (0.524) looks to be slightly off, but it's close
enough. The polar projection is fine.
2. When saving the output in eps format, the Aitoff, Hammer, and Lambert
projections are producing postscript that cannot be interpreted by
ghostview.
I've copied a minimal script to demonstrate the problem at the end of this
email.
Any ideas?
Thanks in advance!
--Chad
import numpy as np
from numpy import arccos, sin, cos, pi
import matplotlib.pyplot as plt
# setup the contour grid
X = np.arange(-180.0,180.0, 1) * pi/180
Y = np.arange(-90.0,90.0, 1) * pi/180
Z = np.zeros([len(Y), len(X)])
# compute the contour field
for x in range(0,360,1):
 for y in range(0,180,1):
 Z[y,x] = arccos(sin(pi/4)*sin(Y[y]) +
 cos(pi/4)*cos(Y[y])*cos(-90*pi/180 - X[x]))
f=[];
ax=[];
for projection in ["hammer", "aitoff", "lambert", "mollweide", "polar"]:
 f.append(plt.figure())
 ax.append(plt.subplot(111, projection=projection))
 plt.grid("on")
 Cplot = plt.contour(X,Y,Z, np.array([30.0, 60, 90, 120, 150])*pi/180)
 plt.clabel(Cplot, inline=1)
 plt.savefig("contourLabels{}.png".format(projection))
 plt.savefig("contourLabels{}.eps".format(projection))

Hi,
I would like to add a label or tick label for an axhline(), in such a 
way that the labels follows the location of the hline.
The x-coordinate of the label should be in screen coordinates (a little 
to the right of the plot) - but the y-coordinate should be in data 
coordinates.
Any good suggestions?
Best regards,
Mads
-- 
+-----------------------------------------------------+
| Mads Ipsen |
+----------------------+------------------------------+
| Gåsebæksvej 7, 4. tv | |
| DK-2500 Valby | phone: +45-29716388 |
| Denmark | email: mad...@gm... |
+----------------------+------------------------------+

Hi Phil,
Le 28/11/2012 12:58, Phil Elson a écrit :
> I've just submitted a pull request
> (https://github.com/matplotlib/matplotlib/pull/1540) to get the mpl
> docs to link to the nabble archive instead
> (http://matplotlib.1069221.n5.nabble.com/matplotlib-users-f3.html
> <http://matplotlib.1069221.n5.nabble.com/matplotlib-users-f3.html>).
Great ! I'm not so familiar with the nabble archive service, but at
first glance it looks way better than sourceforge (beyond the fact their
archiving service is pretty dead ;-) ).
Best,
Pierre

Hi Pierre,
Thanks for raising this (sorry we haven't got back to you sooner).
The mailing list isn't dead, but the archiving link certainly makes it look
like it is. I've just submitted a pull request (
https://github.com/matplotlib/matplotlib/pull/1540) to get the mpl docs to
link to the nabble archive instead (http://
matplotlib.1069221.n5.nabble.com/matplotlib-users-f3.html).
FYI There are a couple of topics related to this which I will raise in the
mpl-devel mailing list sortly.
Thanks,
Phil
On 4 October 2012 08:55, Pierre Haessig <pie...@cr...> wrote:
> Hi,
>
> Is it just my web browser getting crazy or is there a real issue with
> the ML archive on sourceforge:
> http://sourceforge.net/mailarchive/forum.php?forum_name=matplotlib-users
>
> I only see email records until July 16th 2012 !!
>
> If there is another ML archive website in better shape, it would be
> worth updating the link on matplotlib.org front page
> ("Documentation/need help?" section)
>
> Best,
> Pierre
>
>
>
>
>
> ------------------------------------------------------------------------------
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> Matplotlib-users mailing list
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> https://lists.sourceforge.net/lists/listinfo/matplotlib-users
>
>

Hi I just wanted to let you know that I did some work on tkinter to have a
tabbed backend,
It is in early stages but "it works"
If you want to give it a try it is available on
https://github.com/fariza/MPL-Experimental-Backend
Let me know if you have any comments.
Cheers
Federico
-- 
Y yo que culpa tengo de que ellas se crean todo lo que yo les digo?
-- Antonio Alducin --

On Monday, November 26, 2012 14:10:31 Eric Firing wrote:
> But how many colors can you actually distinguish on the screen, or in a
> plot? My impression is that the problem is not lack of colors, but
> rather mapping to the color you want. There is no reason that having a
> value in your *data* of 1e10 has to affect how numbers in your data over
> a "normal" range are mapped.
> 
> You are trying to illustrate the problem with an example using 3 colors,
> so how can the number of colors in the colormap be the fundamental
> limitation?
Ok, I understand.
I think that my linear interpolation code has to somewhat be written in a norm 
instead.
At some time, I have looked at examples on Matplotlib website, and at the code 
of pyshared/matplotlib/colors.py, but without having the "flash" to write the 
norm. The next time I will try to write a norm instead (I will put the code 
here of course).
TP

On 2012年11月26日 12:18 PM, TP wrote:
> On Monday, November 26, 2012 12:06:40 Eric Firing wrote:
>> I'm glad you found a solution, but my sense is that the problem is that
>> you are trying to make the colormap do the work of the norm. The
>> colormap is just a set of discrete colors, with a linear mapping to the
>> 0-1 scale (apart from the special under, over, and invalid values). The
>> norm is for mapping your data to those colors, however you like, by
>> mapping your data to the 0-1 range (again with possible under, over, and
>> invalid values). Did you consider making a custom norm instead of
>> modifying the colormap?
>
> Yes, I did.
> The problem with the default colormap is that it has not enough colors. I have
> found (I may be wrong) that no norm can change this state of affair. If you are
> able to find a norm to make my example work, i.e. to obtain the middle point in
> blue when large_value is for example 1e10, I am interested.
But how many colors can you actually distinguish on the screen, or in a 
plot? My impression is that the problem is not lack of colors, but 
rather mapping to the color you want. There is no reason that having a 
value in your *data* of 1e10 has to affect how numbers in your data over 
a "normal" range are mapped.
You are trying to illustrate the problem with an example using 3 colors, 
so how can the number of colors in the colormap be the fundamental 
limitation?
Eric
>
> TP
>
> ------------------------------------------------------------------------------
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>

On Monday, November 26, 2012 12:06:40 Eric Firing wrote:
> I'm glad you found a solution, but my sense is that the problem is that
> you are trying to make the colormap do the work of the norm. The
> colormap is just a set of discrete colors, with a linear mapping to the
> 0-1 scale (apart from the special under, over, and invalid values). The
> norm is for mapping your data to those colors, however you like, by
> mapping your data to the 0-1 range (again with possible under, over, and
> invalid values). Did you consider making a custom norm instead of
> modifying the colormap?
Yes, I did.
The problem with the default colormap is that it has not enough colors. I have 
found (I may be wrong) that no norm can change this state of affair. If you are 
able to find a norm to make my example work, i.e. to obtain the middle point in 
blue when large_value is for example 1e10, I am interested.
TP

On Tue, Nov 20, 2012 at 8:12 PM, Stephen Gibson
<Ste...@an...>wrote:
> Ok. Adding an NaN as the last data point did not help.
>
> However, I notice that the return path is two segments that go through
> (0,0).
>
> i.e. the baseline (or return) path may actually start/finish at (0,0)
>
> The attached image shows my data offset in y-direction by +1. The end
> points
> have been set to y=0.5. The baseline (or return path) is the line segment
> that
> starts at the first data point, passes through (x=0,y=0), and ends at the
> last
> data point.
>
> Steve.
>
>
Actually, this might be related to a bug that was pointed out to me a while
back that I just could not figure out. Having this example might help in
narrowing down the cause. Essentially, the (0,0) vertex was being added
even when it shouldn't have been. The key difference in this example is
that zdir='y' is used, which causes the (0,0) vertex to refer to the x,z
coordinate. Interesting...
I will have to investigate further.
Ben Root

On 2012年11月26日 11:37 AM, TP wrote:
> On Thursday, November 22, 2012 23:51:08 TP wrote:
>> Thus it seems to me that my dummy example given in the previous post covers
>> exactly the problem encountered in my real-world imshow function.
>>
>> Is there a memory-efficient workaround in my dummy example (instead of
>> increasing N)?
>
> I have modified LinearSegmentedColormap so as to solve my problem. The
> difference is that I do not create an huge array in my test case, but instead I
> interpolate linearly in the colormap. This is a quick and dirty code that
> does work in my case, but which does not deal with all cases (no management of
> transparency, no discontinuity in the colormap, etc.)
I'm glad you found a solution, but my sense is that the problem is that 
you are trying to make the colormap do the work of the norm. The 
colormap is just a set of discrete colors, with a linear mapping to the 
0-1 scale (apart from the special under, over, and invalid values). The 
norm is for mapping your data to those colors, however you like, by 
mapping your data to the 0-1 range (again with possible under, over, and 
invalid values). Did you consider making a custom norm instead of 
modifying the colormap?
Eric
>
> #####################
> from __future__ import division
> from pylab import *
> from matplotlib.colors import LinearSegmentedColormap
> from matplotlib.collections import CircleCollection
>
> from scipy.interpolate import interp1d
>
>
> class ContinuousLinearSegmentedColormap( LinearSegmentedColormap ):
>
> def __init__(self, name, segmentdata, gamma=1.0):
>
> LinearSegmentedColormap.__init__( self
> , name, segmentdata, gamma = gamma )
>
> def _init(self):
>
> self.N = len( self._segmentdata['red'] )
> self._lut = np.ones((self.N, 5), np.float)
> for i in range( self.N ):
> self._lut[i, 0] = self._segmentdata['red'][i][0]
> # 2 because I do not manage discontinuities in color
> self._lut[i, 1] = self._segmentdata['red'][i][2]
> self._lut[i, 2] = self._segmentdata['green'][i][2]
> self._lut[i, 3] = self._segmentdata['blue'][i][2]
>
> self._isinit = True
>
>
> def __call__(self, X, alpha=None, bytes=False):
>
> if not self._isinit: self._init()
> mask_bad = None
> if not cbook.iterable(X):
> vtype = 'scalar'
> xa = np.array([X])
> else:
> vtype = 'array'
> xma = ma.array(X, copy=False)
> mask_bad = xma.mask
> xa = xma.data.copy() # Copy here to avoid side effects.
> del xma
>
> lut = self._lut.copy()
> rgba = np.empty(shape=xa.shape+(4,), dtype=lut.dtype)
>
> # We construct interpolation functions.
> fred = interp1d( lut[:,0], lut[:,1])
> fgreen = interp1d( lut[:,0], lut[:,2])
> fblue = interp1d( lut[:,0], lut[:,3])
>
> rgba[:,3] = 1 # alpha=1 for the time being
> for i in range( xa.shape[0] ):
> rgba[i,0] = fred( xa[i] )
> rgba[i,1] = fgreen( xa[i] )
> rgba[i,2] = fblue( xa[i] )
>
> if vtype == 'scalar':
> rgba = tuple(rgba[0,:])
> return rgba
>
>
> ioff()
>
>
> large_value = 257 # blue above this value
> large_value = 258 # black above this value
> large_value = 1e8
>
> cdict = { 'blue': [(0.0, 0.0, 0.0)
> , (2*1/large_value, 1, 1)
> , (1.0, 1.0, 1.0)]
> , 'green': [(0.0, 0.0, 0.0)
> , (2*1/large_value, 0, 0)
> , (1.0, 1.0, 1.0)]
> , 'red': [(0.0, 0.0, 0.0)
> , (2*1/large_value, 0, 0)
> , (1.0, 1.0, 1.0)] }
>
> measures= array( [[ 0.2, 0.3, 0],
> [ 0.3, 0.4, 2],
> [ 0.5, 0.6, large_value]] )
>
> cmap = ContinuousLinearSegmentedColormap( "cmap foobar"
> , cdict
> )
>
> fig = figure()
> axes = fig.add_subplot(111)
> ec = CircleCollection( [80]
> , offsets = measures[:,:2]
> , transOffset = axes.transData
> )
>
> ec.set_array( measures[:,2] )
> ec.set_cmap( cmap )
> axes.add_collection( ec )
>
> show()
> #####################
>
> ------------------------------------------------------------------------------
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On Thursday, November 22, 2012 23:51:08 TP wrote:
> Thus it seems to me that my dummy example given in the previous post covers
> exactly the problem encountered in my real-world imshow function.
> 
> Is there a memory-efficient workaround in my dummy example (instead of
> increasing N)?
I have modified LinearSegmentedColormap so as to solve my problem. The 
difference is that I do not create an huge array in my test case, but instead I 
interpolate linearly in the colormap. This is a quick and dirty code that 
does work in my case, but which does not deal with all cases (no management of 
transparency, no discontinuity in the colormap, etc.)
#####################
from __future__ import division
from pylab import *
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.collections import CircleCollection
from scipy.interpolate import interp1d
class ContinuousLinearSegmentedColormap( LinearSegmentedColormap ):
 def __init__(self, name, segmentdata, gamma=1.0):
 LinearSegmentedColormap.__init__( self
 , name, segmentdata, gamma = gamma )
 def _init(self):
 self.N = len( self._segmentdata['red'] )
 self._lut = np.ones((self.N, 5), np.float)
 for i in range( self.N ):
 self._lut[i, 0] = self._segmentdata['red'][i][0]
 # 2 because I do not manage discontinuities in color
 self._lut[i, 1] = self._segmentdata['red'][i][2]
 self._lut[i, 2] = self._segmentdata['green'][i][2]
 self._lut[i, 3] = self._segmentdata['blue'][i][2]
 self._isinit = True
 def __call__(self, X, alpha=None, bytes=False):
 if not self._isinit: self._init()
 mask_bad = None
 if not cbook.iterable(X):
 vtype = 'scalar'
 xa = np.array([X])
 else:
 vtype = 'array'
 xma = ma.array(X, copy=False)
 mask_bad = xma.mask
 xa = xma.data.copy() # Copy here to avoid side effects.
 del xma
 lut = self._lut.copy()
 rgba = np.empty(shape=xa.shape+(4,), dtype=lut.dtype)
 # We construct interpolation functions.
 fred = interp1d( lut[:,0], lut[:,1])
 fgreen = interp1d( lut[:,0], lut[:,2])
 fblue = interp1d( lut[:,0], lut[:,3])
 rgba[:,3] = 1 # alpha=1 for the time being
 for i in range( xa.shape[0] ):
 rgba[i,0] = fred( xa[i] )
 rgba[i,1] = fgreen( xa[i] )
 rgba[i,2] = fblue( xa[i] )
 if vtype == 'scalar':
 rgba = tuple(rgba[0,:])
 return rgba
ioff()
large_value = 257 # blue above this value
large_value = 258 # black above this value
large_value = 1e8
cdict = { 'blue': [(0.0, 0.0, 0.0)
 , (2*1/large_value, 1, 1)
 , (1.0, 1.0, 1.0)]
 , 'green': [(0.0, 0.0, 0.0)
 , (2*1/large_value, 0, 0)
 , (1.0, 1.0, 1.0)]
 , 'red': [(0.0, 0.0, 0.0)
 , (2*1/large_value, 0, 0)
 , (1.0, 1.0, 1.0)] }
measures= array( [[ 0.2, 0.3, 0],
 [ 0.3, 0.4, 2],
 [ 0.5, 0.6, large_value]] )
cmap = ContinuousLinearSegmentedColormap( "cmap foobar"
 , cdict
 )
fig = figure()
axes = fig.add_subplot(111)
ec = CircleCollection( [80]
 , offsets = measures[:,:2]
 , transOffset = axes.transData
 )
ec.set_array( measures[:,2] )
ec.set_cmap( cmap )
axes.add_collection( ec )
show()
#####################

In article <955...@gm...>,
 Ludwig Schwardt 
 <lud...@gm...> wrote:
> Pip works beautifully on the Mac since Lion, once you install pkg-config. 
> This allows matplotlib to pick up the dependencies from the system (i.e. 
> libpng, libfreetype and zlib)....
I had not heard of pkg-config before. It looks very useful.
>...
> [1] For me the only downside of the installer is the use of Python.org Python 
> instead of the default "system" Python, as the latter makes more sense to me 
> for a standard installation (and avoids having multiple Pythons on your 
> system, which is a Good Thing). Python.org Python used to be a mandatory 
> install on older Mac systems such as Tiger / 10.3, but this is no longer a 
> compelling argument for me on newer systems.
Simplicity is nice, and it's a shame there are so many 3rd party 
versions of python now (python.org, Enthought, ActiveState).
Nonetheless, there are some good reasons for avoiding system python, 
including:
- Apple rarely updates system python, so one does not get bug fixes
- If the operating system uses python for anything then it's safest to 
leave it alone
Regards,
-- Russell

On Wed, Nov 21, 2012 at 6:00 PM, Jeffrey Melloy <jm...@gm...> wrote:
> I'm currently using matplotlib to generate .PNG files, and the
> javascript library flot to do point hover & zooming on the same data
> (after click through). Flot is starting to show its age, and I'd like
> a little more control.
>
> I'd like to get to only one library generating graphs, so I only have
> to change code in one place. d3.js looks interesting & dynamic, but
> I don't want to just replace one javascript library with another. I
> may be able to use d3.js to generate the thumbnails.
>
> My other option is to use matplotlib for the clicking & zooming -- If
> i use it to generate an svg and then do clicking, zooming, etc on the
> svg, am I in for a world of hurt? I see an html5 backend, but that
> hasn't been updated in a year. I also see the svg_histogram example,
> but that didn't work cleanly for me.
>
> Thanks,
> Jeff
>
>
You might be interested in this experimental branch:
https://github.com/matplotlib/matplotlib/pull/1426
Cheers!
Ben Root

Hi,
I wanted to share a little experiment of mine with you to see what you
think and whether there are other ways (or better ways) to do that sort of
thing.
Here is my blog post on embedding matplotlib plots in Cocoa applications:
http://www.streylab.com/blog/2012/11/22/matplotlib-running-in-cocoa-under-xcode.html
Let me know what you think.
Helmut Strey

On Monday, November 19, 2012 13:53:21 Eric Firing wrote:
> It is not entirely clear to me what you are trying to do, but it sounds
> like increasing N is not the right way to do it. Three things might help
> you find a better way:
> 
> 1) The colormap is intended to work with a norm that handles the
> translation from your data numbers to the 0-1.0 range used to select
> values from the colormap (with exceptions--see below). You can choose a
> non-default norm, you can write your own, or you can set the parameters
> (vmin, vmax) of the standard linear norm.
> 
> 2) By creating a colormap and calling its set_under, set_over, and
> set_invalid methods, you can control the colors assigned to data values
> that your norm maps respectively to negative numbers, numbers greater
> than 1, and masked values. See
> http://matplotlib.org/examples/pylab_examples/contourf_demo.html for an
> example of using set_under and set_over. See
> http://matplotlib.org/examples/pylab_examples/image_masked.html for
> another example, and for an example of controlling the norm parameters
> or using an alternative norm.
> 
> 3) It is also possible to index directly into the colormap if you use a
> norm that returns an integer data type. An example of such is the
> BoundaryNorm.
> http://matplotlib.org/examples/pylab_examples/multicolored_line.html
> 
> If all you need is a single assignment of a color to a "large value",
> then using the set_over method will take care of it.
> 
> Eric
Thanks for your answer.
My goal is to keep the correct color, i.e. blue, for the point located at 
(x=0.3, y=0.4), even if there are very large values of z on the plot. As I 
said, increasing N is not satisfying because it leads to large amounts of 
memory to be used. But for the time being, this is the only solution I have 
found.
I cannot use the set_over method to do that, because the "large value" is not 
the only one. Indeed, what I want to do is an imshow plot, with a colorbar 
containing three different linear portions:
* one portion for the values of z contained between the minimum and maximum 
value of z in some measure points.
* one portion for the values of z below the minimum z in the measure points.
* one portion for the values of z above the maximum z in the measure points.
My problem is that I may have very large values on the plot in the range below 
or above the measures z. So I have exactly the problem shown in my dummy 
example of the previous post: all my measures have the same color, although 
they should not, because I have created a colormap that should handle this 
situation (three different linear portions in the map). The only workaround I 
have found is to increase the value of N, but in my case it has to be very 
large, such that the plot is very slow to display, or even can ask for huge 
amounts of memory.
Thus it seems to me that my dummy example given in the previous post covers 
exactly the problem encountered in my real-world imshow function.
Is there a memory-efficient workaround in my dummy example (instead of 
increasing N)?
Thanks,
TP

2012年11月4日 Brickle Macho <bri...@gm...>:
> [...] When I show() a plot form within a Qt application I get the
> following message printed on the console:
>
> QCoreApplication::exec: The event loop is already running
>
> I think I understand the error, obviously the application I calling form
> control the even loop. I suppose I need to somehow supply a parent
> window to pylab plot or the show() function. Is there a way to create
> a plot and show so as not to use the main loop?
Do not use show() in a GUI application.
If you have a FigureCanvas instance embedded in your app, call its
draw() method.
If you use pyplot.figure() to create a matplotlib window from your
app, call pyplot.draw().
Goyo

2012年11月22日 Jeffrey Melloy <jm...@gm...>:
> I'm graphing data from a web service, and seem to have stumbled upon a
> bug when dates are graphed without any values.
>
> Here's a minimum repro:
>
> import datetime
> import matplotlib.pyplot as plt
>
> fig = plt.figure()
> ax = fig.add_subplot(111)
>
> x = []
> st = datetime.datetime(2012,11,21)
> while st < datetime.datetime(2012,11,21, 16, 00):
> x.append(st)
> st = st + datetime.timedelta(minutes=30)
> y = [None] * len(x)
>
> ax.plot(x,y)
> fig.autofmt_xdate()
> plt.show()
>
>
> The stack trace I get:
>
> Traceback (most recent call last):
> File "min_mpl.py", line 15, in <module>
> fig.autofmt_xdate()
> File "c:\python26\lib\site-packages\matplotlib\figure.py", line 318,
> in autofmt_xdate
> for label in ax.get_xticklabels():
> File "c:\python26\lib\site-packages\matplotlib\axes.py", line 2507,
> in get_xticklabels
> self.xaxis.get_ticklabels(minor=minor))
> File "c:\python26\lib\site-packages\matplotlib\axis.py", line 1104,
> in get_ticklabels
> return self.get_majorticklabels()
> File "c:\python26\lib\site-packages\matplotlib\axis.py", line 1088,
> in get_majorticklabels
> ticks = self.get_major_ticks()
> File "c:\python26\lib\site-packages\matplotlib\axis.py", line 1186,
> in get_major_ticks
> numticks = len(self.get_major_locator()())
> File "c:\python26\lib\site-packages\matplotlib\dates.py", line 749,
> in __call__
> self.refresh()
> File "c:\python26\lib\site-packages\matplotlib\dates.py", line 758, in refresh
> dmin, dmax = self.viewlim_to_dt()
> File "c:\python26\lib\site-packages\matplotlib\dates.py", line 530,
> in viewlim_to_dt
> return num2date(vmin, self.tz), num2date(vmax, self.tz)
> File "c:\python26\lib\site-packages\matplotlib\dates.py", line 289,
> in num2date
> if not cbook.iterable(x): return _from_ordinalf(x, tz)
> File "c:\python26\lib\site-packages\matplotlib\dates.py", line 203,
> in _from_ordinalf
> dt = datetime.datetime.fromordinal(ix)
> ValueError: ordinal must be >= 1
>
> Adding a 0 & the current date stops getting the exception, but the
> range seems wildly messed up. (2011 - 2014).
I can't figure out what's going on here, the calls in the stack trace
seem unrelated to Y data.
As a quick and dirty workaround you can set the first and last Y
values to 0 --won't work well if you use markers.
Goyo

This may help you if I understand your basic problem. I use a lot of
interactive plots. This is an example of the work around to show() that I
use:
 
import matplotlib.pyplot as plt
plt.ion()
fig = plt.figure(figsize=(10,8))
ax = fig.add_axes([.15,.1,.8,.65])
ax.plot([1,2,3])
ax.set_title('Fisrt Plot')
raw_input('Enter to close and Continue: ')
plt.close(fig)
When I use this method when connecting to the axes I re-draw the figure
after updating using:
fig.canvas.draw()
I hope this was useful
Regards,
Bob
--
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