matplotlib-users Mailing List for matplotlib

Hi,
I want to read images and do some processing with them. While learning how to do this, i.e. opening images, displaying them, transforming them tu numpy arrays, etc., I came across a strange behaviour. If I open an image and use imshow() to display it, it comes upside down. See this thread in the numpy mailing list for more details: http://thread.gmane.org/gmane.comp.python.numeric.general/30148 . Someone on that list suggested to check here if this behavior was correct. Is it normal that the image appears upside down? If yes, can someone explain what's going on?
jorge
 

João,
Thank you for commenting on the code. sys.argv check is a wise one. Since I
was the only user of this little snippet I didn't give attention of this
detail, however eventually some other people will start using the code too.
I thought it is more efficient to only import functions that I use from
modules. This said, I don't see much speed difference on two codes. Is
import statement (or Python itself) intelligent enough to import only the
modules that are used inside a program or does it load all content at the
original import time?
Mouse-hover readings are really nice to have while analysing the data, and
ability of setting its sensitivity comes handier sometimes. I wish I could
find a way to put such a mechanism on my recent poster presentation. Hah,
maybe I should use a flexible lcd and let viewers to interract with the
poster themselves instead of viewing things on a dry big sheet of paper.
Yes, the IPython call from shell still stays a mystery...
Gökhan
On Thu, May 14, 2009 at 12:52 PM, João Luís Silva <js...@fc...> wrote:
> Gökhan SEVER wrote:
>
>> Hello,
>>
>> After solving the boxplotting mystery, and figuring out how to change the
>> mouse hover reading sensitivities, I have finished my small script which
>> creates boxplots from a given file. I can call it either by issueing
>> ./splot.py file or from inside ipython -pylab with run command. However I
>> still couldn't figure out how to drop in ipython from the bash shell call
>> while all my variable context visible in the ipython namespace.
>>
>> I am attaching the script and a sample file I used. Could you please
>> comment whether I am on the right track? I am not very sure my locals() use
>> is correct to create a variable name from a given file name. There might be
>> other points that seem weaker in the code as well.
>>
>> Thank you.
>>
>> Gökhan
>>
>>
>>
> The code looks pretty good to me. I've done some minor style changes on the
> imports, and added an error check to see if the user passed a parameter or
> not. I didn't know how to change the mouseover format, this will be useful
> for me, thanks!
>
> I'm only a casual user of ipython, and I don't know how to do what you ask,
> so I'll leave that to someone else.
>
> (new splot.py attached)
>
> Best regards,
> João Silva
>

Ok, but can anyone please help me with how to find nlons and nlats
dynamically.I need to do this becuase I have no idea on how many records the
datasets contains.
i tried user Timmes idea (but it aint working,not even starting):
from matplotlib.mlab import load
import matplotlib.pyplot as plt 
import numpy as np
data = np.loadtxt("C:/Users/Linda/data.txt")
y=data[:,0]
x=data[:,1]
z=data[:,2]
### data is loaded from a CSV file
###
lats = y # data[:,0]
## lon => x
lons = x # data[:,1]
## values => z
values = z #
###
lat_uniq = list(set(lats.tolist()))
nlats = len(lat_uniq)
lon_uniq = list(set(lons.tolist()))
nlons = len(lon_uniq)
color_map = plt.cm.spectral
print lats.shape, nlats, nlons
yre = lats.reshape(nlats,nlons)
xre = lons.reshape(nlats,nlons)
zre = values.reshape(nlats,nlons)
#### later in the defined map
CT = plt.contourf(xre, yre, zre, cmap=color_map) 
cbar = plt.colorbar()
plt.show()
I have also tried to use griddata but no luck
xi = np.linspace(-100,100,100)
yi = np.linspace(-100,100,100)
# grid the data.
zi = griddata(x,y,z,xi,yi)
here is the datafile
40 7 10.65251434
41 7.20 16.841345477
42 7.5 10.923153289
46 7.75 13.644313708
45 8 3.550977951
45 8.25 3.352525137
45 8.5 3.080082094
45 8.75 2.971992657
45 9 2.998723785
45 9.25 3.080082094
45 9.5 3.185687405
45 9.75 3.102075854
45 10 3.185687405
45 10.25 3.213960325
45 10.5 3.32326373
45 10.75 3.465643983
45 11 3.612980369
45 11.25 3.644313708
45 11.5 30.701277511
45 11.75 30.923153289
45 12 3.797848342
45 12.25 3.612980369
45 12.5 3.435577844
45 12.75 3.294210812
45 13 3.26536503
45.25 7 16.485050223
45.25 7.25 16.343081631
45.25 7.5 13.856783573
45.25 7.75 13.405725407
45.25 8 13.550977951
45.25 8.25 13.294210812
45.25 8.5 3.294210812
45.25 8.75 3.185687405
45.25 9 3.15761656
45.25 9.25 3.213960325
45.25 9.5 3.15761656
45.25 9.75 3.32326373
45.25 10 3.405725407
45.25 10.25 3.495925216
45.25 10.5 3.465643983
45.25 10.75 3.550977951
45.25 11 3.465643983
45.25 11.25 3.765429652
45.25 11.5 3.95669157
45.25 11.75 3.797848342
45.25 12 3.923153289
45.25 12.25 3.733239867
45.25 12.5 3.550977951
45.25 12.75 3.520306012
45.25 13 3.376085288
45.5 7 6.383367092
45.5 7.25 6.383367092
45.5 7.5 6.009422688
45.5 7.75 4.679469855
45.5 8 3.435577844
45.5 8.25 3.435577844
45.5 8.5 3.236725042
45.5 8.75 3.236725042
45.5 9 3.185687405
45.5 9.25 3.102075854
45.5 9.5 3.102075854
45.5 9.75 3.185687405
45.5 10 3.352525137
45.5 10.25 3.405725407
45.5 10.5 3.376085288
45.5 10.75 3.612980369
45.5 11 3.520306012
45.5 11.25 3.352525137
45.5 11.5 3.823949103
45.5 11.75 3.856783573
45.5 12 3.856783573
45.5 12.25 3.765429652
45.5 12.5 3.669541114
45.5 12.75 3.550977951
45.5 13 3.435577844
45.75 7 5.309043916
45.75 7.25 6.057519881
45.75 7.5 5.030958443
45.75 7.75 4.836570243
45.75 8 4.836570243
45.75 8.25 2.724965001
45.75 8.5 2.607751091
45.75 8.75 3.26536503
45.75 9 2.898163214
45.75 9.25 2.872155245
45.75 9.5 1.893252754
45.75 9.75 2.043669061
45.75 10 1.75488883
45.75 10.25 2.004264146
45.75 10.5 2.971992657
45.75 10.75 1.804949998
45.75 11 2.846334614
45.75 11.25 5.519419657
45.75 11.5 2.517818813
45.75 11.75 3.733239867
45.75 12 3.376085288
45.75 12.25 3.550977951
45.75 12.5 14.612980369
45.75 12.75 10.520306012
45.75 13 31.495925216
46 7 5.06399168
46 7.25 4.949174095
46 7.5 5.266087828
46 7.75 5.352298328
46 8 4.757472437
46 8.25 2.800325674
46 8.5 3.612980369
46 8.75 3.185687405
46 9 2.323282473
46 9.25 1.671485743
46 9.5 3.856783573
46 9.75 4.572079662
46 10 4.679469855
46 10.25 4.679469855
46 10.5 5.309043916
46 10.75 3.294210812
46 11 3.405725407
46 11.25 3.669541114
46 11.5 3.495925216
46 11.75 4.255093726
46 12 3.495925216
46 12.25 3.185687405
46 12.5 3.213960325
46 12.75 3.550977951
46 13 3.520306012
46.25 7 1.969297411
46.25 7.25 14.908706364
46.25 7.5 3.052767233
46.25 7.75 3.765429652
46.25 8 3.95669157
46.25 8.25 5.06399168
46.25 8.5 5.266087828
46.25 8.75 3.669541114
46.25 9 3.185687405
46.25 9.25 3.797848342
46.25 9.5 13.352525137
46.25 9.75 15.439709782
46.25 10 25.69098301
46.25 10.25 4.949174095
46.25 10.5 5.736883145
46.25 10.75 5.105542055
46.25 11 4.255093726
46.25 11.25 3.701277511
46.25 11.5 4.255093726
46.25 11.75 4.572079662
46.25 12 3.98369323
46.25 12.25 4.148941623
46.25 12.5 3.129746478
46.25 12.75 3.236725042
46.25 13 3.550977951
46.5 7 2.872155245
46.5 7.25 3.701277511
46.5 7.5 3.15761656
46.5 7.75 3.765429652
46.5 8 5.18951259
46.5 8.25 6.105948261
46.5 8.5 5.266087828
46.5 8.75 5.69098301
46.5 9 16.009422688
46.5 9.25 5.147381739
46.5 9.5 5.829636932
46.5 9.75 5.654489904
46.5 10 6.243327668
46.5 10.25 5.395852976
46.5 10.5 5.736883145
46.5 10.75 6.057519881
46.5 11 5.147381739
46.5 11.25 3.520306012
46.5 11.5 3.856783573
46.5 11.75 4.148941623
46.5 12 4.71833512
46.5 12.25 4.71833512
46.5 12.5 3.701277511
46.5 12.75 3.889851131
46.5 13 3.32326373
46.75 7 1.859766825
46.75 7.25 2.198852355
46.75 7.5 2.345277833
46.75 7.75 2.517818813
46.75 8 3.856783573
46.75 8.25 3.856783573
46.75 8.5 5.06399168
46.75 8.75 4.184077131
46.75 9 5.829636932
46.75 9.25 3.644313708
46.75 9.5 3.765429652
46.75 9.75 5.309043916
46.75 10 6.009422688
46.75 10.25 5.147381739
46.75 10.5 5.609155594
46.75 10.75 5.783100444
46.75 11 5.147381739
46.75 11.25 3.581868928
46.75 11.5 4.908706364
46.75 11.75 3.465643983
46.75 12 3.465643983
46.75 12.25 4.148941623
46.75 12.5 3.98369323
46.75 12.75 3.581868928
46.75 13 3.644313708 
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This one is from Jeff here at the forum and it does work. But the smoothing
of the color values inbetween the countour is not so good. How to improve
that ?
Also,i am still searching for a load .csv example to use with pcolor or
countour.
from matplotlib.mlab import load
import matplotlib.pyplot as plt 
import numpy as np
data = load("C:/Users/Pontus/data.txt")
# need to know nlons and nlats beforehand!
nlons = 8; nlats = 25
X = data[0::nlats,0]
Y = data[0:nlats,1]
# data is in nlons,nlats order in file, need to transpose
Z = data[:,2].reshape(nlons,nlats).transpose()
X,Y = np.meshgrid(X,Y)
CS = plt.contourf(X,Y,Z,20)
plt.show()
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On Sat, May 16, 2009 at 6:57 AM, amrbekhit <amr...@gm...> wrote:
>
> Hello,
>
> I am trying to write an application that measures data from an external
> device and then displays the data on a graph, updating the graph when new
> measurements arrive. Searching the web has led to matplotlib and so I've
> been having a go at using that for my program. After searching around on the
> forums, I have had some success in implementing the functionality I am
> aiming for, but the application gets very very slow the longer it runs.
By default matplotlib overplots, meaning it keeps the old data around
in addition to the new data, so you are plotting on the i-th iteration
 0: [d0]
 1: [d0], [d0, d1][d0], [d0, d1]
 2: [d0], [d0, d1], [d0, d1, d2], ....
You probably don't see it because the new points overlap the old.
If you turn overplotting off
 ax.hold(False)
before issuing the plot commands you should not see the dramatic slowing.
You can speed up the performance further by reusing the same line object, eg
somelimit = 1000
line, = ax.plot([], [])
xs = []
ys = []
for row in mydata:
 xs.append(row['newx'])
 ys.append(row['newy'])
 if len(xs)>somelimit:
 del xs[0]
 del ys[0]
 line.set_data(xs, ys)
 ax.figure.canvas.draw()
See also the animation tutorial and examples
 http://www.scipy.org/Cookbook/Matplotlib/Animations
 http://matplotlib.sourceforge.net/examples/animation/index.html
JDH

On Fri, May 15, 2009 at 4:23 PM, Christopher Barker
<Chr...@no...> wrote:
> Sandro Tosi wrote:
>>>
>>> mpl.ticker.AutoDateLocator
>>> mpl.ticker.AutoDateFormatter
>>>
>>> Where might I find these now? They don't seem to be in matplotlib.ticker
>>> or matplotlib.date. do they have a new name? Is there a new way to get
>>> automatic date tic location and formating?
>
>> dates.AutoDateFormatter dates.AutoDateLocator
>
> thanks -- I did just find it myself, by looking at the code in
> axes.plot_dates -- however, the plot_dates docstring is wrong, and they
> don't seem to be here:
I updated the plot_dates docstring in svn to point to matplotlib.dates
> http://matplotlib.sourceforge.net/api/dates_api.html
>
> They are there in the diagram on top, but don't seem to be on the page.
That is odd -- any sphinx gurus have any idea why these are not being
picked up by the automodule code?
>>> note: I'm trying to do this for the x axis of a quiver plot, which
>>> doesn't seem to support passing in dates directly.
>
> by the way, is that a bug that should be tracked down and fixed?
Not all of the methods currently support direct date/unit plotting,
but we do try to extend support when we have reported failures, so if
you post a script which exposes the problem I'll try and fix it.
> However, in this case, I'm trying to write method that takes an axes object,
> and plots to that -- how can I do this without the figure reference?
Most mpl objects have a reference to their parent, so you can walk up
the containment hierarchy, eg
 line.axes.figure.canvas
so in your case
 ax.figure.autofmt_xdate()
but note that autofmt_xdate is designed to work for subplots with only
one column, not in the case of general axes configurations, so calling
this from an axes in a method that does not know how the axes are
layed out does not make sense. But you can do the main part yourself
 for label in ax.get_xticklabels():
 label.set_rotation(30)
 label.set_horizontalalignment('right')
> But it does work well if you apply it to the figure after my plotting
> routines.
That is the best place to apply it, because there you know the subplot
layout is suitable for the call.
JDH

Hello,
I am trying to write an application that measures data from an external
device and then displays the data on a graph, updating the graph when new
measurements arrive. Searching the web has led to matplotlib and so I've
been having a go at using that for my program. After searching around on the
forums, I have had some success in implementing the functionality I am
aiming for, but the application gets very very slow the longer it runs.
I have attached the test program I wrote at the bottom of this post. The
application uses Tkinter for the GUI. Whenever you type something into the
text box at the top, the ascii code of the last character in the text is
added to the data list and plotted. The more things you type, the
application gets slower and slower.
I initially thought that the slow down might be due to the data list getting
bigger and bigger, but I no longer think that this is the case. If you
comment out line 31 (self.data.append(ord(self.text.get()[-1]))) and replace
it with something like self.data = [1, 2, 3], the application still exhibits
the same slow down with time.
Is there a 'proper' way of doing this?
Thanks,
--Amr
PS remove the comment at the bottom of the file (root.mainloop())
http://www.nabble.com/file/p23573077/pylog.py pylog.py 
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Hi all,
I have been using matplotlib for about 5 hours :-/ so please forgive me if
the question has been asked several times (and i have been trying to find it
in the forum)
But i downloaded phyton(xy) and from there i found the poormans_countour and
some pcolor scripts. I think this is what i need but how do i load a
textfile (.csv or -txt with columns etc) that contains UV-coordinates
(ordinary xy) with a assigned value and put them into the Z variables in the
.py scripts ?? i.e
from numpy import *
from pylab import load 
from pylab import save
read_data = load("myfile.txt")
// here how to assign Z to read_data
cmap = cm.get_cmap('jet', 35) # 35 discrete colors
im = imshow(Z, cmap=cmap, interpolation='bilinear')
axis('off')
colorbar()
show()
The same goes for the pcolor plot, how can i replace the func3 so it contais
an array of my values from the loaded file ?
def func3(x,y):
 return (1- x/2 + x**5 + y**3)*exp(-x**2-y**2)
# make these smaller to increase the resolution
dx, dy = 0.05, 0.05
x = arange(-3.0, 3.0, dx)
y = arange(-3.0, 3.0, dy)
X,Y = meshgrid(x, y)
Z = func3(X, Y)
ax = subplot(111)
im = imshow(Z, cmap=cm.jet)
#im.set_interpolation('nearest')
#im.set_interpolation('bicubic')
im.set_interpolation('bilinear')
Thanks in advance :handshake:
/Linda
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Christopher Barker wrote:
> Christopher Barker wrote:
>> Hi all,
>>
>> I've been messing with quiver a bit, and have some confusions:
> 
> one more issue with quiver -- autoscaling fails if there is a NaN in the 
> data:
> 
I just committed a change to ensure that nans and infs are treated as 
masked points.
If you still see the problem (and I suspect you will), then it is 
inherent in the autoscaling.
Eric
> 
> 
> x = (1,2)
> y = (1,2)
> u = (2,2)
> v = (-2,2)
> 
> 
> fig = plt.figure(1)
> fig.clear()
> 
> ax = fig.add_subplot(2,2,3)
> 
> # fails for a nan data point if auto-scaling
> u = (2, np.nan)
> props = {'units' : "dots",
> 'scale' : .1,
> 'width' : 2,
> 'headwidth': 2,
> 'headlength': 3,
> 'headaxislength': 4,
> }
> 
> ax.quiver( x, y, u, v, **props )
> 
> 
> 
> 
> -Chris
> 
> 

On Fri, May 15, 2009 at 4:24 PM, Jae-Joon Lee <lee...@gm...> wrote:
> It may be doable, depending on what you exactly want.
> For example
>
> http://dl.getdropbox.com/u/178748/AxesGrid/htdocs/users/overview.html#rgb-axes
>
> But my emphasis was on displaying images, and may not be suitable for
> general axes.
FYI, several weeks ago I updated the official site-docs after you
contributes the axes grid toolkit and docs:
 http://matplotlib.sourceforge.net/mpl_toolkits/axes_grid/users/overview.html#rgb-axes
Which only increases the pressure on us to get the 98 bug fix release
out and then the next trunk release out so that the code keeps up with
the docs...
JDH

On Fri, May 15, 2009 at 5:16 PM, Jae-Joon Lee <lee...@gm...> wrote:
> Attached is a patch that I want to commit to the trunk.
> It introduces new attribute "_check_contains" (any name suggestion?)
I'm not keen on the name either -- something more explicit would be
better. Maybe, set_annotation_clip or something like that, which is
more descriptive to me.
> for Annotation class.
>
>     * True : the annotation will only be drawn when self.xy is
> inside the axes.
>     * False : the annotation will always be drawn regardless of
> its position.
>     * None : the self.xy will be checked only if *xycoords* is "data"
>
> The default value is None, i.e., position is only checked if the
> xycoords is "data".
>
> I'll commit this soon if others don't object.
You don't need permission :-) but I took a look at the patch. Inline
comments below
+ def get_check_contains(self):
+ " retrun *check_contains* attribute. "
+ return self._check_contains
+
retrun is misspelled
+
+
 def update_positions(self, renderer):
+ xy_pixel = self._get_position_xy(renderer)
+ self._update_position_xytext(renderer, xy_pixel)
+
+
+ def _get_position_xy(self, renderer):
+ x, y = self.xy
+ return self._get_xy(x, y, self.xycoords)
+
When you are extending/fixing existing code and come across methods
with no docs, could you write a one or two line doc string for them?
I wrote many of these and at the time they were so obvious that they
didn't need docstrings, but as time passes and I reencounter them, I
wish there was a simple line explaining them. As you are digging
through the code figuring them all out, it is a great time to drop in
a simple one-liner docstring (eg explaining what coord system is being
returned by _get_position_xy). As the famous coding quip says, the
literal wording or author of which I cannot dig up right now, "Leave
comments in your code -- someone may read it someday, and that someone
may be you!"
+ class _SimpleEvent:
+ def __init__(self, xy):
+ self.x, self.y = xy
+
+ def _check_xy(self, renderer, xy_pixel):
+ "given the xy coordinate, check if the annotation need to be drawn"
+
+ b = self.get_check_contains()
+ if b or (b is None and self.xycoords == "data"):
+ # check if self.xy is inside the axes.
+ if not self.axes.contains(Annotation._SimpleEvent(xy_pixel))[0]:
+ return False
+
I'm not wild about this -- I find myself doing similar hacks when
writing GUIs and doing other stuff where the quick-and-dirty is easier
than the right way, but for mpl, which is a library and is growing
rapidly and has already reached the point where no one developer has
their head around the whole thing, I think we need to be careful about
these kinds of hacks that can result in subtle bugs down the road.
You are relying on the event infrastructure API via the Axes.contain
method, but are hacking around it by just providing the minimal set of
things that are needed for the contains call with the _SimpleEvent.
The problem is, if the event API or the contains method changes later,
this is a latent bug which is difficult to unit test since it is
primarily exposed interactively. Axes.contains expects a MouseEvent
according to the signature -- I haven't dug to deeply here but with a
little more work can we provide what it is expecting rather than the
stripped down proxy class (and does "contains" really need a
MouseEvent or would a LocationEvent would suffice -- this may be
simply a matter of renaming the arg to contains....). Or perhaps the
Axes.contains method itself is misguided, requiring a full-fledged
event when an x/y location would suffice. If it is fairly easy, I
would like to fix this here and now rather than hack around a bad
design decision.
Thanks for the patch!
JDH

Christopher Barker wrote:
> Hi all,
> 
> I've been messing with quiver a bit, and have some confusions:
No, you hit a bug. Thanks for the report and test script. It is fixed 
in r7103.
If you are not running from svn, a workaround may be to specify the 
angles as an ndarray or masked array with the shape set to (N,1) where N 
is the number of arrows. I haven't tested it, but based on the changes 
I made, I think this will work with the version you have.
Eric
> 
> according to the docs:
> 
> """
> units: [‘width’ | ‘height’ | ‘dots’ | ‘inches’ | ‘x’ | ‘y’ ]
> 
> arrow units; the arrow dimensions except for length are in 
> multiples of this unit.
> """
> 
> and yes, when I change units from 'dots' for 'inched', the length of the 
> arrows do indeed change.
> 
> 
> """
> angles: [‘uv’ | ‘xy’ | array]
> ... Alternatively, arbitrary angles may be specified as an array of 
> values in degrees, CCW from the x-axis.
> """
> 
> This crashes for me with:
> 
> Traceback (most recent call last):
> File "quiver_test.py", line 72, in <module>
> plt.draw()
> File 
> "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib/pyplot.py", 
> line 341, in draw
> get_current_fig_manager().canvas.draw()
> File 
> "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib/backends/backend_tkagg.py", 
> line 215, in draw
> FigureCanvasAgg.draw(self)
> File 
> "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib/backends/backend_agg.py", 
> line 279, in draw
> self.figure.draw(self.renderer)
> File 
> "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib/figure.py", 
> line 772, in draw
> for a in self.axes: a.draw(renderer)
> File 
> "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib/axes.py", 
> line 1601, in draw
> a.draw(renderer)
> File 
> "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib/quiver.py", 
> line 425, in draw
> verts = self._make_verts(self.U, self.V)
> File 
> "/Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site-packages/matplotlib/quiver.py", 
> line 484, in _make_verts
> theta = ma.asarray(self.angles*np.pi/180.0).filled(0)
> TypeError: can't multiply sequence by non-int of type 'float'
> 
> I've enclosed a small test script...
> 
> Thanks,
> 
> -Chris
> 
> 
> 
> 
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Christopher Barker wrote:
> Hi all,
> 
> I've been messing with quiver a bit, and have some confusions:
one more issue with quiver -- autoscaling fails if there is a NaN in the 
data:
x = (1,2)
y = (1,2)
u = (2,2)
v = (-2,2)
fig = plt.figure(1)
fig.clear()
ax = fig.add_subplot(2,2,3)
# fails for a nan data point if auto-scaling
u = (2, np.nan)
props = {'units' : "dots",
 'scale' : .1,
 'width' : 2,
 'headwidth': 2,
 'headlength': 3,
 'headaxislength': 4,
 }
ax.quiver( x, y, u, v, **props )
-Chris
-- 
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
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Chr...@no...