matplotlib-users Mailing List for matplotlib

Greetings. I've been having lots of luck with my date plots.  But  
I've been having a problem getting the dateformatter to work. I'm  
using the code below. The dates keep getting formatted with the  
default, "Sep 28 2006" instead of what I want, "Sep 28"

Any thoughts?

from datetime import date,timedelta
from matplotlib.dates import MonthLocator, WeekdayLocator,  
DateFormatter,MONDAY,SATURDAY
from pylab import *


def dateplot():
     dates = drange(date(2006,10,1),date(2006,12,1),timedelta(days=1))
     vals  = 500*(randn(len(dates))+2)

     figure(num=1, figsize=(6.5,4))
     axes([.15,.3,.8,.5])

     ax = gca()                          # get the current graphics  
region

     title(r"Average daily bandwidth")

     ax.xaxis.set_major_formatter(DateFormatter('%b %d'))

     ax.yaxis.set_major_formatter(FormatStrFormatter('%3.0f KBps'))

     plot_date(dates, vals, 'bo')

     # Rotate the labels

     labels = ax.get_xticklabels()
     setp(labels,'rotation',90,fontsize=8)
     grid(True)
     savefig("x.pdf",format='pdf')




if(__name__=='__main__'):
     dateplot()

I've been able to figure out how to easily do error bars on a plot_date.

Here is how I do it:

The variables coming in are "dates" which is an array of my dates (in  
days since 0001-01-01), averages, p10 (which is the bottom of my  
error bars), and p90 (which is the top of my error bars)

     plot_date(dates, averages, 'bo')

     # Draw the tops of the error bars
     ax.vlines(dates,averages,p90)
     ax.hlines(p90,dates-.25,dates+.25)

     # Draw the bottom part of the error bars
     ax.vlines(dates,averages,p10)
     ax.hlines(p10,dates-.25,dates+.25)

It's pretty sweet.

I'm having other problems which I will post separately, but this is  
working well.


On Dec 3, 2006, at 12:02 PM, Pierre GM wrote:

> On Saturday 02 December 2006 17:39, Simson Garfinkel wrote:
>> Hi. I'm interested in creating a date plot showing bandwidth along a
>> link. I want to have a dot in the center of each date with the
>> average bandwidth and use the error bars to show the 25th and 75th
>> percentiles. I've been trying to figure out how to do this and am
>> having problems.
>
> My 2c:
> Don't bother yet about dates: first get the plot as you want it,  
> assuming that
> your x data are floats (use date2num if needed). Then you can  
> tackle the
> problem of displaying dates.
>
> If you poke around the sources (axes.py). you'll find that  
> 'plot_date' is only
> 'plot', where a couple of extra parameters are set:
> if xdate:
>        self.xaxis_date(tz)
> 'xdate' is a flag indicating whether the data on the x axis are  
> dates (True)
> or not (False), 'tz' is the timezone flag (default to None), and  
> 'self' is
> your current axes object (you can get its handle by gca() if you  
> haven't
> specified it otherwise).
>
> Combining these pieces of information should to the trick (or most  
> of it).
> Let us know how it goes anyway.
> P.
>

David,

I have made some changes in svn that address all but one of the points 
you made:

[....]
> if self.clip:
>                mask = ma.getmaskorNone(val)
>                if mask == None:
>                    val = ma.array(clip(val.filled(vmax), vmin, vmax))
>                else:
>                    val = ma.array(clip(val.filled(vmax), vmin, vmax),
>                                mask=mask)

The real problem here is that I should not have been using 
getmaskorNone().  In numpy.ma, we need nomask, not None, so we want an 
ordinary getmask() call.  ma.array(...., mask=ma.nomask) is very fast, 
so the problem goes away.

> 
> Actually, the problem is in ma.array: with a value of mask to None, it 
> should not make a difference between mask = None or no mask arg, right ? 
But it does, because for numpy it needs to be nomask; it does something 
with None, but whatever it is, it is very slow.

> I didn't change ma.array to keep my change as local as possible. To 
> change only this operation as above gives a speed up from 1.8 s to ~ 1.0 
> s for to_rgba, which means calling show goes from ~ 2.2 s to ~1.4 s. I 
> also changed  
> result = (val-vmin)/float(vmax-vmin)
> 
> to
> 
> invcache    = 1.0 / (vmax - vmin)
> result = (val-vmin) * invcache

This is the one I did not address.  I don't understand how this could be 
making much difference, and some testing using ipython and %prun with 
1-line operations showed little difference with variations on this 
theme.  The fastest would appear to be (and logically should be, I 
think) result = (val-vmin)*(1.0/(vmax-vmin)), but I don't think it makes 
much difference--it looks to me like maybe 10-20 msec, not 100, on my 
Pentium M 1.6 Ghz.  Maybe still worthwhile, so I may yet make the change 
after more careful testing.


> 
> which gives a moderate speed up (around 100 ms for a 8000x256 points 
> array). Once you make both those changes, the clip call is by far the 
> most expensive operation in normalize functor, but the functor is not 
> really expensive anymore compared to the rest, so this is not where I 
> looked at.
> 
> For the where calls in Colormap functor, I was wondering if they are 
> necessary in all cases: some of those calls seem redundant, and it may 
> be possible to detect that before calling them. This should be both 
> easier and faster, at least in this case, than having a fast where ?
> 

You hit the nail squarely: where() is the wrong function to use, and I 
have eliminated it from colors.py.  The much faster replacement is 
putmask, which does as well as direct indexing with a Boolean but works 
with all three numerical packages.  I think that using the fast putmask 
is better than trying to figure out special cases in which there would 
be nothing to put, although I could be convinced otherwise.


> I understand that support of multiple array backend, support of mask 
> arrays have cost consequences. But it looks like it may be possible to 
> speed things up for cases where an array has only meaningful values/no 
> mask.

The big gains here were essentially bug fixes--picking the appropriate 
function (getmask versus getmaskorNone and putmask versus where).

Here is the colors.py diff:

--- trunk/matplotlib/lib/matplotlib/colors.py	2006/12/03 21:54:38	2906
+++ trunk/matplotlib/lib/matplotlib/colors.py	2006/12/14 08:27:04	2923
@@ -30,9 +30,9 @@
  """
  import re

-from numerix import array, arange, take, put, Float, Int, where, \
+from numerix import array, arange, take, put, Float, Int, putmask, \
       zeros, asarray, sort, searchsorted, sometrue, ravel, divide,\
-     ones, typecode, typecodes, alltrue
+     ones, typecode, typecodes, alltrue, clip
  from numerix.mlab import amin, amax
  import numerix.ma as ma
  import numerix as nx
@@ -536,8 +536,9 @@
      lut[0] = y1[0]
      lut[-1] = y0[-1]
      # ensure that the lut is confined to values between 0 and 1 by 
clipping it
-    lut = where(lut > 1., 1., lut)
-    lut = where(lut < 0., 0., lut)
+    clip(lut, 0.0, 1.0)
+    #lut = where(lut > 1., 1., lut)
+    #lut = where(lut < 0., 0., lut)
      return lut


@@ -588,16 +589,16 @@
              vtype = 'array'
              xma = ma.asarray(X)
              xa = xma.filled(0)
-            mask_bad = ma.getmaskorNone(xma)
+            mask_bad = ma.getmask(xma)
          if typecode(xa) in typecodes['Float']:
-            xa = where(xa == 1.0, 0.9999999, xa) # Tweak so 1.0 is in 
range.
+            putmask(xa, xa==1.0, 0.9999999) #Treat 1.0 as slightly less 
than 1.
              xa = (xa * self.N).astype(Int)
-        mask_under = xa < 0
-        mask_over = xa > self.N-1
-        xa = where(mask_under, self._i_under, xa)
-        xa = where(mask_over, self._i_over, xa)
-        if mask_bad is not None: # and sometrue(mask_bad):
-            xa = where(mask_bad, self._i_bad, xa)
+        # Set the over-range indices before the under-range;
+        # otherwise the under-range values get converted to over-range.
+        putmask(xa, xa>self.N-1, self._i_over)
+        putmask(xa, xa<0, self._i_under)
+        if mask_bad is not None and mask_bad.shape == xa.shape:
+            putmask(xa, mask_bad, self._i_bad)
          rgba = take(self._lut, xa)
          if vtype == 'scalar':
              rgba = tuple(rgba[0,:])
@@ -752,7 +753,7 @@
              return 0.*value
          else:
              if clip:
-                mask = ma.getmaskorNone(val)
+                mask = ma.getmask(val)
                  val = ma.array(nx.clip(val.filled(vmax), vmin, vmax),
                                  mask=mask)
              result = (val-vmin)/float(vmax-vmin)
@@ -804,7 +805,7 @@
              return 0.*value
          else:
              if clip:
-                mask = ma.getmaskorNone(val)
+                mask = ma.getmask(val)
                  val = ma.array(nx.clip(val.filled(vmax), vmin, vmax),
                                  mask=mask)
              result = 
(ma.log(val)-nx.log(vmin))/(nx.log(vmax)-nx.log(vmin))


Eric

I am getting this weird message. This is a linux machine. the matplotlib
version 0.87.7



>>> from pylab import *

Traceback (most recent call last):

  File "<stdin>", line 1, in ?

  File "/foo/python/site-packages/lib/python/pylab.py", line 1, in ?

    from matplotlib.pylab import *

  File "/foo/python/site-packages/lib/python/matplotlib/pylab.py", line 201,
in ?

    from axes import Axes, PolarAxes

  File "/foo/python/site-packages/lib/python/matplotlib/axes.py", line 15,
in ?

    from axis import XAxis, YAxis

  File "/foo/python/site-packages/lib/python/matplotlib/axis.py", line 16,
in ?

    from lines import Line2D, TICKLEFT, TICKRIGHT, TICKUP, TICKDOWN

  File "/foo/python/site-packages/lib/python/matplotlib/lines.py", line 11,
in ?

    import matplotlib.agg as agg

  File "/foo/python/site-packages/lib/python/matplotlib/agg.py", line 106,
in ?

    pi = cvar.pi

AttributeError: 'swigvarlink' object has no attribute 'pi'

>>>

You need to update your mpl to the current release or svn.  This was 
fixed quite a few months ago, but I don't remember exactly when.

Eric

Brian Blais wrote:
> Hello,
> 
> If I do the following:
> 
> plot([1],[1],'o')
> 
> it plots the one dot correctly.
> 
> if, however, one of those numbers is zero:
> 
> 
> plot([1],[0],'o')
> 
> I get a floating point/divide by zero error:
> 
> 
> /usr/lib/python2.4/site-packages/matplotlib/ticker.py in scale_range(vmin, vmax, n, 
> threshold)
>      731     dv = abs(vmax - vmin)
>      732     meanv = 0.5*(vmax+vmin)
> --> 733     var = dv/max(abs(vmin), abs(vmax))
>      734     if var < 1e-12:
>      735         return 1.0, 0.0
> 
> ZeroDivisionError: float division
> 
> 
> Is there a fix for this?
> 
> 
> In [11]:matplotlib.__version__
> Out[11]:'0.87.2'
> 
> 
> running linux, python 2.4.
> 
> 
> 
> 
> 			thanks,
> 
> 
> 				Brian Blais
> 
> 

Brian Blais wrote:

> 
> plot([1],[0],'o')
> 
> I get a floating point/divide by zero error:
> 
> 
> /usr/lib/python2.4/site-packages/matplotlib/ticker.py in scale_range(vmin, vmax, n, 
> threshold)
>      731     dv = abs(vmax - vmin)
>      732     meanv = 0.5*(vmax+vmin)
> --> 733     var = dv/max(abs(vmin), abs(vmax))
>      734     if var < 1e-12:
>      735         return 1.0, 0.0
> 
> ZeroDivisionError: float division
> 
> 
> Is there a fix for this?
> 


You have to upgrade.

In [39]: plot([1],[0],'o')
Out[39]: [<matplotlib.lines.Line2D instance at 0xa38ac80c>]

In [40]: matplotlib.__version__
Out[40]: '0.87.7'

In [41]: matplotlib.__revision__
Out[41]: '$Revision: 2835 $'

-- 
cheers,
steve

Random number generation is the art of producing pure gibberish as 
quickly as possible.

Hello,

If I do the following:

plot([1],[1],'o')

it plots the one dot correctly.

if, however, one of those numbers is zero:


plot([1],[0],'o')

I get a floating point/divide by zero error:


/usr/lib/python2.4/site-packages/matplotlib/ticker.py in scale_range(vmin, vmax, n, 
threshold)
     731     dv = abs(vmax - vmin)
     732     meanv = 0.5*(vmax+vmin)
--> 733     var = dv/max(abs(vmin), abs(vmax))
     734     if var < 1e-12:
     735         return 1.0, 0.0

ZeroDivisionError: float division


Is there a fix for this?


In [11]:matplotlib.__version__
Out[11]:'0.87.2'


running linux, python 2.4.




			thanks,


				Brian Blais


-- 
-----------------

              bb...@br...
              http://web.bryant.edu/~bblais

Hi everybody,
I'm trying to run contour_demo.pp example but I'm having this error.  
Anybody can help?

Thanks,

X Error: BadDevice, invalid or uninitialized input device 166
  Major opcode:  144
  Minor opcode:  3
  Resource id:  0x0
Failed to open device
X Error: BadDevice, invalid or uninitialized input device 166
  Major opcode:  144
  Minor opcode:  3
  Resource id:  0x0
Failed to open device
[-0.8666166 ,-0.49865195,-0.13068729, 0.23727736, 0.60524202, 0.97320667,
       1.34117133,]
type: <type 'array'>
['__copy__', '__deepcopy__', 'astype', 'byteswapped', 'copy', 
'iscontiguous', 'itemsize', 'resize', 'savespace', 'spacesaver', 'tolist', 
'toscalar', 'tostring', 'typecode']
* * * * * * * * * *
<a list of 7 LineCollection objects>
type: <class 'matplotlib.cbook.silent_list'>
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', 
'__delslice__', '__dict__', '__doc__', '__eq__', '__ge__', 
'__getattribute__', '__getitem__', '__getslice__', '__gt__', '__hash__', 
'__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__', 
'__lt__', '__module__', '__mul__', '__ne__', '__new__', '__reduce__', 
'__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', 
'__setitem__', '__setslice__', '__str__', '__weakref__', 'append', 'count', 
'extend', 'index', 'insert', 'mappable', 'pop', 'remove', 'reverse', 'sort', 
'type']
* * * * * * * * * *
Traceback (most recent call last):
  File "./contour_demo.py", line 30, in ?
    clabel(CS, inline=1, fontsize=10)
  File "/usr/lib/python2.4/site-packages/matplotlib/pylab.py", line 1731, in 
clabel
    ret =  gca().clabel(*args, **kwargs)
  File "/usr/lib/python2.4/site-packages/matplotlib/axes.py", line 1241, in 
clabel
    return self._contourLabeler.clabel(*args, **kwargs)
  File "/usr/lib/python2.4/site-packages/matplotlib/contour.py", line 150, 
in clabel
    levels = [con._label for con in contours]
AttributeError: _label



X Error: BadDevice, invalid or uninitialized input device 166
  Major opcode:  144
  Minor opcode:  3
  Resource id:  0x0
Failed to open device
X Error: BadDevice, invalid or uninitialized input device 166
  Major opcode:  144
  Minor opcode:  3
  Resource id:  0x0
Failed to open device
[-0.8666166 ,-0.49865195,-0.13068729, 0.23727736, 0.60524202, 0.97320667,
       1.34117133,]
type: <type 'array'>
['__copy__', '__deepcopy__', 'astype', 'byteswapped', 'copy', 
'iscontiguous', 'itemsize', 'resize', 'savespace', 'spacesaver', 'tolist', 
'toscalar', 'tostring', 'typecode']
* * * * * * * * * *
<a list of 7 LineCollection objects>
type: <class 'matplotlib.cbook.silent_list'>
['__add__', '__class__', '__contains__', '__delattr__', '__delitem__', 
'__delslice__', '__dict__', '__doc__', '__eq__', '__ge__', 
'__getattribute__', '__getitem__', '__getslice__', '__gt__', '__hash__', 
'__iadd__', '__imul__', '__init__', '__iter__', '__le__', '__len__', 
'__lt__', '__module__', '__mul__', '__ne__', '__new__', '__reduce__', 
'__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', 
'__setitem__', '__setslice__', '__str__', '__weakref__', 'append', 'count', 
'extend', 'index', 'insert', 'mappable', 'pop', 'remove', 'reverse', 'sort', 
'type']
* * * * * * * * * *
Traceback (most recent call last):
  File "./contour_demo.py", line 30, in ?
    clabel(CS, inline=1, fontsize=10)
  File "/usr/lib/python2.4/site-packages/matplotlib/pylab.py", line 1731, in 
clabel
    ret =  gca().clabel(*args, **kwargs)
  File "/usr/lib/python2.4/site-packages/matplotlib/axes.py", line 1241, in 
clabel
    return self._contourLabeler.clabel(*args, **kwargs)
  File "/usr/lib/python2.4/site-packages/matplotlib/contour.py", line 150, 
in clabel
    levels = [con._label for con in contours]
AttributeError: _label

_________________________________________________________________
It's Hotmail's 10th Birthday! Come and play Pass the Parcel 
http://www.msnpasstheparcel.com

Eric Firing wrote:

>
> Regarding the clip line, I think that your test for mask is None is 
> not the right solution because it knocks out the clipping operation, 
> but the clipping is intended regardless of the state of the mask.  I 
> had expected it to be a very fast operation, so I am surprised it is a 
> bottleneck; in any case I can take a look to see how it can be sped 
> up, or whether it can be bypassed in some cases.  Maybe it is also 
> using "where" internally.
(again, sorry for the double posting, I always forget that some ML do 
not reply automatically to the ML)

My wordings were vague at best :) The clipping operation is *not* 
removed, and it was not the culprit (it becomes a bottleneck once you 
get the 4x speed issue, though). What I did was:

if self.clip:
               mask = ma.getmaskorNone(val)
               if mask == None:
                   val = ma.array(clip(val.filled(vmax), vmin, vmax))
               else:
                   val = ma.array(clip(val.filled(vmax), vmin, vmax),
                               mask=mask)

Actually, the problem is in ma.array: with a value of mask to None, it 
should not make a difference between mask = None or no mask arg, right ? 
I didn't change ma.array to keep my change as local as possible. To 
change only this operation as above gives a speed up from 1.8 s to ~ 1.0 
s for to_rgba, which means calling show goes from ~ 2.2 s to ~1.4 s. I 
also changed
 
result = (val-vmin)/float(vmax-vmin)

to

invcache    = 1.0 / (vmax - vmin)
result = (val-vmin) * invcache

which gives a moderate speed up (around 100 ms for a 8000x256 points 
array, still in the 5-10 % range of the whole cost, though, and is not 
likely to cause any hidden bug). Once you make both those changes, the 
clip call is by far the most expensive operation in normalize functor, 
but the functor is not really expensive anymore compared to the rest, so 
this is not where I looked at after.

For the where calls in Colormap functor, I was wondering if they are 
necessary in all cases: some of those calls seem redundant, and it may 
be possible to detect that before calling them. This should be both 
easier and faster, at least in this case, than having a fast where ?

I understand that support of multiple array backend, support of mask 
arrays have cost consequences. But it looks like it may be possible to 
speed things up for cases where an array has only meaningful values/no 
mask.

cheers,

David

It's fixed now.

Eric

Rob Hetland wrote:
> fill(x, y) returns an error like:

[....]

> /Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
> packages/matplotlib/axes.py in update_datalim(self, xys)
>      966         # and the data in xydata
>      967         xys = asarray(xys)
> --> 968         self.dataLim.update_numerix_xy(xys, -1)
>      969
>      970
> 
> <type 'exceptions.TypeError'>: Bbox::update_numerix_xy expected  
> numerix array

Rob,

OK, thanks.  That sounds like something resulting from the change I made 
to support 2D array input to plot.  I will check it.

Eric

Rob Hetland wrote:
> fill(x, y) returns an error like:
> 
> 
> /Users/rob/Projects/Merrimack/Grid/landfill.py in <module>()
>       24 for filename in filenames:
>       25     x, y, = pl.load(filename).T
> ---> 26     pl.fill(x, y, facecolor=fillcolor, alpha=fillalpha)
>       27
>       28
> 
> /Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
> packages/matplotlib/pylab.py in fill(*args, **kwargs)
>     1869         hold(h)
>     1870     try:
> -> 1871         ret =  gca().fill(*args, **kwargs)
>     1872         draw_if_interactive()
>     1873     except:
> 
> /Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
> packages/matplotlib/axes.py in fill(self, *args, **kwargs)
>     3677         patches = []
>     3678         for poly in self._get_patches_for_fill(*args,  
> **kwargs):
> -> 3679             self.add_patch( poly )
>     3680             patches.append( poly )
>     3681         self.autoscale_view()
> 
> /Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
> packages/matplotlib/axes.py in add_patch(self, p)
>      951         xys = self._get_verts_in_data_coords(
>      952             p.get_transform(), p.get_verts())
> --> 953         self.update_datalim(xys)
>      954         self.patches.append(p)
>      955
> 
> /Library/Frameworks/Python.framework/Versions/2.5/lib/python2.5/site- 
> packages/matplotlib/axes.py in update_datalim(self, xys)
>      966         # and the data in xydata
>      967         xys = asarray(xys)
> --> 968         self.dataLim.update_numerix_xy(xys, -1)
>      969
>      970
> 
> <type 'exceptions.TypeError'>: Bbox::update_numerix_xy expected  
> numerix array
> WARNING: Failure executing file: <landfill.py>
> 
> 
> ----
> Rob Hetland, Associate Professor
> Dept. of Oceanography, Texas A&M University
> http://pong.tamu.edu/~rob
> phone: 979-458-0096, fax: 979-845-6331
> 
> 
> 
> -------------------------------------------------------------------------
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