matplotlib-devel Mailing List for matplotlib

Tony S Yu wrote:
> On Sep 26, 2008, at 2:28 PM, John Hunter wrote:
> 
>> On Fri, Sep 26, 2008 at 12:39 PM, Tony S Yu <to...@mi...> wrote:
>>
>>> + if all(nonzero == False):
>>> + raise ValueError('spy cannot plot sparse zeros
>>> matrix')
>> Is raising an exception the right choice here -- why can't we plot an
>> all zeros image?
>>
>> JDH
> 
> I guess you could plot sparse all-zero matrices with image mode. My 
> only hesitation is that sparse arrays tend to be very large and (I 
> imagine) this would lead to very slow performance. I assumed this was 
> the reason image mode wasn't adapted to use sparse arrays.
Also, if an image cannot be resolved by the output device, info is 
lost--one might not see anything at a location where there actually is a 
value--whereas with markers, a marker will always show up, and the only 
problem is that one can't necessarily distinguish a single point from a 
cluster.
The real problem with all-zero values is that plot can't handle 
"plot([],[])". One can work around this by putting in bogus values to 
plot a single point, saving the line, and then setting the line data to 
empty; or, better, by not using the high-level plot command, but by 
generating the Line2D object and adding it to the axes. The Line2D 
initializer is happy with empty x and y sequences. I think if you use 
this approach it will kill two bugs (failure on all-zeros with sparse 
and full arrays) with one very simple stone.
Eric
> 
> Actually, now that I think about it: you could plot a trivially small 
> image and just adjust the coordinates so that they correspond to the 
> original matrix shape. Is this what you were thinking?
> 
> I should note that a dense zero array also fails to plot with spy *if 
> marker mode is used*.
> 
> -T
> 
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On Sep 26, 2008, at 3:38 PM, John Hunter wrote:
> On Fri, Sep 26, 2008 at 2:36 PM, Tony S Yu <to...@mi...> wrote:
>>
>> Actually, now that I think about it: you could plot a trivially 
>> small image
>> and just adjust the coordinates so that they correspond to the 
>> original
>> matrix shape. Is this what you were thinking?
>
> This is something I considered, but I was thinking less about the
> implementation and more about the functionality. I don't want to
> raise an exception unless the input doesn't make sense. I would
> rather the user start at a boring image and figure out why it is blank
> that deal with an exception.
Yeah, I agree this is much friendlier.
>> I should note that a dense zero array also fails to plot with spy 
>> *if marker
>> mode is used*.
>
> Can you fix this along with spy2?
I assume you mean spy, not spy2 (I just searched through the 
matplotlib files and saw that spy2 hasn't existed since 2006). I'll 
work on a patch to return a blank plot using the method described 
above (unless someone chimes in with a better suggestion).
-Tony

On Fri, Sep 26, 2008 at 2:36 PM, Tony S Yu <to...@mi...> wrote:
> I guess you could plot sparse all-zero matrices with image mode. My only
> hesitation is that sparse arrays tend to be very large and (I imagine) this
> would lead to very slow performance. I assumed this was the reason image
> mode wasn't adapted to use sparse arrays.
>
> Actually, now that I think about it: you could plot a trivially small image
> and just adjust the coordinates so that they correspond to the original
> matrix shape. Is this what you were thinking?
This is something I considered, but I was thinking less about the
implementation and more about the functionality. I don't want to
raise an exception unless the input doesn't make sense. I would
rather the user start at a boring image and figure out why it is blank
that deal with an exception.
> I should note that a dense zero array also fails to plot with spy *if marker
> mode is used*.
Can you fix this along with spy2?
JDH

On Sep 26, 2008, at 2:28 PM, John Hunter wrote:
> On Fri, Sep 26, 2008 at 12:39 PM, Tony S Yu <to...@mi...> wrote:
>
>> + if all(nonzero == False):
>> + raise ValueError('spy cannot plot sparse zeros
>> matrix')
>
> Is raising an exception the right choice here -- why can't we plot an
> all zeros image?
>
> JDH
I guess you could plot sparse all-zero matrices with image mode. My 
only hesitation is that sparse arrays tend to be very large and (I 
imagine) this would lead to very slow performance. I assumed this was 
the reason image mode wasn't adapted to use sparse arrays.
Actually, now that I think about it: you could plot a trivially small 
image and just adjust the coordinates so that they correspond to the 
original matrix shape. Is this what you were thinking?
I should note that a dense zero array also fails to plot with spy *if 
marker mode is used*.
-T

On Fri, Sep 26, 2008 at 12:39 PM, Tony S Yu <to...@mi...> wrote:
> + if all(nonzero == False):
> + raise ValueError('spy cannot plot sparse zeros
> matrix')
Is raising an exception the right choice here -- why can't we plot an
all zeros image?
JDH

When sparse matrices have explicit zero values, `axes.spy` plots those 
zero values. This behavior seems unintentional. For example, the 
following code should have a main diagonal with markers missing in the 
middle, but `spy` currently plots a full main diagonal.
#~~~~~~~~~~~
import scipy.sparse as sparse
import matplotlib.pyplot as plt
sp = sparse.spdiags([[1,1,1,0,0,0,1,1,1]], [0], 9, 9)
plt.spy(sp, marker='.')
#~~~~~~~~~~~
Below is a patch which only plots the nonzero entries in a sparse 
matrix. Note, sparse matrices with all zero entries raises an error; 
this behavior differs from dense matrices. I could change this 
behavior, but I wanted to minimize the code changed.
Cheers,
-Tony
PS: this patch also includes two trivial changes to some examples.
Index: lib/matplotlib/axes.py
===================================================================
--- lib/matplotlib/axes.py	(revision 6122)
+++ lib/matplotlib/axes.py	(working copy)
@@ -6723,9 +6723,11 @@
 else:
 if hasattr(Z, 'tocoo'):
 c = Z.tocoo()
- y = c.row
- x = c.col
- z = c.data
+ nonzero = c.data != 0.
+ if all(nonzero == False):
+ raise ValueError('spy cannot plot sparse zeros 
matrix')
+ y = c.row[nonzero]
+ x = c.col[nonzero]
 else:
 Z = np.asarray(Z)
 if precision is None: mask = Z!=0.
Index: examples/pylab_examples/masked_demo.py
===================================================================
--- examples/pylab_examples/masked_demo.py	(revision 6122)
+++ examples/pylab_examples/masked_demo.py	(working copy)
@@ -1,4 +1,4 @@
-#!/bin/env python
+#!/usr/bin/env python
 '''
 Plot lines with points masked out.
Index: examples/misc/rec_groupby_demo.py
===================================================================
--- examples/misc/rec_groupby_demo.py	(revision 6122)
+++ examples/misc/rec_groupby_demo.py	(working copy)
@@ -2,7 +2,7 @@
 import matplotlib.mlab as mlab
-r = mlab.csv2rec('data/aapl.csv')
+r = mlab.csv2rec('../data/aapl.csv')
 r.sort()
 def daily_return(prices):

Robert Kern <rob...@gm...>
writes:
 > L*u*v* or its cylindrical-coordinate cousin L*t*theta* (or
 > LCH_uv). "Choosing Color Palettes for Statistical Graphics" is a
 > nice paper talking about an implementation in R (although they do
 > seem to misname L*t*theta* as HCL, which officially is different):
 >
 > http://eeyore.ucdavis.edu/stat250/epub-wu-01_abd.pdf
That link did not work for me, this looks to be an alternative:
http://epub.wu-wien.ac.at/dyn/virlib/wp/eng/mediate/epub-wu-01_abd.pdf?ID=epub-wu-01_abd
-- 
Pete Forman -./\.- Disclaimer: This post is originated
WesternGeco -./\.- by myself and does not represent
pet...@we... -./\.- the opinion of Schlumberger or
http://petef.22web.net -./\.- WesternGeco.