[Numpy-discussion] Problem with array assignment

[Todd Miller]

Francesc Altet wrote:

>Now, let's test the conversion for large objects, with data copy and
>without data copy.
>  
>
I didn't consider that case but was thinking the semantics of the array 
interface would be "buffer-like" and therefore non-copying.   So at the 
moment numarray *never* copies and completely ignores the non-sequence 
array() parameters for the "array interface" case.

>For numarray-->Numeric:
>
>  
>
>>>>t2=timeit.Timer("num=Numeric.array(na,copy=0)","import
>>>>        
>>>>
>Numeric;import numarray; na=numarray.arange(100000)")
>  
>
>>>>t2.repeat(3,100)
>>>>        
>>>>
>[0.0025169849395751953, 0.0025219917297363281, 0.0024950504302978516]
>  
>
>>>>t2_copy=timeit.Timer("num=Numeric.array(na,copy=1)","import
>>>>        
>>>>
>Numeric;import numarray; na=numarray.arange(100000)")
>  
>
>>>>t2_copy.repeat(3,100)
>>>>        
>>>>
>[0.50105500221252441, 0.49400091171264648, 0.49266600608825684]
>
>So it seems like if the data copy is taking place.
>
>  
>
I agree and was to be able to verify this by making buffer()s out of the 
resulting arrays to examine their data addresses.

>For Numeric-->numarray:
>
>  
>
>>>>t4=timeit.Timer("na=numarray.array(num,copy=0)","import
>>>>        
>>>>
>Numeric;import numarray; num=Numeric.arange(100000)")
>  
>
>>>>t4.repeat(3,100)
>>>>        
>>>>
>[0.0054900646209716797, 0.0044760704040527344, 0.0048201084136962891]
>  
>
>>>>t4_copy=timeit.Timer("na=numarray.array(num,copy=1)","import
>>>>        
>>>>
>Numeric;importnumarray; num=Numeric.arange(100000)")
>  
>
>>>>t4_copy.repeat(3,100)
>>>>        
>>>>
>[0.0063569545745849609, 0.004302978515625, 0.0042738914489746094]
>
>Ooops! the times for conversion with copy and without copy are more or
>less the same. Perhaps the copy is not done? It seems so:
>
>  
>
>>>>num=Numeric.arange(10)
>>>>na=numarray.array(num,copy=0)
>>>>na_copy=numarray.array(num,copy=1)
>>>>na.info()
>>>>        
>>>>
>...
>data pointer: 0x08312280 (DEBUG ONLY)
>  
>
>>>>na_copy.info()
>>>>        
>>>>
>...
>data pointer: 0x08312280 (DEBUG ONLY)
>
>i.e. the same data pointer. So it seems that the Numeric-->numarray is
>not copying the data even in the case that we are asking to do that.
>  
>
Yeah,  the copy=X flag is totally ignored at the moment.

>Other minor things (maybe this is just because you are in the middle of
>refactoring):
>
>  
>
>>>>na._data
>>>>        
>>>>
>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>
>while I would expect:
>
>  
>
>>>>na._data
>>>>        
>>>>
><memory at 0x081e3618 with size:0x00000028 held by object 0x401f06a0
>aliasing object 0x00000000>
>
>Also:
>  
>
This is actually correct I think.  Since a Numeric array supports the 
buffer protocol,  it can be used as a buffer.  The "memory" output shown 
above is the output from one particular kind of buffer;  i.e. it's the 
repr of numarray's memory object.

>  
>
>>>>na
>>>>        
>>>>
>Traceback (most recent call last):
>  File "<stdin>", line 1, in ?
>  File "/usr/lib/python2.4/site-packages/numarray/numarraycore.py", line
>930, in __repr__
>    return array_repr(self)
>  File "/usr/lib/python2.4/site-packages/numarray/numarraycore.py", line
>1660, in array_repr
>    lst = arrayprint.array2string(
>  File "/usr/lib/python2.4/site-packages/numarray/arrayprint.py", line
>188, in array2string
>    separator, prefix)
>  File "/usr/lib/python2.4/site-packages/numarray/arrayprint.py", line
>140, in _array2string
>    data = _gen.ravel(a)
>  File "/usr/lib/python2.4/site-packages/numarray/numarraycore.py", line
>918, in copy
>    c = _gen.NDArray.copy(self)
>  File "/usr/lib/python2.4/site-packages/numarray/generic.py", line 724,
>in copy
>    arr._itemsize)
>TypeError: NA_maybeLongsFromIntTuple: must be a sequence of integers.
>  
>
For some reason Numeric was returning None for __array_strides__ and 
hence this message.   I fixed array() so that strides are not set when 
__array_strides__ returns None.

With the added complexity of handling the shape, type, and copy 
parameters in array(),   I'm now seeing performance like this from CVS:

numarray-->Numeric array_if: [0.40103912353515625, 0.41119098663330078, 
0.41148614883422852]
numarray-->Numeric fromstring: [0.24139499664306641, 
0.27034401893615723, 0.21657609939575195]
Numeric-->numarray array_if: [0.81009912490844727, 0.79906082153320312, 
0.74358081817626953]
Numeric-->numarray buffer_if: [0.27320504188537598, 0.22041201591491699, 
0.22667884826660156]

Todd


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