numpy-discussion — Discussion list for all users of Numerical Python

Re: [Numpy-discussion] ndarray.count() ?

[Robert K. <rob...@gm...>]

Martin Spacek wrote:
> What's the most straightforward way to count, say, the number of 1s or 
> Trues in the array? Or the number of any integer?
> 
> I was surprised to discover recently that there isn't a count() method 
> as there is for Python lists. Sorry if this has been discussed already, 
> but I'm wondering if there's a reason for its absence.

Mostly, it's simply easy enough to implement yourself. Not all one-liners should 
be methods on the array object.

   (a == value).sum()

Of course, there are several different things you might do. You might want to 
have multiple values broadcast across the array. You might want to reduce the 
count along a given axis. You might want to use floating point comparison with a 
tolerance. Putting all of those options into one method reduces the convenience 
of that method. Putting a crippled implementation (i.e. just that one-liner) 
expands the already-enormous API of the ndarray object without much benefit.

-- 
Robert Kern

"I have come to believe that the whole world is an enigma, a harmless enigma
  that is made terrible by our own mad attempt to interpret it as though it had
  an underlying truth."
   -- Umberto Eco

Re: [Numpy-discussion] ndarray.count() ?

[Charles R H. <cha...@gm...>]

On 9/7/06, Martin Spacek <sc...@ms...> wrote:
>
> What's the most straightforward way to count, say, the number of 1s or
> Trues in the array? Or the number of any integer?




I was surprised to discover recently that there isn't a count() method
> as there is for Python lists. Sorry if this has been discussed already,
> but I'm wondering if there's a reason for its absence.


I don't know about count, but you can gin up something like this

In [78]: a = ran.randint(0,2, size=(10,))

In [79]: a
Out[79]: array([0, 1, 0, 1, 1, 0, 0, 1, 1, 1])

In [80]: b = sort(a)

In [81]: b.searchsorted(1, side='right') - b.searchsorted(1, side='left')
Out[81]: 6


Which counts the number of ones in a.

I came across a thread in March:
>
> http://aspn.activestate.com/ASPN/Mail/Message/numpy-discussion/3066460
>
> that talked a bit about this in terms of speed, but what about just the
> convenience of having a count() method?
>
> Looks like masked arrays have a count method, don't know much about them
> though.
>
> Also, I understand the inaccuracies when converting between binary and
> decimal floating point representations, and therefore making counting of
> a specific float value in an array somewhat undefined, yet it seems to
> work in Python lists:
>
> >>> 1.1
> 1.1000000000000001
> >>> a=[1.1, 1.1, 1.2]
> >>> a
> [1.1000000000000001, 1.1000000000000001, 1.2]
> >>> a.count(1.1)
> 2
> >>> a.count(1.1000000000000001)
> 2
> >>> a.count(1.2)
> 1


Well, 1.1 == 1.1000000000000001 and that doesn't change. You probably need
to use different precisions to run into problems.

Chuck

[Numpy-discussion] ndarray.count() ?

[Martin S. <sc...@ms...>]

What's the most straightforward way to count, say, the number of 1s or 
Trues in the array? Or the number of any integer?

I was surprised to discover recently that there isn't a count() method 
as there is for Python lists. Sorry if this has been discussed already, 
but I'm wondering if there's a reason for its absence.

I came across a thread in March:

http://aspn.activestate.com/ASPN/Mail/Message/numpy-discussion/3066460

that talked a bit about this in terms of speed, but what about just the 
convenience of having a count() method?

Looks like masked arrays have a count method, don't know much about them 
though.

Also, I understand the inaccuracies when converting between binary and 
decimal floating point representations, and therefore making counting of 
a specific float value in an array somewhat undefined, yet it seems to 
work in Python lists:

 >>> 1.1
1.1000000000000001
 >>> a=[1.1, 1.1, 1.2]
 >>> a
[1.1000000000000001, 1.1000000000000001, 1.2]
 >>> a.count(1.1)
2
 >>> a.count(1.1000000000000001)
2
 >>> a.count(1.2)
1

Comments?

Martin

[Numpy-discussion] 2 GB limit for memmap'ed files

[Glen W. M. <Gle...@sw...>]

A long time ago, Travis wrote:
>  On a related, but orthogonal note:
> 
>  My understanding is that using memory-mapped files for *very* large 
>  files will require modification to the mmap module in Python --- 
>  something I think we should push.  One part of that process would be 
>  to add the C-struct array interface to the mmap module and the buffer 
>  object -- perhaps this is how we get the array interface into Python 
>  quickly.   Then, if we could make a base-type mmap that did not use 
>  the buffer interface or the sequence interface (similar to the 
>  bigndarray in scipy_core) and therefore by-passed the problems with 
>  Python in those areas, then the current mmap object could inherit from 
>  the base class and provide current functionality while still exposing 
>  the array interface for access to >2GB files on 64-bit systems.
> 
>  Who would like to take up the ball for modifying mmap in Python in 
>  this fashion?
> 
>  -Travis

Did anyone ever "pick up the ball" on this issue?

Glen

Re: [Numpy-discussion] Problem with concatenate and object arrays

[Charles R H. <cha...@gm...>]

On 9/7/06, Charles R Harris <cha...@gm...> wrote:
>
>
>
> On 9/7/06, Travis Oliphant <oli...@ie...> wrote:
> >
> > Charles R Harris wrote:
> > > On 9/6/06, *Charles R Harris* <cha...@gm...
> > > <mailto:cha...@gm... >> wrote:
> > >
> > >
> > >
> > >     On 9/6/06, *Travis Oliphant* < oli...@ie...
> > >     <mailto: oli...@ie...>> wrote:
> > >
> > >         Charles R Harris wrote:
> > >         >
> > >         > Where is array at this point?
> > >         Basically it supports the old Numeric behavior wherein object
> > >         array's
> > >         are treated as before *except* for when an error would have
> > >         occurred
> > >         previously when the "new behavior" kicks in.  Anything that
> > >         violates
> > >         that is a bug needing to be fixed.
> > >
> > >         This leaves the new object-array constructor used less
> > >         often.  It could
> > >         be exported explicitly into an oarray constructor, but I'm not
> >
> > >         sure
> > >         about the advantages of that approach.   There are benefits to
> > >         having
> > >         object arrays constructed in the same way as other arrays.  It
> > >         turns out
> > >         many people actually like that feature of Numeric, which is
> > >         the reason I
> > >         didn't go the route of numarray which pulled object arrays
> > out.
> > >
> > >         At this point, however, object arrays can even be part of
> > >         records and so
> > >         need to be an integral part of the data-type description.
> > >         Pulling that
> > >         out is not going to happen.  A more intelligent object-array
> > >         constructor, however, may be a useful tool.
> > >
> > >
> > >     OK. I do have a couple of questions. Let me insert the docs for
> > >     array and asarray :
> > >
> > >         """array(object, dtype=None, copy=1,order=None,
> > subok=0,ndmin=0)
> > >
> > >         Return an array from object with the specified date-type.
> > >
> > >         Inputs:
> > >           object - an array, any object exposing the array interface,
> > any
> > >                     object whose __array__ method returns an array, or
> > any
> > >                     (nested) sequence.
> > >           dtype  - The desired data-type for the array.  If not given,
> > >     then
> > >                     the type will be determined as the minimum type
> > >     required
> > >                     to hold the objects in the sequence.  This
> > >     argument can only
> > >                     be used to 'upcast' the array.  For downcasting,
> > >     use the
> > >                     .astype(t) method.
> > >           copy   - If true, then force a copy.  Otherwise a copy will
> > >     only occur
> > >                     if __array__ returns a copy, obj is a nested
> > >     sequence, or
> > >                     a copy is needed to satisfy any of the other
> > >     requirements
> > >           order  - Specify the order of the array.  If order is 'C',
> > >     then the
> > >                     array will be in C-contiguous order (last-index
> > >     varies the
> > >                     fastest).  If order is 'FORTRAN', then the
> > >     returned array
> > >                     will be in Fortran-contiguous order (first-index
> > >     varies the
> > >                     fastest).  If order is None, then the returned
> > >     array may
> > >                     be in either C-, or Fortran-contiguous order or
> > even
> > >                     discontiguous.
> > >           subok  - If True, then sub-classes will be passed-through,
> > >     otherwise
> > >                     the returned array will be forced to be a
> > >     base-class array
> > >           ndmin  - Specifies the minimum number of dimensions that the
> > >     resulting
> > >                     array should have.  1's will be pre-pended to the
> > >     shape as
> > >                     needed to meet this requirement.
> > >
> > >         """)
> > >
> > >     asarray(a, dtype=None, order=None)
> > >         Returns a as an array.
> > >
> > >         Unlike array(), no copy is performed if a is already an array.
> > >     Subclasses
> > >         are converted to base class ndarray.
> > >
> > >     1) Is it true that array doesn't always return a copy except by
> > >     default? asarray says it contrasts with array in this regard.
> > >     Maybe copy=0 should be deprecated.
> > >
> > >     2) Is asarray is basically array with copy=0?
> > >
> > >     3) Is asanyarray basically array with copy=0 and subok=1?
> > >
> > >     4) Is there some sort of precedence table for conversions? To me
> > >     it looks like the most deeply nested lists are converted to arrays
> > >     first, numeric if they contain all numeric types, object
> > >     otherwise. I assume the algorithm then ascends up through the
> > >     hierarchy like traversing a binary tree in postorder?
> > >
> > >     5) All nesting must be to the same depth and the deepest nested
> > >     items must have the same length.
> > >
> > >     6) How is the difference between lists and "lists" determined, i.e
> > .,
> > >
> > >     In [3]: array([list([1,2,3]),list([1,2])], dtype = object)
> > >     Out[3]: array([[1, 2, 3], [1, 2]], dtype=object)
> > >
> > >     In [8]: array([array([1,2,3]),array([1,2])], dtype = object)
> > >     Out[8]: array([[1 2 3], [1 2]], dtype=object)
> > >
> > >
> > >     In [9]: array([1,2,3],[1,2]], dtype = object)
> > >     ------------------------------------------------------------
> > >        File "<ipython console>", line 1
> > >          array([1,2,3],[1,2]], dtype = object)
> > >                             ^
> > >     SyntaxError: invalid syntax
> > >
> > >     Is the difference that list(...) and array(...) are passed as
> > >     functions (lazy evaluation), but a list is just a list?
> > >
> > >     Sorry to be asking all these questions, but I would like to try
> > >     making the documentation be a bit of a reference. I am sure I will
> > >     have more questions ;)
> > >
> > >         -Travis
> > >
> > >
> > > And, voila, ragged arrays:
> > >
> > > In [9]: a = array([array([1,2,3]),array([1,2])], dtype = object)
> > >
> > > In [10]: a*2
> > > Out[10]: array([[2 4 6], [2 4]], dtype=object)
> > >
> > > In [11]: a + a
> > > Out[11]: array([[2 4 6], [2 4]], dtype=object)
> >
> > Now I remember that this was my original motivation for futzing with the
> >
> > object-array constructor in the first place.  So, now you get there only
> > after an attempt to make a "rectangular" array first.
> >
> > -Travis
>
>
> So is this intentional?
>
> In [24]: a = array([[],[],[]], dtype=object)
>
> In [25]: a.shape
> Out[25]: (3, 0)
>
> In [26]: a = array([], dtype=object)
>
> In [27]: a.shape
> Out[27]: (0,)
>
> One could argue that the first array should have shape (3,)
>

And this doesn't look quite right:

In [38]: a = array([[1],[2],[3]], dtype=object)

In [39]: a.shape
Out[39]: (3, 1)

In [40]: a = array([[1],[2,3],[4,5]], dtype=object)

In [41]: a.shape
Out[41]: (3,)

Chuck

Re: [Numpy-discussion] Problem with concatenate and object arrays

[Charles R H. <cha...@gm...>]

On 9/7/06, Travis Oliphant <oli...@ie...> wrote:
>
> Charles R Harris wrote:
> > On 9/6/06, *Charles R Harris* <cha...@gm...
> > <mailto:cha...@gm...>> wrote:
> >
> >
> >
> >     On 9/6/06, *Travis Oliphant* < oli...@ie...
> >     <mailto:oli...@ie...>> wrote:
> >
> >         Charles R Harris wrote:
> >         >
> >         > Where is array at this point?
> >         Basically it supports the old Numeric behavior wherein object
> >         array's
> >         are treated as before *except* for when an error would have
> >         occurred
> >         previously when the "new behavior" kicks in.  Anything that
> >         violates
> >         that is a bug needing to be fixed.
> >
> >         This leaves the new object-array constructor used less
> >         often.  It could
> >         be exported explicitly into an oarray constructor, but I'm not
> >         sure
> >         about the advantages of that approach.   There are benefits to
> >         having
> >         object arrays constructed in the same way as other arrays.  It
> >         turns out
> >         many people actually like that feature of Numeric, which is
> >         the reason I
> >         didn't go the route of numarray which pulled object arrays out.
> >
> >         At this point, however, object arrays can even be part of
> >         records and so
> >         need to be an integral part of the data-type description.
> >         Pulling that
> >         out is not going to happen.  A more intelligent object-array
> >         constructor, however, may be a useful tool.
> >
> >
> >     OK. I do have a couple of questions. Let me insert the docs for
> >     array and asarray :
> >
> >         """array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0)
> >
> >         Return an array from object with the specified date-type.
> >
> >         Inputs:
> >           object - an array, any object exposing the array interface,
> any
> >                     object whose __array__ method returns an array, or
> any
> >                     (nested) sequence.
> >           dtype  - The desired data-type for the array.  If not given,
> >     then
> >                     the type will be determined as the minimum type
> >     required
> >                     to hold the objects in the sequence.  This
> >     argument can only
> >                     be used to 'upcast' the array.  For downcasting,
> >     use the
> >                     .astype(t) method.
> >           copy   - If true, then force a copy.  Otherwise a copy will
> >     only occur
> >                     if __array__ returns a copy, obj is a nested
> >     sequence, or
> >                     a copy is needed to satisfy any of the other
> >     requirements
> >           order  - Specify the order of the array.  If order is 'C',
> >     then the
> >                     array will be in C-contiguous order (last-index
> >     varies the
> >                     fastest).  If order is 'FORTRAN', then the
> >     returned array
> >                     will be in Fortran-contiguous order (first-index
> >     varies the
> >                     fastest).  If order is None, then the returned
> >     array may
> >                     be in either C-, or Fortran-contiguous order or even
> >                     discontiguous.
> >           subok  - If True, then sub-classes will be passed-through,
> >     otherwise
> >                     the returned array will be forced to be a
> >     base-class array
> >           ndmin  - Specifies the minimum number of dimensions that the
> >     resulting
> >                     array should have.  1's will be pre-pended to the
> >     shape as
> >                     needed to meet this requirement.
> >
> >         """)
> >
> >     asarray(a, dtype=None, order=None)
> >         Returns a as an array.
> >
> >         Unlike array(), no copy is performed if a is already an array.
> >     Subclasses
> >         are converted to base class ndarray.
> >
> >     1) Is it true that array doesn't always return a copy except by
> >     default? asarray says it contrasts with array in this regard.
> >     Maybe copy=0 should be deprecated.
> >
> >     2) Is asarray is basically array with copy=0?
> >
> >     3) Is asanyarray basically array with copy=0 and subok=1?
> >
> >     4) Is there some sort of precedence table for conversions? To me
> >     it looks like the most deeply nested lists are converted to arrays
> >     first, numeric if they contain all numeric types, object
> >     otherwise. I assume the algorithm then ascends up through the
> >     hierarchy like traversing a binary tree in postorder?
> >
> >     5) All nesting must be to the same depth and the deepest nested
> >     items must have the same length.
> >
> >     6) How is the difference between lists and "lists" determined, i.e.,
> >
> >     In [3]: array([list([1,2,3]),list([1,2])], dtype = object)
> >     Out[3]: array([[1, 2, 3], [1, 2]], dtype=object)
> >
> >     In [8]: array([array([1,2,3]),array([1,2])], dtype = object)
> >     Out[8]: array([[1 2 3], [1 2]], dtype=object)
> >
> >
> >     In [9]: array([1,2,3],[1,2]], dtype = object)
> >     ------------------------------------------------------------
> >        File "<ipython console>", line 1
> >          array([1,2,3],[1,2]], dtype = object)
> >                             ^
> >     SyntaxError: invalid syntax
> >
> >     Is the difference that list(...) and array(...) are passed as
> >     functions (lazy evaluation), but a list is just a list?
> >
> >     Sorry to be asking all these questions, but I would like to try
> >     making the documentation be a bit of a reference. I am sure I will
> >     have more questions ;)
> >
> >         -Travis
> >
> >
> > And, voila, ragged arrays:
> >
> > In [9]: a = array([array([1,2,3]),array([1,2])], dtype = object)
> >
> > In [10]: a*2
> > Out[10]: array([[2 4 6], [2 4]], dtype=object)
> >
> > In [11]: a + a
> > Out[11]: array([[2 4 6], [2 4]], dtype=object)
>
> Now I remember that this was my original motivation for futzing with the
> object-array constructor in the first place.  So, now you get there only
> after an attempt to make a "rectangular" array first.
>
> -Travis


So is this intentional?

In [24]: a = array([[],[],[]], dtype=object)

In [25]: a.shape
Out[25]: (3, 0)

In [26]: a = array([], dtype=object)

In [27]: a.shape
Out[27]: (0,)

One could argue that the first array should have shape (3,)

Chuck

[Numpy-discussion] reshape: missing arg check?

[Sven S. <sve...@gm...>]

Hi,
never mind that the following syntax is wrong, but is it supposed to
yield that SystemError instead of something more informative?

(This is with b5 on win32 and python 2.4.3)

>>> b.reshape(3,3,axis = 1)
Traceback (most recent call last):
  File "<interactive input>", line 1, in ?
SystemError: NULL result without error in PyObject_Call

-sven

[Numpy-discussion] Fix to Int64/string numexpr support

[Ivan V. i B. <iv...@ca...>]

Hi all, I've detected some small errors in the patches I sent some time
ago for adding Int64 and string support to numexpr (see
http://www.mail-archive.com/numpy-discussion%40lists.sourceforge.net/msg01551.html).
 Basically:

* ``numpy.string`` was accessed instead of ``numpy.string_`` (looks like
  one of those not-so-just-aesthetical last-time changes).
* The ``copy_args`` argument to ``evaluate()`` and others was no longer
  needed since discontiguous/unaligned arrays are no longer a special
  case.

I have attached the necessary patch.  Bye!

::

	Ivan Vilata i Balaguer   >qo<   http://www.carabos.com/
	       Cárabos Coop. V.  V  V   Enjoy Data
	                          ""

[Numpy-discussion] =?GB2312?B?RE9FzO+/2sq9xrfWyrmks8w=?=

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            <font size="2"><b><font color="#FF0000">1.田口式品质工程的思想方法</font></b><br>
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   设计出总成本最低的最优化的制造方法(参数)<br>
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            正交表数据分析<br>
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            望小特性的信号杂音比法数据处理和最优化选择<br>
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            如何通过确认实验确定最佳的技术条件<br>
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  某电路板厂回流焊工序的工艺研究<br>
  某橡胶制品公司的配方研究</font><br>
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Re: [Numpy-discussion] Problem with concatenate and object arrays

[Travis O. <oli...@ie...>]

Charles R Harris wrote:
> OK. I do have a couple of questions. Let me insert the docs for array 
> and asarray :
>
>     """array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0)
>
>     Return an array from object with the specified date-type.
>
> 1) Is it true that array doesn't always return a copy except by 
> default? asarray says it contrasts with array in this regard. Maybe 
> copy=0 should be deprecated.
array is the main creation function.   It is a loose wrapper around 
PyArray_fromAny.   copy=0 means don't copy unless you have to.

> 2) Is asarray is basically array with copy=0?
Yes.
>
> 3) Is asanyarray basically array with copy=0 and subok=1?
Yes.
>
> 4) Is there some sort of precedence table for conversions? To me it 
> looks like the most deeply nested lists are converted to arrays first, 
> numeric if they contain all numeric types, object otherwise. I assume 
> the algorithm then ascends up through the hierarchy like traversing a 
> binary tree in postorder?
I'm not sure I understand what you mean.  The discover-depth and 
discover-dimensions algorithm figures out what the shape should be and 
then recursive PySequence_GetItem and PySequence_SetItem is used to copy 
the information over to the ndarray from the nested sequence.

>
> 5) All nesting must be to the same depth and the deepest nested items 
> must have the same length.
Yes, there are routines discover_depth and discover_dimensions that are 
the actual algorithm used.  These are adapted from Numeric.
>
> 6) How is the difference between lists and "lists" determined, i.e.,
>
> In [3]: array([list([1,2,3]),list([1,2])], dtype = object)
> Out[3]: array([[1, 2, 3], [1, 2]], dtype=object)
>
> In [8]: array([array([1,2,3]),array([1,2])], dtype = object)
> Out[8]: array([[1 2 3], [1 2]], dtype=object)
>
>
> In [9]: array([1,2,3],[1,2]], dtype = object)
> ------------------------------------------------------------
>    File "<ipython console>", line 1
>      array([1,2,3],[1,2]], dtype = object)
>                         ^
> SyntaxError: invalid syntax

I think this is just due to a missing [ in In [9].   There is no 
semantic difference between
list([1,2,3]) and [1,2,3] (NumPy will see those things as exactly the 
same).
>
> Is the difference that list(...) and array(...) are passed as 
> functions (lazy evaluation), but a list is just a list?
There is nothing like "lazy evaluation" going on.  array([1,2,3]) is 
evaluated returning an object and array([1,2]) is evaluated returning an 
object and then the two are put into another object array.  Equivalent code

a = array([1,2,3])
b = array([1,2])
c = array([a,b],dtype=object)


Thanks for all your help with documentation.  It is very-much appreciated.

-Travis

Re: [Numpy-discussion] Problem with concatenate and object arrays

[Travis O. <oli...@ie...>]

Charles R Harris wrote:
> On 9/6/06, *Charles R Harris* <cha...@gm... 
> <mailto:cha...@gm...>> wrote:
>
>
>
>     On 9/6/06, *Travis Oliphant* < oli...@ie...
>     <mailto:oli...@ie...>> wrote:
>
>         Charles R Harris wrote:
>         >
>         > Where is array at this point?
>         Basically it supports the old Numeric behavior wherein object
>         array's
>         are treated as before *except* for when an error would have
>         occurred
>         previously when the "new behavior" kicks in.  Anything that
>         violates
>         that is a bug needing to be fixed.
>
>         This leaves the new object-array constructor used less
>         often.  It could
>         be exported explicitly into an oarray constructor, but I'm not
>         sure
>         about the advantages of that approach.   There are benefits to
>         having
>         object arrays constructed in the same way as other arrays.  It
>         turns out
>         many people actually like that feature of Numeric, which is
>         the reason I
>         didn't go the route of numarray which pulled object arrays out.
>
>         At this point, however, object arrays can even be part of
>         records and so
>         need to be an integral part of the data-type description.  
>         Pulling that
>         out is not going to happen.  A more intelligent object-array
>         constructor, however, may be a useful tool. 
>
>
>     OK. I do have a couple of questions. Let me insert the docs for
>     array and asarray :
>
>         """array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0)
>
>         Return an array from object with the specified date-type.
>
>         Inputs:
>           object - an array, any object exposing the array interface, any
>                     object whose __array__ method returns an array, or any
>                     (nested) sequence.
>           dtype  - The desired data-type for the array.  If not given,
>     then
>                     the type will be determined as the minimum type
>     required
>                     to hold the objects in the sequence.  This
>     argument can only
>                     be used to 'upcast' the array.  For downcasting,
>     use the
>                     .astype(t) method.
>           copy   - If true, then force a copy.  Otherwise a copy will
>     only occur
>                     if __array__ returns a copy, obj is a nested
>     sequence, or
>                     a copy is needed to satisfy any of the other
>     requirements
>           order  - Specify the order of the array.  If order is 'C',
>     then the
>                     array will be in C-contiguous order (last-index
>     varies the
>                     fastest).  If order is 'FORTRAN', then the
>     returned array
>                     will be in Fortran-contiguous order (first-index
>     varies the
>                     fastest).  If order is None, then the returned
>     array may
>                     be in either C-, or Fortran-contiguous order or even
>                     discontiguous.
>           subok  - If True, then sub-classes will be passed-through,
>     otherwise
>                     the returned array will be forced to be a
>     base-class array
>           ndmin  - Specifies the minimum number of dimensions that the
>     resulting
>                     array should have.  1's will be pre-pended to the
>     shape as
>                     needed to meet this requirement.
>
>         """)
>
>     asarray(a, dtype=None, order=None)
>         Returns a as an array.
>
>         Unlike array(), no copy is performed if a is already an array.
>     Subclasses
>         are converted to base class ndarray.
>
>     1) Is it true that array doesn't always return a copy except by
>     default? asarray says it contrasts with array in this regard.
>     Maybe copy=0 should be deprecated.
>
>     2) Is asarray is basically array with copy=0?
>
>     3) Is asanyarray basically array with copy=0 and subok=1?
>
>     4) Is there some sort of precedence table for conversions? To me
>     it looks like the most deeply nested lists are converted to arrays
>     first, numeric if they contain all numeric types, object
>     otherwise. I assume the algorithm then ascends up through the
>     hierarchy like traversing a binary tree in postorder?
>
>     5) All nesting must be to the same depth and the deepest nested
>     items must have the same length.
>
>     6) How is the difference between lists and "lists" determined, i.e.,
>
>     In [3]: array([list([1,2,3]),list([1,2])], dtype = object)
>     Out[3]: array([[1, 2, 3], [1, 2]], dtype=object)
>
>     In [8]: array([array([1,2,3]),array([1,2])], dtype = object)
>     Out[8]: array([[1 2 3], [1 2]], dtype=object)
>
>
>     In [9]: array([1,2,3],[1,2]], dtype = object)
>     ------------------------------------------------------------
>        File "<ipython console>", line 1
>          array([1,2,3],[1,2]], dtype = object)
>                             ^
>     SyntaxError: invalid syntax
>
>     Is the difference that list(...) and array(...) are passed as
>     functions (lazy evaluation), but a list is just a list?
>
>     Sorry to be asking all these questions, but I would like to try
>     making the documentation be a bit of a reference. I am sure I will
>     have more questions ;)
>
>         -Travis
>
>
> And, voila, ragged arrays:
>
> In [9]: a = array([array([1,2,3]),array([1,2])], dtype = object)
>
> In [10]: a*2
> Out[10]: array([[2 4 6], [2 4]], dtype=object)
>
> In [11]: a + a
> Out[11]: array([[2 4 6], [2 4]], dtype=object)

Now I remember that this was my original motivation for futzing with the 
object-array constructor in the first place.  So, now you get there only 
after an attempt to make a "rectangular" array first.

-Travis

Re: [Numpy-discussion] Problem with concatenate and object arrays

[A. M. A. <per...@gm...>]

On 06/09/06, Charles R Harris <cha...@gm...> wrote:
> On 9/6/06, Charles R Harris <cha...@gm...> wrote:
>
> >       order  - Specify the order of the array.  If order is 'C', then the
> >                 array will be in C-contiguous order (last-index varies the
> >                 fastest).  If order is 'FORTRAN', then the returned array
> >                 will be in Fortran-contiguous order (first-index varies
> the
> >                 fastest).  If order is None, then the returned array may
> >                 be in either C-, or Fortran-contiguous order or even
> >                 discontiguous.

This one's a bit complicated. If array() is passed a list of lists,
there are two different orders that are relevant - the output order of
the array, and the order used to interpret the input. I suppose that
if L is a lost of lists, array(L)[2,3]==L[2][3], that is, in some
sense the arrays are always logically C-ordered even if the underlying
representation is different. Does it make sense to specify this
somewhere in the docstring? At least it would be good to make it clear
that the order parameter affects only the underlying storage format,
and not the indexing of the array.

A. M. Archibald

Re: [Numpy-discussion] Problem with concatenate and object arrays

[Charles R H. <cha...@gm...>]

On 9/6/06, Charles R Harris <cha...@gm...> wrote:
>
>
>
> On 9/6/06, Travis Oliphant <oli...@ie...> wrote:
> >
> > Charles R Harris wrote:
> > >
> > > Where is array at this point?
> > Basically it supports the old Numeric behavior wherein object array's
> > are treated as before *except* for when an error would have occurred
> > previously when the "new behavior" kicks in.  Anything that violates
> > that is a bug needing to be fixed.
> >
> > This leaves the new object-array constructor used less often.  It could
> > be exported explicitly into an oarray constructor, but I'm not sure
> > about the advantages of that approach.   There are benefits to having
> > object arrays constructed in the same way as other arrays.  It turns out
> > many people actually like that feature of Numeric, which is the reason I
> > didn't go the route of numarray which pulled object arrays out.
> >
> > At this point, however, object arrays can even be part of records and so
> > need to be an integral part of the data-type description.   Pulling that
> > out is not going to happen.  A more intelligent object-array
> > constructor, however, may be a useful tool.
>
>
> OK. I do have a couple of questions. Let me insert the docs for array and
> asarray :
>
>     """array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0)
>
>     Return an array from object with the specified date-type.
>
>     Inputs:
>       object - an array, any object exposing the array interface, any
>                 object whose __array__ method returns an array, or any
>                 (nested) sequence.
>       dtype  - The desired data-type for the array.  If not given, then
>                 the type will be determined as the minimum type required
>                 to hold the objects in the sequence.  This argument can
> only
>                 be used to 'upcast' the array.  For downcasting, use the
>                 .astype(t) method.
>       copy   - If true, then force a copy.  Otherwise a copy will only
> occur
>                 if __array__ returns a copy, obj is a nested sequence, or
>                 a copy is needed to satisfy any of the other requirements
>       order  - Specify the order of the array.  If order is 'C', then the
>                 array will be in C-contiguous order (last-index varies the
>                 fastest).  If order is 'FORTRAN', then the returned array
>                 will be in Fortran-contiguous order (first-index varies
> the
>                 fastest).  If order is None, then the returned array may
>                 be in either C-, or Fortran-contiguous order or even
>                 discontiguous.
>       subok  - If True, then sub-classes will be passed-through, otherwise
>                 the returned array will be forced to be a base-class array
>       ndmin  - Specifies the minimum number of dimensions that the
> resulting
>                 array should have.  1's will be pre-pended to the shape as
>                 needed to meet this requirement.
>
>     """)
>
> asarray(a, dtype=None, order=None)
>     Returns a as an array.
>
>     Unlike array(), no copy is performed if a is already an array.
> Subclasses
>     are converted to base class ndarray.
>
> 1) Is it true that array doesn't always return a copy except by default?
> asarray says it contrasts with array in this regard. Maybe copy=0 should be
> deprecated.
>
> 2) Is asarray is basically array with copy=0?
>
> 3) Is asanyarray basically array with copy=0 and subok=1?
>
> 4) Is there some sort of precedence table for conversions? To me it looks
> like the most deeply nested lists are converted to arrays first, numeric if
> they contain all numeric types, object otherwise. I assume the algorithm
> then ascends up through the hierarchy like traversing a binary tree in
> postorder?
>
> 5) All nesting must be to the same depth and the deepest nested items must
> have the same length.
>
> 6) How is the difference between lists and "lists" determined, i.e.,
>
> In [3]: array([list([1,2,3]),list([1,2])], dtype = object)
> Out[3]: array([[1, 2, 3], [1, 2]], dtype=object)
>
> In [8]: array([array([1,2,3]),array([1,2])], dtype = object)
> Out[8]: array([[1 2 3], [1 2]], dtype=object)
>
>
> In [9]: array([1,2,3],[1,2]], dtype = object)
> ------------------------------------------------------------
>    File "<ipython console>", line 1
>      array([1,2,3],[1,2]], dtype = object)
>                         ^
> SyntaxError: invalid syntax
>
> Is the difference that list(...) and array(...) are passed as functions
> (lazy evaluation), but a list is just a list?
>
> Sorry to be asking all these questions, but I would like to try making the
> documentation be a bit of a reference. I am sure I will have more questions
> ;)
>
> -Travis
>
>
And, voila, ragged arrays:

In [9]: a = array([array([1,2,3]),array([1,2])], dtype = object)

In [10]: a*2
Out[10]: array([[2 4 6], [2 4]], dtype=object)

In [11]: a + a
Out[11]: array([[2 4 6], [2 4]], dtype=object)

Chuck

Re: [Numpy-discussion] Problem with concatenate and object arrays

[Charles R H. <cha...@gm...>]

On 9/6/06, Travis Oliphant <oli...@ie...> wrote:
>
> Charles R Harris wrote:
> >
> > Where is array at this point?
> Basically it supports the old Numeric behavior wherein object array's
> are treated as before *except* for when an error would have occurred
> previously when the "new behavior" kicks in.  Anything that violates
> that is a bug needing to be fixed.
>
> This leaves the new object-array constructor used less often.  It could
> be exported explicitly into an oarray constructor, but I'm not sure
> about the advantages of that approach.   There are benefits to having
> object arrays constructed in the same way as other arrays.  It turns out
> many people actually like that feature of Numeric, which is the reason I
> didn't go the route of numarray which pulled object arrays out.
>
> At this point, however, object arrays can even be part of records and so
> need to be an integral part of the data-type description.   Pulling that
> out is not going to happen.  A more intelligent object-array
> constructor, however, may be a useful tool.


OK. I do have a couple of questions. Let me insert the docs for array and
asarray :

    """array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0)

    Return an array from object with the specified date-type.

    Inputs:
      object - an array, any object exposing the array interface, any
                object whose __array__ method returns an array, or any
                (nested) sequence.
      dtype  - The desired data-type for the array.  If not given, then
                the type will be determined as the minimum type required
                to hold the objects in the sequence.  This argument can only
                be used to 'upcast' the array.  For downcasting, use the
                .astype(t) method.
      copy   - If true, then force a copy.  Otherwise a copy will only occur
                if __array__ returns a copy, obj is a nested sequence, or
                a copy is needed to satisfy any of the other requirements
      order  - Specify the order of the array.  If order is 'C', then the
                array will be in C-contiguous order (last-index varies the
                fastest).  If order is 'FORTRAN', then the returned array
                will be in Fortran-contiguous order (first-index varies the
                fastest).  If order is None, then the returned array may
                be in either C-, or Fortran-contiguous order or even
                discontiguous.
      subok  - If True, then sub-classes will be passed-through, otherwise
                the returned array will be forced to be a base-class array
      ndmin  - Specifies the minimum number of dimensions that the resulting
                array should have.  1's will be pre-pended to the shape as
                needed to meet this requirement.

    """)

asarray(a, dtype=None, order=None)
    Returns a as an array.

    Unlike array(), no copy is performed if a is already an array.
Subclasses
    are converted to base class ndarray.

1) Is it true that array doesn't always return a copy except by default?
asarray says it contrasts with array in this regard. Maybe copy=0 should be
deprecated.

2) Is asarray is basically array with copy=0?

3) Is asanyarray basically array with copy=0 and subok=1?

4) Is there some sort of precedence table for conversions? To me it looks
like the most deeply nested lists are converted to arrays first, numeric if
they contain all numeric types, object otherwise. I assume the algorithm
then ascends up through the hierarchy like traversing a binary tree in
postorder?

5) All nesting must be to the same depth and the deepest nested items must
have the same length.

6) How is the difference between lists and "lists" determined, i.e.,

In [3]: array([list([1,2,3]),list([1,2])], dtype = object)
Out[3]: array([[1, 2, 3], [1, 2]], dtype=object)

In [8]: array([array([1,2,3]),array([1,2])], dtype = object)
Out[8]: array([[1 2 3], [1 2]], dtype=object)


In [9]: array([1,2,3],[1,2]], dtype = object)
------------------------------------------------------------
   File "<ipython console>", line 1
     array([1,2,3],[1,2]], dtype = object)
                        ^
SyntaxError: invalid syntax

Is the difference that list(...) and array(...) are passed as functions
(lazy evaluation), but a list is just a list?

Sorry to be asking all these questions, but I would like to try making the
documentation be a bit of a reference. I am sure I will have more questions
;)

-Travis


Chuck

[Numpy-discussion] f2py with xmingw

[Flavio C. <fcc...@gm...>]

Hi,

I have a module that uses a Fortran extension which I would like to compile
for windows with f2py.
I wonder If I could do this from Linux using xmingw. Has anyone tried this?

thanks,

--=20
Fl=E1vio Code=E7o Coelho
registered Linux user # 386432
---------------------------
"Laws are like sausages. It's better not to see them being made."
Otto von Bismark

Re: [Numpy-discussion] Numpy that is compatible with Scipy windows installer version

[Ryan K. <rya...@gm...>]

Thanks Chris, that worked great.

Ryan

On 9/6/06, Christopher Barker <Chr...@no...> wrote:
> Ryan Krauss wrote:
> > Is there a compatible matplotlib as well?  I was o.k. with mpl from
> > enthought until I switched numerix to numpy.  That made mpl unhappy.
> > I downloaded 0.87.5 but I broke something in the process because now
> > even switching back to Numeric doesn't make mpl happy.
>
> There was an error with the first MPL 0.87.5 builds uploaded. See the
> MPL list for more info, but these builds should work:
>
> http://euclid.uits.iupui.edu/mplfiles/
>
> -Chris
>
>
> --
> Christopher Barker, Ph.D.
> Oceanographer
>
> NOAA/OR&R/HAZMAT         (206) 526-6959   voice
> 7600 Sand Point Way NE   (206) 526-6329   fax
> Seattle, WA  98115       (206) 526-6317   main reception
>
> Chr...@no...
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] Problem with concatenate and object arrays

[Travis O. <oli...@ie...>]

Charles R Harris wrote:
>
> Where is array at this point?
Basically it supports the old Numeric behavior wherein object array's 
are treated as before *except* for when an error would have occurred 
previously when the "new behavior" kicks in.  Anything that violates 
that is a bug needing to be fixed.

This leaves the new object-array constructor used less often.  It could 
be exported explicitly into an oarray constructor, but I'm not sure 
about the advantages of that approach.   There are benefits to having 
object arrays constructed in the same way as other arrays.  It turns out 
many people actually like that feature of Numeric, which is the reason I 
didn't go the route of numarray which pulled object arrays out.

At this point, however, object arrays can even be part of records and so 
need to be an integral part of the data-type description.   Pulling that 
out is not going to happen.  A more intelligent object-array 
constructor, however, may be a useful tool.

-Travis

Re: [Numpy-discussion] numpy.fftpack: axis=None ?

[Travis O. <oli...@ie...>]

PGM wrote:
> Folks,
> I was playing around the numpy.fftpack when I ran into the problem below:
> it seems that axis=None is not valid with fft. Is there a reason for that ?
Not really, other than history and the fact that sub-packages of NumPy 
will often define their own behavior. 

Perhaps we could implement it, but would this create more confusion as 
usually when axis=None is accepted then it is the default. 

-Travis

Re: [Numpy-discussion] Problem with concatenate and object arrays

[Charles R H. <cha...@gm...>]

On 9/5/06, Travis Oliphant <oli...@ie...> wrote:
>
> Matthew Brett wrote:
> > Hi,
> >
> >
> >> This is a result of PyArray_FromAny changing when object arrays are
> >> explicitly requested (which they are in this case --- although behind
> >> the scenes).
> >>
> >
> > Hmm - I think I am hitting a related bug/feature/surprising change in
> > behavior, which is showing up rather obscurely in a failure of the
> > scipy.io matlab loading tests:
> >
> > http://projects.scipy.org/scipy/scipy/ticket/258
> >
> > Here's the change I wasn't expecting, present with current SVN:
> >
> > a = arange(2)
> > b = arange(1)
> > c = array([a, b], dtype=object)
> > c
> > ->
> > array([[0, 1],
> >        [0, 0]], dtype=object)
> >
> > On a previous version of numpy (1.02b.dev2975) I get the answer I was
> expecting:
> >
> > array([[0], [0 1]], dtype=object)
> >
>
> This should now be fixed.  The code was inappropriately not checking for
> dimensions when object arrays were being constructed.  Now, it raises
> the appropriate error and then interprets it correctly using the extra
> object creation code.
>
> Users of scipy 0.5.1 will only have to upgrade NumPy to get the fix (the
> SciPy install won't have to be re-built).
>
> -Travis


Where is array at this point? I would like to review the documented
behaviour and make modifications to the document string if required. What
about Robert's idea of a separate constructor for object arrays? Is it
something we could introduce on top of the current array constructor? I
realize that if we restrict the current array constructor there might be
compatibility problems with Numeric code, but introducing something like
oarray as a shorthand for object arrays might incourage it's use. Robert
also said that Numarray dealt with object arrays as a separate issue and I
wonder what they did that we should think about.

Chuck

Re: [Numpy-discussion] Numpy that is compatible with Scipy windows installer version

[Christopher B. <Chr...@no...>]

Ryan Krauss wrote:
> Is there a compatible matplotlib as well?  I was o.k. with mpl from
> enthought until I switched numerix to numpy.  That made mpl unhappy.
> I downloaded 0.87.5 but I broke something in the process because now
> even switching back to Numeric doesn't make mpl happy.

There was an error with the first MPL 0.87.5 builds uploaded. See the 
MPL list for more info, but these builds should work:

http://euclid.uits.iupui.edu/mplfiles/

-Chris


-- 
Christopher Barker, Ph.D.
Oceanographer
                                     		
NOAA/OR&R/HAZMAT         (206) 526-6959   voice
7600 Sand Point Way NE   (206) 526-6329   fax
Seattle, WA  98115       (206) 526-6317   main reception

Chr...@no...

Re: [Numpy-discussion] Numpy that is compatible with Scipy windows installer version

[bryce h. <bhe...@en...>]

Ryan,

The Enthought 1.0.0 release shipped a pre-release of matplotlib 0.87.3, 
svn revision 2478. If you update numpy, you have to update to matching 
versions of scipy and matplotlib. If you are using binary releases, 
guessing which version of each is needed is difficult unless builders of 
the binaries specify exactly which version of numpy was used.

If you're comfortable building these packages yourself, the simple 
solution is to download the latest numpy, install it over the existing 
numpy, get the latest scipy & matplot lib sources, build them and 
install them over the existing packages.

Bryce

Ryan Krauss wrote:
> O.K. I think I am up and running.  I actually installed Entought and
> Numpy and Scipy worked together there.  I then upgraded the scipy and
> numpy packages.
>
> Is there a compatible matplotlib as well?  I was o.k. with mpl from
> enthought until I switched numerix to numpy.  That made mpl unhappy.
> I downloaded 0.87.5 but I broke something in the process because now
> even switching back to Numeric doesn't make mpl happy.  I may switch
> to the mpl list if this keeps giving me problems.
>
> Ryan
>
> On 9/6/06, James Graham <jg...@ca...> wrote:
>   
>> Ryan Krauss wrote:
>>     
>>> I would be glad to try that.  Where do I get the installer for scipy 0.51?
>>>       
>> http://prdownloads.sourceforge.net/scipy/scipy-0.5.1.win32-py2.4.exe?download
>>
>> The SciPy website needs to be updated.
>>
>> --
>> "Eternity's a terrible thought. I mean, where's it all going to end?"
>>   -- Tom Stoppard, Rosencrantz and Guildenstern are Dead
>>
>> -------------------------------------------------------------------------
>> Using Tomcat but need to do more? Need to support web services, security?
>> Get stuff done quickly with pre-integrated technology to make your job easier
>> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
>> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
>> _______________________________________________
>> Numpy-discussion mailing list
>> Num...@li...
>> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>>
>>     
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
>   

Re: [Numpy-discussion] Numpy that is compatible with Scipy windows installer version

[Ryan K. <rya...@gm...>]

O.K. I think I am up and running.  I actually installed Entought and
Numpy and Scipy worked together there.  I then upgraded the scipy and
numpy packages.

Is there a compatible matplotlib as well?  I was o.k. with mpl from
enthought until I switched numerix to numpy.  That made mpl unhappy.
I downloaded 0.87.5 but I broke something in the process because now
even switching back to Numeric doesn't make mpl happy.  I may switch
to the mpl list if this keeps giving me problems.

Ryan

On 9/6/06, James Graham <jg...@ca...> wrote:
> Ryan Krauss wrote:
> > I would be glad to try that.  Where do I get the installer for scipy 0.51?
>
> http://prdownloads.sourceforge.net/scipy/scipy-0.5.1.win32-py2.4.exe?download
>
> The SciPy website needs to be updated.
>
> --
> "Eternity's a terrible thought. I mean, where's it all going to end?"
>   -- Tom Stoppard, Rosencrantz and Guildenstern are Dead
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] Numpy that is compatible with Scipy windows installer version

[James G. <jg...@ca...>]

Ryan Krauss wrote:
> I would be glad to try that.  Where do I get the installer for scipy 0.51?

http://prdownloads.sourceforge.net/scipy/scipy-0.5.1.win32-py2.4.exe?download

The SciPy website needs to be updated.

-- 
"Eternity's a terrible thought. I mean, where's it all going to end?"
  -- Tom Stoppard, Rosencrantz and Guildenstern are Dead

Re: [Numpy-discussion] Numpy that is compatible with Scipy windows installer version

[Ryan K. <rya...@gm...>]

I would be glad to try that.  Where do I get the installer for scipy 0.51?

Thanks,

Ryan

On 9/6/06, Martin Spacek <sc...@ms...> wrote:
> Ryan,
>
> Try installing the latest scipy version 0.51. There's a windows binary
> for it. Worked fine for me.
>
> Martin
>
> Ryan Krauss wrote:
> > I am a Linux user trying to install Numpy/Scipy on a Windows machine
> > in my office.
> >
> > I went to the website and grabbed the two latest versions:
> > scipy = scipy-0.5.0.win32-py2.4.exe
> > numpy = numpy-1.0b5.win32-py2.4.exe
> >
> >
>
> -------------------------------------------------------------------------
> Using Tomcat but need to do more? Need to support web services, security?
> Get stuff done quickly with pre-integrated technology to make your job easier
> Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo
> http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642
> _______________________________________________
> Numpy-discussion mailing list
> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] Numpy that is compatible with Scipy windows installer version

[Martin S. <sc...@ms...>]

Ryan,

Try installing the latest scipy version 0.51. There's a windows binary 
for it. Worked fine for me.

Martin

Ryan Krauss wrote:
> I am a Linux user trying to install Numpy/Scipy on a Windows machine
> in my office.
> 
> I went to the website and grabbed the two latest versions:
> scipy = scipy-0.5.0.win32-py2.4.exe
> numpy = numpy-1.0b5.win32-py2.4.exe
> 
>