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

[Numpy-discussion] Remco Jager/mapper is out of the office.

[<R....@ma...>]

I will be out of the office starting  09/04/2006 and will not return until
09/25/2006.

I will respond to your message when I return. For urgent matters please
contact Ton van de Peut: T.v...@ma...

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

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

Charles R Harris wrote:
> On 9/3/06, *Fernando Perez* <Fer...@co... 
> <mailto:Fer...@co...>> wrote:
> 
>     Hi all,
> 
>     I'm wondering if the following difference in behavior of object
>     arrays should
>     be considered a bug.  Let a and b be:
> 
>     In [21]: a = [0,1]
> 
>     In [22]: b = [ None, None]
> 
>     If we concatenate a with an empty list, it works:
> 
>     In [23]: numpy.concatenate(([],a))
>     Out[23]: array([0, 1])
> 
>     But not so for b:
> 
>     In [24]: numpy.concatenate(([],b))
>     ---------------------------------------------------------------------------
>     exceptions.ValueError                                 Traceback
>     (most recent
>     call last)
> 
>     /home/fperez/<ipython console>
> 
>     ValueError: 0-d arrays can't be concatenated
> 
> 
> I think it's propably a bug:
> 
>  >>> concatenate((array([]),b))
> array([None, None], dtype=object)

Well, if you can fix it without breaking anything else, then it's a bug.

However, I would suggest that a rule of thumb for using object arrays is to 
always be explicit. Never rely on automatic conversion from Python containers to 
object arrays. Since Python containers are also objects, it is usually ambiguous 
what the user meant.

I kind of liked numarray's choice to move the object array into a separate 
constructor. I think that gave them some flexibility to choose different syntax 
and semantics from the generic array() constructor. Since constructing object 
arrays is so different from constructing numeric arrays, I think that difference 
is warranted.

-- 
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] Problem with concatenate and object arrays

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

On 9/3/06, Fernando Perez <Fer...@co...> wrote:
>
> Hi all,
>
> I'm wondering if the following difference in behavior of object arrays
> should
> be considered a bug.  Let a and b be:
>
> In [21]: a = [0,1]
>
> In [22]: b = [ None, None]
>
> If we concatenate a with an empty list, it works:
>
> In [23]: numpy.concatenate(([],a))
> Out[23]: array([0, 1])
>
> But not so for b:
>
> In [24]: numpy.concatenate(([],b))
>
> ---------------------------------------------------------------------------
> exceptions.ValueError                                Traceback (most
> recent
> call last)
>
> /home/fperez/<ipython console>
>
> ValueError: 0-d arrays can't be concatenated


I think it's propably a bug:

>>> concatenate((array([]),b))
array([None, None], dtype=object)

Chuck

Re: [Numpy-discussion] diagflat

[Nils W. <nw...@ia...>]

On Sun, 3 Sep 2006 12:20:09 -0600
  "Charles R Harris" <cha...@gm...> wrote:
> Travis has introduced the function diagflat for creating=20
>diagonal arrays. It
> flattens whatever array is supplied and returns its=20
>values as the diagonal
> of a 2-D array or matrix. As the function is currently=20
>undocumented I
> thought now might be a good time to discuss other=20
>possible choices for the
> name. Here is a quick list that comes to mind
>=20
> diagflat (current name)
> asdiagonal
> todiagonal
> asdiag
> todiag
> ...
>=20
> Chuck

+1 for todiag

Nils

[Numpy-discussion] Problem with concatenate and object arrays

[Fernando P. <Fer...@co...>]

Hi all,

I'm wondering if the following difference in behavior of object arrays should 
be considered a bug.  Let a and b be:

In [21]: a = [0,1]

In [22]: b = [ None, None]

If we concatenate a with an empty list, it works:

In [23]: numpy.concatenate(([],a))
Out[23]: array([0, 1])

But not so for b:

In [24]: numpy.concatenate(([],b))
---------------------------------------------------------------------------
exceptions.ValueError                                Traceback (most recent 
call last)

/home/fperez/<ipython console>

ValueError: 0-d arrays can't be concatenated


This behavior changed recently (it used to work with r2788), and I realize 
it's probably part of all the reworkings of the object arrays which have been 
discussed on the list, and all of whose details I have to admit I haven't 
followed.  But this behavior strikes me as a bit inconsistent, since 
concatenation with a non-empty object array works fine:

In [26]: numpy.concatenate(([None],b))
Out[26]: array([None, None, None], dtype=object)

This is biting us in some code which keeps object arrays, because when 
operations of the kind

N.concatenate((some_list_of_objects[:nn],other_object_array))

are taken and nn happens to be 0, the code just explodes.  In our case, the 
variable nn is a runtime computed quantity that comes from a numerical 
algorithm, for which 0 is a perfectly reasonable value.

Are we just misusing things and is there a reasonable alternative, or should 
this be considered a numpy bug?  The r2788 behavior was certainly a lot less 
surprising as far as our code was concerned.

I realize that one alternative is to wrap everything into arrays:

N.concatenate((N.asarray(some_list_of_objects[:nn]),other_object_array))

Is this the only solution moving forward, or could the previous behavior be 
restored without breaking other areas of the new code/design?

Thanks for any input,

f

[Numpy-discussion] diagflat

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

Travis has introduced the function diagflat for creating diagonal arrays. It
flattens whatever array is supplied and returns its values as the diagonal
of a 2-D array or matrix. As the function is currently undocumented I
thought now might be a good time to discuss other possible choices for the
name. Here is a quick list that comes to mind

diagflat (current name)
asdiagonal
todiagonal
asdiag
todiag
...

Chuck

[Numpy-discussion] user_array.container and __eq__ [was: Re: (no subject)]

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

Bill Spotz wrote:
> I think I see a bug in lib.user_array.container class.  The __eq__  
> method is
> 
>      def __eq__(self,other): return self._rc(equal(self.array,other))
> 
> the expression equal(self.array,other) will return an ndarray of  
> bools, which is then converted, by way of self._rc(), to whatever the  
> derived class is.  In my case, this does not make sense, because an  
> array of bools is not a valid argument to the constructor (actually,  
> the data buffer is accepted, but the results are not reliable).  What  
> I want, and it seem most people would want in this situation, is just  
> the array of bools; i.e. don't apply the self._rc() method.

No, it's not a bug. It's just the design of that class: always return the same 
class of object. Of course, since that class only exists to be subclassed, if 
you need different behavior from those methods, override them.

-- 
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

[Numpy-discussion] (no subject)

[Bill S. <wf...@sa...>]

I think I see a bug in lib.user_array.container class.  The __eq__  
method is

     def __eq__(self,other): return self._rc(equal(self.array,other))

the expression equal(self.array,other) will return an ndarray of  
bools, which is then converted, by way of self._rc(), to whatever the  
derived class is.  In my case, this does not make sense, because an  
array of bools is not a valid argument to the constructor (actually,  
the data buffer is accepted, but the results are not reliable).  What  
I want, and it seem most people would want in this situation, is just  
the array of bools; i.e. don't apply the self._rc() method.

Assuming there is agreement that this is the desirable behavior, the  
same would be true for __lt__, __le__, etc.

I will probably override this behavior by defining my own __eq__,  
etc., in my derived class, just for safety.

** Bill Spotz                                              **
** Sandia National Laboratories  Voice: (505)845-0170      **
** P.O. Box 5800                 Fax:   (505)284-5451      **
** Albuquerque, NM 87185-0370    Email: wf...@sa... **

Re: [Numpy-discussion] Keyword added to searchsorted.

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

Charles R Harris wrote:
> Hi all,
> 
> I added the keyword side to the searchsorted method and functions. 

Thank you! Just the other day, I was wishing that we had such a thing.

-- 
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

[Numpy-discussion] Keyword added to searchsorted.

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

Hi all,

I added the keyword side to the searchsorted method and functions.
Documentation:

"""a.searchsorted(values=v, side='left') -> array of indices.

    Required Arguments:
        v -- keys to be searched for in a.

    Keyword arguments
        side -- {'left', 'right'}, default('left').

    If a is a 1-D array in ascending order, then

        a.searchsorted(v, side='left')

    returns an array of indices i such that for each element of values the
    following holds:

           a[j] < key <= a[i] for all j < i,

    If such an index does not exist, a.size() is used. The result is such
that
    if the key were to be inserted in the slot before the index i, then the
    order of a would be preserved and i would be the smallest index with
that
    property.

    If a is a 1-D array in ascending order, then

        a.searchsorted(v, side='right')

    returns an array of indices i such that for each element of values the
    following holds:

           a[j] <= key < a[i] for all j < i,

    If such an index does not exist, a.size() is used. The result is that if
the
    key were to be inserted in the slot before the index i, then the order
of a
    would be preserved and i would be the largest index with that property.

"""

I also replaced the old search routine as it was linear time worst case:

In [27]: t1 = t.Timer('N.searchsorted(a,1,side="left")','import numpy as N;
a = N.ones(1000000)')

In [28]: t2 = t.Timer('N.searchsorted(a,1,side="right")','import numpy as N;
a = N.ones(1000000)')

In [29]: t1.repeat(3,100)
Out[29]: [0.5301368236541748, 0.4924161434173584, 0.46317386627197266]

In [30]: t2.repeat(3,100)
Out[30]: [0.0011379718780517578, 0.00081586837768554688,
0.00083994865417480469]

where left was the original routine. It is now noticeably faster in some
situations:

In [2]: t1 = t.Timer('N.searchsorted(a,1,side="left")','import numpy as N; a
= N.ones(1000000)')

In [3]: t1.repeat(3,100)
Out[3]: [0.00082802772521972656, 0.00077795982360839844,
0.00076913833618164062]

I am open to suggestions as to better names for the keyword kind. It also
might be worth it to make type-specific routines if anyone commonly uses
large lists of keys and large sorted arrays.

Chuck

Re: [Numpy-discussion] iiR Xje

[Guendolen H. <em...@fi...>]

Hi
=20
All yo e ur PHA o RRMAC s Y d f irec g tly from the m t anufa a cture j
r,
Your ch v an b ce to eco i nomiz v e wit o h us
http://omgandesunkerion.com

  r=20

  z=20

  y=20

floor was smooth and hard, as were the walls when I brushed my fingers
What about her? Steengo asked.
be a crackup in the old kaserne tonight!

Re: [Numpy-discussion] amd64 support

[David M. C. <co...@ph...>]

On Aug 30, 2006, at 11:53 , Keith Goodman wrote:

> I plan to build an amd64 box and run debian etch. Are there any big,
> 64-bit, show-stopping problems in numpy? Any minor annoyances?

Shouldn't be; I regularly build and test it on an amd64 box running  
Debian unstable, and I know several others use amd64 boxes too.

-- 
|>|\/|<
/------------------------------------------------------------------\
|David M. Cooke              http://arbutus.physics.mcmaster.ca/dmc/
|co...@ph...

[Numpy-discussion] =?GB2312?B?cmU61MvTw0VYQ0VMuMS9+MrQs6HTqs/60+uyxs7xudzA7Q==?=

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Re: [Numpy-discussion] dtype=object behavior change from 0.9.6 to beta 1

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

On 8/31/06, Christopher Barker <Chr...@no...> wrote:
>
> Tom Denniston wrote:
> > I would think one would want to throw an error when the data has
> > inconsistent dimensions.
>
> But it doesn't have inconsistent dimensions - they are perfectly
> consistent with a (2,) array of objects. How is the code to know what
> you intended?


Same as it produces a float array from array([1,2,3.0]). Array is a
complicated function for precisely these sort of reasons, but the
convenience makes it worthwhile. So, if a list contains something that can
only be interpreted as an object, dtype should be set to object.

With numeric types, it is unambiguous to march down through the
> sequences until you get a number. As a sequence is an object, there no
> way to unambiguously do this automatically.
>
> Perhaps the way to solve this is for the array constructor to take a
> "shape" or "rank" argument, so you could specify what you intend. But
> that's really just syntactic sugar to avoid for calling numpy.empty()
> first.
>
> Perhaps a numpy.object_array() constructor would be useful, although as
> I think about it, even specifying a shape or rank would not be
> unambiguous!
>
> This is a useful discussion. If we ever get a nd-array into the standard
> lib, I suspect that object arrays will get heavy use -- better to clean
> up the semantics now.
>
> Perhaps a Wiki page on building object arrays is called for.
>
> -Chris


Chuck

Re: [Numpy-discussion] dtype=object behavior change from 0.9.6 to beta 1

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

Tom Denniston wrote:
> I would think one would want to throw an error when the data has
> inconsistent dimensions.

But it doesn't have inconsistent dimensions - they are perfectly 
consistent with a (2,) array of objects. How is the code to know what 
you intended?

With numeric types, it is unambiguous to march down through the 
sequences until you get a number. As a sequence is an object, there no 
way to unambiguously do this automatically.

Perhaps the way to solve this is for the array constructor to take a 
"shape" or "rank" argument, so you could specify what you intend. But 
that's really just syntactic sugar to avoid for calling numpy.empty() first.

Perhaps a numpy.object_array() constructor would be useful, although as 
I think about it, even specifying a shape or rank would not be unambiguous!

This is a useful discussion. If we ever get a nd-array into the standard 
lib, I suspect that object arrays will get heavy use -- better to clean 
up the semantics now.

Perhaps a Wiki page on building object arrays is called for.

-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] dtype=object behavior change from 0.9.6 to beta 1

[Tom D. <tom...@al...>]

I would think one would want to throw an error when the data has
inconsistent dimensions.  This is what numpy does for other dtypes:



In [10]: numpy.array(([1,2,3], [4,5,6]))
Out[10]:
array([[1, 2, 3],
       [4, 5, 6]])

In [11]: numpy.array(([1,3], [4,5,6]))
---------------------------------------------------------------------------
exceptions.TypeError                                 Traceback (most recent
call last)

TypeError: an integer is required





On 8/31/06, Christopher Barker <Chr...@no...> wrote:
>
> Tom Denniston wrote:
> > So my question is what is the _advantage_ of the new semantics?
>
> what if the list don't have the same length, and therefor can not be
> made into an array, now you get a weird result:
>
> >>>N.array([N.array([1,'A',None],dtype=object),N.array
> ([2,2,'Somestring',5],dtype=object)]).shape
> ()
>
> Now you get an Object scalar.
>
> but:
> >>>N.array([N.array([1,'A',None],dtype=object),N.array
> ([2,2,'Somestring',5],dtype=object)],dtype=object).shape
> (2,)
>
> Now you get a length 2 array, just like before: far more consistent.
> With the old semantics, if you test your code with arrays of different
> lengths, you'll get one thing, but if they then happen to be the same
> length in some production use, the whole thing breaks -- this is a Bad
> Idea.
>
> Object arrays are just plain weird, there is nothing you can do that
> will satisfy every need. I think it's best for the array constructor to
> not try to guess at what the hierarchy of sequences you *meant* to use.
> You can (and probably should) always be explicit with:
>
> >>> A = N.empty((2,), dtype=object)
> >>> A
> array([None, None], dtype=object)
> >>> A[:] = [N.array([1,'A', None],
> dtype=object),N.array([2,2,'Somestring',5],dtype=object)]
> >>> A
> array([[1 A None], [2 2 Somestring 5]], dtype=object)
>
> -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
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>

Re: [Numpy-discussion] dtype=object behavior change from 0.9.6 to beta 1

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

Tom Denniston wrote:
> So my question is what is the _advantage_ of the new semantics? 

what if the list don't have the same length, and therefor can not be 
made into an array, now you get a weird result:

 >>>N.array([N.array([1,'A',None],dtype=object),N.array([2,2,'Somestring',5],dtype=object)]).shape
()

Now you get an Object scalar.

but:
 >>>N.array([N.array([1,'A',None],dtype=object),N.array([2,2,'Somestring',5],dtype=object)],dtype=object).shape
(2,)

Now you get a length 2 array, just like before: far more consistent. 
With the old semantics, if you test your code with arrays of different 
lengths, you'll get one thing, but if they then happen to be the same 
length in some production use, the whole thing breaks -- this is a Bad Idea.

Object arrays are just plain weird, there is nothing you can do that 
will satisfy every need. I think it's best for the array constructor to 
not try to guess at what the hierarchy of sequences you *meant* to use. 
You can (and probably should) always be explicit with:

 >>> A = N.empty((2,), dtype=object)
 >>> A
array([None, None], dtype=object)
 >>> A[:] = [N.array([1,'A', None], 
dtype=object),N.array([2,2,'Somestring',5],dtype=object)]
 >>> A
array([[1 A None], [2 2 Somestring 5]], dtype=object)

-Chris





-- 
Christopher Barker, Ph.D.
Oceanographer
                                     		
NOAA/OR&R/HAZMAT         (206) 526-6959   voice
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Seattle, WA  98115       (206) 526-6317   main reception

Chr...@no...

Re: [Numpy-discussion] possible bug with numpy.object_

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

Tim Hochberg wrote:
>> That's what I'd expect, but what if you start with a (0,) array:
>>  >>> a = N.array([]).sum(); a.shape; a.size; a
>> ()
>> 1
>> 0
>>
>> where did that zero come from?
>>   
> More or less from:
> 
>      >>> numpy.add.identity
>     0

I'm not totally sure, but I think I'd rather it raise an exception. 
However, if it's not going to, then 0 is really the only reasonable answer.

-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] fromiter shape argument -- was Re: For loop tips

[Tim H. <tim...@ie...>]

Torgil Svensson wrote:
>> Yes. fromiter(iterable, dtype, count) works.
>>     
>
> Oh. Thanks. I probably had too old documentation to see this (15
> June). If it's not updated since I'll give Travis a rest about this,
> at least until 1.0 is released :)
>   
Actually I just knew 'cause I wrote it. I don't see a docstring for 
fromiter, although I though I wrote one. Maybe I just forgot?

>> Regardless, L is only iterated over once.
>>     
>
> How can this be true? If no size is given, mustn't numpy either loop
> over L twice or build an internal representation on which it'll
> iterate or copy in chunks?
>   
Well, it can't in general loop over L twice since the only method that L 
is guaranteed to have is next(); that's the extent of the iterator 
protocol. What it does is allocate an initial chunk of memory (the size 
of which I forget -- I did some tuning) and start filling it up. Once 
it's full, it does a realloc, which expands the existing chunk or 
memory, if possible, or returns a new, larger, chunk of memory with the 
data copied into it. Then we iterate on L some more until we fill up the 
new larger chunk, in which case we go get another one, etc. This is 
exactly how list.append works, although in that case the chunk of data 
is acutally a chunk of pointers to objects.

-tim
>
> I just found out that this works
>   
>>>> import numpy,itertools
>>>> rec_dt=numpy.dtype(">i4,S10,f8")
>>>> rec_iter=itertools.cycle([(1,'s',4.0),(5,'y',190.0),(2,'h',-8)])
>>>> numpy.fromiter(rec_iter,rec_dt,10).view(recarray)
>>>>         
> recarray([(1, 's', 4.0), (5, 'y', 190.0), (2, 'h', -8.0), (1, 's', 4.0),
>        (5, 'y', 190.0), (2, 'h', -8.0), (1, 's', 4.0), (5, 'y', 190.0),
>        (2, 'h', -8.0), (1, 's', 4.0)],
>       dtype=[('f0', '>i4'), ('f1', '|S10'), ('f2', '<f8')])
>
> but what's wrong with this?
>
>   
>>>> d2_dt=numpy.dtype("4f8")
>>>> d2_iter=itertools.cycle([(1.0,numpy.nan,-1e10,14.0)])
>>>> numpy.fromiter(d2_iter,d2_dt,10)
>>>>         
> Traceback (most recent call last):
>   File "<stdin>", line 1, in ?
> TypeError: a float is required
>   
>>>> numpy.__version__
>>>>         
> '1.0b4'
>
> //Torgil
>
>
>
> On 8/30/06, Tim Hochberg <tim...@ie...> wrote:
>   
>> Torgil Svensson wrote:
>>     
>>>>    return uL,asmatrix(fromiter((idx[x] for x in L),dtype=int))
>>>>
>>>>         
>>> Is it possible for fromiter to take an optional shape (or count)
>>> argument in addition to the dtype argument?
>>>       
>> Yes. fromiter(iterable, dtype, count) works.
>>
>>     
>>> If both is given it could
>>> preallocate memory and we only have to iterate over L once.
>>>
>>>       
>> Regardless, L is only iterated over once. In general you can't rewind
>> iterators, so that's a requirement. This is accomplished by doing
>> successive overallocation similar to the way appending to a list is
>> handled. By specifying the count up front you save a bunch of reallocs,
>> but no iteration.
>>
>> -tim
>>
>>
>>
>>     
>>> //Torgil
>>>
>>> On 8/29/06, Keith Goodman <kwg...@gm...> wrote:
>>>
>>>       
>>>> On 8/29/06, Torgil Svensson <tor...@gm...> wrote:
>>>>
>>>>         
>>>>> something like this?
>>>>>
>>>>> def list2index(L):
>>>>>    uL=sorted(set(L))
>>>>>    idx=dict((y,x) for x,y in enumerate(uL))
>>>>>    return uL,asmatrix(fromiter((idx[x] for x in L),dtype=int))
>>>>>
>>>>>           
>>>> Wow. That's amazing. Thank you.
>>>>
>>>> -------------------------------------------------------------------------
>>>> 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
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>>> _______________________________________________
>>> 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
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>> _______________________________________________
>> Numpy-discussion mailing list
>> Num...@li...
>> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>>
>>     
>
> -------------------------------------------------------------------------
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> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
>
>   

Re: [Numpy-discussion] Strange exp

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

Angelo Secchi wrote:
> Hi,
> I have the following script 
> 
> import fileinput
> import string
> from math import *
> from scipy import *
> from rpy import *
> import Numeric
> import shelve
> import sys
> 
> def dpolya1(n,N,b,a):
>     a=float(a)
>     b=float(b)
>     L=784
>     probs=((special.gammaln(N+1)+special.gammaln(L*(a/b))+special.gammaln((a/b)+n)+special.gammaln((a/b)*(L-1)+N-n))-(special.gammaln(L*(a/b)+N)+special.gammaln(a/b)+special.gammaln(n+1)+special.gammaln(N-n+1)+special.gammaln(L*(a/b)-(a/b))))#)
>     return probs
> 
> and I observe the following "strange" (for me of course) behaviour
> 
>>>> dpolya1(1,2,0.5,0.4)
> -5.9741312822170585
>>>> type(dpolya1(1,2,0.5,0.4))
> <type 'float64scalar'>
>>>> exp(dpolya1(1,2,0.5,0.4))
> Traceback (most recent call last):
>   File "<stdin>", line 1, in ?
> AttributeError: 'numpy.ndarray' object has no attribute 'exp'
> 
> I do not understand what's wrong. Any help?

Probably rpy (which still uses Numeric, right?) is exposing Numeric's exp() 
implementation and overriding the one that you got from scipy (which is numpy's, 
I presume). When Numeric's exp() is confronted with an object that it doesn't 
recognize, it looks for a .exp() method to call.

If you want to avoid this situation in the future, don't use the "from foo 
import *" form. It makes debugging problems like this difficult.

-- 
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] dtype=object behavior change from 0.9.6 to beta 1

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

I submitted a ticket for this.

On 8/31/06, Tom Denniston <tom...@al...> wrote:
>
> wrote the last email before reading your a = array([1,'A', None])
> comment.  I definately agree with you on that.
>
>
> On 8/31/06, Tom Denniston <tom...@al...> wrote:
> >
> >  Yes one can take a toy example and hack it to work but I don't
> > necessarily have control over the input as to whether it is a list of object
> > arrays, list of 1d heterogenous arrays, etc.  Before I didn't need to worry
> > about the input because numpy understood that a list of 1d arrays is a
> > 2d piece of data.  Now it understands this for all dtypes except object.  My
> > question was is this new set of semantics preferable to the old.
> >
> > I think your example kind of proves my point.  Does it really make any
> > sense for the following two ways of specifying an array give such different
> > results?  They strike me as _meaning_ the same thing.  Doesn't it seem
> > inconsistent to you?
> >
> >
> > In [13]: array([array([1,'A', None], dtype=object),array([2,2,'Some
> > string'],dtype=object)], dtype=object).shape
> > Out[13]: (2,)
> >
> > and
> >
> > In [14]: array([array([1,'A', None], dtype=object),array([2,2,'Some
> > string'],dtype=object)]).shape
> > Out[14]: (2, 3)
> > So my question is what is the _advantage_ of the new semantics?  The two
> > examples above used to give the same results.  In what cases is it
> > preferable for them to give different results?  How does it make life
> > simpler?
> >
> >
> > On 8/31/06, Charles R Harris <cha...@gm... > wrote:
> >
> > >  On 8/31/06, Tom Denniston <tom...@al... > wrote:
> >
> > > But i have hetergenious arrays that have numbers and strings and
> > > NoneType, etc.
> > >
> > > Take for instance:
> > >
> > > In [11]: numpy.array([numpy.array([1,'A', None]),
> > > numpy.array([2,2,'Some string'])], dtype=object)
> > > Out[11]:
> > > array([[1, A, None],
> > >        [2, 2, Some string]], dtype=object)
> > >
> > > In [12]: numpy.array([ numpy.array([1,'A', None]),
> > > numpy.array([2,2,'Some string'])], dtype=object).shape
> > > Out[12]: (2, 3)
> > >
> > > Works fine in Numeric and pre beta numpy but in beta numpy versions i
> > > get:
> >
> >
> > I think you want:
> >
> > In [59]: a = array([array([1,'A', None],dtype=object),array([2,2,'Some
> > string'],dtype=object)])
> >
> > In [60]: a.shape
> > Out[60]: (2, 3)
> >
> >
> > Which makes good sense to me.
> >
> > Chuck
> >
> >
> >
> >
> >
> >
> > -------------------------------------------------------------------------
> > 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
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>
> _______________________________________________
> Numpy-discussion mailing list
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> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>
>
>

Re: [Numpy-discussion] For loop tips

[Torgil S. <tor...@gm...>]

def list2index(L):
    uL=sorted(set(L))
    idx=dict((y,x) for x,y in enumerate(uL))
    return uL,asmatrix(fromiter((idx[x] for x in L),dtype=int,count=len(L)))

adding the count will save you a little more time, and temporary
memory [see related thread].

//Torgil

On 8/29/06, Keith Goodman <kwg...@gm...> wrote:
> On 8/29/06, Torgil Svensson <tor...@gm...> wrote:
> > something like this?
> >
> > def list2index(L):
> >    uL=sorted(set(L))
> >    idx=dict((y,x) for x,y in enumerate(uL))
> >    return uL,asmatrix(fromiter((idx[x] for x in L),dtype=int))
>
> Wow. That's amazing. Thank you.
>
> -------------------------------------------------------------------------
> 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
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> _______________________________________________
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> Num...@li...
> https://lists.sourceforge.net/lists/listinfo/numpy-discussion
>

Re: [Numpy-discussion] fromiter shape argument -- was Re: For loop tips

[Torgil S. <tor...@gm...>]

> Yes. fromiter(iterable, dtype, count) works.

Oh. Thanks. I probably had too old documentation to see this (15
June). If it's not updated since I'll give Travis a rest about this,
at least until 1.0 is released :)


> Regardless, L is only iterated over once.

How can this be true? If no size is given, mustn't numpy either loop
over L twice or build an internal representation on which it'll
iterate or copy in chunks?


I just found out that this works
>>> import numpy,itertools
>>> rec_dt=numpy.dtype(">i4,S10,f8")
>>> rec_iter=itertools.cycle([(1,'s',4.0),(5,'y',190.0),(2,'h',-8)])
>>> numpy.fromiter(rec_iter,rec_dt,10).view(recarray)
recarray([(1, 's', 4.0), (5, 'y', 190.0), (2, 'h', -8.0), (1, 's', 4.0),
       (5, 'y', 190.0), (2, 'h', -8.0), (1, 's', 4.0), (5, 'y', 190.0),
       (2, 'h', -8.0), (1, 's', 4.0)],
      dtype=[('f0', '>i4'), ('f1', '|S10'), ('f2', '<f8')])

but what's wrong with this?

>>> d2_dt=numpy.dtype("4f8")
>>> d2_iter=itertools.cycle([(1.0,numpy.nan,-1e10,14.0)])
>>> numpy.fromiter(d2_iter,d2_dt,10)
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
TypeError: a float is required
>>> numpy.__version__
'1.0b4'

//Torgil



On 8/30/06, Tim Hochberg <tim...@ie...> wrote:
> Torgil Svensson wrote:
> >>    return uL,asmatrix(fromiter((idx[x] for x in L),dtype=int))
> >>
> >
> > Is it possible for fromiter to take an optional shape (or count)
> > argument in addition to the dtype argument?
> Yes. fromiter(iterable, dtype, count) works.
>
> > If both is given it could
> > preallocate memory and we only have to iterate over L once.
> >
> Regardless, L is only iterated over once. In general you can't rewind
> iterators, so that's a requirement. This is accomplished by doing
> successive overallocation similar to the way appending to a list is
> handled. By specifying the count up front you save a bunch of reallocs,
> but no iteration.
>
> -tim
>
>
>
> > //Torgil
> >
> > On 8/29/06, Keith Goodman <kwg...@gm...> wrote:
> >
> >> On 8/29/06, Torgil Svensson <tor...@gm...> wrote:
> >>
> >>> something like this?
> >>>
> >>> def list2index(L):
> >>>    uL=sorted(set(L))
> >>>    idx=dict((y,x) for x,y in enumerate(uL))
> >>>    return uL,asmatrix(fromiter((idx[x] for x in L),dtype=int))
> >>>
> >> Wow. That's amazing. Thank you.
> >>
> >> -------------------------------------------------------------------------
> >> 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
> >
> >
> >
>
>
>
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[Numpy-discussion] Strange exp

[Angelo S. <se...@ss...>]

Hi,
I have the following script 

import fileinput
import string
from math import *
from scipy import *
from rpy import *
import Numeric
import shelve
import sys

def dpolya1(n,N,b,a):
    a=float(a)
    b=float(b)
    L=784
    probs=((special.gammaln(N+1)+special.gammaln(L*(a/b))+special.gammaln((a/b)+n)+special.gammaln((a/b)*(L-1)+N-n))-(special.gammaln(L*(a/b)+N)+special.gammaln(a/b)+special.gammaln(n+1)+special.gammaln(N-n+1)+special.gammaln(L*(a/b)-(a/b))))#)
    return probs

and I observe the following "strange" (for me of course) behaviour

>>> dpolya1(1,2,0.5,0.4)
-5.9741312822170585
>>> type(dpolya1(1,2,0.5,0.4))
<type 'float64scalar'>
>>> exp(dpolya1(1,2,0.5,0.4))
Traceback (most recent call last):
  File "<stdin>", line 1, in ?
AttributeError: 'numpy.ndarray' object has no attribute 'exp'

I do not understand what's wrong. Any help?
Thanks
Angelo

Re: [Numpy-discussion] dtype=object behavior change from 0.9.6 to beta 1

[Tom D. <tom...@al...>]

wrote the last email before reading your a = array([1,'A', None]) comment.
I definately agree with you on that.

On 8/31/06, Tom Denniston <tom...@al...> wrote:
>
>  Yes one can take a toy example and hack it to work but I don't
> necessarily have control over the input as to whether it is a list of object
> arrays, list of 1d heterogenous arrays, etc.  Before I didn't need to worry
> about the input because numpy understood that a list of 1d arrays is a
> 2d piece of data.  Now it understands this for all dtypes except object.  My
> question was is this new set of semantics preferable to the old.
>
> I think your example kind of proves my point.  Does it really make any
> sense for the following two ways of specifying an array give such different
> results?  They strike me as _meaning_ the same thing.  Doesn't it seem
> inconsistent to you?
>
>
> In [13]: array([array([1,'A', None], dtype=object),array([2,2,'Some
> string'],dtype=object)], dtype=object).shape
> Out[13]: (2,)
>
> and
>
> In [14]: array([array([1,'A', None], dtype=object),array([2,2,'Some
> string'],dtype=object)]).shape
> Out[14]: (2, 3)
> So my question is what is the _advantage_ of the new semantics?  The two
> examples above used to give the same results.  In what cases is it
> preferable for them to give different results?  How does it make life
> simpler?
>
>
> On 8/31/06, Charles R Harris <cha...@gm...> wrote:
>
> >  On 8/31/06, Tom Denniston <tom...@al... > wrote:
>
> > But i have hetergenious arrays that have numbers and strings and
> > NoneType, etc.
> >
> > Take for instance:
> >
> > In [11]: numpy.array([numpy.array([1,'A', None]),
> > numpy.array([2,2,'Some string'])], dtype=object)
> > Out[11]:
> > array([[1, A, None],
> >        [2, 2, Some string]], dtype=object)
> >
> > In [12]: numpy.array([ numpy.array([1,'A', None]),
> > numpy.array([2,2,'Some string'])], dtype=object).shape
> > Out[12]: (2, 3)
> >
> > Works fine in Numeric and pre beta numpy but in beta numpy versions i
> > get:
>
>
> I think you want:
>
> In [59]: a = array([array([1,'A', None],dtype=object),array([2,2,'Some
> string'],dtype=object)])
>
> In [60]: a.shape
> Out[60]: (2, 3)
>
>
> Which makes good sense to me.
>
> Chuck
>
>
>
>
>
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