[Numpy-discussion] Propose modification to binary_repr
Hi, The current behavior of numpy's binary_repr is the following: binary_repr(1,width=2) '01' binary_repr(0,width=2) '0' This seems inconsistent and I'd suggest always padding with zeros to make sure that the return string always has length=width. Objections ? David ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] resize in old Numeric
Thank you all for your valuable input. Learned something 'bout Numeric again. And my problem is solved ;-). Thanks Christian ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Traceback on divide by zero error
Tony S Yu wrote:
Hello,
This is something that's been bothering for awhile. When numpy raises
the following divide by zero error:
Warning: divide by zero encountered in double_scalars
is there a way to get a Traceback on where that warning occurred.
In [1]: from numpy import *
In [2]: seterr?
Type: function
Base Class: type 'function'
Namespace:Interactive
File: /Users/rkern/svn/numpy/numpy/core/numeric.py
Definition: seterr(all=None, divide=None, over=None, under=None,
invalid=None)
Docstring:
Set how floating-point errors are handled.
Valid values for each type of error are the strings
ignore, warn, raise, and call. Returns the old settings.
If 'all' is specified, values that are not otherwise specified
will be set to 'all', otherwise they will retain their old
values.
Note that operations on integer scalar types (such as int16) are
handled like floating point, and are affected by these settings.
Example:
seterr(over='raise') # doctest: +SKIP
{'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore', 'under':
'ignore'}
seterr(all='warn', over='raise') # doctest: +SKIP
{'over': 'raise', 'divide': 'ignore', 'invalid': 'ignore', 'under':
'ignore'}
int16(32000) * int16(3) # doctest: +SKIP
Traceback (most recent call last):
File stdin, line 1, in ?
FloatingPointError: overflow encountered in short_scalars
seterr(all='ignore') # doctest: +SKIP
{'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore', 'under':
'ignore'}
In [3]: seterr(divide='raise')
Out[3]: {'divide': 'print', 'invalid': 'print', 'over': 'print', 'under':
'ignore'}
In [4]: ones(10) / zeros(10)
---
FloatingPointErrorTraceback (most recent call last)
/Users/rkern/svn/mpl-toolkits/ipython console in module()
FloatingPointError: divide by zero encountered in divide
In [5]: seterr(all='ignore')
Out[5]: {'divide': 'raise', 'invalid': 'print', 'over': 'print', 'under':
'ignore'}
In [6]: ones(10) / zeros(10)
Out[6]: array([ Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf])
--
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
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Re: [Numpy-discussion] Propose modification to binary_repr
Change done. All tests pass. Should I document the change somewhere ? Although it's a small change, I'm guessing it could be very annoying to debug for someone depending on the previous behavior. 2007/12/13, Travis E. Oliphant [EMAIL PROTECTED]: David Huard wrote: Hi, The current behavior of numpy's binary_repr is the following: binary_repr(1,width=2) '01' binary_repr(0,width=2) '0' This seems inconsistent and I'd suggest always padding with zeros to make sure that the return string always has length=width. Objections ? This sounds like a good idea. I don't think it would break anything, but it would be good to test. -Travis O. ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Traceback on divide by zero error
On Dec 13, 2007, at 2:21 PM, Robert Kern wrote: Tony S Yu wrote: Hello, This is something that's been bothering for awhile. When numpy raises the following divide by zero error: Warning: divide by zero encountered in double_scalars is there a way to get a Traceback on where that warning occurred. In [1]: from numpy import * In [3]: seterr(divide='raise') seterr(divide='warn') is exactly what I was looking for. Thanks! -Tony ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Propose modification to binary_repr
On Thu, Dec 13, 2007 at 02:33:01PM -0500, David Huard wrote: Change done. All tests pass. Now's a good time to fix that :) Cheers Stéfan ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Faster array version of ndindex
I was needing an array representation of ndindex since ndindex only gives an iterator but array(list(ndindex)) takes too long. There is prob some obvious way to do this I am missing but if not feel free to include this code which is much faster. In [252]: time a=np.array(list(np.ndindex(10,10,10,10,10,10))) CPU times: user 11.61 s, sys: 0.09 s, total: 11.70 s Wall time: 11.82 In [253]: time a=ndtuples(10,10,10,10,10,10) CPU times: user 0.32 s, sys: 0.21 s, total: 0.53 s Wall time: 0.60 def ndtuples(*dims): Fast implementation of array(list(ndindex(*dims))). # Need a list because we will go through it in reverse popping # off the size of the last dimension. dims = list(dims) # N will keep track of the current length of the indices. N = dims.pop() # At the beginning the current list of indices just ranges over the # last dimension. cur = np.arange(N) cur = cur[:,np.newaxis] while dims != []: d = dims.pop() # This repeats the current set of indices d times. # e.g. [0,1,2] - [0,1,2,0,1,2,...,0,1,2] cur = np.kron(np.ones((d,1)),cur) # This ranges over the new dimension and 'stretches' it by N. # e.g. [0,1,2] - [0,0,...,0,1,1,...,1,2,2,...,2] front = np.arange(d).repeat(N)[:,np.newaxis] # This puts these two together. cur = np.column_stack((front,cur)) N *= d return cur ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
