There's already been some dance around this topic, maybe you will find
the concept behind it:
http://search.gmane.org/?query=unravel_index&group=gmane.comp.python.numeric.general
2013/9/13 Mark Bakker :
> Thanks, Gregorio.
> I would like it if argmax had a keyword option to return the row,colum
Hi Mark,
You're looking for "np.unravel_index" function.
Cheers,
Gregorio
2013/9/13 Mark Bakker :
> Hello list,
>
> I am trying to find the indices of the maximum value in a 2D array.
> argmax works fine, but returns the index in the flattened array.
> That is often not very convenient.
> Is the
to a float64
array in the second case (same behaviour with np.array). Anyone knows
the reason behind?
python 2.7.4 win32
numpy 1.7.1
Gregorio
2013/9/4 Gregorio Bastardo :
> @Stéfan: the 'np.all' calls are now unnecessary on line 26
>
> @Stéfan, Robert: Is it worth to bring th
@Stéfan: the 'np.all' calls are now unnecessary on line 26
@Stéfan, Robert: Is it worth to bring this solution into numpy? I mean
it's probably not a rare problem, and now users have to bring this
snippet into their codebase.
Gregorio
2013/9/3 Stéfan van der Walt :
> On Tue, Sep 3, 2013 at 2:47
was only curious why this feature
is not provided by the function itself, as returning the input array's
dtype seems not so useful (can't imagine such a use case).
Gregorio
2013/9/2 Stéfan van der Walt :
> On Mon, Sep 2, 2013 at 4:21 PM, Gregorio Bastardo
> wrote:
>>>>>
Hi,
I'd like to find the smallest possible representation of an array
given a set of possible values. I've checked the function
'np.min_scalar_type', it works well for scalar input, but contrary to
my assumption when array-like param is given: array's dtype is simply
returned, instead of finding a
> Ouch…
> Quick workaround: use `x.harden_mask()` *then* `x.mask.flags.writeable=False`
Thanks for the update and the detailed explanation. I'll try this trick.
> This may change in the future, depending on a yet-to-be-achieved consensus on
> the definition of 'least-surprising behaviour'. Rig
Hi,
On Mon, Jun 10, 2013 at 3:47 PM, Nathaniel Smith wrote:
> Hi all,
>
> Is there anyone out there using numpy masked arrays, who has an
> opinion on how empty_like (and its friends ones_like, zeros_like)
> should handle the mask?
>
> Right now apparently if you call np.ma.empty_like on a masked
Hi Pierre,
> Note as well that hardening the mask only prevents unmasking: you can still
> grow the mask, which may not be what you want. Use
> `x.mask.flags.writeable=False` to make the mask really read-only.
I ran into an unmasking problem with the suggested approach:
>>> np.version.version
Hi Pierre,
> I'm a bit surprised, though. Here's what I tried
>
np.version.version
> <<< 1.7.0
x = np.ma.array([1,2,3], mask=[0,1,0])
x.flags.writeable=False
x[0]=-1
> <<< ValueError: assignment destination is read-only
Thanks, it works perfectly =) Sorry, probably have overlo
ution for this problem?
Thanks,
Gregorio
2013/7/12 Stéfan van der Walt :
> On Fri, Jul 12, 2013 at 4:41 PM, Gregorio Bastardo
> wrote:
>> array.flags.writeable = False
>>
>> is perfectly fine, but it does not work on ma-s. Moreover, mask
>> hardening only prot
Hi,
I use masked arrays to mark missing values in data and found it very
convenient, although sometimes counterintuitive.
I'd like to make a pool of masked arrays (shared between several
processing steps) read-only (both data and mask property) to protect
the arrays from accidental modification (
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