[Numpy-discussion] Generalize hstack/vstack --> stack; Block matrices like in matlab

[josef.pktd at gmail.com]

On Tue, Sep 9, 2014 at 8:30 AM, <josef.pktd at gmail.com> wrote:

>
>
>
> On Tue, Sep 9, 2014 at 5:42 AM, Stefan Otte <stefan.otte at gmail.com> wrote:
>
>> Hey,
>>
>> @Josef, I wasn't aware of `bmat` and `np.asarray(np.bmat(....))` does
>> basically what I want and what I'm already using.
>>
>
> I never needed any tetris or anything similar except for the matched block
> version.
>
> Just to point out two more related functions
>
> scipy.sparse also has `bmat` for sparse block matrices
> scipy.linalg and scipy.sparse have `block_diag` to complement bmat.
>
>
> What I sometimes wish for is a sparse pseudo kronecker product as
> convenience to bmat, where the first (or the second) matrix contains (0,1)
> flags where the 1's specify where to put the blocks.
> (I'm not sure what I really mean, similar to block_diag but with a
> different filling pattern.)
>

(or in analogy to kronecker, np.nonzero provides the filling pattern, but
the submatrix is multiplied by the value.)

(application: unbalanced repeated measures or panel data)

Josef
(....)


>
> Josef
>
>
>
>>
>> Regarding the Tetris problem: that never happened to me, but stack, as
>> Josef pointed out, can handle that already :)
>>
>> I like the idea of removing the redundant square brackets:
>>     stack([[a, b], [c, d]]) --> stack([a, b], [c, d])
>> However, if the brackets are there there is no difference between
>> creating a `np.array` and stacking arrays with `np.stack`.
>>
>> If we want to get fancy and turn this PR into something bigger
>> (working our way up to a NP-complete problem ;)) then how about this.
>> I sometimes have arrays that look like:
>>   AB0
>>   0 C
>> Where 0 is a scalar but is supposed to fill the rest of the array.
>> Having something like 0 in there might lead to ambiguities though. What
>> does
>>   ABC
>>   0D0
>> mean? One could limit the "filler" to appear only on the left or the
>> right:
>>   AB0
>>   0CD
>> But even then the shape is not completely determined. So we could
>> require to have one row that only consists of arrays and determines
>> the shape. Alternatively we could have a keyword parameter `shape`:
>>   stack([A, B, 0], [0, C, D], shape=(8, 8))
>>
>> Colin, with `bmat` you can do what you're asking for. Directly taken
>> from the example:
>> >>> np.bmat('A,B; C,D')
>> matrix([[1, 1, 2, 2],
>>         [1, 1, 2, 2],
>>         [3, 4, 7, 8],
>>         [5, 6, 9, 0]])
>>
>>
>> General question: If `bmat` already offers something like `stack`
>> should we even bother implementing `stack`? More code leads to more
>> bugs and maintenance work.
>>
>>
>> Best,
>>  Stefan
>>
>>
>>
>> On Tue, Sep 9, 2014 at 12:14 AM, cjw <cjw at ncf.ca> wrote:
>> >
>> > On 08-Sep-14 4:40 PM, Joseph Martinot-Lagarde wrote:
>> >> Le 08/09/2014 15:29, Stefan Otte a écrit :
>> >>> Hey,
>> >>>
>> >>> quite often I work with block matrices. Matlab offers the convenient
>> notation
>> >>>
>> >>>       [ a b; c d ]
>> > This would appear to be a desirable way to go.
>> >
>> > Numpy has something similar for strings.  The above is neater.
>> >
>> > Colin W.
>> >>> to stack matrices. The numpy equivalent is kinda clumsy:
>> >>>
>> >>> vstack([hstack([a,b]), hstack([c,d])])
>> >>>
>> >>> I wrote the little function `stack` that does exactly that:
>> >>>
>> >>>       stack([[a, b], [c, d]])
>> >>>
>> >>> In my case `stack` replaced `hstack` and `vstack` almost completely.
>> >>>
>> >>> If you're interested in including it in numpy I created a pull request
>> >>> [1]. I'm looking forward to getting some feedback!
>> >>>
>> >>>
>> >>> Best,
>> >>>    Stefan
>> >>>
>> >>>
>> >>>
>> >>> [1] https://github.com/numpy/numpy/pull/5057
>> >>>
>> >> The outside brackets are redundant, stack([[a, b], [c, d]]) should be
>> >> stack([a, b], [c, d])
>> >>
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