[Numpy-discussion] Using the C-API iterator with object arrays
Hi there,
I have a question concerning the numpy iterator C-API. I want to create
a numpy
string array using NPY_OBJECT as a datatype for creating the array (the
reason I am going for this
approach is that I do not know the length of the individual strings at
the time I construct
the array, I only know its shape). The original strings are stored
in a std::vector instance. The approach I took was something like
this
std::vector buffer = ;
NpyIter *iter = NpyIter_New(numpy_array,
NPY_ITER_READWRITE | NPY_ITER_C_INDEX |
NPY_ITER_REFS_OK,
NPY_CORDER , NPY_NO_CASTING,nullptr);
if(iter==NULL)
{
return;
}
NpyIter_IterNextFunc *iternext = NpyIter_GetIterNext(iter,nullptr);
if(iternext == NULL)
{
std::cerr<<"Could not instantiate next iterator function"<
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Re: [Numpy-discussion] Using the C-API iterator with object arrays
On Mon, Feb 12, 2018 at 12:13 PM Eugen Wintersberger <
eugen.wintersber...@gmail.com> wrote:
> Hi there,
> I have a question concerning the numpy iterator C-API. I want to create a
> numpy
> string array using NPY_OBJECT as a datatype for creating the array (the
> reason I am going for this
> approach is that I do not know the length of the individual strings at the
> time I construct
> the array, I only know its shape). The original strings are stored
> in a std::vector instance. The approach I took was something like
> this
>
> std::vector buffer = ;
> NpyIter *iter = NpyIter_New(numpy_array,
> NPY_ITER_READWRITE | NPY_ITER_C_INDEX |
> NPY_ITER_REFS_OK,
> NPY_CORDER , NPY_NO_CASTING,nullptr);
> if(iter==NULL)
> {
> return;
> }
> NpyIter_IterNextFunc *iternext = NpyIter_GetIterNext(iter,nullptr);
> if(iternext == NULL)
> {
> std::cerr<<"Could not instantiate next iterator function"< return;
> }
> PyObject **dataptr = (PyObject**)NpyIter_GetDataPtrArray(iter);
> for(auto string: buffer)
> {
> dataptr[0] = PyString_FromSting(string); // this string construction
> seem to work
> iternext(iter);
> }
> NpyIter_Deallocate(iter);
>
>
> This code snippet is a bit stripped down with all the safety checks
> removed to make
> things more readable.
> However, the array I get back still contains only a None instance. Does
> anyone have an idea
> what I am doing wrong here?
>
I think you have the indirections wrong in dataptr?
NpyIter_GetDataPtrArray returns a char**, that hold the address of the
variable where the iterator holds the address of the first byte of the
current item being iterated.
When you write to dataptr[0] you are not writing to the array, but to where
the iterator holds the address of the first byte of the current item.
So you would have to write to dataptr[0][0], or **dataptr, to actually
affect the contents of the array. Of course, your dataptr being a PyObject**,
the compiler would probably complaint about such an assignment.
I think that if you define dataptr as a PyObject*** (yay, three star
programming !) and then assign to
**dataptr, everything will work fine. If that doesn't work, maybe try to
make dataptr a char**, and then assign to (PyObject *)(**dataptr) = ...
Jaime
> Thanks in advance.
>
> best regards
>Eugen
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Re: [Numpy-discussion] Using the C-API iterator with object arrays
On Mon, Feb 12, 2018 at 6:13 AM, Eugen Wintersberger <
eugen.wintersber...@gmail.com> wrote:
> Hi there,
> I have a question concerning the numpy iterator C-API. I want to create a
> numpy
> string array using NPY_OBJECT as a datatype for creating the array (the
> reason I am going for this
> approach is that I do not know the length of the individual strings at the
> time I construct
> the array, I only know its shape). The original strings are stored
> in a std::vector instance. The approach I took was something like
> this
>
> std::vector buffer = ;
> NpyIter *iter = NpyIter_New(numpy_array,
> NPY_ITER_READWRITE | NPY_ITER_C_INDEX |
> NPY_ITER_REFS_OK,
> NPY_CORDER , NPY_NO_CASTING,nullptr);
> if(iter==NULL)
> {
> return;
> }
> NpyIter_IterNextFunc *iternext = NpyIter_GetIterNext(iter,nullptr);
> if(iternext == NULL)
> {
> std::cerr<<"Could not instantiate next iterator function"< return;
> }
> PyObject **dataptr = (PyObject**)NpyIter_GetDataPtrArray(iter);
> for(auto string: buffer)
> {
> dataptr[0] = PyString_FromSting(string); // this string construction
> seem to work
> iternext(iter);
> }
> NpyIter_Deallocate(iter);
>
>
> This code snippet is a bit stripped down with all the safety checks
> removed to make
> things more readable.
> However, the array I get back still contains only a None instance. Does
> anyone have an idea
> what I am doing wrong here?
> Thanks in advance.
>
> best regards
>Eugen
>
>
Honestly, given that you're iterating over a single 1D array you've just
constructed, I don't think I would even bother trying to use the iterator.
For this case, a simple loop will be both concise and clear.
Eric
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Re: [Numpy-discussion] improving arange()? introducing fma()?
I think it's all been said, but a few comments: On Sun, Feb 11, 2018 at 2:19 PM, Nils Becker wrote: > Generating equidistantly spaced grids is simply not always possible. > exactly -- and linspace gives pretty much teh best possible result, guaranteeing tha tthe start an end points are exact, and the spacing is within an ULP or two (maybe we could make that within 1 ULP always, but not sure that's worth it). > The reason is that the absolute spacing of the possible floating point > numbers depends on their magnitude [1]. > Also that the exact spacing may not be exactly representable in FP -- so you have to have at least one space that's a bit off to get the end points right (or have the endpoints not exact). > If you - for some reason - want the same grid spacing everywhere you may > choose an appropriate new spacing. > well, yeah, but usually you are trying to fit to some other constraint. I'm still confused as to where these couple of ULPs actually cause problems, unless you are doing in appropriate FP comparisons elsewhere. Curiously, either by design or accident, arange() seems to do something > similar as was mentioned by Eric. It creates a new grid spacing by adding > and subtracting the starting point of the grid. This often has similar > effect as adding and subtracting N*dx (e.g. if the grid is symmetric around > 0.0). Consequently, arange() seems to trade keeping the grid spacing > constant for a larger error in the grid size and consequently in the end > point. > interesting -- but it actually makes sense -- that is the definition of arange(), borrowed from range(), which was designed for integers, and, in fact, pretty much mirroered the classic C index for loop: for (int i=0; i 1. Comparison to calculations with decimal can be difficult as not all > simple decimal step sizes are exactly representable as > finite floating point numbers. > yeah, this is what I mean by inappropriate use of Decimal -- decimal is not inherently "more accurate" than fp -- is just can represent _decimal_ numbers exactly, which we are all use to -- we want 1 / 10 to be exact, but dont mind that 1 / 3 isn't. Decimal also provided variable precision -- so it can be handy for that. I kinda wish Python had an arbitrary precision binary floating point built in... -CHB -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] @xoviat
Anyone know what happened to xoviat? He seems to have been disappeared. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] @xoviat
On Mon, Feb 12, 2018 at 7:42 PM, Charles R Harris wrote: > Anyone know what happened to xoviat? He seems to have been disappeared. > His account was flagged by GitHub after scipy issue gh-8373. Flagging means your profile, and all your issue comments, PRs, etc. are all made private. It is quite bad if they don't come back; GitHub shouldn't break conversations like that by removing comments on a different repo made well prior to an incident. We'll be contacting GitHub about this. Ralf ___ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
