Jaime has helped me and PR#5117 is getting ready to be merged <https://github.com/numpy/numpy/pull/5117>...
2014-10-02 19:00 GMT+02:00 <numpy-discussion-requ...@scipy.org>: > Send NumPy-Discussion mailing list submissions to > numpy-discussion@scipy.org > > To subscribe or unsubscribe via the World Wide Web, visit > http://mail.scipy.org/mailman/listinfo/numpy-discussion > or, via email, send a message with subject or body 'help' to > numpy-discussion-requ...@scipy.org > > You can reach the person managing the list at > numpy-discussion-ow...@scipy.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of NumPy-Discussion digest..." > > > Today's Topics: > > 1. Re: Interpolation using `np.interp()` with periodic > x-coordinates (Charles R Harris) > 2. Re: Proposal: add ndarray.keys() to return dtype.names > (Sebastian Wagner) > 3. Re: Proposal: add ndarray.keys() to return dtype.names > (John Zwinck) > 4. skip samples in random number generator (Brad Buran) > 5. Re: skip samples in random number generator (Robert Kern) > 6. Re: skip samples in random number generator (Nathaniel Smith) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 1 Oct 2014 18:12:56 -0600 > From: Charles R Harris <charlesr.har...@gmail.com> > Subject: Re: [Numpy-discussion] Interpolation using `np.interp()` with > periodic x-coordinates > To: Discussion of Numerical Python <numpy-discussion@scipy.org> > Message-ID: > <CAB6mnxJwuw8k=NCkkUyKioCvLUy3XfUQkR= > o-dgkxwqfvwo...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > On Wed, Sep 24, 2014 at 3:57 PM, Saullo Castro <saullogiov...@gmail.com> > wrote: > > > From the closed pull request PR #5109: > > > > https://github.com/numpy/numpy/pull/5109 > > > > it came out that the a good implementation would be adding a parameter > > `period`. I would like to know about the community's interest for this > > implementation. > > > > The modification are shown here: > > > > > https://github.com/saullocastro/numpy/compare/interp_with_period?expand=1 > > > > Please, let me know about your feedback. > > > > I don't have any problem with allowing periodic interpolation. > > Chuck > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: > http://mail.scipy.org/pipermail/numpy-discussion/attachments/20141001/519ac77f/attachment-0001.html > > ------------------------------ > > Message: 2 > Date: Thu, 02 Oct 2014 12:27:26 +0200 > From: Sebastian Wagner <se...@sebix.at> > Subject: Re: [Numpy-discussion] Proposal: add ndarray.keys() to return > dtype.names > To: Discussion of Numerical Python <numpy-discussion@scipy.org> > Message-ID: <0294ae9ec5a919857265bed55ebff...@fizeau.net> > Content-Type: text/plain; charset=US-ASCII; format=flowed > > So, for non-structured arrays, the consens is an Exception. The question > is, which one. > AttributeError would be fully backwards compatible. Existing code checks > for the method and if it exists, the object has fields. > ValueError would make more sense, as the value - the array - is in wrong > format/structure/type. > > regards, > > On 2014-10-01 16:47, Eelco Hoogendoorn wrote: > > Ah yes; you can use.. > > > > from types import MethodType > > > > ...to dynamically add methods to specific instances of a type. This > > may be cleaner or more pythonic than performing a check within the > > method, I dunno. > > > > On Wed, Oct 1, 2014 at 4:41 PM, Benjamin Root <ben.r...@ou.edu> wrote: > > > >> Actually, if I remember correctly, special methods show up in the > >> ndarray object when the dtype is datetime64, right? > >> > >> On Wed, Oct 1, 2014 at 10:13 AM, Eelco Hoogendoorn > >> <hoogendoorn.ee...@gmail.com> wrote: > >> > >> Well, the method will have to be present on all ndarrays, since > >> structured arrays do not have a different type from regular arrays, > >> only a different dtype. Thus the attribute has to be present > >> regardless, but some Exception will have to be raised depending on > >> the dtype, to make it quack like the kind of duck it is, so to > >> speak. Indeed it seems like an atypical design pattern; but I don't > >> see a problem with it. > >> > >> On Wed, Oct 1, 2014 at 4:08 PM, John Zwinck <jzwi...@gmail.com> > >> wrote: > >> > >> On 1 Oct 2014 04:30, "Stephan Hoyer" <sho...@gmail.com> wrote: > >>> > >>> On Tue, Sep 30, 2014 at 1:22 PM, Eelco Hoogendoorn > >> <hoogendoorn.ee...@gmail.com> wrote: > >>>> > >>>> On more careful reading of your words, I think we agree; indeed, > >> if keys() is present is should return an iterable; but I don't think > >> it should be present for non-structured arrays. > >>> > >>> Indeed, I think we do agree. The attribute can simply be missing > >> (e.g., accessing it raises AttributeError) for non-structured > >> arrays. > >> > >> I'm generally fine with this, though I would like to know if there > >> is precedent for methods being present on structured arrays only. > >> Even if there is no precedent I am still OK with the idea, I just > >> think we should understand if how novel this will be. > >> _______________________________________________ > >> NumPy-Discussion mailing list > >> NumPy-Discussion@scipy.org > >> http://mail.scipy.org/mailman/listinfo/numpy-discussion [1] > >> > >> _______________________________________________ > >> NumPy-Discussion mailing list > >> NumPy-Discussion@scipy.org > >> http://mail.scipy.org/mailman/listinfo/numpy-discussion [1] > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion [1] > > > > > > > > Links: > > ------ > > [1] http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > ------------------------------ > > Message: 3 > Date: Thu, 2 Oct 2014 19:09:47 +0800 > From: John Zwinck <jzwi...@gmail.com> > Subject: Re: [Numpy-discussion] Proposal: add ndarray.keys() to return > dtype.names > To: Discussion of Numerical Python <numpy-discussion@scipy.org> > Message-ID: > <CAK_AF3A9aONKmu8RKgOZWgLKpmTgD=wyB6xkh0+v12nzRP= > s...@mail.gmail.com> > Content-Type: text/plain; charset=UTF-8 > > On Thu, Oct 2, 2014 at 6:27 PM, Sebastian Wagner <se...@sebix.at> wrote: > > So, for non-structured arrays, the consens is an Exception. The question > > is, which one. > > AttributeError would be fully backwards compatible. Existing code checks > > for the method and if it exists, the object has fields. > > ValueError would make more sense, as the value - the array - is in wrong > > format/structure/type. > > If a non-structured array has its keys() raise AttributeError, I think > that hasattr(arr, "keys") should return False, which implies that > getattr(arr, "keys") should throw AttributeError. This would require > that ndarray.__getattribute__ raise AttributeError, meaning "the > attribute isn't here," not merely "the attribute doesn't have a value > now." Otherwise people may rightly complain when they interrogate an > array to see if it has keys(), find out that it does, but then get an > error upon calling it which could have been known ahead of time. > > Now, to actually implement it this way would seem to require setting > the "tp_getattro" function pointer > (https://docs.python.org/3/c-api/typeobj.html#c.PyTypeObject.tp_getattro). > Currently PyArray_Type has this field as null. If I understand > everything correctly, adding a non-null function pointer here would > mean some small runtime overhead to resolve every attribute on > ndarray. I could easily be missing some detail which would allow us > to do what I described above without a performance hit, but someone > better versed would need to explain how/why. > > If I'm right about all that, and if the consensus is that keys() > should raise an exception when dtype.names is None, then perhaps > raising ValueError is the only viable option. > > I'd appreciate opinions from those experienced in the details of the C API. > > > ------------------------------ > > Message: 4 > Date: Thu, 2 Oct 2014 08:42:46 -0700 > From: Brad Buran <bbu...@alum.mit.edu> > Subject: [Numpy-discussion] skip samples in random number generator > To: Discussion of Numerical Python <numpy-discussion@scipy.org> > Message-ID: > < > cahb_y2lalg3a4jch9erkmcdmhmzhkcvs0w20cenkxbxbhl1...@mail.gmail.com> > Content-Type: text/plain; charset=UTF-8 > > Given the following: > > from numpy import random > rs = random.RandomState(seed=1) > # skip the first X billion samples > x = rs.uniform(0, 10) > > How do I accomplish "skip the first X billion samples" (e.g. 7.2 > billion)? I see that there's a numpy.random.RandomState.set_state > which accepts (among other parameters) a value called "pos". This > sounds promising, but the other parameters I'm not sure how to compute > (e.g. the 1D array of 624 unsigned integers, etc.). I need to be able > to skip ahead in the sequence to reproduce some signals that were > generated for experiments. I could certainly consume and discard the > first X billion samples; however, that seems to be computationally > inefficient. > > Brad > > > ------------------------------ > > Message: 5 > Date: Thu, 2 Oct 2014 16:52:11 +0100 > From: Robert Kern <robert.k...@gmail.com> > Subject: Re: [Numpy-discussion] skip samples in random number > generator > To: Discussion of Numerical Python <numpy-discussion@scipy.org> > Message-ID: > < > caf6fjivcx3q+twyolxbdzbcgc2uk8isv83zu-+28ldyndh5...@mail.gmail.com> > Content-Type: text/plain; charset=UTF-8 > > On Thu, Oct 2, 2014 at 4:42 PM, Brad Buran <bbu...@alum.mit.edu> wrote: > > Given the following: > > > > from numpy import random > > rs = random.RandomState(seed=1) > > # skip the first X billion samples > > x = rs.uniform(0, 10) > > > > How do I accomplish "skip the first X billion samples" (e.g. 7.2 > > billion)? I see that there's a numpy.random.RandomState.set_state > > which accepts (among other parameters) a value called "pos". This > > sounds promising, but the other parameters I'm not sure how to compute > > (e.g. the 1D array of 624 unsigned integers, etc.). I need to be able > > to skip ahead in the sequence to reproduce some signals that were > > generated for experiments. I could certainly consume and discard the > > first X billion samples; however, that seems to be computationally > > inefficient. > > Unfortunately, it requires some significant number-theoretical > precomputation for any given N number of steps that you want to skip. > > http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/JUMP/index.html > > It's also unreliable for your purpose. You don't know how many random > integers were actually pulled from the core PRNG if you ever used any > of the nonuniform distributions (they usually will consume a variable > number of uniform pseudorandom numbers to give you a single nonuniform > number). > > Instead, you should just pickle the RandomState object just before you > start using it for anything that you want to reproduce. The unpickled > RandomState will reproduce the same numbers the first one did. > > -- > Robert Kern > > > ------------------------------ > > Message: 6 > Date: Thu, 2 Oct 2014 17:28:47 +0100 > From: Nathaniel Smith <n...@pobox.com> > Subject: Re: [Numpy-discussion] skip samples in random number > generator > To: Discussion of Numerical Python <numpy-discussion@scipy.org> > Message-ID: > < > capjvwbmzbqu-nyo1se6scvn+6eotw9mrzruwdkkuf188bgf...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > On 2 Oct 2014 16:52, "Robert Kern" <robert.k...@gmail.com> wrote: > > > > On Thu, Oct 2, 2014 at 4:42 PM, Brad Buran <bbu...@alum.mit.edu> wrote: > > > Given the following: > > > > > > from numpy import random > > > rs = random.RandomState(seed=1) > > > # skip the first X billion samples > > > x = rs.uniform(0, 10) > > > > > > How do I accomplish "skip the first X billion samples" (e.g. 7.2 > > > billion)? I see that there's a numpy.random.RandomState.set_state > > > which accepts (among other parameters) a value called "pos". This > > > sounds promising, but the other parameters I'm not sure how to compute > > > (e.g. the 1D array of 624 unsigned integers, etc.). I need to be able > > > to skip ahead in the sequence to reproduce some signals that were > > > generated for experiments. I could certainly consume and discard the > > > first X billion samples; however, that seems to be computationally > > > inefficient. > > > > Unfortunately, it requires some significant number-theoretical > > precomputation for any given N number of steps that you want to skip. > > > > http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/JUMP/index.html > > If someone really wanted this functionality then I suppose it would be > possible to precompute the special jump coefficients for lengths 2, 4, 8, > 16, 32, ..., and then perform arbitrary jumps using a sequence of smaller > jumps. (The coefficient table could be shipped with the source code.) > > -n > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: > http://mail.scipy.org/pipermail/numpy-discussion/attachments/20141002/d58de1c1/attachment-0001.html > > ------------------------------ > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > End of NumPy-Discussion Digest, Vol 97, Issue 3 > *********************************************** >
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