Re: [Numpy-discussion] return type from ufuncs
Dnia czwartek, 20 listopada 2014 18:47:41 Marek Wojciechowski pisze: Hi! I wrote a simple subclass of np.ndarray and now i do call np.sum() on it. I expected that the result will be a python float (or int) just like when summing up regular arrays. Instead i obtain the (scalar) view of my subclass. How can i change this behavior? I tried writing __array_wrap__ method like this: def __array_wrap__(self, out_arr, context=None): selfv = self.view(np.ndarray) return np.ndarray.__array_wrap__(selfv, out_arr, context) but this just returns np.ndarray type and not float. Hi! I'm back with the problem of returning types form ndarray subclasses, now with ravelling the array. I wrote the __array_wrap__ function like this: def __array_wrap__(self, out_arr, context=None): arr = out_arr.view(np.ndarray) if arr.ndim == 0: arr = arr[()] return arr I was convinced that now ufuncs wil return always numpy arrays. But now i discovered that for example: a.ravel() returns still the subclass type, not ndarray type. Ravel method apparently does not call __array_wrap__ (whereas np.ravel(a) does!). Is there a systematic way to resolve this or i need to write new ravel method? Regards, -- Marek Wojciechowski ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Setting up a newcomers label on the issue tracker ?
KDE calls themjunior jobs. On Nov 27, 2014 2:29 AM, Benjamin Root ben.r...@ou.edu wrote: FWIW, matplotlib calls it low hanging fruit. I think it is a better name than newcomers. On Wed, Nov 26, 2014 at 1:19 PM, Aldcroft, Thomas aldcr...@head.cfa.harvard.edu wrote: On Wed, Nov 26, 2014 at 8:24 AM, Charles R Harris charlesr.har...@gmail.com wrote: On Wed, Nov 26, 2014 at 2:36 AM, Sebastian Berg sebast...@sipsolutions.net wrote: On Mi, 2014-11-26 at 08:44 +, David Cournapeau wrote: Hi, Would anybody mind if I create a label newcomers on GH, and start labelling simple issues ? We actually have an easy fix label, which I think had this in mind. However, I admit that I think some of these issues may not be easy at all (I guess it depends on what you consider easy ;)). In any case, I think just go ahead with creating a new label or reusing the current one. easy fix might be a starting point to find some candidate issues. - Sebsatian This is in anticipation to the bloomberg lab event in London this WE. I will try to give a hand to people interested in numpy/scipy, There is also a documentation label, and about 30 tickets with that label. That should be good for just practicing the mechanics. FWIW in astropy we settled on two properties, level of effort and level of sub-package expertise, with corresponding labels: - effort-low, effort-medium, and effort-high - package-novice, package-intermediate, package-expert This has been used with reasonable success. - Tom Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Finding values in an array
I probably miss something very basic, but how given two arrays a and b, can I find positions in a where elements of b are located? If a were sorted, I could use searchsorted, but I don't want to get valid positions for elements that are not in a. In my case, a has unique elements, but in the general case I would accept the first match. In other words, I am looking for an array analog of list.index() method. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Finding values in an array
On Thu, Nov 27, 2014 at 10:15 PM, Alexander Belopolsky ndar...@mac.com wrote: I probably miss something very basic, but how given two arrays a and b, can I find positions in a where elements of b are located? If a were sorted, I could use searchsorted, but I don't want to get valid positions for elements that are not in a. In my case, a has unique elements, but in the general case I would accept the first match. In other words, I am looking for an array analog of list.index() method. I don't know an easy solution to this problem in pure numpy, but if you could do this pretty easily (and quite efficiently) if you are willing to use pandas. Something like: locs = pd.Index(a).get_indexer(b) Note that -1 is used to denote a non-match, and get_indexer will raise if the match is non-unique instead of returning the first element. If your array is not 1d, you can still make this work but you'll need to use np.ravel and np.unravel_index. Actually, you may find that putting your data into pandas data structures is a good solution, since pandas is designed to make exactly these sort of alignment operations easy (and automatic). I suppose the simplest solution to this problem would be to convert your data into a list and use list.index() repeatedly (or you could even write it yourself in a few lines), but I'd guess that was never implemented for ndarrays because it's rather slow -- better to use a hash-table like a dict or pandas.Index for repeated lookups. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
