Thank you Joseph El vie, 18 dic 2020 a las 16:56, Joseph Fox-Rabinovitz (< jfoxrabinov...@gmail.com>) escribió:
> There is: np.floor_divide. > > On Fri, Dec 18, 2020, 14:38 Martín Chalela <tinchochal...@gmail.com> > wrote: > >> Right! I just thought there would/should be a "digitize" function that >> did this. >> >> El vie, 18 dic 2020 a las 14:16, Joseph Fox-Rabinovitz (< >> jfoxrabinov...@gmail.com>) escribió: >> >>> Bin index is just value floor divided by the bin size. >>> >>> On Fri, Dec 18, 2020, 09:59 Martín Chalela <tinchochal...@gmail.com> >>> wrote: >>> >>>> Hi all! I was wondering if there is a way around to using np.digitize >>>> when dealing with equidistant bins. For example: >>>> bins = np.linspace(0, 1, 20) >>>> >>>> The main problem I encountered is that digitize calls np.searchsorted. >>>> This is the correct way, I think, for generic bins, i.e. bins that have >>>> different widths. However, in the special, but not uncommon, case of >>>> equidistant bins, the searchsorted call can be very expensive and >>>> unnecessary. One can perform a simple calculation like the following: >>>> >>>> def digitize_eqbins(x, bins): >>>> """ >>>> Return the indices of the bins to which each value in input array >>>> belongs. >>>> Assumes equidistant bins. >>>> """ >>>> nbins = len(bins) - 1 >>>> digit = (nbins * (x - bins[0]) / (bins[-1] - bins[0])).astype(np.int) >>>> return digit + 1 >>>> >>>> Is there a better way of computing this for equidistant bins? >>>> >>>> Thank you! >>>> Martin. >>>> _______________________________________________ >>>> NumPy-Discussion mailing list >>>> NumPy-Discussion@python.org >>>> https://mail.python.org/mailman/listinfo/numpy-discussion >>>> >>> _______________________________________________ >>> NumPy-Discussion mailing list >>> NumPy-Discussion@python.org >>> https://mail.python.org/mailman/listinfo/numpy-discussion >>> >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@python.org >> https://mail.python.org/mailman/listinfo/numpy-discussion >> > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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