Yep, that's about 7x faster than what I came up with. Thanks Maciek! -greg
On Thu, Nov 14, 2019 at 4:35 PM Maciek Wójcikowski <mac...@wojcikowski.pl> wrote: > Hi Thomas, > > You could also use SetBitsFromList() method: > >> bv.SetBitsFromList(np.where(ar)[0].tolist()) >> > > ---- > Pozdrawiam, | Best regards, > Maciek Wójcikowski > mac...@wojcikowski.pl > > > czw., 14 lis 2019 o 16:28 Greg Landrum <greg.land...@gmail.com> > napisał(a): > >> Hi Thomas, >> >> There may be more efficient ways to do this, but here's something that >> works (and isn't the slowest thing I came up with): >> def np_to_bv(fv): >> bv = DataStructs.ExplicitBitVect(len(fv)) >> for i,v in enumerate(fv): >> if v: >> bv.SetBit(i) >> return bv >> >> -greg >> >> >> >> On Thu, Nov 14, 2019 at 3:47 PM Thomas Evangelidis <teva...@gmail.com> >> wrote: >> >>> Greetings, >>> >>> I am opening this old thread again for someone to answer my initial >>> question this time, which was "How do I convert numpy.ndarray objects to >>> rdkit.DataStructs.ExplicitBitVect objects?". At the time I asked >>> the question I circumvented the problem by calculating Tanimoto >>> similarities with Scipy, but now I want to utilize all similarity functions >>> offered by rdkit.DataStructs. I am struggling with that for quite some time >>> although I feel that the answer is simple. >>> >>> So basically, I have these arrays and want to calculate their >>> DataStructs.McConnaugheySimilarity similarity. How do I do it? >>> >>> fv1 = numpy.array([1,1,0,0,1,0,1]) >>> >>> >>> fv2 = numpy.array([0,1,1,0,1,0,0]) >>> >>> Thanks in advance. >>> Thomas >>> >>> >>> -- >>> >>> ====================================================================== >>> >>> Dr. Thomas Evangelidis >>> >>> Research Scientist >>> >>> IOCB - Institute of Organic Chemistry and Biochemistry of the Czech >>> Academy of Sciences <https://www.uochb.cz/web/structure/31.html?lang=en> >>> , Prague, Czech Republic >>> & >>> CEITEC - Central European Institute of Technology >>> <https://www.ceitec.eu/>, Brno, Czech Republic >>> >>> email: teva...@gmail.com, Twitter: tevangelidis >>> <https://twitter.com/tevangelidis>, LinkedIn: Thomas Evangelidis >>> <https://www.linkedin.com/in/thomas-evangelidis-495b45125/> >>> >>> website: https://sites.google.com/site/thomasevangelidishomepage/ >>> >>> >>> >>> _______________________________________________ >>> Rdkit-discuss mailing list >>> Rdkit-discuss@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss >>> >> _______________________________________________ >> Rdkit-discuss mailing list >> Rdkit-discuss@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss >> >
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