Re: [Numpy-discussion] Inverting argsort(a, axis=0) to obtain column-wise ranks
D'oh, Zachary already gave that answer. On Sun, Sep 19, 2010 at 10:17 PM, John Schulman wrote: > Argsort twice and you get the rank. > > a1.argsort(axis=0).argsort(axis=0) > > That's because argsort is it's own inverse when applied to the ranks. > > On Tue, Sep 7, 2010 at 1:01 PM, Alexander Michael wrote: >> Calculating ranks by inverting the results of an argsort is >> straightforward and fast for 1D arrays: >> >> indices = argsort(a1) >> ranks = zeros_like(indices) >> ranks[indices] = arange(len(indices)) >> >> I was wondering if there was an equally pithy way to do this for >> multiple data samples stored column-wise in a 2D array. That is, is >> there a trick to invert the results of argsort(a2, axis=0) without >> iterating (in python) over the columns? >> >> Thanks, >> Alex >> ___ >> 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
Re: [Numpy-discussion] Inverting argsort(a, axis=0) to obtain column-wise ranks
Argsort twice and you get the rank. a1.argsort(axis=0).argsort(axis=0) That's because argsort is it's own inverse when applied to the ranks. On Tue, Sep 7, 2010 at 1:01 PM, Alexander Michael wrote: > Calculating ranks by inverting the results of an argsort is > straightforward and fast for 1D arrays: > > indices = argsort(a1) > ranks = zeros_like(indices) > ranks[indices] = arange(len(indices)) > > I was wondering if there was an equally pithy way to do this for > multiple data samples stored column-wise in a 2D array. That is, is > there a trick to invert the results of argsort(a2, axis=0) without > iterating (in python) over the columns? > > Thanks, > Alex > ___ > 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] Infix operators for numpy arrays
Hi all, I just posted a recipe (http://code.activestate.com/recipes/577201/) for infix operators that work with numpy arrays, so you can write X *dot* Y. It works by making the Infix class a subclass of ndarray. John ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Scipy09 Sprints
For the numpy/scipyers at Caltech now: I'm wondering what is happening with the scipy09 sprints. According to the website, the sprints start at 8AM in Powell Booth, but that building is locked and there's no sign of life. Best, John ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] switching to float32
I'm trying to reduce the memory used in a calculation, so I'd like to switch my program to float32 instead of float64. Is it possible to change the numpy default float size, so I don't have to explicitly state dtype=np.float32 everywhere? Thanks, John ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] error with large memmap
What's the best way to install a 64-bit python alongside my existing installation? On Tue, Jun 9, 2009 at 7:32 AM, John Schulman wrote: > OK looks like that was the issue > $ python -c "import platform; print platform.machine()" > i386 > > Thanks > > > On Tue, Jun 9, 2009 at 2:52 AM, David > Cournapeau wrote: >> Charles R Harris wrote: >>> >>> >>> On Tue, Jun 9, 2009 at 2:32 AM, John Schulman >> <mailto:jos...@caltech.edu>> wrote: >>> >>> I'm getting the error >>> OverflowError: cannot fit 'long' into an index-sized integer >>> when I try to memmap a 6gb file >>> >>> top of the stack trace is >>> mm = mmap.mmap(fid.fileno(), bytes, access=acc) >>> where bytes = 652800L >>> >>> I thought that 64-bit systems with python>2.5 could memmap large >>> files. I'm running the latest EPD python distribution (4.2.30201), >>> which uses python 2.5.4 and numpy 1.2.1 >>> Macbook Pro Core 2 Duo, OS X 10.5.6 >>> >>> >>> Is your python 64 bits? Try: >>> >>> file `which python` >> >> This is even better: >> >> python -c "import platform; print platform.machine()" >> >> as mac os x can be confusing with fat binaries and all :) >> >> David >> ___ >> 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
Re: [Numpy-discussion] error with large memmap
OK looks like that was the issue $ python -c "import platform; print platform.machine()" i386 Thanks On Tue, Jun 9, 2009 at 2:52 AM, David Cournapeau wrote: > Charles R Harris wrote: >> >> >> On Tue, Jun 9, 2009 at 2:32 AM, John Schulman > <mailto:jos...@caltech.edu>> wrote: >> >> I'm getting the error >> OverflowError: cannot fit 'long' into an index-sized integer >> when I try to memmap a 6gb file >> >> top of the stack trace is >> mm = mmap.mmap(fid.fileno(), bytes, access=acc) >> where bytes = 652800L >> >> I thought that 64-bit systems with python>2.5 could memmap large >> files. I'm running the latest EPD python distribution (4.2.30201), >> which uses python 2.5.4 and numpy 1.2.1 >> Macbook Pro Core 2 Duo, OS X 10.5.6 >> >> >> Is your python 64 bits? Try: >> >> file `which python` > > This is even better: > > python -c "import platform; print platform.machine()" > > as mac os x can be confusing with fat binaries and all :) > > David > ___ > 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] error with large memmap
I'm getting the error OverflowError: cannot fit 'long' into an index-sized integer when I try to memmap a 6gb file top of the stack trace is mm = mmap.mmap(fid.fileno(), bytes, access=acc) where bytes = 652800L I thought that 64-bit systems with python>2.5 could memmap large files. I'm running the latest EPD python distribution (4.2.30201), which uses python 2.5.4 and numpy 1.2.1 Macbook Pro Core 2 Duo, OS X 10.5.6 ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
