I think they are identical, its just that asanyarray appears to be targeted for exactly this use-case, so perhaps it is a little faster. I just posted that asanyarray would probably have been a better choice, the posts must have crossed.
On Sun, Feb 22, 2009 at 6:52 PM, Andrew Straw <straw...@astraw.com> wrote: > Darren, > > What's the difference between asanyarray(y) and array(y, copy=False, > subok=True)? I thought asanyarray would also do what you want. > > -Andrew > > Darren Dale wrote: > > On Sun, Feb 22, 2009 at 3:22 PM, Darren Dale <dsdal...@gmail.com > > <mailto:dsdal...@gmail.com>> wrote: > > > > On Sun, Feb 22, 2009 at 3:17 PM, Darren Dale <dsdal...@gmail.com > > <mailto:dsdal...@gmail.com>> wrote: > > > > Hello, > > > > I am using numpy's assert_array_equal and > > assert_array_almost_equal to unit test my physical quantities > > package. I made a single minor change to assert_array_compare > > that I think might make these functions more useful to ndarray > > subclasses, and thought maybe they could be useful to numpy > > itself. I tried applying this diff to numpy and running the > > test suite, and instead of 9 known failures I got 1 known > > failure, 11 skips, 2 errors and 2 failures. Perhaps it is > > possible that by not forcing the input arrays to be ndarray > > instances, some additional numpy features are exposed. > > > > Thanks, > > Darren > > > > $ svn diff > > Index: numpy/testing/utils.py > > > =================================================================== > > --- numpy/testing/utils.py (revision 6370) > > +++ numpy/testing/utils.py (working copy) > > @@ -240,9 +240,9 @@ > > > > def assert_array_compare(comparison, x, y, err_msg='', > > verbose=True, > > header=''): > > - from numpy.core import asarray, isnan, any > > - x = asarray(x) > > - y = asarray(y) > > + from numpy.core import array, isnan, any > > + x = array(x, copy=False, subok=True) > > + y = array(y, copy=False, subok=True) > > > > def isnumber(x): > > return x.dtype.char in '?bhilqpBHILQPfdgFDG' > > > > > > Actually, my svn checkout was not up to date. With this patch > > applied, I get 1 known failure and 11 skips. > > > > > > I just double checked and I think I get the same results running the > > svn 6456 test suite with and without this patch applied. I tried > > posting an enhancement request at the trac website, but I cant file > > the ticket because I get "500 Internal Server Error", so I'm posting > > it here. > > ------------------------------------------------------------------------ > > > > _______________________________________________ > > Numpy-discussion mailing list > > Numpy-discussion@scipy.org > > http://projects.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion >
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