[Numpy-discussion] weird indexing

Hi Okay, here's a weird one. In Fortran you can specify the upper/lower bounds of an array e.g. REAL A(3:7) What would be the best way to translate this to a Numpy array? I would like to do something like A=numpy.zeros(shape=(5,)) and have the expression A[3] actually return A[0]. Or something.

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

Matthew Brett wrote: >>> So, currently we have all and allclose giving the same answer: >>> >>> In [19]: a = array([]) >>> >>> In [20]: b = array([1]) >>> >>> In [21]: all(a == b) >>> Out[21]: True >>> >>> In [22]: allclose(a, b) >>> Out[22]: True >>> >>> Would we want the answers to be different?

[Numpy-discussion] Error importing from numpy.matlib

Hi all, I can't figure out why this is happening ... I just recently recompiled numpy/scipy from svn just for the heck of it. Anyway, somewhere in my codebase (for a long time now) I'm doing: from numpy.matlib import * Now, when I try to use this code, or just type that in the interpreter,

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

> > So, currently we have all and allclose giving the same answer: > > > > In [19]: a = array([]) > > > > In [20]: b = array([1]) > > > > In [21]: all(a == b) > > Out[21]: True > > > > In [22]: allclose(a, b) > > Out[22]: True > > > > Would we want the answers to be different? > > No. I wasn't thin

Re: [Numpy-discussion] Numpy code coverage

Stefan van der Walt wrote: > Hi all, > > I read about Titus Brown's Figleaf code coverage tool [1] on the > Planet SciPy aggregator [2]. The results of running figleaf on the > numpy test-suite [3] covers Python code only. > > What the best way of discovering the C and C++ code coverage as well?

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

Matthew Brett wrote: > So, currently we have all and allclose giving the same answer: > > In [19]: a = array([]) > > In [20]: b = array([1]) > > In [21]: all(a == b) > Out[21]: True > > In [22]: allclose(a, b) > Out[22]: True > > Would we want the answers to be different? No. I wasn't thinkin

Re: [Numpy-discussion] planet.scipy.org

On Jan 2, 2008 8:02 PM, Ondrej Certik <[EMAIL PROTECTED]> wrote: > On Jan 2, 2008 4:20 PM, Scott Ransom <[EMAIL PROTECTED]> wrote: > > Hmmm. When I try to load: > > > > http://planet.scipy.org/ > > > > I get: "Unknown host planet.scipy.org" > > > > However, http://planet.scipy.org (without the f

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

So, currently we have all and allclose giving the same answer: In [19]: a = array([]) In [20]: b = array([1]) In [21]: all(a == b) Out[21]: True In [22]: allclose(a, b) Out[22]: True Would we want the answers to be different? ___ Numpy-discussion mai

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

On Jan 3, 2008 2:37 PM, Matthew Brett <[EMAIL PROTECTED]> wrote: > Just to ask - is there a reason why this: > > > In [39]: all([]) > > Out[39]: True > > is the case? Because it's True. Anything is true about the elements of an empty set, because there aren't any. In this case, all asks if all e

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

Just to ask - is there a reason why this: > In [39]: all([]) > Out[39]: True is the case? ___ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

> On Thu, 3 Jan 2008, Charles R Harris apparently wrote: >> Isn't it trivially true that all elements of an empty >> array are close to any number? On Thu, 3 Jan 2008, Alan G Isaac apparently wrote: > Sure, but might not one expect a ValueError due to > shape mismatch? (Doesn't allclose usuall

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

Hi, > > > import numpy as np > > > print np.__version__ > > > a=np.int32(42) > > > b=np.array([],dtype=np.int32) > > > assert np.allclose(a,b) > > > > > > Is this expected behavior of numpy or is this a bug I should report? > > > > Bug, I think. I think this bug - which may be mine - follows from

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

On Thu, 3 Jan 2008, Charles R Harris apparently wrote: > Isn't it trivially true that all elements of an empty > array are close to any number? Sure, but might not one expect a ValueError due to shape mismatch? (Doesn't allclose usually use normal broadcasting rules?) Cheers, Alan Isaac ___

Re: [Numpy-discussion] non-intuitive behaviour in numpy.array([list], numpy.object_)

Thanks - that's clear I guess, although I still think that it might be less confusing if numpy did not try to be clever! On 1/3/08, Christopher Barker <[EMAIL PROTECTED]> wrote: > Garnet Chan wrote: > > When constructing an numpy object array from a list of numpy arrays, > > one observes the follo

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

On Jan 3, 2008 1:06 PM, Robert Kern <[EMAIL PROTECTED]> wrote: > Andrew Straw wrote: > > Apologies if I've missed the discussion of this, but I was recently > > surprised by the following behavior (in svn trunk 4673). The following > > code runs without triggering the assertion. > > > > import num

[Numpy-discussion] MaskedArray and the min,max,sum,prod Methods

Working with the new MaskedArray, I noticed the following differences with numpy.array behavior: >>> masked_array([1, 2, 3], mask=True).min() 2147483647 >>> array([]).min() Traceback (most recent call last): File "", line 1, in ValueError: zero-size array to ufunc.reduce without identity >>> m

[Numpy-discussion] MaskedArray and Record Arrays

I am experimenting with the new MaskedArray (from ) as a replacement for my own home-brewed masked data handling mechanisms. In what I have built myself, I often work with record arrays that have a single mask for the whole record (no fieldmask).

Re: [Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

Andrew Straw wrote: > Apologies if I've missed the discussion of this, but I was recently > surprised by the following behavior (in svn trunk 4673). The following > code runs without triggering the assertion. > > import numpy as np > print np.__version__ > a=np.int32(42) > b=np.array([],dtype=np.i

[Numpy-discussion] unexpected behavior with allclose( scalar, empty array)

Apologies if I've missed the discussion of this, but I was recently surprised by the following behavior (in svn trunk 4673). The following code runs without triggering the assertion. import numpy as np print np.__version__ a=np.int32(42) b=np.array([],dtype=np.int32) assert np.allclose(a,b) Is th

Re: [Numpy-discussion] non-intuitive behaviour in numpy.array([list], numpy.object_)

Garnet Chan wrote: > When constructing an numpy object array from a list of numpy arrays, > one observes the following behaviour > import numpy as N a=[N.zeros([2,2], N.object_), N.zeros([2,2], N.object_)] b=N.array(a, N.object_) print b.shape > (2, 2, 2) a=[N.zeros([2,2],

[Numpy-discussion] non-intuitive behaviour in numpy.array([list], numpy.object_)

When constructing an numpy object array from a list of numpy arrays, one observes the following behaviour >>> import numpy as N >>> a=[N.zeros([2,2], N.object_), N.zeros([2,2], N.object_)] >>> b=N.array(a, N.object_) >>> print b.shape (2, 2, 2) >>> a=[N.zeros([2,2], N.object_), N.zeros([2,1], N.ob