Glad I finally found this discussion.
I implemented some of the ideas from the SciPy BOAF discussion, and
Joshua has already merged them into his datarray on GitHub (thanks,
Joshua, for being so fast on the merge button).
To introduce these changes, here's a couple of examples of how you
could in
On Wed, Jul 7, 2010 at 10:13 PM, Christoph Gohlke wrote:
> Dear NumPy developers,
>
> I am trying to solve some scipy.sparse TypeError failures reported in
> [1] and reduced them to the following example:
>
>
> >>> import numpy
> >>> a = numpy.array([[1]])
>
> >>> numpy.dot(a.astype('single'), a.
On Wed, Jul 7, 2010 at 10:13 PM, Christoph Gohlke wrote:
> Dear NumPy developers,
>
> I am trying to solve some scipy.sparse TypeError failures reported in
> [1] and reduced them to the following example:
>
>
> >>> import numpy
> >>> a = numpy.array([[1]])
>
> >>> numpy.dot(a.astype('single'), a.
On Thu, Jul 8, 2010 at 6:13 AM, Christoph Gohlke wrote:
> Dear NumPy developers,
>
> I am trying to solve some scipy.sparse TypeError failures reported in
> [1] and reduced them to the following example:
>
>
import numpy
a = numpy.array([[1]])
>
numpy.dot(a.astype('single'), a.astyp
Dear NumPy developers,
I am trying to solve some scipy.sparse TypeError failures reported in
[1] and reduced them to the following example:
>>> import numpy
>>> a = numpy.array([[1]])
>>> numpy.dot(a.astype('single'), a.astype('longdouble'))
array([[1.0]], dtype=float64)
>>> numpy.dot(a.astyp
Sorry to be super-brief, I've been offline for days and only have a
brief window of access for now.
Many thanks to Jonathan for the summary!
On Tue, Jul 6, 2010 at 9:42 AM, Skipper Seabold wrote:
>
>
> I think what Josh said is right. However, we proposed having all of
> the new labeled axis ac
I've been trying to embed extra information into dtype, such that whether I have
structured arrays or not, I can alwas have access to:
* A name of the field.
This is already present on structured arrays, but lost whenever you access a
field.
* A description of the field.
Tried
Bruce Southey writes:
> 4) How should this interact with the rest of numpy?
BTW, now I rememberd something I wanted to implement but required too much
monkeypatching right now.
For all functions accepting the 'axis' argument, I'd like to provide a string
that uniquely identifies a dimension/axis,
Bruce Southey writes:
> 1) Indexing especially related to slicing and broadcasting.
1.1) Absolute indexing/slicing
a[0], a['tickvalue']
1.2) Partial slicing
For the case of "compund" ticks that is merging multiple ticks into a
single one:
a['subtick1value-subtick2value']
On Wed, Jul 7, 2010 at 5:52 AM, Bruce Southey wrote:
> On 07/06/2010 01:09 PM, Gael Varoquaux wrote:
>> Just to give a data point, my research group and I would be very excited
>> at the idea of having Fernando's data arrays in Numpy. We can't offer to
>> maintain it, because we are already fairly
On 2010-07-07, at 9:52 AM, Bruce Southey wrote:
> This is very interesting work especially if can be used to extend or
> replace the current record arrays (and perhaps structured arrays).
It's unlikely that this would happen. They serve a different purpose, as far as
I can tell. It would be pe
On 07/06/2010 01:09 PM, Gael Varoquaux wrote:
> Just to give a data point, my research group and I would be very excited
> at the idea of having Fernando's data arrays in Numpy. We can't offer to
> maintain it, because we are already fairly involved in machine learning
> and neuroimaging specific c
Thanks for your answers.
> Three solutions:
> - ask your users to build the software and install zlib by
> themselves. On windows, I am afraid it means you concretely limit your
> userbase to practically 0.
> - build zlib as part of the build process, and keep zlib internally.
> - include a cop
On Tue, Jul 6, 2010 at 11:33 PM, David Goldsmith wrote:
> >>> np.finfo('float64').eps # returns a scalar
> 2.2204460492503131e-16
> >>> np.finfo('float64').epsneg # returns an array
> array(1.1102230246251565e-16)
>
> Bug or feature?
>
>
Looks like a bug.
Chuck
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