On Sat, Aug 20, 2011 at 4:17 PM, David Warde-Farley
wrote:
>
> There are functions for this available in scikits.image:
>
> http://stefanv.github.com/scikits.image/api/scikits.image.color.html
>
> Although you may need to reshape it with reshape(arr, (width, height, 4)) or
> something similar fir
On Sat, Aug 20, 2011 at 17:49, Chris Withers wrote:
> On 20/08/2011 15:38, Robert Kern wrote:
>> On Sat, Aug 20, 2011 at 17:37, Chris Withers wrote:
>>> Hi All,
>>>
>>> What's the best type of array to use for decimal values?
>>> (ie: where I care about precision and want to avoid any possible
>>
On Sat, Aug 20, 2011 at 4:49 PM, Chris Withers wrote:
> On 20/08/2011 15:38, Robert Kern wrote:
> > On Sat, Aug 20, 2011 at 17:37, Chris Withers
> wrote:
> >> Hi All,
> >>
> >> What's the best type of array to use for decimal values?
> >> (ie: where I care about precision and want to avoid any po
Hi All,
I've got a tree of nested dicts that at their leaves end in numpy arrays
of identical sizes.
What's the easiest way to persist these to disk so that I can pick up
with them where I left off?
What's the most "correct" way to do so?
I'm using IPython if that makes things easier...
I ha
On 20/08/2011 15:38, Robert Kern wrote:
> On Sat, Aug 20, 2011 at 17:37, Chris Withers wrote:
>> Hi All,
>>
>> What's the best type of array to use for decimal values?
>> (ie: where I care about precision and want to avoid any possible
>> rounding errors)
>
> dtype=object
Thanks!
What are the pe
On Sat, Aug 20, 2011 at 17:37, Chris Withers wrote:
> Hi All,
>
> What's the best type of array to use for decimal values?
> (ie: where I care about precision and want to avoid any possible
> rounding errors)
dtype=object
--
Robert Kern
"I have come to believe that the whole world is an enigma
Hi All,
What's the best type of array to use for decimal values?
(ie: where I care about precision and want to avoid any possible
rounding errors)
cheers,
Chris
--
Simplistix - Content Management, Batch Processing & Python Consulting
- http://www.simplistix.co.uk
On Sat, Aug 20, 2011 at 2:47 AM, Olivier Verdier wrote:
> Your syntax is not as intuitive as you may think.
>
> Suppose I take a matrix instead
>
> a = np.array([1,2,3,4]).reshape(2,2)
> b = (a>1) # np.array([[False,True],[True,True]])
>
> How would a[b,np.newaxis] be supposed to work?
>
> Note t
On Fri, Aug 19, 2011 at 4:52 PM, Bruce Southey wrote:
> On Fri, Aug 19, 2011 at 3:05 PM, Mark Wiebe wrote:
> > On Fri, Aug 19, 2011 at 11:44 AM, Charles R Harris
> > wrote:
> >>
> >>
> >>
> >>
> >> My main peeve is that NA is upper case ;) I suppose that could use some
> >> discussion.
> >
> >
On Fri, Aug 19, 2011 at 11:37 AM, Bruce Southey wrote:
> Hi,
> Just some immediate minor observations that are really about trying to
> be consistent:
>
> 1) Could you keep the display of the NA dtype be the same as the array?
> For example, NA dtype is displayed as ' 'float64' as that is the arr
On Sun, Aug 14, 2011 at 09:15:35PM +0200, Charanpal Dhanjal wrote:
> Incidentally, I am confused as to why numpy calls the lapack lite
> routines - when I call numpy.show_config() it seems to have detected my
> ATLAS libraries and I would have expected it to use those.
My rule of thumb is to nev
On 2011-08-20, at 4:01 AM, He Shiming wrote:
> Hi,
>
> I'm wondering how to do RGB <-> HSV conversion in numpy. I found a
> couple solutions through stackoverflow, but somehow they can't be used
> in my array format. I understand the concept of conversion, but I'm
> not that familiar with numpy.
Hi,
I'm wondering how to do RGB <-> HSV conversion in numpy. I found a
couple solutions through stackoverflow, but somehow they can't be used
in my array format. I understand the concept of conversion, but I'm
not that familiar with numpy.
My source buffer format is 'RGBA' sequence. I can take it
Your syntax is not as intuitive as you may think.
Suppose I take a matrix instead
a = np.array([1,2,3,4]).reshape(2,2)
b = (a>1) # np.array([[False,True],[True,True]])
How would a[b,np.newaxis] be supposed to work?
Note that other (simple) slices work perfectly with newaxis, such as
a[:1,np.new
14 matches
Mail list logo
