On 14-Jul-09, at 3:33 PM, Greg Fiske wrote:
> Dear list,
>
>
>
> I'm learning to work with numpy arrays. Can somebody explain how to
> get the
> average of two separate arrays while ignoring a user defined value
> in one
> array?
>
>
>
> For example:
>
a = numpy.array([1,5,4,99])
>
On Tue, Jul 14, 2009 at 14:42, Chris Colbert wrote:
> for your particular case:
>
a = np.array([1, 5, 4, 99], 'f')
b = np.array([3, 7, 2, 8], 'f')
c = b.copy()
d = a!=99
c[d] = (a[d] + b[d])/2.
c
> array([ 2., 6., 3., 8.], dtype=float32)
A more general answer
2009/7/14 Greg Fiske :
> Dear list,
>
> I’m learning to work with numpy arrays. Can somebody explain how to get the
> average of two separate arrays while ignoring a user defined value in one
> array?
>
> For example:
>
a = numpy.array([1,5,4,99])
b = numpy.array([3,7,2,8])
>
> Ignoring th
for your particular case:
>>> a = np.array([1, 5, 4, 99], 'f')
>>> b = np.array([3, 7, 2, 8], 'f')
>>> c = b.copy()
>>> d = a!=99
>>> c[d] = (a[d] + b[d])/2.
>>> c
array([ 2., 6., 3., 8.], dtype=float32)
>>>
On Tue, Jul 14, 2009 at 3:36 PM, Chris Colbert wrote:
> index with a boolean array?
>
index with a boolean array?
>>> import numpy as np
>>> a = np.array([3, 3, 3, 4, 4, 4])
>>> a
array([3, 3, 3, 4, 4, 4])
>>> np.average(a)
3.5
>>> b = a != 3
>>> b
array([False, False, False, True, True, True], dtype=bool)
>>> np.average(a[b])
4.0
>>>
On Tue, Jul 14, 2009 at 3:33 PM, Greg Fisk
Dear list,
I'm learning to work with numpy arrays. Can somebody explain how to get the
average of two separate arrays while ignoring a user defined value in one
array?
For example:
>>>a = numpy.array([1,5,4,99])
>>>b = numpy.array([3,7,2,8])
Ignoring the value 99, the result should b
On Sun, Jun 7, 2009 at 2:52 AM, Gabriel Beckers wrote:
> OK, perhaps I drank that beer too soon...
>
> Now, numpy.test() hangs at:
>
> test_pinv (test_defmatrix.TestProperties) ...
>
> So perhaps something is wrong with ATLAS, even though the building went
> fine, and "make check" and "make ptcheck
All,
Consider the following code:
>>> a = np.array(zip(np.arange(3)),dtype=[('a',float)])
>>> np.isfinite(a)
NotImplemented
That is, when the input is a structured array, np.isfinite returns an
object of type NotImplementedType. I would have expected it to raise a
NotImplementedError excepti
Dave wrote:
> I got stung when taking an ordinary python integer to the power of a numpy
> integer - the result wasn't what I was expecting (see below)!
From the results below, it seems to be okay if the base is a long.
Note the type of the returned result in each case. Does it seem
inconsiste
I got stung when taking an ordinary python integer to the power of a numpy
integer - the result wasn't what I was expecting (see below)!
Taking a wild guess I expect this is due to integer overflow (since it doesn't
show up with int64). When working with an int32 type one has to be aware of such
i
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