Hi Neil,
sure...I aeh, knew this...of course...[?]
I'm using shuffle with a list of indices now...
Thanks,
Jan
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Dear Josef and Keith,
thank you both for your suggestions. I think intersect would be what I want
for it makes clean code.
I have, however, spotted the problem:
I was mistakenly under the assumption that random_integers returns unique
entries, which is of course not guaranteed, so that the random
Dear List,
I'm trying to speed up a piece of code that selects a subsample based on some
criteria:
Setup:
I have two samples, raw and cut. Cut is a pure subset of raw, all elements in
cut are also in raw, and cut is derived from raw by applying some cuts.
Now I would like to select a random subs
>
>> a) Can you guys tell me briefly about the kind of problems you are
>> tackling with numpy and scipy?
>
> I'm using python with numpy,scipy, pytables and matplotlib for data
> analysis in the field of high energy particle physics. Most of the
> work is histograming millions of events, fitting
I'm afraid I'm not quite done with this, yet.
There's still more I am confused about, even after reading the manual
(again).
There is something fundamentally weird about record arrays, that doesn't
seem to click with me.
Please have a look at this piece of code:
import numpy as N
newtype = N.dtyp
Thanks Stefan and Travis for their explanations.
First a request: Could both explanations be added to the manual, please?
Thanks.
So the problem I was having was that I thought this difference in behavior
would be caused by two different types: recarray and ndarray.
I feel that there is still som
There seems to be a fundamental lack of understanding on my behalf when it
comes to dtypes and record arrays.
Please consider the following snippet:
import numpy as N
newtype = N.dtype([('x', N.float64), ('y', N.float64), ('z', N.float64)])
a = N.random.random((100,3))
a.dtype=newtype
b = N.colum
I'm having a difficult time understanding the following behavior:
import numpy as N
# create a new array 4 rows, 3 columns
x = N.random.random((4, 3))
# elementwise multiplication
x*x
newtype = N.dtype([('x', N.float64), ('y', N.float64), ('z', N.float64)])
# interpret the array as an array of
Hi list,
maybe this is a really stupid idea, and I don't want to advertise this, but
what actually happens when I reassign the dtype of an object?
Does it return the same as array.view?
say I have the following code
In [64]: b
Out[64]:
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
> For instance
>
> In [7]: def countmembers(a1, a2) :
> ...: a = sort(a2)
> ...: il = a.searchsorted(a1, side='l')
> ...: ir = a.searchsorted(a1, side='r')
> ...: return ir - il
> ...:
>
> In [8]: a2 = random.randint(0,10,(100,))
>
> In [9]: a1 = arange(11)
>
> In [11]:
I would also like to see a method that doesn't have the unique members
requirement.
If setmember1d cannot be modified to do that, is there another method
that doesn't have these restrictions. Or could one be added?
Thanks,
Jan
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