On Wed, 21 Jun 2006, Bill Baxter apparently wrote:
ew to numpy.nonzero
I agree that having the method and function behave so
differently is awkward; this was discussed before on this
list.
It does allow Simon's nicer solution, however.
I'm not sure why bool arrays cannot be used as
Hi,
Can someone give a brief example of the Numeric
function LittleEndian?
I have written two separate functions to read binary
data that can be either LittleEndian or BigEndian (using byteswapped() ) but it
would be great with just one function.
Much obliged,
Sheldon
Hi,
I'm not sure why bool arrays cannot be used as indices.
The natural solution to the original problem seemed to be:
M[:,V0]
but this is not allowed.
I started a thread on this earlier this year. Try searching the archive for
boolean indexing (if it comes back online somewhen).
Travis
Hey Sheldon
With NumPy you can use dtype's newbyteorder method to convert any dtype's
byte order to an order you specify:
In [1]: import numpy as N
In [2]: x = N.array([1],dtype='i4')
In [3]: y = N.array([1],dtype='i4')
In [4]: xle = N.asarray(x, dtype=x.dtype.newbyteorder(''))
In [5]: yle =
On 6/20/06, Bill Baxter [EMAIL PROTECTED] wrote:
a[:,num.where(v0.5)[0]]
array([[1, 2, 4],
[6, 7, 9]])
I'll put that up on the Matlab-Numpy page.
That's a great addition to the Matlab to Numpy page.
But it only works if v is a column vector. If v is a row vector, then
where(v.A
All,
As part of this year's SciPy 2006 Conference, we've planned Coding
Sprints on Monday and Tuesday (August 14-15) and a Tutorial Day
Wednesday (August 16)--the normal conference presentations follow on
Thursday and Friday (August 17-18).
For this year at least, the Tutorials (and Sprints)
Bill Baxter wrote:
On 6/21/06, *Simon Burton* [EMAIL PROTECTED]
mailto:[EMAIL PROTECTED] wrote:
On Wed, 21 Jun 2006 13:48:48 +0900
Bill Baxter [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] wrote:
a[:,num.where(v0.5)[0]]
array([[1, 2, 4],
[6, 7, 9]])
Not sure if this one has been addressed. There appears to be a
problem with cumsum(dtype=), with reasonably small numbers. Both PPC
and x86 Macs.
import numpy
print numpy version:, numpy.__version__
v = numpy.arange(10002) # 10001 is OK, larger is worse
print
On 6/21/06, Travis Oliphant [EMAIL PROTECTED] wrote:
Johannes Loehnert wrote:
Hi,
I'm not sure why bool arrays cannot be used as indices.
The natural solution to the original problem seemed to be:
M[:,V0]
but this is not allowed.
I started a thread on this earlier this year.
The NumPy for Matlab Users page suggests mat(a.A * b.A) for
element-by-element matrix multiplication. I think it would be helpful
to also include multiply(a, b).
a.*b
mat(a.A * b.A) or
multiply(a, b)
___
Numpy-discussion mailing list
Keith Goodman wrote:
The NumPy for Matlab Users page suggests mat(a.A * b.A) for
element-by-element matrix multiplication. I think it would be helpful
to also include multiply(a, b).
a.*b
mat(a.A * b.A) or
multiply(a, b)
It is a wiki page. You may edit it yourself without needing to ask
Pau Gargallo wrote:
On 6/21/06, Travis Oliphant [EMAIL PROTECTED] wrote:
Johannes Loehnert wrote:
Hi,
I'm not sure why bool arrays cannot be used as indices.
The natural solution to the original problem seemed to be:
M[:,V0]
but this is not allowed.
I started
I am trying to install numpy on Gentoo (see my info below for version
etc). It all seems to go fine, but when I try to import it and run
the tests, I get the following error (in ipython):
In [1]: import numpy
import linalg - failed: libg2c.so.0: cannot open shared object file:
No such file or
On 6/21/06, Robert Kern [EMAIL PROTECTED] wrote:
Keith Goodman wrote:
The NumPy for Matlab Users page suggests mat(a.A * b.A) for
element-by-element matrix multiplication. I think it would be helpful
to also include multiply(a, b).
a.*b
mat(a.A * b.A) or
multiply(a, b)
It is a
[EMAIL PROTECTED] wrote:
Hi Travis
Not sure if you've had a chance to look at the previous code I sent or not,
but I was able to reduce the code (see below) to its smallest size and still
have the problem, albeit at a slower rate. The problem appears to come from
changing values in the
Alan G Isaac wrote:
M.transpose()[V0]
If you want the columns as columns,
you can transpose again.
On Wed, 21 Jun 2006, Keith Goodman apparently wrote:
I can't get that to work when M is a n by m matrix:
The problem is not M being a matrix.
You made V a matrix (i.e., 2d).
So you need
I do not understand how to think about this:
x=arange(3).flat
x
numpy.flatiter object at 0x01BD0C58
x2
True
x10
True
Why? (I realize this behaves like xrange,
so this may not be a numpy question,
but I do not understand that behavior either.)
What I expected:
I was setting the fill_value as 'NA' when constructing the array so
the masked values would be printed as 'NA'. It is not a big deal to
avoid doing this.
Nevertheless, the differences between a masked array with a boolean
mask and a mask of booleans have caused me trouble before. Especially
when
Hello all,
I'm encountering some (relatively new?) behavior with masked arrays that
strikes me as bizarre. Raising zero to a floating-point value is triggering
a mask to be set, even though the result should be well-defined. When using
fixed-point integers for powers, everything works as
Simon Burton wrote:
On Wed, 21 Jun 2006 10:50:26 -0600
Travis Oliphant [EMAIL PROTECTED] wrote:
So, in SVN NumPy, you will be able to do
a[:,V0]
a[V0,:]
The V0 will be replaced with integer arrays as if nonzero(V0) had been
called.
OK.
But just for the record, we should
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