Integer exponentiation fails (i.e., changes type)
with boolean dtype. See below. Expected?
Alan Isaac
a = np.array([[0,1,0],[0,0,1],[1,0,0]], dtype=np.bool_)
a2 = a*a
a3 = a2*a
print(a3)
[[False True False]
[False False True]
[ True False False]]
On 10/6/2010 5:52 PM, Charles R Harris wrote:
What would you want in it's place? For consistancy, a**0 == all_true, a**i ==
a, i 0?
Yes.
Alan Isaac
PS Boolean matrix powers are well defined and useful.
I found this oddity by accidentally creating an array instead of a matrix.
On 9/8/2010 6:38 AM, John Reid wrote:
def safe_x_log_x(x):
@return: x log(x) but replaces -inf with 0.
l = np.log(x)
result = x * l
result[np.isneginf(l)] = 0.
return result
Assuming x is known to contain nonnegative floats:
def safe_x_log_x(x):
x =
On 8/17/2010 12:13 AM, Alex Ter-Sarkissov wrote:
I have an array (say, mat=rand(3,5)) from which I 'pull out' a row
(say, s1=mat[1,]). The problem is, the shape of this row s1 is not
[1,5], as I would expect, but rather [5,], which means that I can't,
for example, concateante mat and s1
Two warnings remain; no failures.
Details below.
Alan Isaac
(Python 2.7 on Vista)
Python 2.7 (r27:82525, Jul 4 2010, 09:01:59) [MSC v.1500 32 bit (Intel)] on
win32
Type help, copyright, credits or license for more information.
import numpy as np
np.test()
Running unit tests for numpy
NumPy
On 8/17/2010 12:07 PM, Pierre GM wrote:
What does a np.ma.test() give you ?
No problems. (Aren't those always run?)
Alan
np.ma.test()
Running unit tests for numpy.ma
NumPy version 1.5.0b2
NumPy is installed in C:\Python27\lib\site-packages\numpy
Python version 2.7 (r27:82525, Jul 4 2010,
On 8/17/2010 11:53 AM, Nikolaus Rath wrote:
I want to find the first i such that x[i] y and x[i+1]= y. Is there
a way to do this without using a Python loop?
argmax? (to get i+1):
d = np.linspace(0,10,101)
x = np.sin(d)
np.argmax(x=0.5)
6
fwiw,
Alan
On 8/3/2010 12:23 PM, Wayne Watson wrote:
How do I access 1.2 in such a way as to end up with a float? I keep
getting a matrix.
from numpy import matrix
m = matrix([[1.2],[2.3]])
Matrices have the odd (and imo undesirable) property that
m[0,0] != m[0][0]
You want the former.
For a
On 8/3/2010 1:29 PM, Joshua Holbrook wrote:
What's the
advantage of the matrix datatype?
As it turns out, that's a controversial question. ;-)
One answer: pedagogy (for those used to matrices).
A related answer: succinctness and readability of *some* code.
a,b,c =
On 8/3/2010 5:40 PM, Robert Faryabi wrote:
I'm using Tabular Package for manipulating tab-delimited data.
There is a small problem that I cannot get my head around it.
When I construct my tabarray from file, the blank fields are replaced by
nan.
Does any one knows how to just keep them as
On 8/1/2010 12:38 PM, Ralf Gommers wrote:
Binaries, sources and release notes can be found at
https://sourceforge.net/projects/numpy/files/
https://sourceforge.net/projects/numpy/files/
I'm not seeing them.
Alan
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Tests produce a few failures and a couple warnings.
Details below.
Alan Isaac
Python 2.7 (r27:82525, Jul 4 2010, 09:01:59) [MSC v.1500 32 bit (Intel)] on
win32
Type help, copyright, credits or license for more information.
import numpy as np
np.test()
Running unit tests for numpy
NumPy
On 7/30/2010 8:21 AM, Guillaume Chérel wrote:
is there no
implementation of an exact type
You could use the fraction module:
f = [Fraction(i,10) for i in range(10)]
a = np.array(f, dtype=object)
a
array([0, 1/10, 1/5, 3/10, 2/5, 1/2, 3/5, 7/10, 4/5, 9/10], dtype=object)
But I don't see why
On 7/30/2010 12:59 PM, Guillaume Chérel wrote:
my problem may involve
many more than 2 disks at a time
You cd approximate each disc by a polygon, as accurately
as you need, and test each point for membership in each disc:
from matplotlib.nxutils import pnpoly
hth,
Alan Isaac
On 7/29/2010 4:04 AM, Nadav Horesh wrote:
a = np.arange(5)
a[a0] = a
This has nothing to do with reusing ``a``::
b = np.arange(50)
a[a0] = b
a
array([0, 0, 1, 2, 3])
Note however that reusing ``a`` is unsafe.
(You will get all zeros.)
fwiw,
Alan Isaac
Rather than just looking for a new name (e.g., znormal),
would it not be better to decide on a syntax for specifying
PRNG algorithms? (E.g., MATLAB takes such an
approach:
http://www.mathworks.com/access/helpdesk/help/techdoc/math/brt5wsv.html)
Wouldn't this meet the need for replicability with
On 7/29/2010 4:37 PM, Robert Kern wrote:
this MATLAB API is deprecated
The old API has been replaced by a constructor that still takes a
string literal argument to determine the PRNG algorithm.
See the bottom of
http://www.mathworks.com/access/helpdesk/help/techdoc/math/brt5wsv.html
This
On 7/28/2010 8:26 AM, Mark Bakker wrote:
I don't understand why ceil and floor return real values
The same for ``round``.
(Note that Python 3 rounds to int.)
Furthermore, it would be nice if each took a ``dtype`` argument.
Alan Isaac
___
Wed, 28 Jul 2010 14:26:36 +0200, Mark Bakker wrote:
I don't understand why ceil and floor return real values [snip]
Wouldn't an integer make more sense?
On 7/28/2010 9:39 AM, Pauli Virtanen wrote:
Which integer? Only arbitrary-size integers (Python longs) are able to
span the whole
On Mon, Jul 26, 2010 at 10:05 PM, Alan G Isaacais...@american.edu wrote:
But I am still confused about the use case.
What is the scalar- (or 1d-array-) returning procedure
invoked before taking the determinant?
On 7/27/2010 8:51 AM, Skipper Seabold wrote:
Recently I ran into this trying to
On 7/26/2010 9:41 AM, Johann Hibschman wrote:
if reduce were defined as a *right* fold, then it would make sense for
subtract (and divide) to use the right identity
Instead of deviating from the Python definition of reduce,
it would imo make more sense to introduce new functions,
sayfoldl and
On 7/26/2010 12:45 PM, Skipper Seabold wrote:
Right now np.linalg.det does not handle scalars or 1d (scalar) arrays.
I don't have a real opinion on changing this, but I am curious
to know the use case, as the current behavior seems
a) correct and b) to provide an error check.
Cheers,
Alan
On 7/26/2010 8:22 PM, Joshua Holbrook wrote:
imo, the determinant of a scalar should be defined as itself, based on
the definition of the determinant.
What definition do you have in mind?
Alan Isaac
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On 7/26/2010 8:18 PM, Skipper Seabold wrote:
np.linalg.det(np.array([[2]]))
#2.0
which should either fail or if not, then I think np.linalg.det should
handle scalars and scalars as 1d arrays
It should not fail, because it follows from standard definitions.
(E.g., it is the base case of a
On 7/22/2010 4:00 PM, Johann Hibschman wrote:
I'm trying to understand numpy.subtract.reduce. The documentation
doesn't seem to match the behavior. The documentation claims
For a one-dimensional array, reduce produces results equivalent to:
r = op.identity
for i in
Fri, 23 Jul 2010 10:29:47 -0400, Alan G Isaac wrote:
np.subtract.reduce([])
0.0
Getting a right identity for an empty array is surprising. Matching
Python's behavior (raising a TypeError) seems desirable. (?)
On 7/23/2010 10:37 AM, Pauli Virtanen wrote:
I don't
On 7/5/2010 11:13 AM, Ralf Gommers wrote:
The failure is yet another case of test precision set slightly too high.
Thought we had got them all... Not sure about the matlab thing. Which
version of Windows are you running?
Vista 64bit (with 32 bit Python 2.6).
Alan
On 6/9/2010 12:49 PM, greg whittier wrote:
Is there a way to do A*A.T without two
copies of A?
Does this do what you want?
Alan Isaac
a
array([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
np.tensordot(a,a,axes=(-1,-1))
array([[ 1, 3, 5, 7, 9],
[
On 5/21/2010 8:34 PM, Christoph Gohlke wrote:
the 32-bit Python
interpreter can only use 2 GB of your memory
Why?
2**32/1e9
4.294967296003
Thanks,
Alan
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On 5/23/2010 9:33 PM, Christoph Gohlke wrote:
2 GB is the memory limit for 32-bit processes running in
user-mode under 64-bit Windows, unless the executable was specifically
built with 'IMAGE_FILE_LARGE_ADDRESS_AWARE' set.
Seehttp://msdn.microsoft.com/en-us/library/aa366778%28VS.85%29.aspx
On 5/21/2010 4:13 PM, Matthew Turk wrote:
a1 = numpy.random.random((512,512,512)).astype(float32)
This consistently gives me a MemoryError.
I believe I have plenty of physical memory.
(That should require about 1.3G during creation, right?
I have 8G.) It seems I'm hitting some kind of 1G
memory
On 5/16/2010 12:03 AM, Gabriel Mihalache wrote:
The eigenvalue should be 1 exactly.
http://floating-point-gui.de/
hth,
Alan Isaac
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On 5/10/2010 5:42 PM, gerardob wrote:
I would like to check whether lower_bound[i]= x[i]= upper_bound[i] for
all i in range(len(x))
import numpy as np
l, m, u = np.arange(12).reshape((3,4))
(l = m) (m = u)
array([ True, True, True, True], dtype=bool)
l[3]=9
(l = m) (m = u)
array([
What information exactly is `isnan` supposed to communicate?
Put another way, given that it raises NotImplemented for unknown types,
and that bool(NotImplemented) is True, is there a reason by it cannot
return a Python bool (which seems more useful)?
Thanks,
Alan Isaac
np.isnan(np.nan)
True
Alan wrote:
There is one change I would not mind: let
A * M be undefined if A is an ndarray and
M is a NumPy matrix.
On 4/28/2010 5:46 PM, David Warde-Farley wrote:
What about the other binary ops? I would say, matrix goes with matrix,
array with array, never the two shall meet unless you
On 4/28/2010 5:46 PM, David Warde-Farley wrote:
Would it be acceptable to retain the matrix class but not have it
imported in the default namespace, and have to import e.g.
numpy.matlib to get at them?
If we can have A * M undefined, then I do not think this is
a needed addition. But I do not
On 4/28/2010 12:05 PM, Travis Oliphant wrote:
A proposal was made to allow calling a NumPy array to infer dot
product:
a(b) is equivalent to dot(a,b)
a(b)(c) would be equivalent to dot(dot(a,b),c)
Here is a related ticket that proposes a more
explicit alternative: adding a ``dot`` method
On 4/28/2010 12:08 PM, Dag Sverre Seljebotn wrote:
it would be good to deprecate the matrix class
from NumPy
Please let us not have this discussion all over again.
The matrix class is very useful for teaching.
In economics for example, the use of matrix algebra
is widespread, while algebra
On 4/13/2010 11:16 PM, jah wrote:
binomial coefficent and factorial function
http://code.google.com/p/econpy/source/browse/trunk/pytrix/pytrix.py
fwiw,
Alan Isaac
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On 4/6/2010 6:46 AM, Yogesh Tomar wrote:
import numpy
import numpy.linalg
x=numpy.eye(1000)
for i in range(10):
eigenvalues,eigenvectors=numpy.linalg.eig(x)
eigenvalues,eigenvectors=numpy.linalg.eig(x)
print str(i),'---'*
I'm not seeing any
On 4/2/2010 8:29 AM, Nadav Horesh wrote:
In python empty sequences are always equivalent to False, and non-empty to
True.
I think that was why the OP objected to this behavior:
bool(np.array([0]))
False
Alan Isaac
___
On 3/30/2010 10:13 AM, Tom K. wrote:
What I want to do is get rid of singleton dimensions, and index into the
last dimension with an array.
x=np.zeros((10,1,1,1,14,1024))
np.squeeze(x).shape
(10, 14, 1024)
hth,
Alan Isaac
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On 3/30/2010 12:56 PM, Sean Mulcahy wrote:
512x512 arrays. I would like to set elements of the array whose value fall
within a specified range to zero (eg 23 x 45).
x[(23x)*(x45)]=0
hth,
Alann Isaac
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On 3/13/2010 8:57 PM, Charles R Harris wrote:
I suspect the change was made, whenever that was, in order to conform to
python.
So is there an actual polcy? When C99 behavior
and Python behavior differ, will NumPy follow
Python as a *rule*?
Thanks,
Alan Isaac
On 3/21/2010 12:24 AM, Charles R Harris wrote:
I really, really want to get rid of the asterisks, they are ugly and
distracting (IMHO).
I agree, which is why my deflist example did not use asterisks.
I consider it readable and only very slightly verbose. (The blank
lines are needed by reST,
On 3/21/2010 12:47 AM, josef.p...@gmail.com wrote:
dashes would be also ok, but I don't think rst would recognize them.
It does.
But again, a definition list (using indentation)
is also a list structure. It needs no markup
besides the indentation.
My main problem with rst is that it doesn't
On 3/21/2010 12:54 AM, Ralf Gommers wrote:
too many blank lines are needed
Please define need after seeing the compact example I posted.
Personally, I think reST makes the right trade-offs,
minimizing markup within the constraint of being unambiguous.
Alan Isaac
On 3/21/2010 12:54 AM, Ralf Gommers wrote:
too many blank lines are needed
On Sun, Mar 21, 2010 at 9:51 PM, Alan G Isaac ais...@american.edu
mailto:ais...@american.edu wrote:
Please define need after seeing the compact example I posted.
On 3/21/2010 9:58 AM, Ralf Gommers wrote
On 3/20/2010 2:15 PM, josef.p...@gmail.com wrote:
As far as I know, stars are the only way to render a list in
restructured txt, otherwise it looses the list formatting.
Try a definition list?
Example below.
Alan
Returns
---
q, r if mode = 'full':
- q : ndarray of float or complex,
On 3/19/2010 10:53 AM, gerardob wrote:
I would like to know a simple way to generate a list containing all the
lists having two 1's at each element.
Example, n = 4
L2 = [[1,1,0,0],[1,0,1,0],[1,0,0,1],[0,1,1,0],[0,1,0,1],[0,0,1,1]]
I like
list(set(itertools.permutations([1,1]+[0]*(n-2
On 3/18/2010 4:56 AM, Sebastian Haase wrote:
How would people feel about unifying the function vs. the method behavior ?
One could add an addition option like
`repeat` or `fillZero`.
One could (at first !?) keep opposite defaults to not change the
current behavior.
But this way it would be
Is the zero-fill intentional?
If so, it is documented?
(NumPy 1.3)
Alan Isaac
a = np.arange(5)
b = a.copy()
c = np.resize(a, (5,2))
b.resize((5,2))
c # as expected
array([[0, 1],
[2, 3],
[4, 0],
[1, 2],
[3, 4]])
b # surprise!
array([[0, 1],
[2, 3],
On 3/17/2010 10:16 AM, josef.p...@gmail.com wrote:
numpy.resize(a, new_shape)
Return a new array with the specified shape.
If the new array is larger than the original array, then the new array
is filled with repeated copied of a. Note that this behavior is
different from a.resize(new_shape)
Francesc Altet once provided an example that for integer
division, numpy uses the C99 rule: round towards 0.
This is different than Python's rule for integer division:
round towards negative infinity.
But I cannot reproduce his example. (NumPy 1.3.)
Did this behavior change at some point?
This behavior does not match the current documentation.
np.random.uniform(low=0.5,high=0.5)
0.5
np.random.uniform(low=0.5,high=0.4)
0.48796883601707464
I assume this behavior is intentional and it is
the documentation that is in error (for the case
when high=low)?
fwiw,
Alan Isaac
On 2/21/2010 5:30 AM, Charles R Harris wrote:
I would be much obliged if some folks would run the attached script and
report the output, numpy version, and python version.
No problem with NumPy 1.3.0 (from superpack) on Python 2.6.4 under Vista.
Alan Isaac
Python 2.6.4 (r264:75708, Oct 26
Wayne wrote:
I just checked the version via import and it's 0.6.0.
Try updating.
Also, the SciPy Reference Guide explains how to turn
off deprecation warnings.
http://docs.scipy.org/doc/scipy/scipy-ref.pdf
Alan Isaac
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1. Should `numpy.cov` use `ddof` instead of `bias`,
like `std` and `mean`?
2. Should the docs for scipy.cov state that it is deprecated?
http://docs.scipy.org/scipy/docs/scipy.stats.stats.cov/#scipy-stats-cov
(Use raises a deprecation warning.)
Thanks,
Alan Isaac
On Mon, Feb 15, 2010 at 8:42 AM, Alan G Isaacais...@american.edu wrote:
1. Should `numpy.cov` use `ddof` instead of `bias`,
like `std` and `mean`?
On 2/15/2010 9:31 AM, josef.p...@gmail.com wrote:
+1
(I just checked scipy stats and the usage of bias versus ddof is also
inconsistent.
On 2/10/2010 1:57 PM, Friedrich Romstedt wrote:
I wonder why there is no response on my e-mail dating back to Feb 4.
Is there nobody interested in it, is somebody working on it, or did it
simply did not come through?
I'm going to guess it is because your actual question is at
the very end of a
I see that NumPy 1.4.0 is still the download
offered on SourceForge. Did I misunderstand
that a decision had been made to withdraw it,
at least until the ongoing discussion about
ABI breakage is resolved?
(Btw, as a user, I'm hoping Jarrod's sensible proposal
prevails in that discussion. That
I have two 1d arrays, say `a` and `b`.
I need to swap elements if a 1d boolean criterion `to_swap` is met.
Here's one way:
a, b = np.choose([to_swap,np.logical_not(to_swap)], [a, b])
Here is a much faster way:
a[to_swap], b[to_swap] = b[to_swap], a[to_swap]
Other better ways?
On 2/7/2010 10:21 AM, Alan Isaac wrote:
I have two 1d arrays, say `a` and `b`.
I need to swap elements if a 1d boolean criterion `to_swap` is met.
[clip]
Here is a much faster way:
a[to_swap], b[to_swap] = b[to_swap], a[to_swap]
On 2/7/2010 10:21 AM, Pauli Virtanen wrote:
That
On 2/7/2010 11:16 AM, Keith Goodman wrote:
Bad:
a = np.array([1, 2, 3])
b = a[1:]
a[to_swap], b[to_swap] = b[to_swap], a[to_swap]
a
array([2, 1, 2])
b
array([1, 2])
So that is an important point:
if `a` and `b` share data, the
swap is not well defined.
But that
You should be able to have Matplotlib in Python 2.5
and in Python 2.6, no problem.
But you need to get the correct installer.
There are separate installers for different Pythons.
Alan Isaac
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On 2/6/2010 9:02 AM, Charles R Harris wrote:
I don't know if things have changed, but long ago when I was using
windows more often I found it best to delete old installations of python
when moving up to later versions.
I have had multiple versions running side by side for years,
with never a
On 2/3/2010 3:08 AM, laurent.fe...@free.fr wrote:
if i multiply two matrix, one with a unique line and the second one
with a unique column, i should have a scalar
What definition of matrix multiplication is that??
If you really want a scalar product,
ask for it::
import numpy as np
On 1/27/2010 7:57 PM, David Cournapeau wrote:
Guido explicitly asked not to break compatibility while staying under
py3k, so we should try to do it once numpy has been ported to py3k (e.g.
if numpy 1.5 still is not py3k compatible, do a 1.6 before a 2.0 -
iterate if necessary:) ).
This
On 1/27/2010 7:57 PM, David Cournapeau wrote:
Guido explicitly asked not to break compatibility while staying under
py3k, so we should try to do it once numpy has been ported to py3k (e.g.
if numpy 1.5 still is not py3k compatible, do a 1.6 before a 2.0 -
iterate if necessary:) ).
Alan G
On 1/27/2010 8:56 PM, David Cournapeau wrote:
one could make the argument that releasing the API would avoid
having to port numpy twice (first to py3k with say numpy 1.5.0, then
to the new API for numpy 2.0). But I am not sure it is a big change in
practice ?
OK, I misunderstood: I thought
Is this a fair test?
I expected shuffle to be much faster
(no array creation).
Alan Isaac
import timeit
setup =
... import numpy as np
... prng = np.random.RandomState()
... N = 10**5
... indexes = np.arange(N)
...
print timeit.timeit('prng.shuffle(indexes)',setup, number=100)
On 1/26/2010 2:00 PM, Alan G Isaac wrote:
Is this a fair test?
I expected shuffle to be much faster
(no array creation).
Alan Isaac
import timeit
setup =
... import numpy as np
... prng = np.random.RandomState()
... N = 10**5
... indexes = np.arange(N)
...
print timeit.timeit
Suppose x and y are conformable 2d arrays.
I now want x to become a duplicate of y.
I could create a new array:
x = y.copy()
or I could assign the values of y to x:
x[:,:] = y
As expected the latter is faster (no array creation).
Are there better ways?
Thanks,
Alan Isaac
As I understand it, numpy.random provides the function ``random`` as an
alias for ``random_state``. Might this be moved into
numpy.random.mtrand.RandomState, for interface consistency?
Right now if I start with ``prng = np.random.RandomState(seed=myseed)``
I cannot use ``prng.random`` as it does
On 1/23/2010 5:01 PM, Anne Archibald wrote:
If both arrays are C contiguous, or more generally contiguous blocks
of memory with the same strided structure, you might get faster
copying by flattening them first, so that it can go in a single
memcpy().
I may misuderstand this. Did you just
On 1/23/2010 6:00 PM, Keith Goodman wrote:
x = y.view()
Thanks, but I'm not looking for a view.
And I need x to own its data.
Alan
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On 1/23/2010 7:29 PM, Anne Archibald wrote:
I had in mind accessing the underlying data through views
that were flat:
In [3]: x = np.random.random((1000,1000))
In [4]: y = np.random.random((1000,1000))
In [5]: xf = x.view()
In [6]: xf.shape = (-1,)
In [7]: yf = y.view()
In [8]:
filter(lambda x: x.startswith('eig'),dir(np.linalg))
['eig', 'eigh', 'eigvals', 'eigvalsh']
import scipy.linalg as spla
filter(lambda x: x.startswith('eig'),dir(spla))
['eig', 'eig_banded', 'eigh', 'eigvals', 'eigvals_banded', 'eigvalsh']
hth,
Alan Isaac
On 1/12/2010 1:35 AM, Jankins wrote:
from scipy.sparse.linalg.eigen import eigen
Traceback (most recent call last):
File stdin, line 1, inmodule
ImportError: cannot import name eigen
Look at David's example:
from scipy.sparse.linalg import eigen
hth,
Alan Isaac
Why is s3 F_CONTIGUOUS, and perhaps equivalently,
why is its C_CONTIGUOUS data in s3.base (below)?
Thanks,
Alan Isaac
a3
array([[ 0, 1, 2, 3, 4, 5],
[ 6, 7, 8, 9, 10, 11]])
a3.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED :
On 12/19/2009 11:45 AM, Wayne Watson wrote:
A 4x1, 1x7, and 1x5 would be examples of a 1D array or matrix, right?
Are you saying that instead of using a rotational matrix ...
that I should use a 2-D array for rotCW? So why does numpy have a matrix
class? Is the class only used when working
On 12/18/2009 5:54 PM, Keith Goodman wrote:
On Fri, Dec 18, 2009 at 2:51 PM, Wayne Watson
sierra_mtnv...@sbcglobal.net wrote:
That should do it. Thanks. How do I get the scalar result by itself?
np.dot(x.T,x)[0,0]
14
or
x = np.array([1,2,3])
np.dot(x,x)
14
or
On 12/18/2009 7:12 PM, Wayne Watson wrote:
The point of the scalar product is to produce theta.
As David said, that is just NumPy's `dot`.
a = np.array([0,2])
b = np.array([5,0])
theta = np.arccos(np.dot(a,b)/np.sqrt(np.dot(a,a)*np.dot(b,b)))
theta
1.5707963267948966
theta/np.pi
0.5
hth,
Does NumPy have an equivalent to Python's math.fsum?
Thanks,
Alan Isaac
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On 12/15/2009 1:39 PM, Bruce Southey wrote:
+1 for the function but we can not shorten the name because of existing
numpy.rank() function.
1. Is it a rule that there cannot be a name duplication
in this different namespace?
2. Is there a commitment to keeping both np.rank and np.ndim?
(I.e.,
On 12/3/2009 12:40 AM, Peter Cai wrote:
If I have homogeneous linear equations like this
array([[-0.75, 0.25, 0.25, 0.25],
[ 1. , -1. , 0. , 0. ],
[ 1. , 0. , -1. , 0. ],
[ 1. , 0. , 0. , -1. ]])
And I want to get a non-zero solution for it. How
On 11/26/2009 8:20 AM, Nils Wagner wrote:
a = array(([True,True],[True,True]))
b = array(([False,False],[False,False]))
a+b
NumPy's boolean operations are very well behaved.
a = np.array(([True,True],[True,True]))
a+a
array([[ True, True],
[ True, True]], dtype=bool)
Compare
On 11/7/2009 1:51 PM, Stas K wrote:
Can I get rid of the loop in this example? And what is the fastest way
to get v in the example?
ar = array([1,2,3])
for a in ar:
for b in ar:
v = a**2+b**2
a2 = a*a
np.add.outer(a2,a2)
array([[ 2, 5, 10],
[ 5, 8, 13],
On 11/7/2009 10:56 PM, a...@ajackson.org wrote:
I want to build a 2D array of lists, and so I need to initialize the
array with empty lists :
myarray = array([[[],[],[]] ,[[],[],[]]])
[[[] for i in range(3)] for j in range(2) ]
fwiw,
Alan Isaac
On 11/4/2009 3:09 PM, David Warde-Farley wrote:
I'd like to map every unique element (these could be strings, objects,
or already ints) to a unique integer between 0 and len(unique(d)) - 1.
mymap = dict((k,v) for v,k in enumerate(set(a)))
fwiw,
Alan Isaac
On 10/26/2009 4:04 AM, Nils Wagner wrote:
how can I obtain the multiplicity of an entry in a list
a = ['abc','def','abc','ghij']
That's a Python question, not a NumPy question.
So comp.lang.python would be a better forum.
But here's a simplest solution::
a = ['abc','def','abc','ghij']
for
On 10/21/2009 3:23 PM, Charles R Harris wrote:
What exactly *was* the history of that project and what can we learn
from it?
Imo, what really drove this project forward, is that Skipper
was able to interact regularly with someone else who was actively
using and developing on the code base
On 10/7/2009 10:57 PM, Robert Kern wrote:
it's pimpl
OK: http://en.wikipedia.org/wiki/Opaque_pointer
Thanks,
Alan Isaac
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On 9/25/2009 1:45 PM, Mads Ipsen wrote:
Is there a numpy operation on two arrays, say [1,2,3] and [4,5,6], that
will yield:
[[(1,4),(1,5),(1,6)],[(2,4),(2,5),(2,6)],[(3,4),(3,5),(3,6)]]
from itertools import product
list(product([1,2,3],[4,5,6]))
[(1, 4), (1, 5), (1, 6), (2, 4), (2, 5),
I do not see what is wrong with itertools.product,
but if you hate it, you can use numpy.meshgrid:
np.array(np.meshgrid([1,2,3],[4,5,6])).transpose()
array([[[1, 4],
[1, 5],
[1, 6]],
[[2, 4],
[2, 5],
[2, 6]],
[[3, 4],
[3, 5],
On 9/25/2009 4:01 PM, Mads Ipsen wrote:
a = numpy.array([1,2,3])
b = numpy.array([4,5,6])
(n,m) = (a.shape[0],b.shape[0])
a = numpy.repeat(a,m).reshape(n,m)
b = numpy.repeat(b,n).reshape(m,n).transpose()
ab = numpy.dstack((a,b))
print ab.tolist()
That's just a slow implementation of
OK, sure, for large arrays meshgrid will look bad.
(It creates a large array twice.) If the example
really involves sequential integers, then mgrid
could be used instead to save on this.
Even so, it is just implausible that duplicating
meshgrid functionality will be faster than using meshgrid.
Alan G Isaac wrote:
That's just a slow implementation of meshgrid:
np.meshgrid(a,b).transpose().tolist()
Gives you the same thing.
On 9/25/2009 6:38 PM, Mads Ipsen wrote:
Yes, but it should also work for [2.1,3.2,4.5] combined with
[4.6,-2.3,5.6] - forgot to tell that.
No problem
On 9/16/2009 8:22 PM, Gökhan Sever wrote:
I want to be able to count predefined simple rectangle shapes on an
image as shown like in this one:
http://img7.imageshack.us/img7/2327/particles.png
ch.9 of
On 9/15/2009 7:07 AM, Sebastien Binet wrote:
usage of the exec statement is usually frown upon and can be side stepped.
e.g:
for m in meat:
for c in cut:
locals()['consumed_%s_%s' % (m,c)] = some_array
Additionally, name construction can be pointless.
Maybe::
info = dict()
for
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