, t1-t0, seconds
fileh.close()
assert y1.shape == y2.shape
assert np.alltrue(y1 == y2)
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([tuple(row) for row in results],
dtype=mydescriptor), import numpy; results = [['M',64.0,75.0]]*1;
mydescriptor = {'names': ('gender','age','weight'),
'formats':('S1','f4', 'f4')}).repeat(3,10)
Out[52]:[0.49885106086730957, 0.4325258731842041, 0.43297886848449707]
HTH,
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()])
:
In [66]: A=numpy.array(a).reshape(2,2); B=numpy.array(b).reshape(2,2)
In [67]: A, B
Out[67]:
(array([[ 1., 2.],
[ 3., 9.]]),
array([[ 2., 3.],
[ 4., 4.]]))
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],
[4, 4]])
Yeah. Much, much better indeed.
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A Dijous 02 Novembre 2006 22:26, A. M. Archibald escrigué:
On 02/11/06, Francesc Altet [EMAIL PROTECTED] wrote:
I see this as a major issue in numarray and poses in great danger the
intended support of PyTables for numarray that we planned for some time
(until end of 2007). It would be nice
a
solution for this problem anytime soon, will make this very problematic
to us.
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['f1'].argsort()]
Out[58]:
array([(1, 1), (0, 2)],
dtype=[('f0', 'i2'), ('f1', 'i4')])
HTH,
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).
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of nested types
and recarrays.
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. This is because 'float64' is the
default type in NumPy from some months ago (before the default was 'int_')
HTH,
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])
In [51]: b
Out[51]: array(2)
In [52]: b += 1
In [53]: b
Out[53]: array(3)
In [54]: a
Out[54]: array([1, 3, 3])
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Perhaps raising an error saying something like boolean types cannot be
compared would be nice. Not too important, but worth to notice, IMO.
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A Divendres 13 Octubre 2006 22:20, Lisandro Dalcin va escriure:
On 10/13/06, Francesc Altet [EMAIL PROTECTED] wrote:
Is it possible to test a numpy version directly from the source
directory without having to install it?
I usually do:
$ python setup.py build
$ python setup.py install
to data in memory. However, I lack experience in buffer
protocol, so suggestions for achieving this are welcome.
If there is some other trivial way that I haven't devised (specially if usable
from pyrex), please tell me about.
TIA,
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don't have to book memory
for copying the data area, so I'll use it.
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])
[be sure to use parentesizes appropriately]
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:-)
Cheers!
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El dv 29 de 09 del 2006 a les 16:27 -0600, en/na Travis Oliphant va
escriure:
Francesc Altet wrote:
I see. Thanks for the explanation.
You deserve the thanks for the great testing of less-traveled corners of
NumPy. It's exactly the kind of thing needed to get NumPy ready
],
dtype='|S4')
i.e. it seems like numpy is striping-off NULL chars before building the object
and I don't think this is correct.
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+20, 8.94319890e-39, 2.e+00],
dtype=float32)
i.e. in an array built from ndarray, the default is that it has to be
read-only?
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'
f4.flags.writeable = True
f4[2] = 99
a
'12345123\x00\x00\xc6B34512345'
The original, *immutable* string has been mutated. This could get you
into real trouble in certain situations.
I see. Thanks for the explanation.
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that
properly belongs to trunk, then it's time to create the branch, but
meanwhile you can save yourself quite a few syncronization work.
Anyway, it is my pleasure to help finding bugs for NumPy!
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far so good, but is the next the intended behaviour?
numpy.typeDict['i4']
type 'numpy.int32'
numpy.typeDict['int32']
type 'numpy.int32'
numpy.typeDict['i4'] == numpy.typeDict['int32']
False
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'exceptions.TypeError' Traceback (most recent call last)
/home/faltet/python.nobackup/numpy/ipython console in module()
type 'exceptions.TypeError': function takes exactly 1 argument (0 given)
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(at least, when I'm
aware of it!), but I thought it would be nice to warn other people about
that.
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El dl 25 de 09 del 2006 a les 11:08 -0600, en/na Travis Oliphant va
escriure:
Francesc Altet wrote:
Hi,
Anybody know if there is a map between NumPy types and Numeric
typecodes? Something like 'typecodes' for numarray:
How about
dtype(obj).char?
This doesn't work for many types
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to be only three of such functions in NumPy, namely, argmax,
argmin and argsort. Adding three additional 'combos' doesn't seem a lot to my
mind, but it can be just 'too much' for more common sense minds.
Cheers,
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El dl 18 de 09 del 2006 a les 17:10 +0200, en/na Lionel Roubeyrie va
escriure:
Le lundi 18 septembre 2006 12:17, Francesc Altet a écrit :
You have two problems here. The first is that you shouldn't have missign
entries, or conversion from empty strings to ints (or whatever) will
fail
])
In [3]:a.data
Out[3]:read-write buffer for 0x82a9970, ptr 0x821f630, size 4 at
0xb6dd3300
although I'm not sure which number is the memory address I'd say it's
the last one.
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('i4'), and the
second it's the third column from ra (so the second column from ra has been
stripped out from rb).
HTH,
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],
dtype='|S4')
In [120]: ra['dval']
Out[120]: array([ 9.73041595e-72, 9.73041595e-72])
You can still access the empty spaces if you want (although it is nonsense):
In [121]: ra['unused']
Out[121]:
recarray([, ],
dtype='|V4')
Cheers,
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represents it
now, I'd say that this is a backwards step in readability. Something
like 'i4' would look good for a low-level library, but not for a
high-level one like NumPy, IMO.
Cheers,
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El dt 12 de 09 del 2006 a les 13:17 -0400, en/na Pierre Thibault va
escriure:
Hello again,
On 9/12/06, Francesc Altet [EMAIL PROTECTED] wrote:
Hello Pierre,
[...]
Well, in some way, there is a temporary array creation that is
immediately bound to B, so in the end, the temporary
, install ctypes separately or feel free
to ignore this.
I suppose that a check has to be set up in the tests to avoid ctypes ones to
be checked in case ctypes is not available.
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use of TAB in ipython console).
Cheers,
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Get stuff done quickly with pre-integrated technology to make your job
faster. I'd say that replacing memmove
by memcpy would make .take() much faster.
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===
Announcing PyTables 1.3.3
===
I'm happy to announce a new minor release of PyTables. In this one, we
have focused on improving compatibility with latest beta versions of
NumPy (0.9.8, 1.0b2, 1.0b3 and higher), adding some improvements and the
to thanks (once
more), the excellent work of the NumPy crew, and specially Travis for their
first-class work.
Thanks!
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everything, look at online docstrings and be able to do fast timings added
the cerise sur le gâteau.
Luck!
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)
a-base = obj
Py_DECREF(cobj)
Thanks Travis!
Hey! I checked this morning Travis' patch and seems to work well for me. I'll
add yours as well later on and see... BTW, where exactly I've to add the
above lines?
Many thanks Travis and Todd. You are great!
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this is a relatively important problem, because it somewhat prevents a
smooth transition from numarray to NumPy.
Thanks,
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some testing code for
checking numerical types, and ended with this 'animal'. Sorry about that ;-)
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) because I tend to find
it slightly less confusing.
However, I think that if you choose whatever convention consistently, people
will get used to it and everything will be fine.
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trying to run f2py:
Mmm, perhaps you can try with putting:
[build]
compiler=mingw32
in your local distutils.cfg (see
http://docs.python.org/inst/config-syntax.html)
HTH,
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the attributes and fields so that they
doesn't get mixed.
Implementing this shouldn't be complicated at all, but I'm afraid that I
can't do this right now :-(
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this capability to represent
addresses? I think this would simplify things (specially it will prevent to
use ascii/pointer conversions, which are ugly to my mind).
Cheers,
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A Dimarts 13 Juny 2006 19:47, Francesc Altet va escriure:
- Support for both numpy and numarray (use the flag --force-numarray
in setup.py).
At first glance this looks like it doesn't make things to messy, so I'm
in favor of incorporating this.
Yeah. I thing you are right. It's
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