It is turning out to be much more of a headache moving from
numpy-0.9.x to numpy-1.0b1 than it ever was moving from Numeric
to numpy. Could someone please throw together some release
notes (the scipy & sourceforge RN pages are blank) or a Wiki
page warning what has to be changed? Here are some stumbling
blocks I've come across:
* Type names are all now lower case. I know this started
happening late in 0.9.x, but I only know it by accident, via
some email from Travis. This is just one example of the kind
of change that really needs to be prominently noted somewhere.
* .nonzero() now returns a 1-tuple with an array of indices,
for a 1-d array. Before it returned an array of indices.
* In the C API, ContiguousFromObject is now ContiguousFromAny.
I am surprised that my libraries compile with no errors; I
only get a runtime error. Shouldn't I be warned about this
* mtrand still uses ContiguousFromObject in a few places; these
give exceptions when you call, e.g., setstate.
* rand, fft, ift are no longer in the numpy namespace. Is
this a permanent change or a bug?
There are probably other obstacles I'll come across, but this
is what I encountered in code that ran perfectly with 0.9.6.
After several hours of work it's still not running.
This mail sent through IMP: http://horde.org/imp/
I find myself needing the set operations provided by python 2.4 such as
intersection, difference, or even just the advantages of the data strucure
itself, like that fact that I can try adding something to it and if it's
already there, it won't get added again. Will my decision to use of the
python 'set' datastructure come back to haunt me later by being too slow? Is
there anything equivalent in scipy or numpy that I can use? I find myself
going between numpy arrays and sets a lot because I sometimes need to treat
it like an array to use some of the array functions.
Sorry for cross-posting to scipy and numpy... is that a bad idea?
Is there a new version of official numpybok? Mine dated at March 15,
Gen-Nan Chen, PhD
Research and Development Group
CorTechs Labs Inc (www.cortechs.net)
1020 Prospect St., #304, La Jolla, CA, 92037
Tel: 1-858-459-9700 ext 16
I tried different arcsin functions on a complex number (z=0.52+0j) and obtained
the following results:
cmath.asin(z) gives (0.54685095069594414+0j) #okay
-1j*log(1j*z+sqrt(1-z**2)) gives (0.54685095069594414+0j) #okay, by definition
numarray.arcsin(z) gives (0.54685095069594414+0j) #still okay
numpy.arcsin(z) gives (0.54685095069594414+0.54685095069594414j) #bug??
Is it really a bug in numpy, or is there another explanation?
I am interested in building numpy using the bdist_egg option. Unfortunately
it doesn't appear to be quite working properly. When I tried it with
0.9.8then it successfully build an egg file but this had problems when
using it. I thought I would look at 1.0b1 and discovered that it would not
build the egg at all. The problem is in numpy.distutils.core where it checks
if "setuptools" in sys.modules. In my case, I have setuptools installed
using ez_setup.py and hence it appears to be qualified by a version number
and hence doesn't pass the test. I tried modifying the code to look more
like an earlier option, ie test by trying to import setuptools within a try
block and that worked fine. I then had a look at the trunk and discovered
that the test was still being performed the same way, ie if "setuptools" in
sys.modules, however there is now a setupegg.py file in the root. Now it
seems that this is a bit of an odd workaround and I was wondering if there
was any compelling reason for not reverting to the mechanism for testing
availability of setuptools using a try block?
On PPC Mac OSX universal build 2.4.3, gcc 4.0,
In : import numpy as N
In : print N.__version__
In : N.random.uniform(0,1)
(This originally showed up in the Ticket 83 regression test during
Just in case this was new information:
ERROR: check_singleton (numpy.lib.tests.test_getlimits.test_longdouble)
Traceback (most recent call last):
line 33, in check_singleton
ftype = finfo(longdouble)
line 49, in __new__
obj = object.__new__(cls)._init(dtype)
line 75, in _init
'numpy %s precision floating point number' % precname)
File "/usr/lib64/python2.4/site-packages/numpy/lib/machar.py", line
210, in __init__
FC4_64 on intel.
is it just me, or is matrixmultiply missing from numpy1.0b?
>>> import numpy
Traceback (most recent call last):
File "<stdin>", line 1, in ?
AttributeError: 'module' object has no attribute 'matrixmultiply'
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