Re: [Numpy-discussion] Contribution
On Sun, May 29, 2011 at 07:09:42AM +0400, Dmitriy Rybalkin wrote: Thank you, Gael, I really hope that I'll improve my programming skills. Also I have a question: Will the page with my example be deleted in case the code is not efficient or smth else has been found to be wrong? I cannot really answer this question, as there is no rule: it depends on the goodwill of whoever reads and tries out the page. It's a wiki. In my experience, people are reluctant to delete other's work. Sometimes they fix it, which is the best solution. Gael ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] finding elements that match any in a set
On 5/28/2011 3:40 PM, Robert wrote: (myarray in mylist) turns into mylist.__contains__(myarray). Only the list object is ever checked for this method. There is no paired method myarray.__rcontains__(mylist) so there is nothing that numpy can override to make this operation do anything different from what lists normally do, which is check if the given object is equal to one of the items in the list. This seems to me to slightly miscast the problem. How would an __rcontains__ method really fix things? Would the list type check against a table of stuff that it knows how to contain? That seems horrible. And even if possible, NumPy would then have to break the rule that ``in`` tests for equality, because (I believe) the real problem in this case is that np equality testing does not return a bool. From this perspective, what is missing is not __rcontains__ (since the list already knows what to do) but rather __eeq__ for element-by-element comparison (ideally, along with an element-by-element operator such as say .==). In the meantime the OP could use any(all(a==x) for x in lst) fwiw, Alan Isaac ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] finding elements that match any in a set
On 5/28/2011 3:46 PM, Robert Kern wrote: mylist.__contains__(x), it should treat all objects exactly the same: check if it equals any item that it contains. There is no way for it to say, Oh, I don't know how to deal with this type, so I'll pass it over to x.__contains__(). Which makes my comment redundant ... ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] finding elements that match any in a set
Michael Katz michaeladamkatz at yahoo.com writes: Yes, thanks, np.in1d is what I needed. I didn't know how to find that. Did you check in the documentation? If so, where did you check? Would you have found it if it was in the 'See also' section of where()? (http://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) I ask because people often post to the list needing in1d() after not being able to find it via the docs, so it would be nice to add references in the places people go looking for it. Neil ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] AttributeError in numpy.distutils
On Sun, May 22, 2011 at 8:14 PM, Branimir Sesar bse...@astro.caltech.eduwrote:
On 05/22/2011 04:17 AM, Ralf Gommers wrote:
On Thu, May 19, 2011 at 8:28 PM, Branimir Sesar
bse...@astro.caltech.edu mailto:bse...@astro.caltech.edu wrote:
Dear Numpy users,
I've encountered an AttributeError in numpy.distutils
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py,
line 646, in swig_sources
extension.swig_opts.remove('-c++')
AttributeError: 'str' object has no attribute 'remove'
while compiling some code with Python 2.7.1 and Numpy 1.6.0.
What are you doing here, compiling numpy? Building some of your own
swig-ed code? Please give the details needed to reproduce your issue.
I've been trying to compile Scikits ANN
(http://projects.scipy.org/scikits/wiki/AnnWrapper) with Python 2.7.1,
numpy 1.6.0, and SWIG 2.0.3 but the compilation breaks down down with
the error given below. Previously, I was able to compile Scikits ANN
using Enthought Python Distribution 7.0.2 (Python 2.7.1, numpy 1.5.1,
swig 1.3.40).
running install
running bdist_egg
running egg_info
running build_src
build_src
building extension scikits.ann._ANN sources
Traceback (most recent call last):
File setup.py, line 139, in module
test_suite = 'nose.collector',
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/core.py,
line 186, in setup
return old_setup(**new_attr)
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/core.py, line
152, in setup
dist.run_commands()
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py, line
953, in run_commands
self.run_command(cmd)
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py, line
972, in run_command
cmd_obj.run()
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/install.py,
line 57, in run
r = self.setuptools_run()
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/install.py,
line 51, in setuptools_run
self.do_egg_install()
File build/bdist.linux-x86_64/egg/setuptools/command/install.py,
line 96, in do_egg_install
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/cmd.py, line
326, in run_command
self.distribution.run_command(command)
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py, line
972, in run_command
cmd_obj.run()
File build/bdist.linux-x86_64/egg/setuptools/command/bdist_egg.py,
line 167, in run
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/cmd.py, line
326, in run_command
self.distribution.run_command(command)
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py, line
972, in run_command
cmd_obj.run()
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/egg_info.py,
line 8, in run
self.run_command(build_src)
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/cmd.py, line
326, in run_command
self.distribution.run_command(command)
File /home/bsesar/usr/pydebug/lib/python2.7/distutils/dist.py, line
972, in run_command
cmd_obj.run()
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py,
line 152, in run
self.build_sources()
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py,
line 169, in build_sources
self.build_extension_sources(ext)
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py,
line 332, in build_extension_sources
sources = self.swig_sources(sources, ext)
File
/home/bsesar/usr/pydebug/lib/python2.7/site-packages/numpy/distutils/command/build_src.py,
line 646, in swig_sources
extension.swig_opts.remove('-c++')
AttributeError: 'str' object has no attribute 'remove'
Looks like this is a bug introduced in numpy 1.6.0 by commit ff0822c4.
Right above this line (numpy/distutils/command/build_src.py, line 646) add
this:
if isinstance(extension.swig_opts, basestring):
extension.swig_opts = extension.swig_opts.split()
Then you should be able to compile scikits.ann.
Cheers,
Ralf
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Re: [Numpy-discussion] finding elements that match any in a set
On 5/28/2011 3:40 PM, Robert wrote: (myarray in mylist) turns into mylist.__contains__(myarray). Only the list object is ever checked for this method. There is no paired method myarray.__rcontains__(mylist) so there is nothing that numpy can override to make this operation do anything different from what lists normally do, however, numpy arrays should be able to override in be defining their own.__contains__ method, so you could do: something in an_array and get a useful, vectorized result. So I thought I'd see what currently happens when I try that: In [24]: a Out[24]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) In [25]: 3 in a Out[25]: True So the simple case works just like a list. But what If I want what the OP wants: In [26]: b Out[26]: array([3, 6, 4]) In [27]: b in a Out[27]: False OK, so the full b array is not in a, and it doesn't vectorize it, either. But: In [29]: a Out[29]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) In [30]: b in a Out[30]: True HUH? I'm not sure by what definition we would say that b is contained in a. but maybe.. In [41]: b Out[41]: array([ 4, 2, 345]) In [42]: b in a Out[42]: False so it's are all of the elements in b in a somewhere? but only for 2-d arrays? So what does it mean? The docstring is not helpful: In [58]: np.ndarray.__contains__? Type: wrapper_descriptor Base Class: type 'wrapper_descriptor' String Form:slot wrapper '__contains__' of 'numpy.ndarray' objects Namespace: Interactive Docstring: x.__contains__(y) == y in x If nothing useful, maybe it could provide a vectorized version of in for this sort of use case. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] finding elements that match any in a set
Yes, in this case I definitely would have found in1d() if it was referenced in the where() section, either as a see also or even better as an example where where() is combined with np.in1d(): indexes_of_interest = np.where( np.in1d( my_records.integer_field, my_values_of_interest ) ) I think the where() documentation page must be a place where a lot of people/newbies spend a lot of time. Perhaps like me they are focusing on the solution being where() + some python stuff I already know, instead of thinking of other numpy functions, like in1d(), that might come into play. It makes sense that in1d() is under the Set section. However (just to try to explain further why I didn't look and find it there), somehow I think of set when I am focused on having a list without duplicates. In my case I wasn't worried about duplicates, just about I want all the guys that match any of these other guys. I did google for numpy member, numpy membership, numpy in, but none led me to in1d(). Also, it's worth saying that, as a newcomer to numpy and relative newcomer to python, I often think that what I'm looking for isn't going to end up being a function with a name -- often some use of slices or (fancy) indexing, or some other pure syntax mechanism, ends up doing what you want. So that's one reason I didn't simply scan all the available numpy function names. From: Neil Crighton neilcrigh...@gmail.com To: numpy-discussion@scipy.org Sent: Sun, May 29, 2011 10:03:25 AM Subject: Re: [Numpy-discussion] finding elements that match any in a set Michael Katz michaeladamkatz at yahoo.com writes: Yes, thanks, np.in1d is what I needed. I didn't know how to find that. Did you check in the documentation? If so, where did you check? Would you have found it if it was in the 'See also' section of where()? (http://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html) I ask because people often post to the list needing in1d() after not being able to find it via the docs, so it would be nice to add references in the places people go looking for it. Neil ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] finding elements that match any in a set
On Sun, May 29, 2011 at 10:58 PM, Chris Barker chris.bar...@noaa.govwrote: On 5/28/2011 3:40 PM, Robert wrote: (myarray in mylist) turns into mylist.__contains__(myarray). Only the list object is ever checked for this method. There is no paired method myarray.__rcontains__(mylist) so there is nothing that numpy can override to make this operation do anything different from what lists normally do, however, numpy arrays should be able to override in be defining their own.__contains__ method, so you could do: something in an_array and get a useful, vectorized result. So I thought I'd see what currently happens when I try that: In [24]: a Out[24]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) In [25]: 3 in a Out[25]: True So the simple case works just like a list. But what If I want what the OP wants: In [26]: b Out[26]: array([3, 6, 4]) In [27]: b in a Out[27]: False OK, so the full b array is not in a, and it doesn't vectorize it, either. But: In [29]: a Out[29]: array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) In [30]: b in a Out[30]: True HUH? I'm not sure by what definition we would say that b is contained in a. but maybe.. In [41]: b Out[41]: array([ 4, 2, 345]) In [42]: b in a Out[42]: False so it's are all of the elements in b in a somewhere? but only for 2-d arrays? So what does it mean? FWIW, a short prelude on the theme seems quite promising, indeed: In []: A Out[]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) In []: [0, 1, 2] in A Out[]: True In []: [0, 3, 6] in A Out[]: True In []: [0, 4, 8] in A Out[]: True In []: [8, 4, 0] in A Out[]: True In []: [2, 4, 6] in A Out[]: True In []: [6, 4, 2] in A Out[]: True In []: [3, 1, 5] in A Out[]: True In [1061]: [3, 1, 4] in A Out[1061]: True But In []: [1, 2, 3] in A Out[]: False In []: [3, 2, 1] in A Out[]: True So, obviously the logic behind __contains__ is not so very straightforward. Perhaps just a bug? Regards, eat The docstring is not helpful: In [58]: np.ndarray.__contains__? Type: wrapper_descriptor Base Class: type 'wrapper_descriptor' String Form:slot wrapper '__contains__' of 'numpy.ndarray' objects Namespace: Interactive Docstring: x.__contains__(y) == y in x If nothing useful, maybe it could provide a vectorized version of in for this sort of use case. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
