Mailman 3 February 2006 - NumPy-Discussion

repmat equivalent
by Alex Liberzon Feb. 23, 2006

Feb. 23, 2006

I am also mostly Matlab user and I like repmat() a lot. I just realized that in SciPy, i am confused about NumPy/SciPy, but it is possible in both :-)) it is much, much easier. Just use r_[a,a] and c_[a,a] and you get a concatination like repmat() does. if you need 'm' times row concatenation of matrix, you can use (sorry for the ugly way, Pythoners): eval('r_['+m*'a,'+']') then, the repmat is just: (qute, isn't it) def repmat(a,m,n): from scipy import r_, c_ a = eval('r_['+m*'a,'+']') return eval('c_['+n*'a,'+']') the test is: >>> from scipy import * numerix Numeric 24.2 >>> a = array([[0,1],[2,3]]) >>> a array([[0, 1], [2, 3]]) >>> repmat(a,2,3) array([[0, 1, 0, 1, 0, 1], [2, 3, 2, 3, 2, 3], [0, 1, 0, 1, 0, 1], [2, 3, 2, 3, 2, 3]]) Best, Alex Liberzon <alex.liberzonNOSPAMFORMEPLEASE(a)gmail.com>

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Up-to-date bugtracker for NumPy?
by Norbert Nemec Feb. 23, 2006

Feb. 23, 2006

Hi there, is there any bugtracker for NumPy actually in active use? Sourceforge has one for numarray and one for "numpy", but the latter one contains only old bugs (probably for Numeric?) Greetings, Norbert

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A question
by Colin J. Williams Feb. 23, 2006

Feb. 23, 2006

Bar is a sub-class of ArrayType (ndarray) and bar is an instance of Bar [Dbg]>>> self.bar Bar([ 5, 1, 1, 11, 3, 7, 14, 0, 5, 2, 4, 12, 9, 10, 4, 12], dtype=uint16) [Dbg]>>> z= self.bar [Dbg]>>> z 1 << Is this expected? [Dbg]>>> type(self.bar) <class '__main__.Bar'> [Dbg]>>> self.bar.__class__.base <attribute 'base' of 'numpy.bigndarray' objects> [Dbg]>>> Colin W.

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Success with SVN on Cygwin
by Jose Gomez-Dans Feb. 23, 2006

Feb. 23, 2006

Hi! A few days ago, I asked about compiling NumPy on Cygwin. Travis carried out some modifications, and with last night's SVN, I can happily report that it now compiles and works. The tests produced no errors, so it's all good :) Many thanks to all, and to Travis specially, for his really fast response. Many thanks! Jose

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Timings for various round functions
by Sasha Feb. 23, 2006

Feb. 23, 2006

C99 defines three functions round, rint and nearbyint that are nearly identical. The only difference is in setting the inexact flag and respecting the rounding mode. Nevertheless, these functions differ significantly in their performance. I've wraped these functions into ufuncs and go the following timings: > python -m timeit -s "from numpy import array, round, rint, nearbyint; x = array([1.5]*10000)" "round(x)" 1000 loops, best of 3: 257 usec per loop > python -m timeit -s "from numpy import array, round, rint, nearbyint; x = array([1.5]*10000)" "nearbyint(x)" 1000 loops, best of 3: 654 usec per loop > python -m timeit -s "from numpy import array, round, rint, nearbyint; x = array([1.5]*10000)" "rint(x)" 10000 loops, best of 3: 103 usec per loop Similarly for single precision: > python -m timeit -s "from numpy import array, round, rint, nearbyint; x = array([1.5]*10000,dtype='f')" "round(x)" 10000 loops, best of 3: 182 usec per loop > python -m timeit -s "from numpy import array, round, rint, nearbyint; x = array([1.5]*10000,dtype='f')" "nearbyint(x)" 1000 loops, best of 3: 606 usec per loop > python -m timeit -s "from numpy import array, round, rint, nearbyint; x = array([1.5]*10000,dtype='f')" "rint(x)" 10000 loops, best of 3: 85.5 usec per loop Obviously, I will use rint in my ndarray.round implementation, however, it may be useful to provide all three as ufuncs. The only question is what name to use for round? 1) round (may lead to confusion with ndarray.round or built-in round) 2) roundint (too similar to rint) 3) round0 (ugly) Any suggestions? Another C99 function that may be worth including is "trunc". Any objections to adding it as a ufunc?

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RE: [Numpy-discussion] repmat equivalent?
by Nadav Horesh Feb. 23, 2006

Feb. 23, 2006

You should really use the "repeat" function. Nadav. -----Original Message----- From: numpy-discussion-admin(a)lists.sourceforge.net on behalf of Albert Strasheim Sent: Thu 23-Feb-06 13:31 To: numpy-discussion(a)lists.sourceforge.net Cc: Subject: [Numpy-discussion] repmat equivalent? Hello all I recently started using NumPy and one function that I am really missing from MATLAB/Octave is repmat. This function is very useful for implementing algorithms as matrix multiplications instead of for loops. Here's my first attempt at repmat for 1d and 2d (with some optimization by Stefan van der Walt): def repmat(a, m, n): if a.ndim == 1: a = array([a]) (origrows, origcols) = a.shape rows = origrows * m cols = origcols * n b = a.reshape(1,a.size).repeat(m, 0).reshape(rows, origcols).repeat(n, 0) return b.reshape(rows, cols) print repmat(array([[1,2],[3,4]]), 2, 3) produces: [[1 2 1 2 1 2] [3 4 3 4 3 4] [1 2 1 2 1 2] [3 4 3 4 3 4]] which is the same as in MATLAB. There are various issues with my function that I don't quite know how to solve: - How to handle scalar inputs (probably need asarray here) - How to handle more than 2 dimensions More than 2 dimensions is tricky, since NumPy and MATLAB don't seem to agree on how more-dimensional data is organised? As such, I don't know what a NumPy user would expect repmat to do with more than 2 dimensions. Here are some test cases that the current repmat should pass, but doesn't: a = repmat(1, 1, 1) assert_equal(a, 1) a = repmat(array([1]), 1, 1) assert_array_equal(a, array([1])) a = repmat(array([1,2]), 2, 3) assert_array_equal(a, array([[1,2,1,2,1,2], [1,2,1,2,1,2]])) a = repmat(array([[1,2],[3,4]]), 2, 3) assert_array_equal(a, array([[1,2,1,2,1,2], [3,4,3,4,3,4], [1,2,1,2,1,2], [3,4,3,4,3,4]])) Any suggestions on how do repmat in NumPy would be appreciated. Regards Albert ------------------------------------------------------- This SF.Net email is sponsored by xPML, a groundbreaking scripting language that extends applications into web and mobile media. Attend the live webcast and join the prime developer group breaking into this new coding territory! http://sel.as-us.falkag.net/sel?cmd=k&kid0944&bid$1720&dat1642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion(a)lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion

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system_info problem
by Robert Cimrman Feb. 23, 2006

Feb. 23, 2006

Hi, I am trying to (finally) move UMFPACK out of the sandbox to the scipy proper and so I need checking for its libraries via system_info.py of numpy distutils. I have added a new section to site.cfg: [umfpack] library_dirs = /home/share/software/packages/UMFPACK/UMFPACK/Lib:/home/share/software/packages/UMFPACK/AMD/Lib include_dirs = /home/share/software/packages/UMFPACK/UMFPACK/Include umfpack_libs = umfpack, amd the names of the libraries are libumfpack.a, libamd.a - they are correctly found in 'system_info._check_libs()' by 'self._lib_list(lib_dir, libs, exts)', but then the function fails, since 'len(found_libs) == len(libs)' which is wrong. Can some numpy.distutils expert help me? Below is the new umfpack_info class I have written using the blas_info class as template. yours clueless, r. PS: I do this because I prefer having the umfpack installed separately. It will be used, if present, to replace the default superLU-based sparse solver. Moving its sources under scipy/Lib/sparse would solve this issue, but Tim Davis recently changed the license of UMFPACK to GPL, and so the last version available for the direct inclusion is 4.4. (4.6 is the current one). Opinions are of course welcome. -- class umfpack_info(system_info): section = 'umfpack' dir_env_var = 'UMFPACK' _lib_names = ['umfpack', 'amd'] includes = 'umfpack.h' notfounderror = UmfpackNotFoundError def calc_info(self): info = {} lib_dirs = self.get_lib_dirs() print lib_dirs umfpack_libs = self.get_libs('umfpack_libs', self._lib_names) print umfpack_libs for d in lib_dirs: libs = self.check_libs(d,umfpack_libs,[]) print d, libs if libs is not None: dict_append(info,libraries=libs) break else: return include_dirs = self.get_include_dirs() print include_dirs h = (self.combine_paths(lib_dirs+include_dirs,includes) or [None])[0] if h: h = os.path.dirname(h) dict_append(info,include_dirs=[h]) print info self.set_info(**info)

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Why floor and ceil change the type of the array?
by Sasha Feb. 23, 2006

Feb. 23, 2006

I was looking for a model to implement "round" in C and discovered that floor and ceil functions change the type of their arguments: >>> floor(array([1,2,3],dtype='i2')).dtype dtype('<f4') >>> floor(array([1,2,3],dtype='i4')).dtype dtype('<f8') I know that this is the same behavior as in Numeric, but wouldn't it be more natural if fllor and ceil return the argument unchanged (maybe a copy) if it is already integer?

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simple subclassing of ndarray
by Zachary Pincus Feb. 22, 2006

Feb. 22, 2006

Hello folks, I'm interested in creating a simple subclass of ndarray that just has a few additional methods. I've stared at defmatrix.py, but I'm not sure what is necessary to do. Specifically, I'm not sure how to get new instances of my subclass created properly. e.g.: numpy.matrix([1,2,3]) Out: matrix([[1, 2, 3]]) class m(numpy.ndarray): pass m([1,2,3]) Out: m([[[ 13691, 0, 0], [ 196608, 296292267, 296303312]]]) So clearly I need something else. Looking at the matrix class, it looks like I need a custom __new__ operator. However, looking at matrix's __new__ operator, I see a lot of complexity that I just don't understand. What's the minimum set of things I need in __new__ to get a proper constructor? Or perhaps there's a different and better way to construct instances of my subclass? Something akin to the 'array' function would be perfect. Now, how do I go about creating such a function (or getting 'array' to do it)? Can anyone give me any pointers here? Thanks, Zach Pincus Program in Biomedical Informatics and Department of Biochemistry Stanford University School of Medicine

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dtype
by Colin J. Williams Feb. 22, 2006

Feb. 22, 2006

I've been trying to gain some understanding of dtype from the builtin documentation and would appreciate advice. I don't find anything in http://projects.scipy.org/scipy/numpy or http://wiki.python.org/moin/NumPy Chapter 2.1 of the book has a good overview, but little reference material. In the following, dt= numpy.dtype Some specific problems are flagged ** below. Colin W. [Dbg]>>> h(dt) Help on class dtype in module numpy: class dtype(__builtin__.object) | Methods defined here: | | __cmp__(...) | x.__cmp__(y) <==> cmp(x,y) | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __len__(...) | x.__len__() <==> len(x) | | __reduce__(...) | self.__reduce__() for pickling. | | __repr__(...) | x.__repr__() <==> repr(x) | | __setstate__(...) | self.__setstate__() for pickling. | | __str__(...) | x.__str__() <==> str(x) | | newbyteorder(...) | self.newbyteorder(<endian>) returns a copy of the dtype object | with altered byteorders. If <endian> is not given all byteorders | are swapped. Otherwise endian can be '>', '<', or '=' to force | a byteorder. Descriptors in all fields are also updated in the | new dtype object. | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = <built-in method __new__ of type object> | T.__new__(S, ...) -> a new object with type S, a subtype of T ** What are the parameters? In other words, | what does ... stand for? ** | | alignment = <member 'alignment' of 'numpy.dtype' objects> | | | base = <attribute 'base' of 'numpy.dtype' objects> | The base data-type or self if no subdtype | | byteorder = <member 'byteorder' of 'numpy.dtype' objects> | | | char = <member 'char' of 'numpy.dtype' objects> | | | descr = <attribute 'descr' of 'numpy.dtype' objects> | The array_protocol type descriptor. | | fields = <attribute 'fields' of 'numpy.dtype' objects> | | | hasobject = <member 'hasobject' of 'numpy.dtype' objects> | | | isbuiltin = <attribute 'isbuiltin' of 'numpy.dtype' objects> | Is this a buillt-in data-type descriptor? | | isnative = <attribute 'isnative' of 'numpy.dtype' objects> | Is the byte-order of this descriptor native? | | itemsize = <member 'itemsize' of 'numpy.dtype' objects> | | | kind = <member 'kind' of 'numpy.dtype' objects> | | | name = <attribute 'name' of 'numpy.dtype' objects> | The name of the true data-type | | num = <member 'num' of 'numpy.dtype' objects> | | | shape = <attribute 'shape' of 'numpy.dtype' objects> | The shape of the subdtype or (1,) | | str = <attribute 'str' of 'numpy.dtype' objects> | The array_protocol typestring. | | subdtype = <attribute 'subdtype' of 'numpy.dtype' objects> | A tuple of (descr, shape) or None. | | type = <member 'type' of 'numpy.dtype' objects> [Dbg]>>> dt.num.__doc__ ** no doc string ** [Dbg]>>> help(dt.num) Help on member_descriptor object: num = class member_descriptor(object) | Methods defined here: | | __delete__(...) | descr.__delete__(obj) | | __get__(...) | descr.__get__(obj[, type]) -> value | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __repr__(...) | x.__repr__() <==> repr(x) | | __set__(...) | descr.__set__(obj, value) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __objclass__ = <member '__objclass__' of 'member_descriptor' objects> [Dbg]>>> help(dt.num) Help on member_descriptor object: num = class member_descriptor(object) | Methods defined here: | | __delete__(...) | descr.__delete__(obj) | | __get__(...) | descr.__get__(obj[, type]) -> value | | __getattribute__(...) | x.__getattribute__('name') <==> x.name | | __repr__(...) | x.__repr__() <==> repr(x) | | __set__(...) | descr.__set__(obj, value) | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __objclass__ = <member '__objclass__' of 'member_descriptor' objects> [Dbg]>>> help(dt.num.__objclass__) Help on class dtype in module numpy: class dtype(__builtin__.object) | Methods defined here: | | __cmp__(...) | x.__cmp__(y) <==> cmp(x,y) | | __getitem__(...) | x.__getitem__(y) <==> x[y] | | __len__(...) | x.__len__() <==> len(x) | | __reduce__(...) | self.__reduce__() for pickling. | | __repr__(...) | x.__repr__() <==> repr(x) | | __setstate__(...) | self.__setstate__() for pickling. | | __str__(...) | x.__str__() <==> str(x) | | newbyteorder(...) | self.newbyteorder(<endian>) returns a copy of the dtype object | with altered byteorders. If <endian> is not given all byteorders | are swapped. Otherwise endian can be '>', '<', or '=' to force | a byteorder. Descriptors in all fields are also updated in the | new dtype object. | | ---------------------------------------------------------------------- | Data and other attributes defined here: | | __new__ = <built-in method __new__ of type object> | T.__new__(S, ...) -> a new object with type S, a subtype of T | | alignment = <member 'alignment' of 'numpy.dtype' objects> | | | base = <attribute 'base' of 'numpy.dtype' objects> | The base data-type or self if no subdtype | | byteorder = <member 'byteorder' of 'numpy.dtype' objects> | | | char = <member 'char' of 'numpy.dtype' objects> | | | descr = <attribute 'descr' of 'numpy.dtype' objects> | The array_protocol type descriptor. | | fields = <attribute 'fields' of 'numpy.dtype' objects> | | | hasobject = <member 'hasobject' of 'numpy.dtype' objects> | | | isbuiltin = <attribute 'isbuiltin' of 'numpy.dtype' objects> | Is this a buillt-in data-type descriptor? | | isnative = <attribute 'isnative' of 'numpy.dtype' objects> | Is the byte-order of this descriptor native? | | itemsize = <member 'itemsize' of 'numpy.dtype' objects> | | | kind = <member 'kind' of 'numpy.dtype' objects> | | | name = <attribute 'name' of 'numpy.dtype' objects> | The name of the true data-type ** How does this differ from what, in common | Python usage, is a class.__name__? ** | | num = <member 'num' of 'numpy.dtype' objects> ** What does this mean? ** | | | shape = <attribute 'shape' of 'numpy.dtype' objects> | The shape of the subdtype or (1,) | | str = <attribute 'str' of 'numpy.dtype' objects> | The array_protocol typestring. | | subdtype = <attribute 'subdtype' of 'numpy.dtype' objects> | A tuple of (descr, shape) or None. | | type = <member 'type' of 'numpy.dtype' objects> [Dbg]>>> ** There is no __module__ attribute. How does one identify the modules holding the code? **

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