Re: [Numpy-discussion] f2py with int8
Hi, 1, 2, 3 are integer literals. 1.0, 3.0e2, -42.0 are real (float) literals 'hello world' is a string literal. As far as I remember, f2py requires a literal variable for the kind. The solution I have landed on is to write a pure fortran module (using int8, or whatever), and then wrap this module either with an f2py compatible fortran module or an interface file. If you want to know what int8 corresponds to, run the following (pure fortran) program through your compiler of choice: program kind_values use iso_fortran_env implicit none print *, 'int8 kind value:', int8 print *, 'int16 kind value:', int16 end program kind_values Paul On 17. apr. 2012, at 19:47, John Mitchell wrote: Thanks Paul. I suppose this is now going slightly out of bounds for f2py. What I am looking for is the fortran kind type for a byte. I thought that this was int8. I guess the question is how to identify the kind type. Although I have verified that integer(1) seems to work for me, I would really like to know why and your answer alludes to that. Please excuse my ignorance on this topic. Can you perhaps educate me a little on 'literal kind values'? I take you to mean that 'int8' is not a literal kind value while 1 and 8 are examples of literal kind values. Thanks, John On Tue, Apr 17, 2012 at 10:12 AM, Paul Anton Letnes paul.anton.let...@gmail.com wrote: Ah, come to think of it, I think that f2py only supports literal kind values. Maybe that's your problem. Paul On 17. apr. 2012, at 07:58, Sameer Grover wrote: On Tuesday 17 April 2012 11:02 AM, John Mitchell wrote: Hi, I am using f2py to pass a numpy array of type numpy.int8 to fortran. It seems like I am misunderstanding something because I just can't make it work. Here is what I am doing. PYTHON b=numpy.array(numpy.zeros(shape=(10,),dtype=numpy.int8),order='F') b[0]=1 b[2]=1 b[3]=1 b array([1, 0, 1, 1, 0, 0, 0, 0, 0, 0], dtype=int8) FORTRAN subroutine print_bit_array(bits,n) use iso_fortran_env integer,intent(in)::n integer(kind=int8),intent(in),dimension(n)::bits print*,'bits = ',bits end subroutine print_bit_array RESULT when calling fortran from python bits = 1000000010 Any Ideas? thanks, John ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion It seems to work if integer(kind=int8) is replaced with integer(8) or integer(1). Don't know why, though. Sameer ___ 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 ___ 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
[Numpy-discussion] documentation bug: Matrix library page not populated
http://docs.scipy.org/doc/numpy/reference/routines.matlib.html#module-numpy.matlib promises a list of functions that does not appear (at the moment, anyway). Alan Isaac ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Casting rules - an awkward case
Hi, I just wanted to point out a situation where the scalar casting rules can be a little confusing: In [113]: a - np.int16(128) Out[113]: array([-256, -1], dtype=int16) In [114]: a + np.int16(-128) Out[114]: array([ 0, -1], dtype=int8) This is predictable from the nice docs here: http://docs.scipy.org/doc/numpy/reference/generated/numpy.result_type.html but I offer it only as a speedbump I hit. On the other hand I didn't find it easy to predict what numpy 1.5.1 was going to do: In [31]: a - np.int16(1) Out[31]: array([127, 126], dtype=int8) In [32]: a + np.int16(-1) Out[32]: array([-129, 126], dtype=int16) As a matter of interest, what was the rule for 1.5.1? See you, Matthew ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Casting rules - an awkward case
Oops, sorry, Keith Goodman kindly pointed out that I had missed out: On Wed, Apr 18, 2012 at 11:03 AM, Matthew Brett matthew.br...@gmail.com wrote: Hi, I just wanted to point out a situation where the scalar casting rules can be a little confusing: In [110]: a = np.array([-128, 127], dtype=np.int8) In [113]: a - np.int16(128) Out[113]: array([-256, -1], dtype=int16) In [114]: a + np.int16(-128) Out[114]: array([ 0, -1], dtype=int8) This is predictable from the nice docs here: http://docs.scipy.org/doc/numpy/reference/generated/numpy.result_type.html but I offer it only as a speedbump I hit. On the other hand I didn't find it easy to predict what numpy 1.5.1 was going to do: In [31]: a - np.int16(1) Out[31]: array([127, 126], dtype=int8) In [32]: a + np.int16(-1) Out[32]: array([-129, 126], dtype=int16) As a matter of interest, what was the rule for 1.5.1? Matthew ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] documentation bug: Matrix library page not populated
Hi, 18.04.2012 19:57, Alan G Isaac kirjoitti: http://docs.scipy.org/doc/numpy/reference/routines.matlib.html#module-numpy.matlib promises a list of functions that does not appear (at the moment, anyway). This doesn't seem to be due to a technical reason, but rather than because nobody has written a list of the functions in the docstring of the module. Pauli ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] YouTrack testbed
12.04.2012 18:43, Ralf Gommers kirjoitti: [clip] My current list of preferences is: 1. Redmine (if admin overhead is not unreasonable) 2. Trac with performance issues solved 3. Github 4. YouTrack 5. Trac with current performance Redmine seems pretty nice, apparently has all the features Trac has, and more. It's actually *easier* to administer than Trac, because you apparently can do most configuration via the web interface. With Trac, you have to drop down to command line and use trac-admin. Just don't deploy it on CGI like the Tracs we currently have :) Pauli ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] mask array and add to list
excellent thank you, that worked perfectly. I just need to remember this feature next time I need it. Thanks again On Thu, Apr 12, 2012 at 11:41 PM, Tim Cera t...@cerazone.net wrote: Use 'ma.max' instead of 'np.max'. This might be a bug OR an undocumented feature. :-) import numpy.ma as ma marr = ma.array(range(10), mask=[0,0,0,0,0,1,1,1,1,1]) np.max(marr) 4 # mask is used a = [] a.append(marr) a.append(marr) np.max(a) 9 # mask is not used ma.max(a) 4 # mask is used Kindest regards, Tim ___ 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] documentation bug: Matrix library page not populated
On Wed, Apr 18, 2012 at 4:14 PM, Pauli Virtanen p...@iki.fi wrote: Hi, 18.04.2012 19:57, Alan G Isaac kirjoitti: http://docs.scipy.org/doc/numpy/reference/routines.matlib.html#module-numpy.matlib promises a list of functions that does not appear (at the moment, anyway). This doesn't seem to be due to a technical reason, but rather than because nobody has written a list of the functions in the docstring of the module. Is it a good idea to use this? Mixing namespaces would completely confuse me. for f in dir(numpy.matlib): ... try: ... if getattr(numpy.matlib, f).__module__ in ['numpy.matlib', 'numpy.matrixlib.defmatrix']: print f ... except: pass ... asmatrix bmat empty eye identity mat matrix ones rand randn repmat zeros Josef Pauli ___ 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
