Re: [Numpy-discussion] ndarray of object dtype
Thanks Robert,
that worked just great.
I was wandering whether anyone has experience with calling Fortran 90
functions that return derived types and cast them to C/Python objects.
I have found excellent reference in a couple of websites, however all
examples deal with returning at best 2D arrays of fundamental/basic
types. When it comes to derived types/structures I have following
dilemma:
should I use Fortran subroutines, allocate memory in C and pass the
structures as arguments or should I use functions, allocate memory in
Fortran and return the data to C/Python.
I should mention I have little experience in C/Fortran.
All thoughts are welcome.
Quoting Robert Kern robert.k...@gmail.com:
On Mon, Oct 4, 2010 at 00:41, Ioan Ferencik ioan.feren...@tkk.fi wrote:
Hello list,
I am trying to pass elements of a custom defined with C API.
I have successfully wrapped a type around a C struct.
I intent to create a list with these objects and process it using
numpy C API.
So i create an array:
array = (PyArrayObject *)PyArray_ContiguousFromObject(input,
PyArray_OBJECT, 0, 0);
to my understanding each el. in this array is a pointer to my type so
a cast to this type
should work.
this is my custom type
typedef struct val{
PyObject_HEAD
float q;
float wl;
int cssid;
int br;
}hm1dval;
I am passing only one element for testing purposes.
in python
a = [hm1d1.hm1dval() for i in range(0,1)]
for c in a:
c.set_values(q=3.0, wl=2.5, cssid=6, br=7)
So following code should be valid in C:
hm1dval s* = PyArray_DATA(array);
but the members are 0 after casting in spite they were set previously.
The elements of a dtype=object array are PyObject* pointers. In C, an
array of pointers would look like this:
hm1dval **s = PyArray_DATA(array);
--
Robert Kern
I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth.
-- Umberto Eco
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Ioan Ferencik
PhD student
Aalto University
School of Science and Technology
Faculty Of Civil and Env. Engineering
Lahti Center
Tel: +358505122707
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Re: [Numpy-discussion] Please help on compilation fortran module using f2py
Hi, On 10/06/2010 04:57 AM, Jing wrote: Hi, everyone: I am new to the python numpy and f2py. I really need help on compiling FORTRAN module using f2py. I have been searched internet without any success. Here is my setup: I have a Ubuntu 10.04 LTS with python 2.6, numpy 1.3.0 and f2py 2 (installed from ubuntu) and gfortran compiler 4.4. I have a simple Fortran subroutine (in CSM_CH01_P1_1a_F.f95 file) as shown below: ! subroutine sim_model_1(ts, n, a) ! !f2py integer, intent(in) :: ts, n !f2py real,dimension(n), intent(inout) :: a implicit none The problem is in Fortran code. The ts, n, and a variables need to be declared for fortran compiler too. The f2py directives are invisible to the fortran compiler, they are just comments that are used by f2py. So, try adding these lines to the Fortran code: integer, intent(in) :: ts, n real, dimension(n) :: a HTH, Pearu ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] index of a value in an array
Hi All, Given an array such as: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) How can I find the index of a particular number in the array? (ie: if it was a list, I'd do [1,2,3,4].index(3)) cheers, Chris ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] alter_code1.py doesn't convert (any more)?
Hi, I'm trying to help a user to migrate from Numeric - which he used up to now - to numpy. I found reference to alter_code1.py on http://numpy.scipy.org/old_array_packages.html. First I tried the conversion script: # python2.6 /path/to/alter_code1.py test.py and alternatively # python2.6 /path/to/alter_code1.py ./ This didn't modify test.py at all. Then I tried to convert from within Python: # python2.6 Python 2.6.2 (r262:71600, Aug 5 2010, 14:21:11) [GCC 4.4.4] on linux2 Type help, copyright, credits or license for more information. import numpy.numarray.alter_code1 as noa noa.convertfile(test.py) ^D # This modified test.py, but only partially. E.g. the import line was still untouched, meaning that it still imported Numeric instead of numpy: # diff ../test-original.py test.py 0a1,2 ## Automatically adapted for numpy.numarray Oct 06, 2010 by 49c51 pyfid.byteswap() --- pyfid.byteswap(True) 74c76 databin.byteswap() --- databin.byteswap(True) # After I've changed 'import os, string, array, Numeric' to 'import os, string, array, numpy' I realized, that the conversion had introduced errors: # python2.6 ./test.py command line parameters Traceback (most recent call last): File ./test.py, line 115, in module combineser3D(td3,td2,td1,ser,ser1,fidnum) File ./test.py, line 21, in combineser3D allser1=readBrukerSer(tdall,td3,ser) File ./test.py, line 51, in readBrukerSer pyfid.byteswap(True) TypeError: byteswap() takes no arguments (1 given) # So either this method doesn't work (any more) or I am doing something wrong. The numpy version we are using is 1.4.1 with Python 2.6.2 from python.org. Any help regarding this is highly appreciated. Thanks in advance frank ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] index of a value in an array
On Wed, Oct 6, 2010 at 5:26 AM, Chris Withers ch...@simplistix.co.ukwrote: Hi All, Given an array such as: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) How can I find the index of a particular number in the array? (ie: if it was a list, I'd do [1,2,3,4].index(3)) cheers, Chris ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Try with numpy.where(array == value) and plain array == value IIRC, the first will return an array the size of the found elements and the second will return an array (or list?) of True/False. Either can be used as index = numpy.where() and index = (array == value) and then array[index]. N ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] index of a value in an array
Chris, You can use where() command to find index of a particular number in the array. For e.g. to find index of number 1 in array a, you can say idx = where(a==1) Regards, Brian -Original Message- From: numpy-discussion-boun...@scipy.org [mailto:numpy-discussion-boun...@scipy.org] On Behalf Of Chris Withers Sent: Wednesday, October 06, 2010 5:27 AM To: numpy-discussion@scipy.org Cc: snezana.pe...@glcuk.com Subject: [Numpy-discussion] index of a value in an array Hi All, Given an array such as: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) How can I find the index of a particular number in the array? (ie: if it was a list, I'd do [1,2,3,4].index(3)) cheers, Chris ___ 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] index of a value in an array
On 06/10/2010 14:53, Thomas, Brian (GE Energy) wrote: For e.g. to find index of number 1 in array a, you can say idx = where(a==1) Exactly what we were looking for, thanks! Chris ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] index of a value in an array
On Wed, Oct 6, 2010 at 7:23 AM, Nicolas Bigaouette nbigaoue...@gmail.comwrote: On Wed, Oct 6, 2010 at 5:26 AM, Chris Withers ch...@simplistix.co.ukwrote: Hi All, Given an array such as: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) How can I find the index of a particular number in the array? (ie: if it was a list, I'd do [1,2,3,4].index(3)) cheers, Chris ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Try with numpy.where(array == value) and plain array == value IIRC, the first will return an array the size of the found elements and the second will return an array (or list?) of True/False. Either can be used as index = numpy.where() and index = (array == value) and then array[index]. N Wouldn't it be more technically correct to use numpy.nonzero()? Returning the index is a special behavior of not specifying the second argument to numpy.where(). Ben Root ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] bug in ceil()
Hi All, I use numpy 1.4.1 on Debian squeeze amd64. I noticed that ceil() is not working properly. If the input to ceil() is a float I expect a float to be returned but for inputs in (-1.0, 0.0) the result is of type integer. In [65]: np.__version__ Out[65]: '1.4.1' In [66]: np.ceil(-1.1) Out[66]: -1.0 In [67]: np.ceil(-0.734) Out[67]: -0 In [68]: np.ceil(-0.256) Out[68]: -0 In [69]: np.ceil(-0.0) Out[69]: -0 In [70]: np.ceil(0.2) Out[70]: 1.0 Best wishes Christian -- Dipl.-Ing. Christian Fischer Institute of Engineering and Computational Mechanics (name in German: Institut für Technische und Numerische Mechanik) University of Stuttgart, Pfaffenwaldring 9, 70569 Stuttgart, Germany mailto:cfisc...@itm.uni-stuttgart.de, tel 0711-685-66565, fax -66400 http://www.itm.uni-stuttgart.de/staff/Fischer smime.p7s Description: S/MIME Cryptographic Signature ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] bug in ceil()
Are you sure? In [4]: type(np.ceil(-0.0)) Out[4]: type 'numpy.float64' Mike On 10/06/2010 01:55 PM, Christian Fischer wrote: Hi All, I use numpy 1.4.1 on Debian squeeze amd64. I noticed that ceil() is not working properly. If the input to ceil() is a float I expect a float to be returned but for inputs in (-1.0, 0.0) the result is of type integer. In [65]: np.__version__ Out[65]: '1.4.1' In [66]: np.ceil(-1.1) Out[66]: -1.0 In [67]: np.ceil(-0.734) Out[67]: -0 In [68]: np.ceil(-0.256) Out[68]: -0 In [69]: np.ceil(-0.0) Out[69]: -0 In [70]: np.ceil(0.2) Out[70]: 1.0 Best wishes Christian ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Michael Droettboom Science Software Branch Space Telescope Science Institute Baltimore, Maryland, USA ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] bug in ceil()
On Wed, Oct 6, 2010 at 11:55 AM, Christian Fischer cfisc...@itm.uni-stuttgart.de wrote: Hi All, I use numpy 1.4.1 on Debian squeeze amd64. I noticed that ceil() is not working properly. If the input to ceil() is a float I expect a float to be returned but for inputs in (-1.0, 0.0) the result is of type integer. In [65]: np.__version__ Out[65]: '1.4.1' In [66]: np.ceil(-1.1) Out[66]: -1.0 In [67]: np.ceil(-0.734) Out[67]: -0 In [68]: np.ceil(-0.256) Out[68]: -0 In [69]: np.ceil(-0.0) Out[69]: -0 In [70]: np.ceil(0.2) Out[70]: 1.0 The -0 is a float type, you can't get it with integers. The printing might could be tweaked to return -0.0, which would be less misleading. The thing that bothers me is that -0 is returned instead of 0.0, but that can be fixed. In [2]: ceil(-.5) Out[2]: -0 In [3]: ceil(-.5) + 0 Out[3]: 0.0 Please open a ticket, maybe two. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] bug in ceil()
On Wed, 06 Oct 2010 19:55:00 +0200 Christian Fischer cfisc...@itm.uni-stuttgart.de wrote: Hi All, I use numpy 1.4.1 on Debian squeeze amd64. I noticed that ceil() is not working properly. If the input to ceil() is a float I expect a float to be returned but for inputs in (-1.0, 0.0) the result is of type integer. In [65]: np.__version__ Out[65]: '1.4.1' In [66]: np.ceil(-1.1) Out[66]: -1.0 In [67]: np.ceil(-0.734) Out[67]: -0 In [68]: np.ceil(-0.256) Out[68]: -0 In [69]: np.ceil(-0.0) Out[69]: -0 In [70]: np.ceil(0.2) Out[70]: 1.0 Best wishes Christian It's a float type(numpy.ceil(-0.4)) type 'numpy.float64' numpy.ceil(-0.4) -0 Nils ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy on 64 bit x86_64
Hi I have a x86_64 Fedora core 3 machine where I want to install numpy but python2.4 has been installed on this machine using Intel compilers instead of gcc Can someone tell me how I can install numpy on this architecture? Thanks ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] point line distance
supose you have a line defined by two points and a point. you want the distance what are easiest possibilities? i am doing it, but its nasty'n ugly -- GNU/Linux User #479299 skype: fabbri.renato ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] puzzle with boolean dtype
Integer exponentiation fails (i.e., changes type) with boolean dtype. See below. Expected? Alan Isaac a = np.array([[0,1,0],[0,0,1],[1,0,0]], dtype=np.bool_) a2 = a*a a3 = a2*a print(a3) [[False True False] [False False True] [ True False False]] print(a**3) [[0 1 0] [0 0 1] [1 0 0]] ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] point line distance
Here's a good list of basic geometry algorithms: http://www.softsurfer.com/algorithms.htm Zach On Oct 6, 2010, at 5:08 PM, Renato Fabbri wrote: supose you have a line defined by two points and a point. you want the distance what are easiest possibilities? i am doing it, but its nasty'n ugly -- GNU/Linux User #479299 skype: fabbri.renato ___ 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] puzzle with boolean dtype
On 10/6/2010 5:52 PM, Charles R Harris wrote: What would you want in it's place? For consistancy, a**0 == all_true, a**i == a, i 0? Yes. Alan Isaac PS Boolean matrix powers are well defined and useful. I found this oddity by accidentally creating an array instead of a matrix. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] known failure test decorator not treated as expected by nose
It appears that the numpy testing decorators for skipping and for known failure should behave similarly to each other, at least from their descriptions here: http://projects.scipy.org/numpy/wiki/TestingGuidelines#known-failures-skipping-tests Yet in the following example, run under nose, they behave quite differently: from numpy.testing import dec @dec.knownfailureif( True, known failure test message) def test_will_fail(): pass @dec.skipif( True, skip test message) def test_to_skip(): pass The skipped test is marked as S as expected, but when the KnownFailureTest exception is raised, nose handles it as an error (marked E rather than K as expected, and printing the stack trace.) It's also interesting that nose has a --no-skip option but no corresponding option for ignoring known failure decorators. Why the discrepancy? Who is out of step with whom? This is with numpy 1.4.0 and nosetests 0.11.3 Thanks, Jonathan March ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] known failure test decorator not treated as expected by nose
On Wed, Oct 6, 2010 at 7:08 PM, Jonathan March jma...@enthought.com wrote: It appears that the numpy testing decorators for skipping and for known failure should behave similarly to each other, at least from their descriptions here: http://projects.scipy.org/numpy/wiki/TestingGuidelines#known-failures-skipping-tests Yet in the following example, run under nose, they behave quite differently: from numpy.testing import dec @dec.knownfailureif( True, known failure test message) def test_will_fail(): pass @dec.skipif( True, skip test message) def test_to_skip(): pass The skipped test is marked as S as expected, but when the KnownFailureTest exception is raised, nose handles it as an error (marked E rather than K as expected, and printing the stack trace.) It's also interesting that nose has a --no-skip option but no corresponding option for ignoring known failure decorators. Why the discrepancy? Who is out of step with whom? This is with numpy 1.4.0 and nosetests 0.11.3 just as additional information This only happens on the command line, with nosetests packagename but not with the function, packagename.test() Josef Thanks, Jonathan March ___ 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] unpack argument in loadtxt/genfromtxt does not work as documented
The documentation for loadtxt and genfromtxt state that the unpack
argument functions as follows:
If True, the returned array is transposed, so that arguments may be
unpacked using x, y, z = loadtxt(...).
In practice, this does not always occur. I have a csv file of mixed
data types, and try importing it via:
genfromtxt(progestogens.csv, delimiter=,, names=True,
dtype=dtype([('id', int),('study', '|S25'),('year', int),('treat',
int),('drug', '|S25'),('form', '|S10'),('ptb', int),('mgest',
int),('lab', int),('n', int),('y', int),('var', '|S5'),('wt',
int),('sdwt', int)]), unpack=True)
With unpack=True, I would expect the data to be presented by columns,
however the resulting array is by rows:
array([(1, 'Meis', 2003, 1, '17P', 'IM', 1, 0, 0, 306, 111, 'ptb'),
(1, 'Meis', 2003, 0, '17P', 'IM', 1, 0, 0, 153, 84, 'ptb'),
(2, 'Rai', 2009, 1, 'Progesterone', 'Oral', 1, 0, 0, 74, 29, 'ptb'),
(2, 'Rai', 2009, 0, 'Progesterone', 'Oral', 1, 0, 0, 74, 44, 'ptb'),
...
The same behaviour occurs using loadtxt. Moreover, this array is
untransposeable, so I am stuck with having to iterate over all the
rows, making genfromtxt no better than csv.reader.
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[Numpy-discussion] Assigning complex value to real array
I came across this gem yesterday from numpy import * R = ones((2)) R[0] = R[0] * 1j R ...array([ 0., 1.]) R = ones((2), 'complex') R[0] = R[0] * 1j R array([ 0.+1.j, 1.+0.j]) and I read that this behavior is actually intended for some reason about how Python wants relations between types to be such that this mistake is unavoidable. So can we have a new abstract floating type which is a complex, but is implemented so that the numbers where the imaginary part is zero represent and operate on that imaginary part implicitly? By containing all these semantics within one type, then presumably we avoid problems with ideas relationships between types. Best regards, Andrew Mullhaupt ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Assigning complex value to real array
Andrew P. Mullhaupt wrote: I came across this gem yesterday from numpy import * R = ones((2)) R[0] = R[0] * 1j R ...array([ 0., 1.]) R = ones((2), 'complex') R[0] = R[0] * 1j R array([ 0.+1.j, 1.+0.j]) and I read that this behavior is actually intended for some reason about how Python wants relations between types to be such that this mistake is unavoidable. So can we have a new abstract floating type which is a complex, but is implemented so that the numbers where the imaginary part is zero represent and operate on that imaginary part implicitly? By containing all these semantics within one type, then presumably we avoid problems with ideas relationships between types. Best regards, Andrew Mullhaupt ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion Sorry, but this post is in the wrong topic since it has nothing to do with my question. -- View this message in context: http://old.nabble.com/Meshgrid-with-Huge-Arrays-tp29902859p29903028.html Sent from the Numpy-discussion mailing list archive at Nabble.com. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Assigning complex value to real array
On 10/7/2010 1:14 AM, sicre wrote: Andrew P. Mullhaupt wrote: (assigning complex value to real array) Sorry, but this post is in the wrong topic since it has nothing to do with my question. How did this happen? I created a new subject. Best regards, Andrew Mullhaupt ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Assigning complex value to real array
On Wed, Oct 6, 2010 at 11:07 PM, Andrew P. Mullhaupt d...@zen-pharaohs.comwrote: I came across this gem yesterday from numpy import * R = ones((2)) R[0] = R[0] * 1j R ...array([ 0., 1.]) R = ones((2), 'complex') R[0] = R[0] * 1j R array([ 0.+1.j, 1.+0.j]) and I read that this behavior is actually intended for some reason about how Python wants relations between types to be such that this mistake is unavoidable. It's because an element of a real array only has space for a real and you can't fit a complex in there. Some other software which is less strict about types may allow such things, but it comes at a cost. So can we have a new abstract floating type which is a complex, but is implemented so that the numbers where the imaginary part is zero represent and operate on that imaginary part implicitly? By containing all these semantics within one type, then presumably we avoid problems with ideas relationships between types. Short answer: no. If you want complex just use a complex array. Changing types like you propose would require making a new copy or reserving space ahead of time, which would be wasteful. It could also be done with lists or objects, but then you would lose speed. Newer versions of numpy will warn you that the imaginary part is going to be discarded in your first example. Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
