Re: [Numpy-discussion] Extract subset from an array
A Wednesday 17 February 2010 08:34:06 Nicola Creati escrigué:
Any kind of improvement is really appreciated.
Well, if you cannot really transpose your matrix, numexpr can also serve as a
good accelerator:
In [1]: import numpy as np
In [2]: import numexpr as ne
In [3]: x_min, x_max, y_min, y_max = .3, .5, .4, .7
In [4]: array = np.random.random((1000, 3))
In [5]: time (array[:,0]x_min) (array[:,0]x_max) (array[:,1]y_min)
(array[:,1]y_max)
CPU times: user 0.23 s, sys: 0.03 s, total: 0.26 s
Wall time: 0.27 s
Out[6]: array([False, False, False, ..., False, False, False], dtype=bool)
In [9]: time ne.evaluate((ax_min) (ax_max) (by_min) (by_max),
{'a': array[:,0], 'b': array[:,1]})
CPU times: user 0.16 s, sys: 0.00 s, total: 0.16 s
Wall time: 0.16 s
Out[10]: array([False, False, False, ..., False, False, False], dtype=bool)
Again, an 1.7x of improvement, but without the need for transposing.
--
Francesc Alted
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Extract subset from an array
Francesc Alted wrote:
A Wednesday 17 February 2010 08:34:06 Nicola Creati escrigué:
Any kind of improvement is really appreciated.
Well, if you cannot really transpose your matrix, numexpr can also serve as a
good accelerator:
In [1]: import numpy as np
In [2]: import numexpr as ne
In [3]: x_min, x_max, y_min, y_max = .3, .5, .4, .7
In [4]: array = np.random.random((1000, 3))
In [5]: time (array[:,0]x_min) (array[:,0]x_max) (array[:,1]y_min)
(array[:,1]y_max)
CPU times: user 0.23 s, sys: 0.03 s, total: 0.26 s
Wall time: 0.27 s
Out[6]: array([False, False, False, ..., False, False, False], dtype=bool)
In [9]: time ne.evaluate((ax_min) (ax_max) (by_min) (by_max),
{'a': array[:,0], 'b': array[:,1]})
CPU times: user 0.16 s, sys: 0.00 s, total: 0.16 s
Wall time: 0.16 s
Out[10]: array([False, False, False, ..., False, False, False], dtype=bool)
Again, an 1.7x of improvement, but without the need for transposing.
Hi,
this morning I tried numexpr and I got a good speed improvement as you
just suggest.
Thanks for keep helping me. :)
Nicola
--
Nicola Creati
Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS
www.inogs.it Dipartimento di Geofisica della Litosfera Geophysics of
Lithosphere Department CARS (Cartography and Remote Sensing) Research Group
http://www.inogs.it/Cars/ Borgo Grotta Gigante 42/c 34010 Sgonico - Trieste -
ITALY ncre...@ogs.trieste.it
off. +39 040 2140 213
fax. +39 040 327307
_
This communication, that may contain confidential and/or legally privileged
information, is intended solely for the use of the intended addressees.
Opinions, conclusions and other information contained in this message, that do
not relate to the official business of OGS, shall be considered as not given or
endorsed by it. Every opinion or advice contained in this communication is
subject to the terms and conditions provided by the agreement governing the
engagement with such a client. Any use, disclosure, copying or distribution of
the contents of this communication by a not-intended recipient or in violation
of the purposes of this communication is strictly prohibited and may be
unlawful. For Italy only: Ai sensi del D.Lgs.196/2003 - T.U. sulla Privacy si
precisa che le informazioni contenute in questo messaggio sono riservate ed a
uso esclusivo del destinatario.
_
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ndarray of complex-like data
Robert Kern wrote: 2) Subclass the ndarray to do what you want. I have subclassed ndarray, but I'm not sure how to continue from there. I was thinking of overriding __getitem__ and casting the complex to my complex subclass. Would that be the way to go? How would that work with slices? I strongly recommend simply implementing an arg() function that works on both arrays and and complex objects. Then just use abs() and arg() instead of trying to get instances of your class and using their attributes. Hmm. I hadn't thought of that option. I guess this makes more sense as this can than also operate on arrays like you say. Still, it would be interesting to know whether it's possible to have an ndarray subclass return instances of my complex subclass, preferably without having to copy the complex data (which would occur if I were to implement this behaviour by overriding __getitem__ as described above). But I assume that would require a user-defined type written in C? Regards, Brecht ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] doc wiki merge request
Hi, Can we have a doc wiki merge before the next release? I reviewed everything, there's changes to about 150 docstrings. See http://docs.scipy.org/numpy/patch/ Thanks, Ralf ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Unit Tests failing daily for 2 months
Hi, I've been informed by our build/installation person that 3 unit tests have been failing in the daily numpy svn installation for the last 2 months. The most recent output from the tests is as follows: == FAIL: Test generic loops. -- Traceback (most recent call last): File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_ufunc.py, line 86, in test_generic_loops assert_almost_equal(fone(x), fone_val, err_msg=msg) File /usr/stsci/pyssgdev/2.5.4/numpy/testing/utils.py, line 435, in assert_almost_equal raise AssertionError(msg) AssertionError: Arrays are not almost equal PyUFunc_F_F ACTUAL: array([ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], dtype=complex64) DESIRED: 1 == FAIL: test_umath.TestComplexFunctions.test_loss_of_precision(type 'numpy.complex64',) -- Traceback (most recent call last): File /usr/stsci/pyssgdev/2.5.4/nose/case.py, line 183, in runTest self.test(*self.arg) File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 721, in check_loss_of_precision check(x_basic, 2*eps/1e-3) File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 691, in check 'arcsinh') AssertionError: (0, 0.0010023052, 0.9987238, 'arcsinh') == FAIL: test_umath.TestComplexFunctions.test_precisions_consistent -- Traceback (most recent call last): File /usr/stsci/pyssgdev/2.5.4/nose/case.py, line 183, in runTest self.test(*self.arg) File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 602, in test_precisions_consistent assert_almost_equal(fcf, fcd, decimal=6, err_msg='fch-fcd %s'%f) File /usr/stsci/pyssgdev/2.5.4/numpy/testing/utils.py, line 435, in assert_almost_equal raise AssertionError(msg) AssertionError: Arrays are not almost equal fch-fcd ufunc 'arcsin' ACTUAL: 2.3561945j DESIRED: (0.66623943249251527+1.0612750619050355j) -- Ran 2512 tests in 18.753s FAILED (KNOWNFAIL=4, failures=3) Running unit tests for numpy NumPy version 2.0.0.dev8116 NumPy is installed in /usr/stsci/pyssgdev/2.5.4/numpy Python version 2.5.4 (r254:67916, Oct 26 2009, 14:36:20) [GCC 3.4.6 20060404 (Red Hat 3.4.6-11)] nose version 0.11.1 errors: failures: (Test(test_ufunc.TestUfunc testMethod=test_generic_loops), 'Traceback (most recent call last):\n File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_ufunc.py, line 86, in test_generic_loops\nassert_almost_equal(fone(x), fone_val, err_msg=msg)\n File /usr/stsci/pyssgdev/2.5.4/numpy/testing/utils.py, line 435, in assert_almost_equal\nraise AssertionError(msg)\nAssertionError: \nArrays are not almost equal PyUFunc_F_F\n ACTUAL: array([ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], dtype=complex64)\n DESIRED: 1\n') (Test(test_umath.TestComplexFunctions.test_loss_of_precision(type 'numpy.complex64',)), 'Traceback (most recent call last):\n File /usr/stsci/pyssgdev/2.5.4/nose/case.py, line 183, in runTest\n self.test(*self.arg)\n File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 721, in check_loss_of_precision\ncheck(x_basic, 2*eps/1e-3)\n File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 691, in check\n\'arcsinh\')\nAssertionError: (0, 0.0010023052, 0.9987238, \'arcsinh\')\n') (Test(test_umath.TestComplexFunctions.test_precisions_consistent), 'Traceback (most recent call last):\n File /usr/stsci/pyssgdev/2.5.4/nose/case.py, line 183, in runTest\n self.test(*self.arg)\n File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 602, in test_precisions_consistent\nassert_almost_equal(fcf, fcd, decimal=6, err_msg=\'fch-fcd %s\'%f)\n File /usr/stsci/pyssgdev/2.5.4/numpy/testing/utils.py, line 435, in assert_almost_equal\nraise AssertionError(msg)\nAssertionError: \nArrays are not almost equal fch-fcd ufunc \'arcsin\'\n ACTUAL: 2.3561945j\n DESIRED: (0.66623943249251527+1.0612750619050355j)\n') -- These failing tests are logged in Trac ticket numbers 1323, 1324, and 1325 respectively. Is anyone else seeing these failures? Any idea what the problem may be? It appears this problem is limited to 64-bit RHE 4 systems. Thank you for your time and help, Chris -- Christopher Hanley Senior Systems Software Engineer Space Telescope Science Institute 3700 San Martin Drive Baltimore MD, 21218 (410) 338-4338 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Unit Tests failing daily for 2 months
On Wed, Feb 17, 2010 at 8:33 AM, Christopher Hanley chan...@stsci.eduwrote: Hi, I've been informed by our build/installation person that 3 unit tests have been failing in the daily numpy svn installation for the last 2 months. The most recent output from the tests is as follows: I don't see these here. What architecture/compiler/os ? == FAIL: Test generic loops. -- Traceback (most recent call last): File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_ufunc.py, line 86, in test_generic_loops assert_almost_equal(fone(x), fone_val, err_msg=msg) File /usr/stsci/pyssgdev/2.5.4/numpy/testing/utils.py, line 435, in assert_almost_equal raise AssertionError(msg) AssertionError: Arrays are not almost equal PyUFunc_F_F ACTUAL: array([ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], dtype=complex64) DESIRED: 1 That's new to me. == FAIL: test_umath.TestComplexFunctions.test_loss_of_precision(type 'numpy.complex64',) -- Traceback (most recent call last): File /usr/stsci/pyssgdev/2.5.4/nose/case.py, line 183, in runTest self.test(*self.arg) File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 721, in check_loss_of_precision check(x_basic, 2*eps/1e-3) File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 691, in check 'arcsinh') AssertionError: (0, 0.0010023052, 0.9987238, 'arcsinh') == FAIL: test_umath.TestComplexFunctions.test_precisions_consistent -- Traceback (most recent call last): File /usr/stsci/pyssgdev/2.5.4/nose/case.py, line 183, in runTest self.test(*self.arg) File /usr/stsci/pyssgdev/2.5.4/numpy/core/tests/test_umath.py, line 602, in test_precisions_consistent assert_almost_equal(fcf, fcd, decimal=6, err_msg='fch-fcd %s'%f) File /usr/stsci/pyssgdev/2.5.4/numpy/testing/utils.py, line 435, in assert_almost_equal raise AssertionError(msg) AssertionError: Arrays are not almost equal fch-fcd ufunc 'arcsin' ACTUAL: 2.3561945j DESIRED: (0.66623943249251527+1.0612750619050355j) -- These two also fail on the buildbot Mac and have for some time. If you are able to reproduce them on an available machine that will be helpful in tracking them down. snip Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Unit Tests failing daily for 2 months
I don't see these here. What architecture/compiler/os ? The system architecture is 2 * Intel Xeon with hyperthreading. The OS is Red Hat Enterprise (RHE) 4 64-bit running Python 2.5.4. The C compiler being used is GCC 3.4.6. No Fortran compiler is being used. That's new to me. Darn. I was hoping this was old news. These two also fail on the buildbot Mac and have for some time. If you are able to reproduce them on an available machine that will be helpful in tracking them down. We can reproduce these problems on our RHE machine. A quick check of our logs from last night doesn't seem to indicate a problem on our Intel Mac 10.5 systems. However if there is something you want me to try just let me know what you need. Thanks, Chris -- Christopher Hanley Senior Systems Software Engineer Space Telescope Science Institute 3700 San Martin Drive Baltimore MD, 21218 (410) 338-4338 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] ndarray of complex-like data
On Wed, Feb 17, 2010 at 02:43, Brecht Machiels brecht.machi...@esat.kuleuven.be wrote: Robert Kern wrote: 2) Subclass the ndarray to do what you want. I have subclassed ndarray, but I'm not sure how to continue from there. I was thinking of overriding __getitem__ and casting the complex to my complex subclass. Would that be the way to go? How would that work with slices? I strongly recommend simply implementing an arg() function that works on both arrays and and complex objects. Then just use abs() and arg() instead of trying to get instances of your class and using their attributes. Hmm. I hadn't thought of that option. I guess this makes more sense as this can than also operate on arrays like you say. Still, it would be interesting to know whether it's possible to have an ndarray subclass return instances of my complex subclass, preferably without having to copy the complex data (which would occur if I were to implement this behaviour by overriding __getitem__ as described above). But I assume that would require a user-defined type written in C? Well, when numpy indexes to get a scalar, the data is always copied (apart from object arrays, of course). It's not something you can or should try to avoid. -- 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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Unit Tests failing daily for 2 months
On Wed, Feb 17, 2010 at 9:07 AM, Christopher Hanley chan...@stsci.eduwrote: I don't see these here. What architecture/compiler/os ? The system architecture is 2 * Intel Xeon with hyperthreading. The OS is Red Hat Enterprise (RHE) 4 64-bit running Python 2.5.4. The C compiler being used is GCC 3.4.6. No Fortran compiler is being used. My mistake on the buildbot os, it was FreeBSD. That's new to me. Darn. I was hoping this was old news. These two also fail on the buildbot Mac and have for some time. If you are able to reproduce them on an available machine that will be helpful in tracking them down. We can reproduce these problems on our RHE machine. A quick check of our logs from last night doesn't seem to indicate a problem on our Intel Mac 10.5 systems. However if there is something you want me to try just let me know what you need. It doesn't seem related to python version, but may be related to compiler version. For FreeBSD: Python version 2.5.2 (r252:60911, Dec 15 2008, 12:04:33) [GCC 3.4.6 [FreeBSD] 20060305] Chuck ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] 1.4 still the candidate for easy_install
Hi, I'm pretty sure this is unintentional but I tried easy_install numpy the other day and it pulled down a 1.4 tarball from PyPI. David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] 1.4 still the candidate for easy_install
Good catch. I just removed the 1.4.0 tarball from PyPI. Thanks, -- Jarrod Millman Helen Wills Neuroscience Institute 10 Giannini Hall, UC Berkeley http://cirl.berkeley.edu/ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Sun Studio Compilers on Linux / atan2 regression
Hi, when compiling numpy-1.40 with the Sun Studio Compilers (v12 Update 1) on Linux (an OpenSUSE 11.1 in my case), about 30 tests in numpy.test() fail; all failures are related to the arctan2 function. I've found that in r7732 a patch was applied to trunk/numpy/core/src/private/npy_config.h in response to #1201, #1202, and #1203, #undef'ing the HAVE_ATAN2 variable in order to fix a broken atan2() implementation on Solaris. It seems that this does no good with the most recent Sun compiler on Linux... The attached patch ensures that the original patch is only applied on Solaris platforms; with this applied, all tests are completed successfully under Linux. BTW, I did not observe #1204 or #1205... As I have no access to a Solaris machine, I also don't know if the original patch is required with Sun Studio 12.1 at all. Something different - I would've loved to enter this in the numpy-Trac, but registration didn't work (I was asked for another username/password at scipy.org during the registration process) :-(( Thanks, Christian. diff -r -C5 numpy-1.4.0.orig/numpy/core/src/private/npy_config.h numpy-1.4.0/numpy/core/src/private/npy_config.h *** numpy-1.4.0.orig/numpy/core/src/private/npy_config.h 2009-12-22 11:59:52.0 + --- numpy-1.4.0/numpy/core/src/private/npy_config.h 2010-02-17 17:47:12.080101518 + *** *** 7,18 #if defined(_MSC_VER) || defined(__MINGW32_VERSION) #undef HAVE_ATAN2 #undef HAVE_HYPOT #endif ! /* Disable broken Sun Workshop Pro math functions */ ! #ifdef __SUNPRO_C #undef HAVE_ATAN2 #endif /* * On Mac OS X, because there is only one configuration stage for all the archs --- 7,18 #if defined(_MSC_VER) || defined(__MINGW32_VERSION) #undef HAVE_ATAN2 #undef HAVE_HYPOT #endif ! /* Disable broken Sun Workshop Pro math functions on Solaris */ ! #if defined(__SUNPRO_C) defined(__sun) #undef HAVE_ATAN2 #endif /* * On Mac OS X, because there is only one configuration stage for all the archs ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] f2py: variable number of arguments of variable lengths
I previously coded a fortran function that needs a variable number of scalar arguments. This number is not known at compile time, but at call time. So I used to pass them within a vector, passing also the length of this vector subroutine systeme(inc,t,nm,Dinc,sn) C C evaluate the derivative of vector x at time t C with complex modes (sn). Used for the calculation C of auto-oscillations in resonator-valve coupled system. C integer nm,np,ny,ind double precision inc(1:2*nm+2), Dinc(1:2*nm+2) complex*16 sn(1:nm) Cf2py double precision, intent(in) :: t Cf2py integer, intent(in), optional :: nm Cf2py double precision, intent(in), dimension(2*nm+2) :: inc Cf2py double precision, intent(out), dimension(2*nm+2) :: Dinc Cf2py complex, intent(in), dimension(nm) :: sn I do now want to pass, not nm float values, but nm arrays of variables lengths. I expect to pass the following objects : - nm: number of arrays - L : a 1d-array (dimension nm) containing the lengths of each array - np: the sum of lengths - X : a 1d-array (dimension np) containing the concatenated arrays. Does anyone have an alternative to this suggestion ? any tip or example? Regards -- Fabrice Silva LMA UPR CNRS 7051 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] f2py: variable number of arguments of variable lengths
On Wed, Feb 17, 2010 at 15:29, Fabrice Silva si...@lma.cnrs-mrs.fr wrote: I previously coded a fortran function that needs a variable number of scalar arguments. This number is not known at compile time, but at call time. So I used to pass them within a vector, passing also the length of this vector subroutine systeme(inc,t,nm,Dinc,sn) C C evaluate the derivative of vector x at time t C with complex modes (sn). Used for the calculation C of auto-oscillations in resonator-valve coupled system. C integer nm,np,ny,ind double precision inc(1:2*nm+2), Dinc(1:2*nm+2) complex*16 sn(1:nm) Cf2py double precision, intent(in) :: t Cf2py integer, intent(in), optional :: nm Cf2py double precision, intent(in), dimension(2*nm+2) :: inc Cf2py double precision, intent(out), dimension(2*nm+2) :: Dinc Cf2py complex, intent(in), dimension(nm) :: sn I do now want to pass, not nm float values, but nm arrays of variables lengths. I expect to pass the following objects : - nm: number of arrays - L : a 1d-array (dimension nm) containing the lengths of each array - np: the sum of lengths - X : a 1d-array (dimension np) containing the concatenated arrays. Yeah, that's pretty much what you would have to do. -- 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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] f2py: variable number of arguments of variable lengths
Le mercredi 17 février 2010 à 15:43 -0600, Robert Kern a écrit : On Wed, Feb 17, 2010 at 15:29, Fabrice Silva si...@lma.cnrs-mrs.fr wrote: I previously coded a fortran function that needs a variable number of scalar arguments. This number is not known at compile time, but at call time. So I used to pass them within a vector, passing also the length of this vector subroutine systeme(inc,t,nm,Dinc,sn) C C evaluate the derivative of vector x at time t C with complex modes (sn). Used for the calculation C of auto-oscillations in resonator-valve coupled system. C integer nm,np,ny,ind double precision inc(1:2*nm+2), Dinc(1:2*nm+2) complex*16 sn(1:nm) Cf2py double precision, intent(in) :: t Cf2py integer, intent(in), optional :: nm Cf2py double precision, intent(in), dimension(2*nm+2) :: inc Cf2py double precision, intent(out), dimension(2*nm+2) :: Dinc Cf2py complex, intent(in), dimension(nm) :: sn I do now want to pass, not nm float values, but nm arrays of variables lengths. I expect to pass the following objects : - nm: number of arrays - L : a 1d-array (dimension nm) containing the lengths of each array - np: the sum of lengths - X : a 1d-array (dimension np) containing the concatenated arrays. Yeah, that's pretty much what you would have to do. What about the next step: a variable number of arguments that are 2d-arrays with different shapes ? -- Fabrice Silva LMA UPR CNRS 7051 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] f2py: variable number of arguments of variable lengths
On Wed, Feb 17, 2010 at 15:55, Fabrice Silva si...@lma.cnrs-mrs.fr wrote: Le mercredi 17 février 2010 à 15:43 -0600, Robert Kern a écrit : On Wed, Feb 17, 2010 at 15:29, Fabrice Silva si...@lma.cnrs-mrs.fr wrote: I previously coded a fortran function that needs a variable number of scalar arguments. This number is not known at compile time, but at call time. So I used to pass them within a vector, passing also the length of this vector subroutine systeme(inc,t,nm,Dinc,sn) C C evaluate the derivative of vector x at time t C with complex modes (sn). Used for the calculation C of auto-oscillations in resonator-valve coupled system. C integer nm,np,ny,ind double precision inc(1:2*nm+2), Dinc(1:2*nm+2) complex*16 sn(1:nm) Cf2py double precision, intent(in) :: t Cf2py integer, intent(in), optional :: nm Cf2py double precision, intent(in), dimension(2*nm+2) :: inc Cf2py double precision, intent(out), dimension(2*nm+2) :: Dinc Cf2py complex, intent(in), dimension(nm) :: sn I do now want to pass, not nm float values, but nm arrays of variables lengths. I expect to pass the following objects : - nm: number of arrays - L : a 1d-array (dimension nm) containing the lengths of each array - np: the sum of lengths - X : a 1d-array (dimension np) containing the concatenated arrays. Yeah, that's pretty much what you would have to do. What about the next step: a variable number of arguments that are 2d-arrays with different shapes ? - nm: number of arrays - ncols : a 1d-array (dimension nm) containing the number of columns in each array - nrows : a 1d-array (dimension nm) containing the number of rows in each array - np: the sum of array sizes [(ncols * nrows).sum() in numpy terms] - X : a 1d-array (dimension np) containing the concatenated arrays. -- 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 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] f2py: variable number of arguments of variable lengths
Le mercredi 17 février 2010 à 16:21 -0600, Robert Kern a écrit : What about the next step: a variable number of arguments that are 2d-arrays with different shapes ? - nm: number of arrays - ncols : a 1d-array (dimension nm) containing the number of columns in each array - nrows : a 1d-array (dimension nm) containing the number of rows in each array - np: the sum of array sizes [(ncols * nrows).sum() in numpy terms] - X : a 1d-array (dimension np) containing the concatenated arrays. I guess I will need to be careful when building the arrays from X. Thanks! -- Fabrice Silva LMA UPR CNRS 7051 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Unable to install numpy-1.3.0 on WinXP (without Administrative rights)
Hi, I'm trying to install numpy on a WinXP system, on which I have no administrative rights. Installation of Python-2.6 went OK, but the windows installer that I downloaded on sourceforge for numpy (numpy-1.3.0-win32 -superpack-python2.6.exe) gives me an error pop-up window Executing numpy installer failed. And when I ask the installer for the details, here's the displayed log : Output folder: C:\Windows\Temp Install dir for actual installers is C:\DOCUME~1\user1\LOCALS~1\Temp Target CPU handles SSE2 Target CPU handles SSE3 native install (arch value: native) Install SSE 3 Extract: *numpy*-1.3.0-sse3.exe... 100% Execute: C:\Windows\Temp\*numpy*-1.3.0-sse3.exe Completed And of course then, typing import numpy in a python shell will just give ImportError: No module named numpy. Seems I'm not the only one that encountered the problem ( see http://old.nabble.com/Failed-installation-on-Windows-XP-td26316987.html#a26316987). I'm wondering if this is known and/or fixed issue (my search on the mailing-list and tickets archives has been fruitless, but I might have not looked at the right places), and if so, what can I do to solve it ? Cheers, T. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Updating Packages in 2.5 (win/numpy) and Related Matters
I don't think I'm on the current version. Does it make sense to move ahead? Is there a way to suppress the messages? On 2/16/2010 9:25 PM, josef.p...@gmail.com wrote: On Wed, Feb 17, 2010 at 12:10 AM, Wayne Watson sierra_mtnv...@sbcglobal.net wrote: Hi, I'm working on a 1800+ line program that uses tkinter. Here are the messages I started getting recently. (I finally figured out how to copy them.). The program goes merrily on its way despite them. s\sentusersentuser_20080716NoiseStudy7.py C:\Python25\lib\site-packages\scipy\misc\__init__.py:25: DeprecationWarning: Num pyTest will be removed in the next release; please update your code to use nose or unittest test = NumpyTest().test C:\Python25\lib\site-packages\scipy\special\__init__.py:23: DeprecationWarning: NumpyTest will be removed in the next release; please update your code to use no se or unittest test = NumpyTest().test C:\Python25\lib\site-packages\scipy\linalg\__init__.py:32: DeprecationWarning: N umpyTest will be removed in the next release; please update your code to use nos e or unittest test = NumpyTest().test C:\Python25\lib\site-packages\scipy\optimize\__init__.py:19: DeprecationWarning: NumpyTest will be removed in the next release; please update your code to use n ose or unittest test = NumpyTest().test C:\Python25\lib\site-packages\scipy\stats\__init__.py:15: DeprecationWarning: Nu mpyTest will be removed in the next release; please update your code to use nose or unittest test = NumpyTest().test Traceback (most recent call last): File C:\Users\Wayne\Sandia_Meteors\Sentinel_Development\Development_Sentuser+ Utilities\sentuser\sentuser_20080716NoiseStudy7.py, line 1993, inmodule Process() File C:\Users\Wayne\Sandia_Meteors\Sentinel_Development\Development_Sentuser+ Utilities\sentuser\sentuser_20080716NoiseStudy7.py, line 1990, in Process root.mainloop() File C:\Python25\lib\lib-tk\Tkinter.py, line 1023, in mainloop DeprecationWarnings mean some some functionality in numpy (or scipy) has changed and the old way of doing things will be removed and be invalid in the next version. During depreciation the old code still works, but before you upgrade you might want to check whether and how much you use these functions and switch to the new behavior. In the case of numpy.test, it means that if you have tests written that use the numpy testing module, then you need to switch them to the new nose based numpy.testing. And you need to install nose for running numpy.test() - What is win32-p3 updates ? Josef On 2/16/2010 4:32 PM, David Cournapeau wrote: Hi Wayne, Wayne Watson wrote: I normally use IDLE on Win, but recently needed to go to command prompt to see all error messages. When I did, I was greeted by a host of deprecation and Numpy messages before things got running. The program otherwise functioned OK, after I found the problem I was after. Are these messages a warning to get to the next update of numpy? I would guess that updating to a higher update does not mean I need to remove the old one, correct? In general for libraries like numpy or scipy, I use win32 updates, but I see win32-p3 updates too on download pages. Since I may be distributing this program to p3 machines, will I need to provide the win32-p3 updates to those users? I am not familiar with IDLE, so I don't really understand your problem: - what triggers numpy warnings ? You talked about a program, but without knowing which program, we can't really help you. - What warnings do you get ? - What is win32-p3 updates ? cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- There is nothing so annoying as to have two people talking when you're busy interrupting. -- Mark Twain ___ 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 -- There is nothing so annoying as to have two people talking when you're busy interrupting. -- Mark Twain ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Unable to install numpy-1.3.0 on WinXP (without Administrative rights)
On Thu, Feb 18, 2010 at 8:24 AM, Touisteur EmporteUneVache touist...@gmail.com wrote: Hi, I'm trying to install numpy on a WinXP system, on which I have no administrative rights. I think it is not possible to install NumPy for python 2.6 if you don't have admin priviledges. I believe the root of the problem is the lack of a right C runtime, and there is no easy way to install it without admin priviledges, and I have no idea how to fix this. The problem is specific to python 2.6 (more exactly because it was built with visual studio 2008), so using python 2.5 or 2.4 should not cause any issue if that's an option for you. The other solution is to ask your administrator to install the redistributable runtime from VS 2008, cheers, David ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Updating Packages in 2.5 (win/numpy) and Related Matters
On 2/16/2010 10:01 PM, Scott Sinclair wrote: On 17 February 2010 07:25,josef.p...@gmail.com wrote: On Wed, Feb 17, 2010 at 12:10 AM, Wayne Watson sierra_mtnv...@sbcglobal.net wrote: Hi, I'm working on a 1800+ line program that uses tkinter. Here are the messages I started getting recently. (I finally figured out how to copy them.). The program goes merrily on its way despite them. s\sentusersentuser_20080716NoiseStudy7.py C:\Python25\lib\site-packages\scipy\misc\__init__.py:25: DeprecationWarning: Num pyTest will be removed in the next release; please update your code to use nose or unittest test = NumpyTest().test DeprecationWarnings mean some some functionality in numpy (or scipy) has changed and the old way of doing things will be removed and be invalid in the next version. During depreciation the old code still works, but before you upgrade you might want to check whether and how much you use these functions and switch to the new behavior. In the case of numpy.test, it means that if you have tests written that use the numpy testing module, then you need to switch them to the new nose based numpy.testing. And you need to install nose for running numpy.test() Wayne - The DeprecationWarnings are being raised by SciPy, not by your code. You probably don't have a recent version of SciPy installed. The most recent release of SciPy is 0.7.1 and works with NumPy 1.3.0. I don't think you will see the warnings if you upgrade SciPy and NumPy on your system. Check your NumPy and SciPy versions at a python prompt as follows: import numpy as np print np.__version__ import scipy as sp print sp.__version__ You will need to completely remove the old versions if you choose to upgrade. You should be able to do this from Add/Remove Programs. You just answered the question I posted to joef moments ago. Interestingly, I'm on win7's Add/Remove numpy. No scipy. I just checked the version via import and it's 0.6.0. These are some of my imports. Note from scipy below. from Tkinter import * from numpy import * import Image import ImageChops import ImageTk import time import binascii import tkMessageBox import tkSimpleDialog from pylab import plot, xlabel, ylabel, title, show, xticks, bar from scipy import stats as stats # scoreatpercentile from matplotlib.pyplot import figure, show #from matplotlib.lines import Line2D #from matplotlib.patches import Patch, Rectangle #from matplotlib.text import Text from matplotlib.image import AxesImage -- There is nothing so annoying as to have two people talking when you're busy interrupting. -- Mark Twain ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Unable to install numpy-1.3.0 on WinXP (without Administrative rights)
On 2/17/2010 7:30 PM, David Cournapeau wrote: On Thu, Feb 18, 2010 at 8:24 AM, Touisteur EmporteUneVache touist...@gmail.com wrote: Hi, I'm trying to install numpy on a WinXP system, on which I have no administrative rights. I think it is not possible to install NumPy for python 2.6 if you don't have admin priviledges. I believe the root of the problem is the lack of a right C runtime, and there is no easy way to install it without admin priviledges, and I have no idea how to fix this. The problem is specific to python 2.6 (more exactly because it was built with visual studio 2008), so using python 2.5 or 2.4 should not cause any issue if that's an option for you. The other solution is to ask your administrator to install the redistributable runtime from VS 2008, If everything else fails you can try to install numpy manually: the file numpy-1.3.0-sse3.exe, which is created in the %TEMP% directory during the numpy-1.3.0-win32-superpack-python2.6.exe installation, is a executable ZIP file and can be opened with any decent archive program, e.g. WinRAR. From numpy-1.3.0-sse3.exe copy PLATLIB\numpy\* to C:\Python26\sitepackages\numpy\ and SCRIPTS\* to C:\Python26\Scripts\. Unlike many other packages, Numpy does not need to have the Microsoft Visual C++ 2008 redistributable package installed to work. To avoid the problem, numpy-1.3.0-sse3.exe could probably be linked statically to MSVCRT9 like the bdist_wininst installers created by Python distutils. Christoph ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Unable to install numpy-1.3.0 on WinXP (without Administrative rights)
On 2/18/2010 at 15:04, Christoph Gohlke wrote: On 2/17/2010 7:30 PM, David Cournapeau wrote: On Thu, Feb 18, 2010 at 8:24 AM, Touisteur EmporteUneVache touist...@gmail.com wrote: Hi, I'm trying to install numpy on a WinXP system, on which I have no administrative rights. I think it is not possible to install NumPy for python 2.6 if you don't have admin priviledges. I believe the root of the problem is the lack of a right C runtime, and there is no easy way to install it without admin priviledges, and I have no idea how to fix this. The problem is specific to python 2.6 (more exactly because it was built with visual studio 2008), so using python 2.5 or 2.4 should not cause any issue if that's an option for you. The other solution is to ask your administrator to install the redistributable runtime from VS 2008, If everything else fails you can try to install numpy manually: the file numpy-1.3.0-sse3.exe, which is created in the %TEMP% directory during the numpy-1.3.0-win32-superpack-python2.6.exe installation, is a executable ZIP file and can be opened with any decent archive program, e.g. WinRAR. From numpy-1.3.0-sse3.exe copy PLATLIB\numpy\* to C:\Python26\sitepackages\numpy\ and SCRIPTS\* to C:\Python26\Scripts\. Unlike many other packages, Numpy does not need to have the Microsoft Visual C++ 2008 redistributable package installed to work. To avoid the problem, numpy-1.3.0-sse3.exe could probably be linked statically to MSVCRT9 like the bdist_wininst installers created by Python distutils. Christoph Hi David and Christoph, Thank you both very much for your suggestions. I went for Christoph's workaround (manual installation) and it seems to work like a charm. Thanks again. Cheers! ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Updating Packages in 2.5 (win/numpy) and Related Matters
On 18 February 2010 05:30, Wayne Watson sierra_mtnv...@sbcglobal.net wrote: On 2/16/2010 10:01 PM, Scott Sinclair wrote: Wayne - The DeprecationWarnings are being raised by SciPy, not by your code. You probably don't have a recent version of SciPy installed. The most recent release of SciPy is 0.7.1 and works with NumPy 1.3.0. I don't think you will see the warnings if you upgrade SciPy and NumPy on your system. Check your NumPy and SciPy versions at a python prompt as follows: import numpy as np print np.__version__ import scipy as sp print sp.__version__ You will need to completely remove the old versions if you choose to upgrade. You should be able to do this from Add/Remove Programs. I'm on win7's Add/Remove numpy. No scipy. I just checked the version via import and it's 0.6.0. You can download the latest NumPy and SciPy installers from: http://sourceforge.net/projects/numpy/files/ and http://sourceforge.net/projects/scipy/files/ You want the win32-superpack for your Python version. Use Add/Remove to remove your current NumPy install (if your version is not already 1.3.0). I'm not sure how SciPy was installed and why it doesn't appear in Add/Remove. You should look in C:\Python25\Lib\site-packages for directories named numpy or scipy (numpy should have been removed already). It is safe to delete C:\Python25\Lib\site-packages\scipy. Then run the superpack installers and you should be good to go. Good luck. Cheers, Scott ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Accessing fields of the object stored in numpy array
Hi, I have a numpy arrays with datetime objects as: a = np.array([dt.datetime(2010, 2, 17), dt.datetime(2010, 2, 16), dt.datetime(2010, 2, 15)]) b = np.array([dt.datetime(2010, 2, 14), dt.datetime(2010, 2, 13), dt.datetime(2010, 2, 12)]) I want doing a-b should give me days difference as numpy array but I get as: array([3 days, 0:00:00, 3 days, 0:00:00, 3 days, 0:00:00], dtype=object) which is a numpy array of timedelta! I can extract days out by using property days of each, but how I do it in a numpy way may like: c=a-b c.days (a numpy array of days difference) like: array([3, 3, 3]) Any pointers? Thanks Vishal Rana Joan Crawfordhttp://www.brainyquote.com/quotes/authors/j/joan_crawford.html - I, Joan Crawford, I believe in the dollar. Everything I earn, I spend. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
