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  • Cocoa App with Python extension which use Scipy -> ImportError: No module named scipy

    - by Snej
    Hi: I have installed Scipy (via macports) for Python on my Mac and it runs fine when running Python scripts. But now I'm using Scipy (via PyObjc) for calculations embedded in a Cocoa App frontend. The following error occurs: ImportError: No module named scipy I am using the "Python.framework" in XCode. Does anybody know why Scipy module is not found? I even added it manually to the module search path via sys.path.append("/opt/local/var/macports/software/py26-scipy/0.7.1_0+gcc43/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/") EDIT: I found the problem myself. The path should be without "/scipy" at the end. But now I got an architecture problem: ImportError: dlopen(/opt/local/var/macports/software/py26-scipy/0.7.1_0+gcc43/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/fftpack/_fftpack.so, 2): no suitable image found. Did find: /opt/local/var/macports/software/py26-scipy/0.7.1_0+gcc43/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/fftpack/_fftpack.so: mach-o, but wrong architecture EDIT 2: I checked the architectures: Yes, sure it is an architecture problem. But when I run: file /opt/local/var/macports/software/py26-scipy/0.7.1_0+gcc43/opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/fftpack/_fftpack.so I get a result Mach-O 64-bit bundle x86_64. And the Mac OS 10.6 PYTHON is: Mach-O universal binary with 3 architectures /usr/bin/python (for architecture x86_64): Mach-O 64-bit executable x86_64 /usr/bin/python (for architecture i386): Mach-O executable i386 /usr/bin/python (for architecture ppc7400): Mach-O executable ppc I build the XCode project as x86_64.

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  • Mac 10.6 Universal Binary scipy: cephes/specfun "_aswfa_" symbol not found

    - by Markus
    Hi folks, I can't get scipy to function in 32 bit mode when compiled as a i386/x86_64 universal binary, and executed on my 64 bit 10.6.2 MacPro1,1. My python setup With the help of this answer, I built a 32/64 bit intel universal binary of python 2.6.4 with the intention of using the arch command to select between the architectures. (I managed to make some universal binaries of a few libraries I wanted using lipo.) That all works. I then installed scipy according to the instructions on hyperjeff's article, only with more up-to-date numpy (1.4.0) and skipping the bit about moving numpy aside briefly during the installation of scipy. Now, everything except scipy seems to be working as far as I can tell, and I can indeed select between 32 and 64 bit mode using arch -i386 python and arch -x86_64 python. The error Scipy complains in 32 bit mode: $ arch -x86_64 python -c "import scipy.interpolate; print 'success'" success $ arch -i386 python -c "import scipy.interpolate; print 'success'" Traceback (most recent call last): File "<string>", line 1, in <module> File "/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/interpolate/__init__.py", line 7, in <module> from interpolate import * File "/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/interpolate/interpolate.py", line 13, in <module> import scipy.special as spec File "/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/special/__init__.py", line 8, in <module> from basic import * File "/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/special/basic.py", line 8, in <module> from _cephes import * ImportError: dlopen(/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/special/_cephes.so, 2): Symbol not found: _aswfa_ Referenced from: /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/special/_cephes.so Expected in: flat namespace in /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/special/_cephes.so Attempt at tracking down the problem It looks like scipy.interpolate imports something called _cephes, which looks for a symbol called _aswfa_ but can't find it in 32 bit mode. Browsing through scipy's source, I find an ASWFA subroutine in specfun.f. The only scipy product file with a similar name is specfun.so, but both that and _cephes.so appear to be universal binaries: $ cd /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/special/ $ file _cephes.so specfun.so _cephes.so: Mach-O universal binary with 2 architectures _cephes.so (for architecture i386): Mach-O bundle i386 _cephes.so (for architecture x86_64): Mach-O 64-bit bundle x86_64 specfun.so: Mach-O universal binary with 2 architectures specfun.so (for architecture i386): Mach-O bundle i386 specfun.so (for architecture x86_64): Mach-O 64-bit bundle x86_64 Ho hum. I'm stuck. Things I may try but haven't figured out how yet include compiling specfun.so myself manually, somehow. I would imagine that scipy isn't broken for all 32 bit machines, so I guess something is wrong with the way I've installed it, but I can't figure out what. I don't really expect a full answer given my fairly unique (?) setup, but if anyone has any clues that might point me in the right direction, they'd be greatly appreciated. (edit) More details to address questions: I'm using gfortran (GNU Fortran from GCC 4.2.1 Apple Inc. build 5646). Python 2.6.4 was installed more-or-less like so: cd /tmp curl -O http://www.python.org/ftp/python/2.6.4/Python-2.6.4.tar.bz2 tar xf Python-2.6.4.tar.bz2 cd Python-2.6.4 # Now replace buggy pythonw.c file with one that supports the "arch" command: curl http://bugs.python.org/file14949/pythonw.c | sed s/2.7/2.6/ > Mac/Tools/pythonw.c ./configure --enable-framework=/Library/Frameworks --enable-universalsdk=/ --with-universal-archs=intel make -j4 sudo make frameworkinstall Scipy 0.7.1 was installed pretty much as described as here, but it boils down to a simple sudo python setup.py install. It would indeed appear that the symbol is undefined in the i386 architecture if you look at the _cephes library with nm, as suggested by David Cournapeau: $ nm -arch x86_64 /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/special/_cephes.so | grep _aswfa_ 00000000000d4950 T _aswfa_ 000000000011e4b0 d _oblate_aswfa_data 000000000011e510 d _oblate_aswfa_nocv_data (snip) $ nm -arch i386 /Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/scipy/special/_cephes.so | grep _aswfa_ U _aswfa_ 0002e96c d _oblate_aswfa_data 0002e99c d _oblate_aswfa_nocv_data (snip) however, I can't yet explain its absence.

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  • Compiling scipy on Windows 32-bit: linker error with libf77blas.a

    - by Sridhar Ratnakumar
    Has anyone tried compiling SciPy 0.7.1 on Windows using numpy-1.3.0 that was built with the pre-built ATLAS libraries (atlas3.6.0_WinNT_P4SSE2.zip) linked in the installation document. I get the following linker error, and have no ideas as to how to fix this issue. $ python setup.py config --compiler=mingw32 build --compiler=mingw32 install --root=i [...] creating build\temp.win32-2.6\Release creating build\temp.win32-2.6\Release\scipy creating build\temp.win32-2.6\Release\scipy\integrate compile options: '-DNO_ATLAS_INFO=2 -I"C:\Documents and Settings\apy\Application Data\Python\Python26\site-packages\numpy\core\inc lude" -IC:\Python26\include -IC:\Python26\PC -c' gcc -mno-cygwin -O2 -Wall -Wstrict-prototypes -DNO_ATLAS_INFO=2 -I"C:\Documents and Settings\apy\Application Data\Python\Python26\ site-packages\numpy\core\include" -IC:\Python26\include -IC:\Python26\PC -c scipy\integrate\_odepackmo dule.c -o build\temp.win32-2.6\Release\scipy\integrate\_odepackmodule.o C:\MinGW\bin\g77.exe -g -Wall -mno-cygwin -g -Wall -mno-cygwin -shared build\temp.win32-2.6\Release\scipy\integrate\_odepackmodule .o -LC:\atlas3.6.0_WinNT_P4SSE2 -LC:\MinGW\lib -LC:\MinGW\lib\gcc\mingw32\3.4.5 -LC:\Python26\libs -LC:\Act ivePython32Python26\PCbuild -Lbuild\temp.win32-2.6 -lodepack -llinpack_lite -lmach -latlas -lcblas -lf77blas -llapack -lpython26 - lg2c -o build\lib.win32-2.6\scipy\integrate\_odepack.pyd C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_daxpy.o):ATL_F77wrap_axpy.c:(.text+0x3c): undefined reference to `ATL _daxpy' C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_dscal.o):ATL_F77wrap_scal.c:(.text+0x26): undefined reference to `ATL _dscal' C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_dcopy.o):ATL_F77wrap_copy.c:(.text+0x3d): undefined reference to `ATL _dcopy' C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_idamax.o):ATL_F77wrap_amax.c:(.text+0x1e): undefined reference to `AT L_idamax' C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_ddot.o):ATL_F77wrap_dot.c:(.text+0x36): undefined reference to `ATL_d dot' collect2: ld returned 1 exit status error: Command "C:\MinGW\bin\g77.exe -g -Wall -mno-cygwin -g -Wall -mno-cygwin -shared build\temp.win32-2.6\Release\scipy\integrat e\_odepackmodule.o -LC:\atlas3.6.0_WinNT_P4SSE2 -LC:\MinGW\lib -LC:\MinGW\lib\gcc\mingw32\3.4.5 -LC:\Python 26\libs -LC:\Python26\PCbuild -Lbuild\temp.win32-2.6 -lodepack -llinpack_lite -lmach -latlas -lcblas -lf77blas -llap ack -lpython26 -lg2c -o build\lib.win32-2.6\scipy\integrate\_odepack.pyd" failed with exit status 1 Does anyone know what could have gone wrong here? Looking for ATL_daxpy, for example, in libf77blas.a resulted in: $ strings libf77blas.a | grep -i daxpy _daxpy_ _atl_f77wrap_daxpy_ ATL_F77wrap_daxpy.o/ daxpy.o/ 1081731936 1003 513 100755 420 ` daxpy.f _daxpy_ _atl_f77wrap_daxpy_ _atl_f77wrap_daxpy_ _ATL_daxpy There is _ATL_daxpy, but no ATL_daxpy.

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  • Compiling scipy on Windows 32-bit

    - by Sridhar Ratnakumar
    Has anyone tried compiling SciPy on Windows using numpy-1.3.0 that was built with the pre-built ATLAS libraries (atlas3.6.0_WinNT_P4SSE2.zip) linked in the installation document. I get the following linker error, and have no ideas as to how to fix this issue. $ python setup.py config --compiler=mingw32 build --compiler=mingw32 install --root=i [...] creating build\temp.win32-2.6\Release creating build\temp.win32-2.6\Release\scipy creating build\temp.win32-2.6\Release\scipy\integrate compile options: '-DNO_ATLAS_INFO=2 -I"C:\Documents and Settings\apy\Application Data\Python\Python26\site-packages\numpy\core\inc lude" -IC:\Python26\include -IC:\Python26\PC -c' gcc -mno-cygwin -O2 -Wall -Wstrict-prototypes -DNO_ATLAS_INFO=2 -I"C:\Documents and Settings\apy\Application Data\Python\Python26\ site-packages\numpy\core\include" -IC:\Python26\include -IC:\Python26\PC -c scipy\integrate\_odepackmo dule.c -o build\temp.win32-2.6\Release\scipy\integrate\_odepackmodule.o C:\MinGW\bin\g77.exe -g -Wall -mno-cygwin -g -Wall -mno-cygwin -shared build\temp.win32-2.6\Release\scipy\integrate\_odepackmodule .o -LC:\atlas3.6.0_WinNT_P4SSE2 -LC:\MinGW\lib -LC:\MinGW\lib\gcc\mingw32\3.4.5 -LC:\Python26\libs -LC:\Act ivePython32Python26\PCbuild -Lbuild\temp.win32-2.6 -lodepack -llinpack_lite -lmach -latlas -lcblas -lf77blas -llapack -lpython26 - lg2c -o build\lib.win32-2.6\scipy\integrate\_odepack.pyd C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_daxpy.o):ATL_F77wrap_axpy.c:(.text+0x3c): undefined reference to `ATL _daxpy' C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_dscal.o):ATL_F77wrap_scal.c:(.text+0x26): undefined reference to `ATL _dscal' C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_dcopy.o):ATL_F77wrap_copy.c:(.text+0x3d): undefined reference to `ATL _dcopy' C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_idamax.o):ATL_F77wrap_amax.c:(.text+0x1e): undefined reference to `AT L_idamax' C:\atlas3.6.0_WinNT_P4SSE2/libf77blas.a(ATL_F77wrap_ddot.o):ATL_F77wrap_dot.c:(.text+0x36): undefined reference to `ATL_d dot' collect2: ld returned 1 exit status error: Command "C:\MinGW\bin\g77.exe -g -Wall -mno-cygwin -g -Wall -mno-cygwin -shared build\temp.win32-2.6\Release\scipy\integrat e\_odepackmodule.o -LC:\atlas3.6.0_WinNT_P4SSE2 -LC:\MinGW\lib -LC:\MinGW\lib\gcc\mingw32\3.4.5 -LC:\Python 26\libs -LC:\Python26\PCbuild -Lbuild\temp.win32-2.6 -lodepack -llinpack_lite -lmach -latlas -lcblas -lf77blas -llap ack -lpython26 -lg2c -o build\lib.win32-2.6\scipy\integrate\_odepack.pyd" failed with exit status 1 Does anyone know what could have gone wrong here?

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  • Python to MATLAB: exporting list of strings using scipy.io

    - by user292461
    I am trying to export a list of text strings from Python to MATLAB using scipy.io. I would like to use scipy.io because my desired .mat file should include both numerical matrices (which I learned to do here) and text cell arrays. I tried: import scipy.io my_list = ['abc', 'def', 'ghi'] scipy.io.savemat('test.mat', mdict={'my_list': my_list) In MATLAB, I load test.mat and get a character array: my_list = adg beh cfi How do I make scipy.io export a list into a MATLAB cell array?

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  • Fitting Gaussian KDE in numpy/scipy in Python

    - by user248237
    I am fitting a Gaussian kernel density estimator to a variable that is the difference of two vectors, called "diff", as follows: gaussian_kde_covfact(diff, smoothing_param) -- where gaussian_kde_covfact is defined as: class gaussian_kde_covfact(stats.gaussian_kde): def __init__(self, dataset, covfact = 'scotts'): self.covfact = covfact scipy.stats.gaussian_kde.__init__(self, dataset) def _compute_covariance_(self): '''not used''' self.inv_cov = np.linalg.inv(self.covariance) self._norm_factor = sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n def covariance_factor(self): if self.covfact in ['sc', 'scotts']: return self.scotts_factor() if self.covfact in ['si', 'silverman']: return self.silverman_factor() elif self.covfact: return float(self.covfact) else: raise ValueError, \ 'covariance factor has to be scotts, silverman or a number' def reset_covfact(self, covfact): self.covfact = covfact self.covariance_factor() self._compute_covariance() This works, but there is an edge case where the diff is a vector of all 0s. In that case, I get the error: File "/srv/pkg/python/python-packages/python26/scipy/scipy-0.7.1/lib/python2.6/site-packages/scipy/stats/kde.py", line 334, in _compute_covariance self.inv_cov = linalg.inv(self.covariance) File "/srv/pkg/python/python-packages/python26/scipy/scipy-0.7.1/lib/python2.6/site-packages/scipy/linalg/basic.py", line 382, in inv if info>0: raise LinAlgError, "singular matrix" numpy.linalg.linalg.LinAlgError: singular matrix What's a way to get around this? In this case, I'd like it to return a density that's essentially peaked completely at a difference of 0, with no mass everywhere else. thanks.

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  • scipy.io typeerror:buffer too small for requested array

    - by kartiku
    I have a problem in python. I'm using scipy, where i use scipy.io to load a .mat file. The .mat file was created using MATLAB. listOfFiles = os.listdir(loadpathTrain) for f in listOfFiles: fullPath = loadpathTrain + '/' + f mat_contents = sio.loadmat(fullPath) print fullPath Here's the error: Traceback (most recent call last): File "tryRankNet.py", line 1112, in demo() File "tryRankNet.py", line 645, in demo mat_contents = sio.loadmat(fullPath) File "/usr/lib/python2.6/dist-packages/scipy/io/matlab/mio.py", line 111, in loadmat matfile_dict = MR.get_variables() File "/usr/lib/python2.6/dist-packages/scipy/io/matlab/miobase.py", line 356, in get_variables getter = self.matrix_getter_factory() File "/usr/lib/python2.6/dist-packages/scipy/io/matlab/mio5.py", line 602, in matrix_getter_factory return self._array_reader.matrix_getter_factory() File "/usr/lib/python2.6/dist-packages/scipy/io/matlab/mio5.py", line 274, in matrix_getter_factory tag = self.read_dtype(self.dtypes['tag_full']) File "/usr/lib/python2.6/dist-packages/scipy/io/matlab/miobase.py", line 171, in read_dtype order='F') TypeError: buffer is too small for requested array The whole thing is in a loop, and I checked the size of the file where it gives the error by loading it interactively in IDLE. The size is (9,521), which is not at all huge. I tried to find if I'm supposed to clear the buffer after each iteration of the loop, but I could not find anything. Any help would be appreciated. Thanks.

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  • Trouble using latex in Matplotlib / Scipy etc.

    - by ajhall
    I'm having some issues with my first attempts at using matplotlib and scipy to make some scatter plots of my data (too many variables, trying to see many things at once). Here's some code of mine that is working fairly well... import numpy from scipy import * import pylab from matplotlib import * import h5py FileID = h5py.File('3DiPVDplot1.mat','r') # (to view the contents of: list(FileID) ) group = FileID['/'] CurrentsArray = group['Currents'].value IvIIIarray = group['IvIII'].value PFarray = group['PF'].value growthTarray = group['growthT'].value fig = pylab.figure() ax = fig.add_subplot(111) cax = ax.scatter(IvIIIarray, growthTarray, PFarray, CurrentsArray, alpha=0.75) cbar = fig.colorbar(cax) ax.set_xlabel('Cu / III') ax.set_ylabel('Growth T') ax.grid(True) pylab.show() I tried to change the code to include latex fonts and interpreting, none of it seems to work for me, however. Here's an example attempt that didn't work: import numpy from scipy import * import pylab from matplotlib import * import h5py rc('text', usetex=True) rc('font', family='serif') FileID = h5py.File('3DiPVDplot1.mat','r') # (to view the contents of: list(FileID) ) group = FileID['/'] CurrentsArray = group['Currents'].value IvIIIarray = group['IvIII'].value PFarray = group['PF'].value growthTarray = group['growthT'].value fig = pylab.figure() ax = fig.add_subplot(111) cax = ax.scatter(IvIIIarray, growthTarray, PFarray, CurrentsArray, alpha=0.75) cbar = fig.colorbar(cax) ax.set_xlabel(r'Cu / III') ax.set_ylabel(r'Growth T') ax.grid(True) pylab.show() I'm using fink installed python26 with corresponding packages for scipy matplotlib etc. I've been using iPython and manual work instead of scripts in python. Since I'm completely new to python and scipy, I'm sure I'm making some stupid simple mistakes. Please enlighten me! I greatly appreciate the help!

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  • SciPy interp1d results are different than MatLab interp1

    - by LMO
    I'm converting a MatLab program to Python, and I'm having problems understanding why scipy.interpolate.interp1d is giving different results than MatLab interp1. In MatLab the usage is slightly different: yi = interp1(x,Y,xi,'cubic') SciPy: f = interp1d(x,Y,kind='cubic') yi = f(xi) For a trivial example the results are the same: MatLab: interp1([0 1 2 3 4], [0 1 2 3 4],[1.5 2.5 3.5],'cubic') 1.5000 2.5000 3.5000 Python: interp1d([1,2,3,4],[1,2,3,4],kind='cubic')([1.5,2.5,3.5]) array([ 1.5, 2.5, 3.5]) But for a real-world example they are not the same: x = 0.0000e+000 2.1333e+001 3.2000e+001 1.6000e+004 2.1333e+004 2.3994e+004 Y = -6 -6 20 20 -6 -6 xi = 0.00000 11.72161 23.44322 35.16484... (2048 data points) Matlab: -6.0000e+000 -1.2330e+001 -3.7384e+000 ... 7.0235e+000 7.0028e+000 6.9821e+000 SciPy: array([[ -6.00000000e+00], [ -1.56304101e+01], [ -2.04908267e+00], ..., [ 1.64475576e+05], [ 8.28360759e+04], [ -5.99999999e+00]]) Any thoughts as to how to can get results that are consistent with MatLab?

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  • Confusion between numpy, scipy, matplotlib and pylab

    - by goFrendiAsgard
    Numpy, scipy, matplotlib, and pylab are common terms among they who use python for scientific computation. I just learn a bit about pylab, and I got a lot of confusion. Whenever I want to import numpy, I can always do: import numpy as np I just consider, that once I do from pylab import * The numpy will be imported as well (with np alias). So basically the second one do more things compared to the first one. There are few things I want to ask. Is it right that pylab is just a wrapper for numpy, scipy and matplotlib? As np is the numpy alias, what is the scipy and matplotlib alias? (as far as I know, plt is alias of matplotlib.pyplot, but I don't know the alias for the matplotlib itself) Thanks in advance.

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  • ANCOVA in Python with Scipy/Numpy stats

    - by Shax
    I would like to know a way of performing ANCOVA(analysis of covariance) using Python with scipy. It is basically a statistical comparison of regression lines. I know Python can do ANOVA and it can also do regression line fitting with Scipy.stats. I'm not sure how to put those together to get an effective ANCOVA though, if it is possible. Regards, Shax

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  • Error installing scipy on Mountain Lion with Xcode 4.5.1

    - by Xster
    Environment: Mountain Lion 10.8.2, Xcode 4.5.1 command line tools, Python 2.7.3, virtualenv 1.8.2 and numpy 1.6.2 When installing scipy with pip install -e "git+https://github.com/scipy/scipy#egg=scipy-dev" on a fresh virtualenv. llvm-gcc: scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c In file included from /System/Library/Frameworks/vecLib.framework/Headers/vecLib.h:43, from /System/Library/Frameworks/Accelerate.framework/Headers/Accelerate.h:20, from scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c:2: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:51:23: error: immintrin.h: No such file or directory In file included from /System/Library/Frameworks/vecLib.framework/Headers/vecLib.h:43, from /System/Library/Frameworks/Accelerate.framework/Headers/Accelerate.h:20, from scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c:2: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h: In function ‘vceilf’: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:53: error: incompatible types in return /System/Library/Frameworks/vecLib.framework/Headers/vfp.h: In function ‘vfloorf’: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:54: error: incompatible types in return /System/Library/Frameworks/vecLib.framework/Headers/vfp.h: In function ‘vintf’: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:55: error: ‘_MM_FROUND_TRUNC’ undeclared (first use in this function) /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:55: error: (Each undeclared identifier is reported only once /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:55: error: for each function it appears in.) /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:55: error: incompatible types in return /System/Library/Frameworks/vecLib.framework/Headers/vfp.h: In function ‘vnintf’: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:56: error: ‘_MM_FROUND_NINT’ undeclared (first use in this function) /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:56: error: incompatible types in return In file included from /System/Library/Frameworks/vecLib.framework/Headers/vecLib.h:43, from /System/Library/Frameworks/Accelerate.framework/Headers/Accelerate.h:20, from scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c:2: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:51:23: error: immintrin.h: No such file or directory In file included from /System/Library/Frameworks/vecLib.framework/Headers/vecLib.h:43, from /System/Library/Frameworks/Accelerate.framework/Headers/Accelerate.h:20, from scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c:2: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h: In function ‘vceilf’: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:53: error: incompatible types in return /System/Library/Frameworks/vecLib.framework/Headers/vfp.h: In function ‘vfloorf’: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:54: error: incompatible types in return /System/Library/Frameworks/vecLib.framework/Headers/vfp.h: In function ‘vintf’: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:55: error: ‘_MM_FROUND_TRUNC’ undeclared (first use in this function) /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:55: error: (Each undeclared identifier is reported only once /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:55: error: for each function it appears in.) /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:55: error: incompatible types in return /System/Library/Frameworks/vecLib.framework/Headers/vfp.h: In function ‘vnintf’: /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:56: error: ‘_MM_FROUND_NINT’ undeclared (first use in this function) /System/Library/Frameworks/vecLib.framework/Headers/vfp.h:56: error: incompatible types in return error: Command "/usr/bin/llvm-gcc -fno-strict-aliasing -Os -w -pipe -march=core2 -msse4 -fwrapv -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -Iscipy/sparse/linalg/eigen/arpack/ARPACK/SRC -I/Users/xiao/.virtualenv/lib/python2.7/site-packages/numpy/core/include -c scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.c -o build/temp.macosx-10.4-x86_64-2.7/scipy/sparse/linalg/eigen/arpack/ARPACK/FWRAPPERS/veclib_cabi_c.o" failed with exit status 1 Is it supposed to be looking for headers from my system frameworks? Is the development version of scipy no longer good for the latest version of Mountain Lion/Xcode?

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  • Scipy interpolation on a numpy array

    - by dassouki
    I have a lookup table that is defined the following way: TR_ua1 = np.array([ [3.6, 6.5, 9.1, 11.5, 13.8], [3.9, 7.3, 10.0, 13.1, 15.9], [4.5, 9.2, 12.2, 14.8, 18.2] ]) The header row elements are (hh) <1,2,3,4,5+ The header column (inc) elements are <10000, 20000, 20001+ The user will input a value ex (1.3, 25,000) or (0.2, 50,000). Scipy.interpolate() should interpolate to determine the correct value. Currently, the only way i can do this is with a bunch of if/elifs as exemplified below. I'm pretty sure there is a better, more efficient way of doing this Here's what i've got so far import numpy as np from scipy import interplate if (ua == 1): if (inc <= low_inc): #low_inc = 10,000 if (hh <= 1): return TR_ua1[0][0] elif (hh >= 1 & hh < 2): return interpolate( (1,2), (TR_ua1[0][1], TR_ua1[0][2]) )

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  • Python: how do I install SciPy on 64 bit Windows?

    - by Peter Mortensen
    How do I install SciPy on my system? Update 1: for the NumPy part (that SciPy depends on) there is actually an installer for 64 bit Windows: numpy-1.3.0.win-amd64-py2.6.msi (is direct download URL, 2310144 bytes). Running the SciPy superpack installer results in this message in a dialog box: "Cannot install. Python version 2.6 required, which was not found in the registry." I already have Python 2.6.2 installed (and a working Django installation in it), but I don't know about any Registry story. The registry entries seems to already exist: REGEDIT4 [HKEY_LOCAL_MACHINE\SOFTWARE\Python] [HKEY_LOCAL_MACHINE\SOFTWARE\Python\PythonCore] [HKEY_LOCAL_MACHINE\SOFTWARE\Python\PythonCore\2.6] [HKEY_LOCAL_MACHINE\SOFTWARE\Python\PythonCore\2.6\Help] [HKEY_LOCAL_MACHINE\SOFTWARE\Python\PythonCore\2.6\Help\Main Python Documentation] @="D:\\Python262\\Doc\\python262.chm" [HKEY_LOCAL_MACHINE\SOFTWARE\Python\PythonCore\2.6\InstallPath] @="D:\\Python262\\" [HKEY_LOCAL_MACHINE\SOFTWARE\Python\PythonCore\2.6\InstallPath\InstallGroup] @="Python 2.6" [HKEY_LOCAL_MACHINE\SOFTWARE\Python\PythonCore\2.6\Modules] [HKEY_LOCAL_MACHINE\SOFTWARE\Python\PythonCore\2.6\PythonPath] @="D:\\Python262\\Lib;D:\\Python262\\DLLs;D:\\Python262\\Lib\\lib-tk" What I have done so far: Step 1 Downloaded the NumPy superpack installer numpy-1.3.0rc2-win32-superpack-python2.6.exe (direct download URL, 4782592 bytes). Running this installer resulted in the same message, "Cannot install. Python version 2.6 required, which was not found in the registry.". Update: there is actually an installer for NumPy that works - see beginning of the question. Step 2 Tried to install NumPy in another way. Downloaded the zip package numpy-1.3.0rc2.zip (direct download URL, 2404011 bytes), extracted the zip file in a normal way to a temporary directory, D:\temp7\numpy-1.3.0rc2 (where setup.py and README.txt is). I then opened a command line window and: d: cd D:\temp7\numpy-1.3.0rc2 setup.py install This ran for a long time and also included use of cl.exe (part of Visual Studio). Here is a nearly 5000 lines long transcript (230 KB). This seemed to work. I can now do this in Python: import numpy as np np.random.random(10) with this result: array([ 0.35667511, 0.56099423, 0.38423629, 0.09733172, 0.81560421, 0.18813222, 0.10566666, 0.84968066, 0.79472597, 0.30997724]) Step 3 Downloaded the SciPy superpack installer, scipy-0.7.1rc3- win32-superpack-python2.6.exe (direct download URL, 45597175 bytes). Running this installer resulted in the message listed in the beginning Step 4 Tried to install SciPy in another way. Downloaded the zip package scipy-0.7.1rc3.zip (direct download URL, 5506562 bytes), extracted the zip file in a normal way to a temporary directory, D:\temp7\scipy-0.7.1 (where setup.py and README.txt is). I then opened a command line window and: d: cd D:\temp7\scipy-0.7.1 setup.py install This did not achieve much - here is a transcript (about 95 lines). And it fails: >>> import scipy as sp2 Traceback (most recent call last): File "<stdin>", line 1, in <module> ImportError: No module named scipy Platform: Python 2.6.2 installed in directory D:\Python262, Windows XP 64 bit SP2, 8 GB RAM, Visual Studio 2008 Professional Edition installed. The startup screen of the installed Python is: Python 2.6.2 (r262:71605, Apr 14 2009, 22:46:50) [MSC v.1500 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. >>> Value of PATH, result from SET in a command line window: Path=D:\Perl64\site\bin;D:\Perl64\bin;C:\Program Files (x86)\PC Connectivity Solution\;D:\Perl\site\bin;D:\Perl\bin;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\Program Files (x86)\ATI Technologies\ATI.ACE\Core-Static;d:\Program Files (x86)\WinSCP\;D:\MassLynx\;D:\Program Files (x86)\Analyst\bin;d:\Python262;d:\Python262\Scripts;D:\Program Files (x86)\TortoiseSVN\bin;D:\Program Files\TortoiseSVN\bin;C:\WINDOWS\system32\WindowsPowerShell\v1.0;D:\Program Files (x86)\IDM Computer Solutions\UltraEdit\

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  • Using scipy.interpolate.splrep function

    - by Koustav Ghosal
    I am trying to fit a cubic spline to a given set of points. My points are not ordered. I CANNOT sort or reorder the points, since I need that information. But since the function scipy.interpolate.splrep works only on non-duplicate and monotonically increasing points I have defined a function that maps the x-coordinates to a monotonically increasing space. My old points are: xpoints=[4913.0, 4912.0, 4914.0, 4913.0, 4913.0, 4913.0, 4914.0, 4915.0, 4918.0, 4921.0, 4925.0, 4932.0, 4938.0, 4945.0, 4950.0, 4954.0, 4955.0, 4957.0, 4956.0, 4953.0, 4949.0, 4943.0, 4933.0, 4921.0, 4911.0, 4898.0, 4886.0, 4874.0, 4865.0, 4858.0, 4853.0, 4849.0, 4848.0, 4849.0, 4851.0, 4858.0, 4864.0, 4869.0, 4877.0, 4884.0, 4893.0, 4903.0, 4913.0, 4923.0, 4935.0, 4947.0, 4959.0, 4970.0, 4981.0, 4991.0, 5000.0, 5005.0, 5010.0, 5015.0, 5019.0, 5020.0, 5021.0, 5023.0, 5025.0, 5027.0, 5027.0, 5028.0, 5028.0, 5030.0, 5031.0, 5033.0, 5035.0, 5037.0, 5040.0, 5043.0] ypoints=[10557.0, 10563.0, 10567.0, 10571.0, 10575.0, 10577.0, 10578.0, 10581.0, 10582.0, 10582.0, 10582.0, 10581.0, 10578.0, 10576.0, 10572.0, 10567.0, 10560.0, 10550.0, 10541.0, 10531.0, 10520.0, 10511.0, 10503.0, 10496.0, 10490.0, 10487.0, 10488.0, 10488.0, 10490.0, 10495.0, 10504.0, 10513.0, 10523.0, 10533.0, 10542.0, 10550.0, 10556.0, 10559.0, 10560.0, 10559.0, 10555.0, 10550.0, 10543.0, 10533.0, 10522.0, 10514.0, 10505.0, 10496.0, 10490.0, 10486.0, 10482.0, 10481.0, 10482.0, 10486.0, 10491.0, 10497.0, 10506.0, 10516.0, 10524.0, 10534.0, 10544.0, 10552.0, 10558.0, 10564.0, 10569.0, 10573.0, 10576.0, 10578.0, 10581.0, 10582.0] Plots: The code for the mapping function and interpolation is: xnew=[] ynew=ypoints for c3,i in enumerate(xpoints): if np.isfinite(np.log(i*pow(2,c3))): xnew.append(np.log(i*pow(2,c3))) else: if c==0: xnew.append(np.random.random_sample()) else: xnew.append(xnew[c3-1]+np.random.random_sample()) xnew=np.asarray(xnew) ynew=np.asarray(ynew) constant1=10.0 nknots=len(xnew)/constant1 idx_knots = (np.arange(1,len(xnew)-1,(len(xnew)-2)/np.double(nknots))).astype('int') knots = [xnew[i] for i in idx_knots] knots = np.asarray(knots) int_range=np.linspace(min(xnew),max(xnew),len(xnew)) tck = interpolate.splrep(xnew,ynew,k=3,task=-1,t=knots) y1= interpolate.splev(int_range,tck,der=0) The code is throwing an error at the function interpolate.splrep() for some set of points like the above one. The error is: File "/home/neeraj/Desktop/koustav/res/BOS5/fit_spline3.py", line 58, in save_spline_f tck = interpolate.splrep(xnew,ynew,k=3,task=-1,t=knots) File "/usr/lib/python2.7/dist-packages/scipy/interpolate/fitpack.py", line 465, in splrep raise _iermessier(_iermess[ier][0]) ValueError: Error on input data But for other set of points it works fine. For example for the following set of points. xpoints=[1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1629.0, 1630.0, 1630.0, 1630.0, 1631.0, 1631.0, 1631.0, 1631.0, 1630.0, 1629.0, 1629.0, 1629.0, 1628.0, 1627.0, 1627.0, 1625.0, 1624.0, 1624.0, 1623.0, 1620.0, 1618.0, 1617.0, 1616.0, 1615.0, 1614.0, 1614.0, 1612.0, 1612.0, 1612.0, 1611.0, 1610.0, 1609.0, 1608.0, 1607.0, 1607.0, 1603.0, 1602.0, 1602.0, 1601.0, 1601.0, 1600.0, 1599.0, 1598.0] ypoints=[10570.0, 10572.0, 10572.0, 10573.0, 10572.0, 10572.0, 10571.0, 10570.0, 10569.0, 10565.0, 10564.0, 10563.0, 10562.0, 10560.0, 10558.0, 10556.0, 10554.0, 10551.0, 10548.0, 10547.0, 10544.0, 10542.0, 10541.0, 10538.0, 10534.0, 10532.0, 10531.0, 10528.0, 10525.0, 10522.0, 10519.0, 10517.0, 10516.0, 10512.0, 10509.0, 10509.0, 10507.0, 10504.0, 10502.0, 10500.0, 10501.0, 10499.0, 10498.0, 10496.0, 10491.0, 10492.0, 10488.0, 10488.0, 10488.0, 10486.0, 10486.0, 10485.0, 10485.0, 10486.0, 10483.0, 10483.0, 10482.0, 10480.0] Plots: Can anybody suggest what's happening ?? Thanks in advance......

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  • Fitting a gamma distribution with (python) Scipy

    - by Archanimus
    Hi folks, Can anyone help me out in fitting a gamma distribution in python? Well, I've got some data : X and Y coordinates, and I want to find the gamma parameters that fit this distribution... In the Scipy doc, it turns out that a fit method actually exists but I don't know how to use it :s.. first, in wich format the argument "data" must be, and how can I provide the seconde argument (the parameters) since this what I'm looking for ??? Thanks a lot!

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  • Calculate Matrix Rank using scipy

    - by Hooked
    I'd like to calculate the mathematical rank of a matrix using scipy. The most obvious function numpy.rank calculates the dimension of an array (ie. scalars have dimension 0, vectors 1, matrices 2, etc...). I am aware that the numpy.linalg.lstsq module has this capability, but I was wondering if such a fundamental operation is built into the matrix class somewhere. Here is an explicit example: from numpy import matrix, rank A = matrix([[1,3,7],[2,8,3],[7,8,1]]) print rank(A) This gives 2 the dimension, where I'm looking for an answer of 3.

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  • Vectorizatoin of index operation for a scipy.sparse matrix

    - by celil
    The following code runs too slowly even though everything seems to be vectorized. from numpy import * from scipy.sparse import * n = 100000; i = xrange(n); j = xrange(n); data = ones(n); A=csr_matrix((data,(i,j))); x = A[i,j] The problem seems to be that the indexing operation is implemented as a python function, and invoking A[i,j] results in the following profiling output 500033 function calls in 8.718 CPU seconds Ordered by: internal time ncalls tottime percall cumtime percall filename:lineno(function) 100000 7.933 0.000 8.156 0.000 csr.py:265(_get_single_element) 1 0.271 0.271 8.705 8.705 csr.py:177(__getitem__) (...) Namely, the python function _get_single_element gets called 100000 times which is really inefficient. Why isn't this implemented in pure C? Does anybody know of a way of getting around this limitation, and speeding up the above code? Should I be using a different sparse matrix type?

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  • computing z-scores for 2D matrices in scipy/numpy in Python

    - by user248237
    How can I compute the z-score for matrices in Python? Suppose I have the array: a = array([[ 1, 2, 3], [ 30, 35, 36], [2000, 6000, 8000]]) and I want to compute the z-score for each row. The solution I came up with is: array([zs(item) for item in a]) where zs is in scipy.stats.stats. Is there a better built-in vectorized way to do this? Also, is it always good to z-score numbers before using hierarchical clustering with euclidean or seuclidean distance? Can anyone discuss the relative advantages/disadvantages? thanks.

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  • hierarchical clustering on correlations in Python scipy/numpy?

    - by user248237
    How can I run hierarchical clustering on a correlation matrix in scipy/numpy? I have a matrix of 100 rows by 9 columns, and I'd like to hierarchically clustering by correlations of each entry across the 9 conditions. I'd like to use 1-pearson correlation as the distances for clustering. Assuming I have a numpy array "X" that contains the 100 x 9 matrix, how can I do this? I tried using hcluster, based on this example: Y=pdist(X, 'seuclidean') Z=linkage(Y, 'single') dendrogram(Z, color_threshold=0) however, pdist is not what I want since that's euclidean distance. Any ideas? thanks.

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  • python- scipy optimization

    - by pear
    In scipy fmin_slsqp (Sequential Least Squares Quadratic Programming), I tried reading the code 'slsqp.py' provided with the scipy package, to find what are the criteria to get the exit_modes 0? I cannot find which statements in the code produce this exit mode? Please help me 'slsqp.py' code as follows, exit_modes = { -1 : "Gradient evaluation required (g & a)", 0 : "Optimization terminated successfully.", 1 : "Function evaluation required (f & c)", 2 : "More equality constraints than independent variables", 3 : "More than 3*n iterations in LSQ subproblem", 4 : "Inequality constraints incompatible", 5 : "Singular matrix E in LSQ subproblem", 6 : "Singular matrix C in LSQ subproblem", 7 : "Rank-deficient equality constraint subproblem HFTI", 8 : "Positive directional derivative for linesearch", 9 : "Iteration limit exceeded" } def fmin_slsqp( func, x0 , eqcons=[], f_eqcons=None, ieqcons=[], f_ieqcons=None, bounds = [], fprime = None, fprime_eqcons=None, fprime_ieqcons=None, args = (), iter = 100, acc = 1.0E-6, iprint = 1, full_output = 0, epsilon = _epsilon ): # Now do a lot of function wrapping # Wrap func feval, func = wrap_function(func, args) # Wrap fprime, if provided, or approx_fprime if not if fprime: geval, fprime = wrap_function(fprime,args) else: geval, fprime = wrap_function(approx_fprime,(func,epsilon)) if f_eqcons: # Equality constraints provided via f_eqcons ceval, f_eqcons = wrap_function(f_eqcons,args) if fprime_eqcons: # Wrap fprime_eqcons geval, fprime_eqcons = wrap_function(fprime_eqcons,args) else: # Wrap approx_jacobian geval, fprime_eqcons = wrap_function(approx_jacobian, (f_eqcons,epsilon)) else: # Equality constraints provided via eqcons[] eqcons_prime = [] for i in range(len(eqcons)): eqcons_prime.append(None) if eqcons[i]: # Wrap eqcons and eqcons_prime ceval, eqcons[i] = wrap_function(eqcons[i],args) geval, eqcons_prime[i] = wrap_function(approx_fprime, (eqcons[i],epsilon)) if f_ieqcons: # Inequality constraints provided via f_ieqcons ceval, f_ieqcons = wrap_function(f_ieqcons,args) if fprime_ieqcons: # Wrap fprime_ieqcons geval, fprime_ieqcons = wrap_function(fprime_ieqcons,args) else: # Wrap approx_jacobian geval, fprime_ieqcons = wrap_function(approx_jacobian, (f_ieqcons,epsilon)) else: # Inequality constraints provided via ieqcons[] ieqcons_prime = [] for i in range(len(ieqcons)): ieqcons_prime.append(None) if ieqcons[i]: # Wrap ieqcons and ieqcons_prime ceval, ieqcons[i] = wrap_function(ieqcons[i],args) geval, ieqcons_prime[i] = wrap_function(approx_fprime, (ieqcons[i],epsilon)) # Transform x0 into an array. x = asfarray(x0).flatten() # Set the parameters that SLSQP will need # meq = The number of equality constraints if f_eqcons: meq = len(f_eqcons(x)) else: meq = len(eqcons) if f_ieqcons: mieq = len(f_ieqcons(x)) else: mieq = len(ieqcons) # m = The total number of constraints m = meq + mieq # la = The number of constraints, or 1 if there are no constraints la = array([1,m]).max() # n = The number of independent variables n = len(x) # Define the workspaces for SLSQP n1 = n+1 mineq = m - meq + n1 + n1 len_w = (3*n1+m)*(n1+1)+(n1-meq+1)*(mineq+2) + 2*mineq+(n1+mineq)*(n1-meq) \ + 2*meq + n1 +(n+1)*n/2 + 2*m + 3*n + 3*n1 + 1 len_jw = mineq w = zeros(len_w) jw = zeros(len_jw) # Decompose bounds into xl and xu if len(bounds) == 0: bounds = [(-1.0E12, 1.0E12) for i in range(n)] elif len(bounds) != n: raise IndexError, \ 'SLSQP Error: If bounds is specified, len(bounds) == len(x0)' else: for i in range(len(bounds)): if bounds[i][0] > bounds[i][1]: raise ValueError, \ 'SLSQP Error: lb > ub in bounds[' + str(i) +'] ' + str(bounds[4]) xl = array( [ b[0] for b in bounds ] ) xu = array( [ b[1] for b in bounds ] ) # Initialize the iteration counter and the mode value mode = array(0,int) acc = array(acc,float) majiter = array(iter,int) majiter_prev = 0 # Print the header if iprint >= 2 if iprint >= 2: print "%5s %5s %16s %16s" % ("NIT","FC","OBJFUN","GNORM") while 1: if mode == 0 or mode == 1: # objective and constraint evaluation requird # Compute objective function fx = func(x) # Compute the constraints if f_eqcons: c_eq = f_eqcons(x) else: c_eq = array([ eqcons[i](x) for i in range(meq) ]) if f_ieqcons: c_ieq = f_ieqcons(x) else: c_ieq = array([ ieqcons[i](x) for i in range(len(ieqcons)) ]) # Now combine c_eq and c_ieq into a single matrix if m == 0: # no constraints c = zeros([la]) else: # constraints exist if meq > 0 and mieq == 0: # only equality constraints c = c_eq if meq == 0 and mieq > 0: # only inequality constraints c = c_ieq if meq > 0 and mieq > 0: # both equality and inequality constraints exist c = append(c_eq, c_ieq) if mode == 0 or mode == -1: # gradient evaluation required # Compute the derivatives of the objective function # For some reason SLSQP wants g dimensioned to n+1 g = append(fprime(x),0.0) # Compute the normals of the constraints if fprime_eqcons: a_eq = fprime_eqcons(x) else: a_eq = zeros([meq,n]) for i in range(meq): a_eq[i] = eqcons_prime[i](x) if fprime_ieqcons: a_ieq = fprime_ieqcons(x) else: a_ieq = zeros([mieq,n]) for i in range(mieq): a_ieq[i] = ieqcons_prime[i](x) # Now combine a_eq and a_ieq into a single a matrix if m == 0: # no constraints a = zeros([la,n]) elif meq > 0 and mieq == 0: # only equality constraints a = a_eq elif meq == 0 and mieq > 0: # only inequality constraints a = a_ieq elif meq > 0 and mieq > 0: # both equality and inequality constraints exist a = vstack((a_eq,a_ieq)) a = concatenate((a,zeros([la,1])),1) # Call SLSQP slsqp(m, meq, x, xl, xu, fx, c, g, a, acc, majiter, mode, w, jw) # Print the status of the current iterate if iprint > 2 and the # major iteration has incremented if iprint >= 2 and majiter > majiter_prev: print "%5i %5i % 16.6E % 16.6E" % (majiter,feval[0], fx,linalg.norm(g)) # If exit mode is not -1 or 1, slsqp has completed if abs(mode) != 1: break majiter_prev = int(majiter) # Optimization loop complete. Print status if requested if iprint >= 1: print exit_modes[int(mode)] + " (Exit mode " + str(mode) + ')' print " Current function value:", fx print " Iterations:", majiter print " Function evaluations:", feval[0] print " Gradient evaluations:", geval[0] if not full_output: return x else: return [list(x), float(fx), int(majiter), int(mode), exit_modes[int(mode)] ]

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  • Dendrogram generated by scipy-cluster does not show

    - by Space_C0wb0y
    I am using scipy-cluster to generate a hierarchical clustering on some data. As a final step of the application, I call the dendrogram function to plot the clustering. I am running on Mac OS X Snow Leopard using the built-in Python 2.6.1 and this matplotlib package. The program runs fine, but at the end the Rocket Ship icon (as I understand, this is the launcher for GUI applications in python) shows up and vanishes immediately without doing anything. Nothing is shown. If I add a 'raw_input' after the call, it just bounces up and down in the dock forever. If I run a simple sample application for matplotlib from the terminal it runs fine. Does anyone have any experiences on this?

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  • sampling integers uniformly efficiently in python using numpy/scipy

    - by user248237
    I have a problem where depending on the result of a random coin flip, I have to sample a random starting position from a string. If the sampling of this random position is uniform over the string, I thought of two approaches to do it: one using multinomial from numpy.random, the other using the simple randint function of Python standard lib. I tested this as follows: from numpy import * from numpy.random import multinomial from random import randint import time def use_multinomial(length, num_points): probs = ones(length)/float(length) for n in range(num_points): result = multinomial(1, probs) def use_rand(length, num_points): for n in range(num_points): rand(1, length) def main(): length = 1700 num_points = 50000 t1 = time.time() use_multinomial(length, num_points) t2 = time.time() print "Multinomial took: %s seconds" %(t2 - t1) t1 = time.time() use_rand(length, num_points) t2 = time.time() print "Rand took: %s seconds" %(t2 - t1) if __name__ == '__main__': main() The output is: Multinomial took: 6.58072400093 seconds Rand took: 2.35189199448 seconds it seems like randint is faster, but it still seems very slow to me. Is there a vectorized way to get this to be much faster, using numpy or scipy? thanks.

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