Mailman 3 February 2008 - SciPy-Dev

Probs with numpy/scipy on Itanium 64Bit with Intel Compilers with SCSL libs.
by Julien Le Sommer Feb. 27, 2008

Feb. 27, 2008

Hello, We are working hard to compile scipy on our box. We have already sent a post on this list but so far we haven't managed to install scipy yet, see : http://projects.scipy.org/pipermail/scipy-dev/2007-November/007998.html here is the problem : we are trying to compile numpy/scipy with intel compilers on a box of itanium processors with the SCSL library. the problem is that both numpy and scipy seem to loose track of the fortran compiler configuration during their build_ext phase even though this configuration is recognized during the build_clib phase, giving this log message. customize IntelItaniumFCompiler warning: build_ext: f77_compiler=intele is not available. the prob is the same when python has been compiled with gcc or with intel c compiler. the prob doesn't seems to be related to scsl lib at all but only to the handling of intel compiler for Itanium . some help would be much appreciated. In particular, is there a Intel/Itanium implementation of scipy/numpy somewhere that we could get some information about ? thanks for your help -- Julien Le Sommer, LEGI/MEOM, BP53, 38041 Grenoble cedex 9, France Tel : +33 4 76 82 50 65 Fax : +33 4 76 82 52 71 http://www.legi.hmg.inpg.fr/~lesommer/<http://www.legi.hmg.inpg.fr/%7Elesommer/>

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splinalg naming
by Stefan van der Walt Feb. 26, 2008

Feb. 26, 2008

Hi all, I see the sparse linear algebra module is called scipy.splinalg. Is this naming preferred above scipy.sparse.linalg? Regards Stéfan

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Variogram code?
by Anand Patil Feb. 20, 2008

Feb. 20, 2008

Hi all, I have a small variogram module I'd like to make available. All it does is compute directional variograms on the sphere. It has a little bit of fortran code. Questions: - Should I be making an effort to interface with any code in the stats module? - The output is essentially a list of bar-graph components. On my computer, I have a function that uses matplotlib to actually plot the variograms. Should I wrap the function in a try block, so that it will be available if 'from pylab import *' works, or get rid of it? - When making directional variograms, the code bins by bearing angle rather than by atan2(delta long, delta lat). Is this the best way to do it? The bearing angle seems to make more sense but - bearing(a,b) isn't necesarily equal to pi + bearing(b,a), so the same pair can go in two different bins, and - the bins tend to look strange on a cylindrical projection if the points are far apart. I haven't been able to find much guidance in the literature. - How should I package the module, and how can I get it to someone who can put it in the repository? Thanks, Anand

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More tickets
by Nils Wagner Feb. 20, 2008

Feb. 20, 2008

Hi Nathan, Thank you for closing the tickets. How about http://projects.scipy.org/scipy/scipy/ticket/314 IIRC, io.read_array is deprecated. And http://projects.scipy.org/scipy/scipy/ticket/525 No problem here. Nils

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Tickets
by Nils Wagner Feb. 20, 2008

Feb. 20, 2008

Hi all, IMHO, the following tickets can be closed http://projects.scipy.org/scipy/scipy/ticket/313 http://projects.scipy.org/scipy/scipy/ticket/513 http://projects.scipy.org/scipy/scipy/ticket/534 http://projects.scipy.org/scipy/scipy/ticket/556 http://projects.scipy.org/scipy/scipy/ticket/588 Nils

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numpy upgrade for enthon next version
by dmitrey Feb. 20, 2008

Feb. 20, 2008

hi all, does anyone know when numpy 0.9.x from latest enthon release will be updated to something more up-to-date? I'm asking because scikits.openopt don't work with the numpy version mentioned, because it fails with "can't import abs from numpy". I think there's rather lot number of people who tried using openopt but turned away to something else because fail to install & run it with Windows + enthon. BTW, I had tried to install more recent numpy version by myself and some errors were obtained, so I guess an ordinary user will futhermore fail as well and switch to something else. (mb scons will simplify the process in future). I guess it's valid not only for openopt but for lots of other numpy-related soft (at least, using numpy.abs()) as well. Regards, D.

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ZVODE, a Double Precision Complex ODE Solver
by Nils Wagner Feb. 19, 2008

Feb. 19, 2008

This might be of interest. Nils From: Alan Hindmarsh <alanh(a)llnl.gov> Date: Wed, 6 Dec 2006 18:20:32 -0500 Subject: ZVODE, a Double Precision Complex ODE Solver Of the many solvers freely available for ODE initial value problems, there seem to be very few, if any, for the case of double precision complex dependent variables, even though there have been occasional user calls for this functionality. (Of course a complex system can be treated as a real system of twice the size, but direct treatment as a complex system is preferable.) I have provided such a solver: ZVODE. ZVODE is a straightforward modification of the Fortran ODE solver DVODE, for double precision real systems, making it part of a trio, SVODE/DVODE/ZVODE. ZVODE is available in two locations: (1) Netlib, in /ode (see file zvode.f), and (2) the LLNL CASC software site, www.llnl.gov/CASC/software.html (see VODE). The latter site also includes a demonstration program, zvdemo. Alan Hindmarsh Center for Applied Scientific Computing Lawrence Livermore National Laboratory

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Extension to PilUtil for inverse radon
by Tisham Dhar Feb. 18, 2008

Feb. 18, 2008

Hi All, We have added a small inverse radon transform to Pilutil(in scipy/misc), it goes along with the forward radon transform already there. The file is below. # Functions which need the PIL import types import numpy from numpy import amin, amax, ravel, asarray, cast, arange, \ ones, newaxis, transpose, mgrid, iscomplexobj, sum, zeros, uint8 import math import Image import ImageFilter __all__ = ['fromimage','toimage','imsave','imread','bytescale', 'imrotate','imresize','imshow','imfilter','radon','inv_radon'] # Returns a byte-scaled image def bytescale(data, cmin=None, cmax=None, high=255, low=0): if data.dtype is uint8: return data high = high - low if cmin is None: cmin = amin(ravel(data)) if cmax is None: cmax = amax(ravel(data)) scale = high *1.0 / (cmax-cmin or 1) bytedata = ((data*1.0-cmin)*scale + 0.4999).astype(uint8) return bytedata + cast[uint8](low) def imread(name,flatten=0): """Read an image file from a filename. Optional arguments: - flatten (0): if true, the image is flattened by calling convert('F') on the resulting image object. This flattens the color layers into a single grayscale layer. """ im = Image.open(name) return fromimage(im,flatten=flatten) def imsave(name, arr): """Save an array to an image file. """ im = toimage(arr) im.save(name) return def fromimage(im, flatten=0): """Takes a PIL image and returns a copy of the image in a numpy container. If the image is RGB returns a 3-dimensional array: arr[:,:,n] is each channel Optional arguments: - flatten (0): if true, the image is flattened by calling convert('F') on the image object before extracting the numerical data. This flattens the color layers into a single grayscale layer. Note that the supplied image object is NOT modified. """ assert Image.isImageType(im), "Not a PIL image." if flatten: im = im.convert('F') mode = im.mode adjust = 0 if mode == '1': im = im.convert(mode='L') mode = 'L' adjust = 1 str = im.tostring() type = uint8 if mode == 'F': type = numpy.float32 elif mode == 'I': type = numpy.uint32 elif mode == 'I;16': type = numpy.uint16 arr = numpy.fromstring(str,type) shape = list(im.size) shape.reverse() if mode == 'P': arr.shape = shape if im.palette.rawmode != 'RGB': print "Warning: Image has invalid palette." return arr pal = numpy.fromstring(im.palette.data,type) N = len(pal) pal.shape = (int(N/3.0),3) return arr, pal if mode in ['RGB','YCbCr']: shape += [3] elif mode in ['CMYK','RGBA']: shape += [4] arr.shape = shape if adjust: arr = (arr != 0) return arr _errstr = "Mode is unknown or incompatible with input array shape." def toimage(arr,high=255,low=0,cmin=None,cmax=None,pal=None, mode=None,channel_axis=None): """Takes a numpy array and returns a PIL image. The mode of the PIL image depends on the array shape, the pal keyword, and the mode keyword. For 2-D arrays, if pal is a valid (N,3) byte-array giving the RGB values (from 0 to 255) then mode='P', otherwise mode='L', unless mode is given as 'F' or 'I' in which case a float and/or integer array is made For 3-D arrays, the channel_axis argument tells which dimension of the array holds the channel data. For 3-D arrays if one of the dimensions is 3, the mode is 'RGB' by default or 'YCbCr' if selected. if the The numpy array must be either 2 dimensional or 3 dimensional. """ data = asarray(arr) if iscomplexobj(data): raise ValueError, "Cannot convert a complex-valued array." shape = list(data.shape) valid = len(shape)==2 or ((len(shape)==3) and \ ((3 in shape) or (4 in shape))) assert valid, "Not a suitable array shape for any mode." if len(shape) == 2: shape = (shape[1],shape[0]) # columns show up first if mode == 'F': data32 = data.astype(numpy.float32) image = Image.fromstring(mode,shape,data32.tostring()) return image if mode in [None, 'L', 'P']: bytedata = bytescale(data,high=high,low=low,cmin=cmin,cmax=cmax) image = Image.fromstring('L',shape,bytedata.tostring()) if pal is not None: image.putpalette(asarray(pal,dtype=uint8).tostring()) # Becomes a mode='P' automagically. elif mode == 'P': # default gray-scale pal = arange(0,256,1,dtype=uint8)[:,newaxis] * \ ones((3,),dtype=uint8)[newaxis,:] image.putpalette(asarray(pal,dtype=uint8).tostring()) return image if mode == '1': # high input gives threshold for 1 bytedata = (data > high) image = Image.fromstring('1',shape,bytedata.tostring()) return image if cmin is None: cmin = amin(ravel(data)) if cmax is None: cmax = amax(ravel(data)) data = (data*1.0 - cmin)*(high-low)/(cmax-cmin) + low if mode == 'I': data32 = data.astype(numpy.uint32) image = Image.fromstring(mode,shape,data32.tostring()) else: raise ValueError, _errstr return image # if here then 3-d array with a 3 or a 4 in the shape length. # Check for 3 in datacube shape --- 'RGB' or 'YCbCr' if channel_axis is None: if (3 in shape): ca = numpy.flatnonzero(asarray(shape) == 3)[0] else: ca = numpy.flatnonzero(asarray(shape) == 4) if len(ca): ca = ca[0] else: raise ValueError, "Could not find channel dimension." else: ca = channel_axis numch = shape[ca] if numch not in [3,4]: raise ValueError, "Channel axis dimension is not valid." bytedata = bytescale(data,high=high,low=low,cmin=cmin,cmax=cmax) if ca == 2: strdata = bytedata.tostring() shape = (shape[1],shape[0]) elif ca == 1: strdata = transpose(bytedata,(0,2,1)).tostring() shape = (shape[2],shape[0]) elif ca == 0: strdata = transpose(bytedata,(1,2,0)).tostring() shape = (shape[2],shape[1]) if mode is None: if numch == 3: mode = 'RGB' else: mode = 'RGBA' if mode not in ['RGB','RGBA','YCbCr','CMYK']: raise ValueError, _errstr if mode in ['RGB', 'YCbCr']: assert numch == 3, "Invalid array shape for mode." if mode in ['RGBA', 'CMYK']: assert numch == 4, "Invalid array shape for mode." # Here we know data and mode is coorect image = Image.fromstring(mode, shape, strdata) return image def imrotate(arr,angle,interp='bilinear'): """Rotate an image counter-clockwise by angle degrees. Interpolation methods can be: 'nearest' : for nearest neighbor 'bilinear' : for bilinear 'cubic' or 'bicubic' : for bicubic """ arr = asarray(arr) func = {'nearest':0,'bilinear':2,'bicubic':3,'cubic':3} im = toimage(arr) im = im.rotate(angle,resample=func[interp]) return fromimage(im) def imresize(arr,newsize,interp='bilinear',mode=None): newsize=list(newsize) newsize.reverse() newsize = tuple(newsize) arr = asarray(arr) func = {'nearest':0,'bilinear':2,'bicubic':3,'cubic':3} im = toimage(arr,mode=mode) im = im.resize(newsize,resample=func[interp]) return fromimage(im) def imshow(arr): """Simple showing of an image through an external viewer. """ im = toimage(arr) if (len(arr.shape) == 3) and (arr.shape[2] == 4): try: import os im.save('/tmp/scipy_imshow.png') if os.system("(xv /tmp/scipy_imshow.png; rm -f /tmp/scipy_imshow.png)&"): raise RuntimeError return except: print "Warning: Alpha channel may not be handled correctly." im.show() return def imresize(arr,size): """Resize an image. If size is an integer it is a percentage of current size. If size is a float it is a fraction of current size. If size is a tuple it is the size of the output image. """ im = toimage(arr) ts = type(size) if ts is types.IntType: size = size / 100.0 if type(size) is types.FloatType: size = (im.size[0]*size,im.size[1]*size) else: size = (size[1],size[0]) imnew = im.resize(size) return fromimage(imnew) def imfilter(arr,ftype): """Simple filtering of an image. type can be: 'blur', 'contour', 'detail', 'edge_enhance', 'edge_enhance_more', 'emboss', 'find_edges', 'smooth', 'smooth_more', 'sharpen' """ _tdict = {'blur':ImageFilter.BLUR, 'contour':ImageFilter.CONTOUR, 'detail':ImageFilter.DETAIL, 'edge_enhance':ImageFilter.EDGE_ENHANCE, 'edge_enhance_more':ImageFilter.EDGE_ENHANCE_MORE, 'emboss':ImageFilter.EMBOSS, 'find_edges':ImageFilter.FIND_EDGES, 'smooth':ImageFilter.SMOOTH, 'smooth_more':ImageFilter.SMOOTH_MORE, 'sharpen':ImageFilter.SHARPEN } im = toimage(arr) if ftype not in _tdict.keys(): raise ValueError, "Unknown filter type." return fromimage(im.filter(_tdict[ftype])) def radon(arr,theta=None): if theta is None: theta = mgrid[0:180] s = zeros((arr.shape[1],len(theta)), float) k = 0 for th in theta: im = imrotate(arr,-th) s[:,k] = sum(im,axis=0) k += 1 return s def inv_radon(arr): theta = mgrid[0:arr.shape[1]] th = (math.pi/180)*theta #set up the image m = arr.shape[1] n = arr.shape[0]-1 inv = zeros( (n, n), int) #set up x and y matices midindex = (n+1)/2 xy = mgrid[1:n+1,1:n+1] xpr = xy[1]-(n+1)/2 ypr = xy[0]-(n+1)/2 for l in theta: filtrindex = numpy.array(midindex + xpr*math.sin(th[l])-ypr*math.cos(th[l])).round() inva=zeros((n,n)) spota=numpy.where((filtrindex > 0) & (filtrindex <= n)) newfiltindex=numpy.array(filtrindex[spota],int) inva[spota]=arr[newfiltindex[:],l] inv=inv+inva return inv/m Regards, Tishampati Dhar Remote Sensing Software Developer APOGEE IMAGING INTERNATIONAL Building 12B 1 Adelaide - Lobethal Road Lobethal SA 5241 Telephone: +61 - 8 - 8389 5499 Fax: +61 - 8 - 8389 5488 Mobile: +61 - 406114165 Email: tisham(a)apogee.com.au mailto:tisham@apogee.com.au> Web: www.apogee.com.au <http://www.apogee.com.au> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ "The information in this e-mail may be confidential and/or commercially privileged. It is intended solely for the addressee. Access to this e-mail by anyone else is unauthorised. If you are not the intendedrecipient, any disclosure, copying, distribution or action taken or omitted to be taken in reliance on it, is prohibited and may be unlawful."

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Difference between A = sparse.lil_matrix((3, 3), dtype=complex) and A = sparse.lil_matrix((3, 3), complex)
by Nils Wagner Feb. 16, 2008

Feb. 16, 2008

Hi all, Can someone explain the difference between A = sparse.lil_matrix((3, 3),dtype=complex) and A = sparse.lil_matrix((3, 3),complex) The first definition produces a complex matrix while the second one results in a r e a l matrix. This is counterintuitive. In [1]: from scipy import sparse In [2]: A = sparse.lil_matrix((3, 3),complex) In [3]: A Out[3]: <3x3 sparse matrix of type '<type 'numpy.float64'>' with 0 stored elements in LInked List format> In [4]: A = sparse.lil_matrix((3, 3),dtype=complex) In [5]: A Out[5]: <3x3 sparse matrix of type '<type 'numpy.complex128'>' with 0 stored elements in LInked List format> Cheers, Nils

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Complex sparse matrices
by Nils Wagner Feb. 15, 2008

Feb. 15, 2008

Hi all, How do I assign complex values to a sparse lil matrix The following code doesn't work from scipy import sparse from scipy.splinalg import spsolve, use_solver from numpy import linalg, ones from numpy.random import rand A = sparse.lil_matrix((3, 3),complex) A.setdiag(ones(3)+1j*ones(3)) A = A.tocsr() print A print A.real() print A.imag() The imaginary part is missing /usr/bin/python -i test_complex.py (0, 0) 1.0 (1, 1) 1.0 (2, 2) 1.0 and I get Traceback (most recent call last): File "test_complex.py", line 9, in ? print A.real() File "/usr/lib/python2.4/site-packages/scipy/sparse/csr.py", line 99, in __getattr__ return _cs_matrix.__getattr__(self, attr) File "/usr/lib/python2.4/site-packages/scipy/sparse/base.py", line 310, in __getattr__ return self._real() File "/usr/lib/python2.4/site-packages/scipy/sparse/data.py", line 30, in _real return self._with_data(numpy.real(self.data),copy=False) NameError: global name 'numpy' is not defined I am using >>> import scipy >>> scipy.__version__ '0.7.0.dev3942' >>> import numpy >>> numpy.__version__ '1.0.5.dev4807'

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