Re: [Numpy-discussion] The NumPy Fortran-ordering quiz
On 18/10/06, Travis Oliphant [EMAIL PROTECTED] wrote: If there are any cases satisfying these rules where a copy does not have to occur then let me know. For example, zeros((4,4))[:,1].reshape((2,2)) need not be copied. I filed a bug in trac and supplied a patch to multiarray.c that avoids copies in PyArray_NewShape unless absolutely necessary. A. M. Archibald - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?
Hi! I am confused with Numpy behavior with its scalar or 0-d arrays objects: numpy.__version__ '1.0rc2' a = numpy.array((1,2,3)) b = a[:2] b += 1 b array([2, 3]) a array([2, 3, 3]) type(b) type 'numpy.ndarray' To this point all is ok for me: subarrays share (by default) memory with their parent array. But: c = a[2] c += 1 c 4 a array([2, 3, 3]) type(c) type 'numpy.int32' id(c) 169457808 c += 1 id(c) 169737448 That's really confusing, because slices (from __getslice__ method) are not copies (they share memory), and items (single elements from __getitem__ ) are copies to one of the scalar objects provided by Numpy. I can understand that numpy.scalars do not provide inplace operations (like Python standard scalars, they are immutable), so I'd like to use 0-d Numpy.ndarrays. But: d = numpy.array(a[2],copy=False) d += 1 d array(4) a array([2, 3, 3]) type(d) type 'numpy.ndarray' d.shape () id(d) 169621280 d += 1 id(d) 169621280 This is not a solution because d is a copy since construction time... My question is: is there a way to get a single element of an array into a 0-d array which shares memory with its parent array? Thx for your help, Sebastien - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] いつでもどこで もイカす出会い天国
中だし体験記！携帯番号ゲットで中だし・・・最高です！ http://carmastorra.com/ika/ - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?
A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure: [snip] I can understand that numpy.scalars do not provide inplace operations (like Python standard scalars, they are immutable), so I'd like to use 0-d Numpy.ndarrays. But: d = numpy.array(a[2],copy=False) d += 1 d array(4) a array([2, 3, 3]) type(d) type 'numpy.ndarray' d.shape () id(d) 169621280 d += 1 id(d) 169621280 This is not a solution because d is a copy since construction time... My question is: is there a way to get a single element of an array into a 0-d array which shares memory with its parent array? One possible solution (there can be more) is using ndarray: In [47]: a=numpy.array([1,2,3], dtype=i4) In [48]: n=1# the position that you want to share In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype=i4) In [50]: a Out[50]: array([1, 2, 3]) In [51]: b Out[51]: array(2) In [52]: b += 1 In [53]: b Out[53]: array(3) In [54]: a Out[54]: array([1, 3, 3]) Cheers, -- 0,0 Francesc Altet http://www.carabos.com/ V V Cárabos Coop. V. Enjoy Data - - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Can' compile numpy 1.02rc3 on OSX 10.3.9
Am 20.10.2006 um 02:53 schrieb Jay Parlar: Hi! I try to compile numpy rc3 on Panther and get following errors. (I start build with python2.3 setup.py build to be sure to use the python shipped with OS X. I din't manage to compile Python2.5 either yet with similar errors) Does anynbody has an Idea? gcc-3.3 XCode 1.5 November gcc updater is installed I couldn't get numpy building with Python 2.5 on 10.3.9 (although I had different compile errors). The solution that ended up working for me was Python 2.4. There's a bug in the released version of Python 2.5 that's preventing it from working with numpy, should be fixed in the next release. You can find a .dmg for Python 2.4 here: http://pythonmac.org/packages/py24-fat/index.html Jay P. I have that installed already but i get some bus errors with that. Furthermore it is built with gcc4 and i need to compile an extra module(pytables) and I fear that will not work, hence I try to compile myself. Python 2.5 dosent't compile either (libSystemStubs is only on Tiger). The linking works when i remove the -lSystemStubs and it compiled clean. Numpy rc3 wass also compiling now with python 2.5, but the tests failed: Python 2.5 (r25:51908, Oct 20 2006, 11:40:08) [GCC 3.3 20030304 (Apple Computer, Inc. build 1671)] on darwin Type help, copyright, credits or license for more information. import numpy numpy.test(10) Found 5 tests for numpy.distutils.misc_util Found 4 tests for numpy.lib.getlimits Found 31 tests for numpy.core.numerictypes Found 32 tests for numpy.linalg Found 13 tests for numpy.core.umath Found 4 tests for numpy.core.scalarmath Found 9 tests for numpy.lib.arraysetops Found 42 tests for numpy.lib.type_check Found 183 tests for numpy.core.multiarray Found 3 tests for numpy.fft.helper Found 36 tests for numpy.core.ma Found 1 tests for numpy.lib.ufunclike Found 12 tests for numpy.lib.twodim_base Found 10 tests for numpy.core.defmatrix Found 4 tests for numpy.ctypeslib Found 41 tests for numpy.lib.function_base Found 2 tests for numpy.lib.polynomial Found 8 tests for numpy.core.records Found 28 tests for numpy.core.numeric Found 4 tests for numpy.lib.index_tricks Found 47 tests for numpy.lib.shape_base Found 0 tests for __main__ Warning: invalid value encountered in divide ..Warning: invalid value encountered in divide ..Warning: divide by zero encountered in divide .Warning: divide by zero encountered in divide ..Warning: invalid value encountered in divide .Warning: divide by zero encountered in divide .Warning: divide by zero encountered in divide .Warning: divide by zero encountered in divide .Warning: divide by zero encountered in divide ..Warning: invalid value encountered in divide ..Warning: invalid value encountered in divide ..Warning: divide by zero encountered in divide .Warning: divide by zero encountered in divide .Warning: divide by zero encountered in divide .Warning: divide by zero encountered in divide Warning: invalid value encountered in divide .Warning: invalid value encountered in divide ..Warning: divide by zero encountered in divide ..Warning: overflow encountered in exp F... ..Warning: divide by zero encountered in divide Warning: divide by zero encountered in divide Warning: divide by zero encountered in divide Warning: divide by zero encountered in divide Warning: divide by zero encountered in divide Warning: divide by zero encountered in divide Warning: divide by zero encountered in divide Warning: divide by zero encountered in divide Warning: divide by zero encountered in divide .Warning: invalid value encountered in sqrt Warning: invalid value encountered in log Warning: invalid value encountered in log10 ..Warning: invalid value encountered in sqrt Warning: invalid value encountered in sqrt Warning: divide by zero encountered in log Warning: divide by zero encountered in log Warning: divide by zero encountered in log10 Warning: divide by zero encountered in log10 Warning: invalid value encountered in arcsin Warning: invalid value encountered in arcsin Warning: invalid value encountered in arccos Warning: invalid value encountered in arccos Warning: invalid value encountered in arccosh Warning: invalid value encountered in arccosh Warning: divide by zero encountered in arctanh Warning: divide by zero encountered in arctanh Warning: invalid value encountered in divide Warning: invalid value encountered in true_divide Warning: invalid value encountered in floor_divide Warning: invalid value encountered in remainder Warning: invalid value encountered in fmod
[Numpy-discussion] Helper function to unroll a array
Hi, There is an operation I do a lot, I would call it unrolling a array. The best way to describe it is probably to give the code: def unroll(M): Flattens the array M and returns a 2D array with the first columns being the indices of M, and the last column the flatten M. return hstack((indices(M.shape).reshape(-1,M.ndim),M.reshape(-1,1))) Example: M array([[ 0.73530097, 0.3553424 , 0.3719772 ], [ 0.83353373, 0.74622133, 0.14748905], [ 0.72023762, 0.32306969, 0.19142366]]) unroll(M) array([[ 0., 0., 0.73530097], [ 0., 1., 0.3553424 ], [ 1., 1., 0.3719772 ], [ 2., 2., 0.83353373], [ 2., 0., 0.74622133], [ 1., 2., 0.14748905], [ 0., 1., 0.72023762], [ 2., 0., 0.32306969], [ 1., 2., 0.19142366]]) The docstring sucks. The function is trivial (when you know numpy a bit). Maybe this function already exists in numpy, if so I couldn't find it. Elsewhere I propose it for inclusion. Cheers, Gaël - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?
On Fri, Oct 20, 2006 at 11:42:26AM +0200, Sebastien Bardeau wrote: a = numpy.array((1,2,3)) b = a[:2] Here you index by a slice. c = a[2] Whereas here you index by a scalar. So you want to do b = a[[2]] b += 1 or in the general case b = a[slice(2,3)] b += 1 Regards Stéfan - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?
Francesc Altet wrote: A Divendres 20 Octubre 2006 11:42, Sebastien Bardeau va escriure: [snip] I can understand that numpy.scalars do not provide inplace operations (like Python standard scalars, they are immutable), so I'd like to use 0-d Numpy.ndarrays. But: d = numpy.array(a[2],copy=False) d += 1 d array(4) a array([2, 3, 3]) type(d) type 'numpy.ndarray' d.shape () id(d) 169621280 d += 1 id(d) 169621280 This is not a solution because d is a copy since construction time... My question is: is there a way to get a single element of an array into a 0-d array which shares memory with its parent array? One possible solution (there can be more) is using ndarray: [SNIP] Here's a slightly more concise version of the same idea: b = a[n:n+1].reshape([]) -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy-scalars vs Numpy 0-d arrays: copy or not copy?
Ooops sorry there was two mistakes with the 'hasslice' flag. This seems now to work for me. def __getitem__(self,index): # Index may be either an int or a tuple # Index length: if type(index) == int: # A single element through first dimension ilen = 1 index = (index,)# A tuple else: ilen = len(index) # Array rank: arank = len(self.shape) # Check if there is a slice: hasslice = False for i in index: if type(i) == slice: hasslice = True # Array is already a 0-d array: if arank == 0 and index == (0,): return self elif arank == 0: raise IndexError, 0-d array has only one element at index 0. # This will return a single element as a 0-d array: elif arank == ilen and not hasslice: # This ugly thing returns a numpy 0-D array AND NOT a numpy scalar! # (Numpy scalars do not share their data with the parent array) newindex = list(index) newindex[0] = slice(index[0],index[0]+1,None) newindex = tuple(newindex) return self[newindex].reshape(()) # This will return a n-D subarray (n=1): else: return self[index] Sebastien Bardeau wrote: One possible solution (there can be more) is using ndarray: In [47]: a=numpy.array([1,2,3], dtype=i4) In [48]: n=1# the position that you want to share In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype=i4) Ok thanks. Actually that was also the solution I found. But this is much more complicated when arrays are N dimensional with N1, and above all if user asks for a slice in one or more dimension. Here is how I redefine the __getitem__ method for my arrays. Remember that the goal is to return a 0-d array rather than a numpy.scalar when I extract a single element out of a N-dimensional (N=1) array: def __getitem__(self,index): # Index may be either an int or a tuple # Index length: if type(index) == int: # A single element through first dimension ilen = 1 index = (index,)# A tuple else: ilen = len(index) # Array rank: arank = len(self.shape) # Check if there is a slice: for i in index: if type(i) == slice: hasslice = True else: hasslice = False # Array is already a 0-d array: if arank == 0 and index == (0,): return self[()] elif arank == 0: raise IndexError, 0-d array has only one element at index 0. # This will return a single element as a 0-d array: elif arank == ilen and hasslice: # This ugly thing returns a numpy 0-D array AND NOT a numpy scalar! # (Numpy scalars do not share their data with the parent array) newindex = list(index) newindex[0] = slice(index[0],index[0]+1,None) newindex = tuple(newindex) return self[newindex].reshape(()) # This will return a n-D subarray (n=1): else: return self[index] Well... I do not think this is very nice. Someone has another idea? My question in my first post was: is there a way to get a single element of an array into a 0-d array which shares memory with its parent array? Sebastien - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion -- - Sebastien Bardeau L3AB - CNRS UMR 5804 2 rue de l'observatoire BP 89 F - 33270 Floirac Tel: (+33) 5 57 77 61 46 - - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] histogram complete makeover
Thanks for the comments, Here is the code for the new histogram, tests included. I'll wait for comments or suggestions before submitting a patch (numpy / scipy) ?CheersDavid
2006/10/18, Tim Hochberg [EMAIL PROTECTED]:
My $0.02:If histogram is going to get a makeover, particularly one that makes itmore complex than at present, it should probably be moved to SciPy.Failing that, it should be moved to a submodule of numpy with similar
statistical tools. Preferably with consistent interfaces for all of thefunctions.-Using Tomcat but need to do more? Need to support web services, security?
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# License: Scipy compatible
# Author: David Huard, 2006
from numpy import *
def histogram(a, bins=10, range=None, normed=False, weights=None, axis=None):
histogram(a, bins=10, range=None, normed=False, weights=None, axis=None)
- H, dict
Return the distribution of sample.
Parameters
--
a: Array sample.
bins:Number of bins, or
an array of bin edges, in which case the range is not used.
range: Lower and upper bin edges, default: [min, max].
normed: Boolean, if False, return the number of samples in each bin,
if True, return a frequency distribution.
weights: Sample weights.
axis:Specifies the dimension along which the histogram is computed.
Defaults to None, which aggregates the entire sample array.
Output
--
H:The number of samples in each bin.
If normed is True, H is a frequency distribution.
dict{
'edges': The bin edges, including the rightmost edge.
'upper': Upper outliers.
'lower': Lower outliers.
'bincenters': Center of bins.
}
Examples
x = random.rand(100,10)
H, Dict = histogram(x, bins=10, range=[0,1], normed=True)
H2, Dict = histogram(x, bins=10, range=[0,1], normed=True, axis=0)
See also: histogramnd
a = asarray(a)
if axis is None:
a = atleast_1d(a.ravel())
axis = 0
# Bin edges.
if not iterable(bins):
if range is None:
range = (a.min(), a.max())
mn, mx = [mi+0.0 for mi in range]
if mn == mx:
mn -= 0.5
mx += 0.5
edges = linspace(mn, mx, bins+1, endpoint=True)
else:
edges = asarray(bins, float)
dedges = diff(edges)
decimal = int(-log10(dedges.min())+6)
bincenters = edges[:-1] + dedges/2.
# apply_along_axis accepts only one array input, but we need to pass the
# weights along with the sample. The strategy here is to concatenate the
# weights array along axis, so the passed array contains [sample, weights].
# The array is then split back in __hist1d.
if weights is not None:
aw = concatenate((a, weights), axis)
weighted = True
else:
aw = a
weighted = False
count = apply_along_axis(__hist1d, axis, aw, edges, decimal, weighted)
# Outlier count
upper = count.take(array([-1]), axis)
lower = count.take(array([0]), axis)
# Non-outlier count
core = a.ndim*[slice(None)]
core[axis] = slice(1, -1)
hist = count[core]
if normed:
normalize = lambda x: atleast_1d(x/(x*dedges).sum())
hist = apply_along_axis(normalize, axis, hist)
return hist, {'edges':edges, 'lower':lower, 'upper':upper, \
'bincenters':bincenters}
def __hist1d(aw, edges, decimal, weighted):
Internal routine to compute the 1d histogram.
aw: sample, [weights]
edges: bin edges
decimal: approximation to put values lying on the rightmost edge in the last
bin.
weighted: Means that the weights are appended to array a.
Return the bin count or frequency if normed.
nbin = edges.shape[0]+1
if weighted:
count = zeros(nbin, dtype=float)
a,w = hsplit(aw,2)
w = w/w.mean()
else:
a = aw
count = zeros(nbin, dtype=int)
w = None
binindex = digitize(a, edges)
# Values that fall on an edge are put in the right bin.
# For the rightmost bin, we want values equal to the right
# edge to be counted in the last bin, and not as an outlier.
on_edge = where(around(a,decimal) == around(edges[-1], decimal))[0]
binindex[on_edge] -= 1
# Count the number of identical
[Numpy-discussion] slicing suggestion
Hello. I have a suggestion that might make slicing using matrices more user-friendly. I often have a matrix of row or column numbers that I wish to use as a slice. If K was a matrix of row numbers (nx1) and M was a nxm matrix, then I would use ans = M[K.A.ravel(),:] to obtain the matrix I want. It turns out that I use .A.ravel() quite a lot in my code, as I usually work with matrices rather than arrays. My suggestion is to create a new attribute, such as .AR, so that the following could be used: M[K.AR,:]. I believe this would be more concise, easier to read, and well used. If slices are made in both directions of the matrix, then the .A.ravel() becomes even more unwieldy. Does anyone else like this idea? John __ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Model and experiment fitting.
Hi! This is probably a silly question but I'm getting confused with a certain problem: a comparison between experimental data points (2D points set) and a model (2D points set - no analytical form). The physical model produces (by a sophisticated simulations done by an external program) some 2D points data and one of my task is to compare those calculated data with an experimental one. The experimental and modeled data have form of 2D curves, build of n 2D-points, i.e.: expDat=[[x1,x2,x3,..xn],[y1,y2,y3,...,yn]] simDat=[[X1,X2,X3,...,Xn],[Y1,Y2,Y3,...,Yn]] The task of determining, let's say, a root mean squarred error (RMSe) is trivial if x1==X1, x2==X2, etc. In general, which is a common situation xk differs from Xk (k=0..n) and one may not simply compare succeeding Yk and yk (k=0..n) to determine the goodness-of-fit. The distance h=Xk-X(k-1) is constant, but similar distance m(k)=xk-x(k-1) depends on k-th point and is not a constant value, although the data array lengths for simulation and experiment are the same. My first idea was to do some interpolations to obtain the missing points, but I held it 'by a hand' (which, BTW gave quite rewarding results) and I suppose, there's some i.g. numpy method to do it for me, isn't it? I suppose to do something like: gfit(expDat,simDat,'measure_type') which I hope will return the number determining the goodness-of-fit (mean squarred error, root mean squarred error,...) of two sets of discrete 2D data points. Is there something like that in any numerical python modules (numpy, pylab) I could use? I can imagine, I can fit the data with some polynomial or whatever, and than compare the fitted data, but my goal is to operate on as raw data as it's possible. Thanks for your comments! Sebastian - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Model and experiment fitting.
Sebastian Żurek wrote: Hi! This is probably a silly question but I'm getting confused with a certain problem: a comparison between experimental data points (2D points set) and a model (2D points set - no analytical form). The physical model produces (by a sophisticated simulations done by an external program) some 2D points data and one of my task is to compare those calculated data with an experimental one. The experimental and modeled data have form of 2D curves, build of n 2D-points, i.e.: expDat=[[x1,x2,x3,..xn],[y1,y2,y3,...,yn]] simDat=[[X1,X2,X3,...,Xn],[Y1,Y2,Y3,...,Yn]] The task of determining, let's say, a root mean squarred error (RMSe) is trivial if x1==X1, x2==X2, etc. In general, which is a common situation xk differs from Xk (k=0..n) and one may not simply compare succeeding Yk and yk (k=0..n) to determine the goodness-of-fit. The distance h=Xk-X(k-1) is constant, but similar distance m(k)=xk-x(k-1) depends on k-th point and is not a constant value, although the data array lengths for simulation and experiment are the same. Your description is a bit vague. Do you mean that you have some model function f that maps X values to Y values? f(x) - y If that is the case, is there some reason that you cannot run your simulation using the same X points as your experimental data? OTOH, is there some other independent variable (say Z) that *is* common between your experimental and simulated data? f(z) - (x, y) -- 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 - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Model and experiment fitting.
On 20/10/06, Sebastian Żurek [EMAIL PROTECTED] wrote: Is there something like that in any numerical python modules (numpy, pylab) I could use? In scipy there are some very convenient spline fitting tools which will allow you to fit a nice smooth spline through the simulation data points (or near, if they have some uncertainty); you can then easily look at the RMS difference in the y values. You can also, less easily, look at the distance from the curve allowing for some uncertainty in the x values. I suppose you could also fit a curve through the experimental points and compare the two curves in some way. I can imagine, I can fit the data with some polynomial or whatever, and than compare the fitted data, but my goal is to operate on as raw data as it's possible. If you want to avoid using an a priori model, Numerical Recipes discuss some possible approaches (Do two-dimensional distributions differ? at http://www.nrbook.com/a/bookcpdf.html is one) but it's not clear how to turn the problem you describe into a solvable one - some assumption about how the models vary between sampled x values appears to be necessary, and that amounts to interpolation. A. M. Archibald - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
[Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
Hi, i am running numpy on aix compiling with xlc. Revision 1.0rc2 works fine and passes all tests. But 1.0rc3 and more recent give the following on import: Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in add Warning: invalid value encountered in not_equal Warning: invalid value encountered in absolute Warning: invalid value encountered in less Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in equal Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in add Warning: invalid value encountered in not_equal Warning: invalid value encountered in absolute Warning: invalid value encountered in less Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in equal Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply [lots more of this] The odd thing is that all tests pass. I have looked, but can't find where this Warning is coming from in the code. Any thoughts on where this is coming from? What can I do to help debug this? I am not sure what revision introduced this issue. Thanks Brian - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
Brian Granger wrote: Hi, i am running numpy on aix compiling with xlc. Revision 1.0rc2 works fine and passes all tests. But 1.0rc3 and more recent give the following on import: Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in add Warning: invalid value encountered in not_equal Warning: invalid value encountered in absolute Warning: invalid value encountered in less Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in equal Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in add Warning: invalid value encountered in not_equal Warning: invalid value encountered in absolute Warning: invalid value encountered in less Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in equal Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply [lots more of this] The odd thing is that all tests pass. I have looked, but can't find where this Warning is coming from in the code. Any thoughts on where this is coming from? What can I do to help debug this? I am not sure what revision introduced this issue. The reason that you are seeing this now is that the default error state has been tightened up. There were some issues with tests failing as a result of this, but I believe I fixed those already and you're seeing this on import, not when running the tests correct? The first thing to do is figure out where the invalids are occurring, and the natural way to do that is to set the error state to raise, but you can't set the error state till you import it, so that's not going to help here. I think the first thing that I would try is to throw in a seterr(all='raise', under='ignore') right after the call to _setdef in numeric.py. If you're lucky, this will point out where the invalids are popping up. As a sanity check, you could instead make this seterr(all='ignore'), which should make all the warnings go away, but won't tell you anything about why there are warnings to begin with. Regards, -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
Also, when I use seterr(all='ignore') the the tests fail: == FAIL: Ticket #112 -- Traceback (most recent call last): File /usr/common/homes/g/granger/usr/local/lib/python/numpy/core/tests/test_regression.py, line 219, in check_longfloat_repr assert(str(a)[1:9] == str(a[0])[:8]) AssertionError -- Ran 516 tests in 0.823s FAILED (failures=1) Thanks for helping out on this. On 10/20/06, Tim Hochberg [EMAIL PROTECTED] wrote: Brian Granger wrote: Hi, i am running numpy on aix compiling with xlc. Revision 1.0rc2 works fine and passes all tests. But 1.0rc3 and more recent give the following on import: Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in add Warning: invalid value encountered in not_equal Warning: invalid value encountered in absolute Warning: invalid value encountered in less Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in equal Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in add Warning: invalid value encountered in not_equal Warning: invalid value encountered in absolute Warning: invalid value encountered in less Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in equal Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply Warning: invalid value encountered in multiply [lots more of this] The odd thing is that all tests pass. I have looked, but can't find where this Warning is coming from in the code. Any thoughts on where this is coming from? What can I do to help debug this? I am not sure what revision introduced this issue. The reason that you are seeing this now is that the default error state has been tightened up. There were some issues with tests failing as a result of this, but I believe I fixed those already and you're seeing this on import, not when running the tests correct? The first thing to do is figure out where the invalids are occurring, and the natural way to do that is to set the error state to raise, but you can't set the error state till you import it, so that's not going to help here. I think the first thing that I would try is to throw in a seterr(all='raise', under='ignore') right after the call to _setdef in numeric.py. If you're lucky, this will point out where the invalids are popping up. As a sanity check, you could instead make this seterr(all='ignore'), which should make all the warnings go away, but won't tell you anything about why there are warnings to begin with. Regards, -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
When I set seterr(all='warn') I see the following: In [1]: import numpy /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/ufunclike.py:46: RuntimeWarning: invalid value encountered in log _log2 = umath.log(2) /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/scimath.py:19: RuntimeWarning: invalid value encountered in log _ln2 = nx.log(2.0) /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:64: RuntimeWarning: invalid value encountered in add two = one + one /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:65: RuntimeWarning: invalid value encountered in subtract zero = one - one /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:71: RuntimeWarning: invalid value encountered in add a = a + a /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:72: RuntimeWarning: invalid value encountered in add temp = a + one /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:73: RuntimeWarning: invalid value encountered in subtract temp1 = temp - a /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:74: RuntimeWarning: invalid value encountered in subtract if any(temp1 - one != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:74: RuntimeWarning: invalid value encountered in not_equal if any(temp1 - one != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:80: RuntimeWarning: invalid value encountered in add b = b + b /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:81: RuntimeWarning: invalid value encountered in add temp = a + b /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:82: RuntimeWarning: invalid value encountered in subtract itemp = int_conv(temp-a) /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:83: RuntimeWarning: invalid value encountered in not_equal if any(itemp != 0): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:95: RuntimeWarning: invalid value encountered in multiply b = b * beta /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:96: RuntimeWarning: invalid value encountered in add temp = b + one /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:97: RuntimeWarning: invalid value encountered in subtract temp1 = temp - b /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:98: RuntimeWarning: invalid value encountered in subtract if any(temp1 - one != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:98: RuntimeWarning: invalid value encountered in not_equal if any(temp1 - one != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:103: RuntimeWarning: invalid value encountered in divide betah = beta / two /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:106: RuntimeWarning: invalid value encountered in add a = a + a /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:107: RuntimeWarning: invalid value encountered in add temp = a + one /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:108: RuntimeWarning: invalid value encountered in subtract temp1 = temp - a /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:109: RuntimeWarning: invalid value encountered in subtract if any(temp1 - one != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:109: RuntimeWarning: invalid value encountered in not_equal if any(temp1 - one != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:113: RuntimeWarning: invalid value encountered in add temp = a + betah /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:115: RuntimeWarning: invalid value encountered in subtract if any(temp-a != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:115: RuntimeWarning: invalid value encountered in not_equal if any(temp-a != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:117: RuntimeWarning: invalid value encountered in add tempa = a + beta /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:118: RuntimeWarning: invalid value encountered in add temp = tempa + betah /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:119: RuntimeWarning: invalid value encountered in subtract if irnd==0 and any(temp-tempa != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:119: RuntimeWarning: invalid value encountered in not_equal if irnd==0 and any(temp-tempa != zero): /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:124: RuntimeWarning: invalid value encountered in divide betain = one / beta /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/machar.py:127: RuntimeWarning: invalid value encountered in multiply a = a * betain
Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
Brian Granger wrote: Also, when I use seterr(all='ignore') the the tests fail: == FAIL: Ticket #112 -- Traceback (most recent call last): File /usr/common/homes/g/granger/usr/local/lib/python/numpy/core/tests/test_regression.py, line 219, in check_longfloat_repr assert(str(a)[1:9] == str(a[0])[:8]) AssertionError -- Ran 516 tests in 0.823s FAILED (failures=1) Thanks for helping out on this. How recent is your version? I just a problem that was causing this same failure yesterday -- if you checkout is older than that, you may want to get the most recent stuff from SVN and see if that fixes this. -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
I have been doing these recent tests with 1.0rc3. I am building from trunk right now and we will see how that goes. Thanks for your help. Brian On 10/20/06, Tim Hochberg [EMAIL PROTECTED] wrote: Brian Granger wrote: Also, when I use seterr(all='ignore') the the tests fail: == FAIL: Ticket #112 -- Traceback (most recent call last): File /usr/common/homes/g/granger/usr/local/lib/python/numpy/core/tests/test_regression.py, line 219, in check_longfloat_repr assert(str(a)[1:9] == str(a[0])[:8]) AssertionError -- Ran 516 tests in 0.823s FAILED (failures=1) Thanks for helping out on this. How recent is your version? I just a problem that was causing this same failure yesterday -- if you checkout is older than that, you may want to get the most recent stuff from SVN and see if that fixes this. -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
Brian Granger wrote: When I set seterr(all='warn') I see the following: In [1]: import numpy /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/ufunclike.py:46: RuntimeWarning: invalid value encountered in log _log2 = umath.log(2) /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/scimath.py:19: RuntimeWarning: invalid value encountered in log _ln2 = nx.log(2.0) [etc, etc] Wow! That looks pretty bad. What do you get if you try just numpy.log(2) or numpy.log(2.0)? Is it producing sane results for scalars at all? I suppose another possibility is that the error reporting is broken on AIX for some reason. Hmmm. I'm betting that is is. The macro UFUNC_CHECK_STATUS is very platform dependent. There is a version from AIX (ufuncobject.h line 301), but perhaps it's broken on your particular configuration and as a result is spitting out all kinds of bogus errors. This is only coming to light now because the default error checking level got cranked up. I gotta call it a night and I'll be out tomorrow, so I won't be much more help, but here's something that you might look into: have you compiled numarray sucessfully? If you haven't you might want to try it. It uses the same default error checking that numpy is now using. If you have, you might want to look for the equivalent of UFUNC_CHECK_STATUS (it might even have the same name) and splice it into numpy and see if it fixes your problems. Of course, if numpy.log(2) is spitting out something bogus, there's something much worse going on, but I suspect you would have noticed that by now. Good luck, -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
Brian Granger wrote: Tim, I just tried everything with r3375. I set seterr(all='warn') and the tests passed. But all the floating point warning are still there. With seterr(all='ignore') the warnings go away and all the tests pass. should I worry about the warnings? Maybe. I just sent you some email on this. But my guess is that the code that checks for FP errors is broken on your particular system. Mainly I suspect this because I think you would have noticed by now if everything was as broken as the warnings seem to indicate. Assuming that's the case, and this will probably become clear if you test a bunch of computations that give correct (and non INF/NAN) results, but still spit out warnings, you have two choices: try to fix the warnings code or disable the warnings. The former would be preferable since then you could actually use the warnings code, but it may be a pain in the neck unless you can find some place to steal the relevant code from. -tim thanks Brian On 10/20/06, Tim Hochberg [EMAIL PROTECTED] wrote: Brian Granger wrote: Also, when I use seterr(all='ignore') the the tests fail: == FAIL: Ticket #112 -- Traceback (most recent call last): File /usr/common/homes/g/granger/usr/local/lib/python/numpy/core/tests/test_regression.py, line 219, in check_longfloat_repr assert(str(a)[1:9] == str(a[0])[:8]) AssertionError -- Ran 516 tests in 0.823s FAILED (failures=1) Thanks for helping out on this. How recent is your version? I just a problem that was causing this same failure yesterday -- if you checkout is older than that, you may want to get the most recent stuff from SVN and see if that fixes this. -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
Re: [Numpy-discussion] Problem introduced after 1.0rc2 on AIX with xlc
Thanks, I will investigate more on these things and get back to you early in the week. But for now numpy seems to be functioning pretty normally (log(2) gives the correct answer). thanks again. It would be great to figure this stuff out before 1.0, but we might not have time. Brian On 10/20/06, Tim Hochberg [EMAIL PROTECTED] wrote: Brian Granger wrote: When I set seterr(all='warn') I see the following: In [1]: import numpy /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/ufunclike.py:46: RuntimeWarning: invalid value encountered in log _log2 = umath.log(2) /usr/common/homes/g/granger/usr/local/lib/python/numpy/lib/scimath.py:19: RuntimeWarning: invalid value encountered in log _ln2 = nx.log(2.0) [etc, etc] Wow! That looks pretty bad. What do you get if you try just numpy.log(2) or numpy.log(2.0)? Is it producing sane results for scalars at all? I suppose another possibility is that the error reporting is broken on AIX for some reason. Hmmm. I'm betting that is is. The macro UFUNC_CHECK_STATUS is very platform dependent. There is a version from AIX (ufuncobject.h line 301), but perhaps it's broken on your particular configuration and as a result is spitting out all kinds of bogus errors. This is only coming to light now because the default error checking level got cranked up. I gotta call it a night and I'll be out tomorrow, so I won't be much more help, but here's something that you might look into: have you compiled numarray sucessfully? If you haven't you might want to try it. It uses the same default error checking that numpy is now using. If you have, you might want to look for the equivalent of UFUNC_CHECK_STATUS (it might even have the same name) and splice it into numpy and see if it fixes your problems. Of course, if numpy.log(2) is spitting out something bogus, there's something much worse going on, but I suspect you would have noticed that by now. Good luck, -tim - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion - Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnkkid=120709bid=263057dat=121642 ___ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
