Mailman 3 December 2009 - NumPy-Discussion

Matlab's griddata3 for numpy?
by reckoner 23 Dec '09

23 Dec '09

Hi, I realize that there is a griddata for numpy via matplotlib, but is there a griddata3 (same has griddata, but for higher dimensions). Any help appreciated.

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dot function or dot notation, matrices, arrays?
by Wayne Watson 23 Dec '09

23 Dec '09

Is it possible to calculate a dot product in numpy by either notation (a ^ b, where ^ is a possible notation) or calling a dot function (dot(a,b)? I'm trying to use a column matrix for both "vectors". Perhaps, I need to somehow change them to arrays? -- Wayne Watson (Watson Adventures, Prop., Nevada City, CA) (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time) Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet "... humans'innate skills with numbers isn't much better than that of rats and dolphins." -- Stanislas Dehaene, neurosurgeon Web Page: <www.speckledwithstars.net/>

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Proposal for matrix_rank function in numpy
by Matthew Brett 22 Dec '09

22 Dec '09

Hi, Following on from the occasional discussion on the list, can I propose a small matrix_rank function for inclusion in numpy/linalg? I suggest it because it seems rather a basic need for linear algebra, and it's very small and simple... I've appended an implementation with some doctests in the hope that it will be acceptable, Robert - I hope you don't mind me quoting you in the notes. Thanks a lot, Matthew def matrix_rank(M, tol=None): ''' Return rank of matrix using SVD method Rank of the array is the number of SVD singular values of the array that are greater than `tol`. Parameters ---------- M : array-like array of <=2 dimensions tol : {None, float} threshold below which SVD values are considered zero. If `tol` is None, and `S` is an array with singular values for `M`, and `eps` is the epsilon value for datatype of `S`, then `tol` set to ``S.max() * eps``. Examples -------- >>> matrix_rank(np.eye(4)) # Full rank matrix 4 >>> matrix_rank(np.c_[np.eye(4),np.eye(4)]) # Rank deficient matrix 4 >>> matrix_rank(np.zeros((4,4))) # All zeros - zero rank 0 >>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0 1 >>> matrix_rank(np.zeros((4,))) 0 >>> matrix_rank([1]) # accepts array-like 1 Notes ----- Golub and van Loan define "numerical rank deficiency" as using tol=eps*S[0] (note that S[0] is the maximum singular value and thus the 2-norm of the matrix). There really is not one definition, much like there isn't a single definition of the norm of a matrix. For example, if your data come from uncertain measurements with uncertainties greater than floating point epsilon, choosing a tolerance of about the uncertainty is probably a better idea (the tolerance may be absolute if the uncertainties are absolute rather than relative, even). When floating point roundoff is your concern, then "numerical rank deficiency" is a better concept, but exactly what the relevant measure of the tolerance is depends on the operations you intend to do with your matrix. [RK, numpy mailing list] References ---------- Matrix Computations by Golub and van Loan ''' M = np.asarray(M) if M.ndim > 2: raise TypeError('array should have 2 or fewer dimensions') if M.ndim < 2: return int(not np.all(M==0)) S = npl.svd(M, compute_uv=False) if tol is None: tol = S.max() * np.finfo(S.dtype).eps return np.sum(S > tol)

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ANN: PyTables 2.2b2 released
by Francesc Alted 22 Dec '09

22 Dec '09

=========================== Announcing PyTables 2.2b2 =========================== PyTables is a library for managing hierarchical datasets and designed to efficiently cope with extremely large amounts of data with support for full 64-bit file addressing. PyTables runs on top of the HDF5 library and NumPy package for achieving maximum throughput and convenient use. This is the second beta version of 2.2 release. The main addition is the support for links. All HDF5 kind of links are supported: hard, soft and external. Hard and soft links are similar to hard and symbolic links in regular UNIX filesystems, while external links are more like mounting external filesystems (in this case, HDF5 files) on top of existing ones. This allows for a considerable degree of flexibility when defining your object tree. See the new tutorial at: http://www.pytables.org/docs/manual-2.2b2/ch03.html#LinksTutorial Also, some other new features (like complete control of HDF5 chunk cache parameters and native compound types in attributes), bug fixes and a couple of (small) API changes happened. In case you want to know more in detail what has changed in this version, have a look at: http://www.pytables.org/moin/ReleaseNotes/Release_2.2b2 You can download a source package with generated PDF and HTML docs, as well as binaries for Windows, from: http://www.pytables.org/download/preliminary For an on-line version of the manual, visit: http://www.pytables.org/docs/manual-2.2b2 Resources ========= About PyTables: http://www.pytables.org About the HDF5 library: http://hdfgroup.org/HDF5/ About NumPy: http://numpy.scipy.org/ Acknowledgments =============== Thanks to many users who provided feature improvements, patches, bug reports, support and suggestions. See the ``THANKS`` file in the distribution package for a (incomplete) list of contributors. Most specially, a lot of kudos go to the HDF5 and NumPy (and numarray!) makers. Without them, PyTables simply would not exist. Share your experience ===================== Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. ---- **Enjoy data!** -- The PyTables Team -- Francesc Alted

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cos -- NameError: global name 'cos' is not defined
by Wayne Watson 22 Dec '09

22 Dec '09

In this code, ===========start import math import numpy as np from numpy import matrix def sinD(D): # given in degrees, convert to radians return sin(radians(D)) def cosD(D): return cos(radians(D)) <<-------------- def acosD(D): acos(radians(D)) return=====end the << line produces, "NameError: global name 'cos' is not defined", but the sin() above it does not? They are both built-in functions. -- Wayne Watson (Watson Adventures, Prop., Nevada City, CA) (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time) Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet "... humans'innate skills with numbers isn't much better than that of rats and dolphins." -- Stanislas Dehaene, neurosurgeon Web Page: <www.speckledwithstars.net/>

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Import error in builds of 7726
by Chris 22 Dec '09

22 Dec '09

I am building Numpy on OSX 10.6 using a recent update from SVN (r7726). Though I was able to build the package successfully, the resulting package generates an ImportError: import umath ImportError: dlopen(/Library/Python/2.6/site-packages/ numpy-1.4.0.dev7726-py2.6-macosx-10.6- universal.egg/numpy/core/umath.so, 2): Symbol not found : _npy_cexp Referenced from: /Library/Python/2.6/site-packages/ numpy-1.4.0.dev7726-py2.6-macosx-10.6-universal.egg/ numpy/core/umath.so Expected in: flat namespace in /Library/Python/2.6/site-packages/ numpy-1.4.0.dev7726-py2.6-macosx-10.6-universal.egg /numpy/core/umath.so Note that this did not occur previously (r7542). For some reason umath is not being built properly.

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nicest way to apply an arbitrary sequence of row deltas to an array
by George Dahl 22 Dec '09

22 Dec '09

Hi everyone, I was wondering if anyone had insight on the best way to solve the following problem. Suppose I have a numpy array called U. U has shape (N,M) Suppose further that I have another array called dU and that dU has shape (P,M) and that P has no particular relationship to N, it could be larger or smaller. Additionally, I have a one dimensional array called idx and idx has shape (P,). Furthermore idx holds integers that are all valid indices into the rows of U. idx almost certainly contains some duplicates. I want, for k in range(P): U[idx[k],:] += dU[k,:] Is there a nice vectorized and efficient way to do this without making the obvious python for loop I included above? For what I am doing, P is usually quite large. I am most interested in a clever use of numpy/scipy commands and Python and not Cython. Thanks in advance for any suggestions. - George

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Another numpy svn installation error
by Gökhan Sever 21 Dec '09

21 Dec '09

Hello, Here are the steps that I went through to install the numpy from the svn-repo: svn co http://svn.scipy.org/svn/numpy/trunk numpy Be "su" and type: python setupegg.py develop Successful installation so far, but import fails with the given error: [gsever@ccn Desktop]$ python Python 2.6 (r26:66714, Jun 8 2009, 16:07:26) [GCC 4.4.0 20090506 (Red Hat 4.4.0-4)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> import numpy Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/gsever/Desktop/python-repo/numpy/numpy/__init__.py", line 132, in <module> import add_newdocs File "/home/gsever/Desktop/python-repo/numpy/numpy/add_newdocs.py", line 9, in <module> from numpy.lib import add_newdoc File "/home/gsever/Desktop/python-repo/numpy/numpy/lib/__init__.py", line 4, in <module> from type_check import * File "/home/gsever/Desktop/python-repo/numpy/numpy/lib/type_check.py", line 8, in <module> import numpy.core.numeric as _nx File "/home/gsever/Desktop/python-repo/numpy/numpy/core/__init__.py", line 6, in <module> import umath ImportError: /home/gsever/Desktop/python-repo/numpy/numpy/core/umath.so: undefined symbol: npy_spacing My platform: Linux-2.6.29.6-217.2.3.fc11.i686.PAE-i686-with-fedora-11-Leonidas Any ideas what might be wrong here? -- Gökhan

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help(numpy.dot) Hmmm.
by Wayne Watson 21 Dec '09

21 Dec '09

I've just become acquainted with the help command in WinXP IDLE. help(numyp.sin) works fine. What's going on with dot? >>> help(numpy.core.multiarray.dot) Help on built-in function dot in module numpy.core.multiarray: dot(...) Is there help for dot? -- Wayne Watson (Watson Adventures, Prop., Nevada City, CA) (121.015 Deg. W, 39.262 Deg. N) GMT-8 hr std. time) Obz Site: 39° 15' 7" N, 121° 2' 32" W, 2700 feet "... humans'innate skills with numbers isn't much better than that of rats and dolphins." -- Stanislas Dehaene, neurosurgeon Web Page: <www.speckledwithstars.net/>

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indexing question
by Alan G Isaac 21 Dec '09

21 Dec '09

Why is s3 F_CONTIGUOUS, and perhaps equivalently, why is its C_CONTIGUOUS data in s3.base (below)? Thanks, Alan Isaac >>> a3 array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11]]) >>> a3.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False >>> ind array([3, 1, 2, 4, 5, 0]) >>> s3 = a3[:,ind] >>> s3.flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : False WRITEABLE : True ALIGNED : True UPDATEIFCOPY : False >>> s3.base array([[ 3, 9], [ 1, 7], [ 2, 8], [ 4, 10], [ 5, 11], [ 0, 6]]) >>> s3 array([[ 3, 1, 2, 4, 5, 0], [ 9, 7, 8, 10, 11, 6]]) >>>

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