Mailman 3 March 2004 - NumPy-Discussion

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Installing Numpy
by Hoa Nguyen March 13, 2004

March 13, 2004

Hi, Please help me with installing NumPy. When I run: python setup.py install there was error. It said that: ImportError: No module named distutils I'm using Mandrake 9. Thanks, Hoa Nguyen __________________________________ Do you Yahoo!? Yahoo! Mail - More reliable, more storage, less spam http://mail.yahoo.com

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ANN: numarray-0.9
by Todd Miller March 13, 2004

March 13, 2004

Release Notes for numarray-0.9 Numarray is an array processing package designed to efficiently manipulate large multi-dimensional arrays. Numarray is modelled after Numeric and features c-code generated from python template scripts, the capacity to operate directly on arrays in files, and improved type promotions. I. ENHANCEMENTS 1. Support for "from __future__ import division" True division has been implemented for numarray. This means that modules that wish to use true division can also use numarray and numarray division will work as follows: a. dividing any two integer arrays using "/" results in a Float32 array. b. dividing two floating point arrays using "//" results in truncation of the result as in: a // b == floor(a/b). 2. C-coded array slicing Array slicing has been re-implemented in C-code as part of the _ndarray module. This means faster slicing. Thanks go to Warren Hack, Chris Hanley, and Ivo Busko for helping debug a huge refcount error. 3. Decreased Ufunc overhead Ufunc execution speed has clawed and scratched its way back to where it was around numarray-0.5. Improvements here included optimization of the ufunc caching, smarter thread handling, and smarter support for subclasses. The ufunc caching is based on a simple 20 element table for each ufunc. 4. Faster array creation from C Code which creates NumArrays from C (including numarray itself) can now do so faster because the API functions have been modified to do the array __init__ inline rather than through an expensive Python callback. II. BUGS FIXED / CLOSED See http://sourceforge.net/tracker/?atid=450446&group_id=1369&func=browse for more details. 913781 Another memory leak in example in Chapter 12 908399 Numarray 0.7: "del a[1]" dumps core 899259 astype Int16 copy4bytes: access beyond buffer 895801 Buffer overflow in sum w/ 0-sized array 894810 MemoryError When Creating Large Arrays 890703 getnan() and getinf() failure 883124 and and operator.and respond differently 865410 Usage of __dict__ 854480 Slice assignment of float to integer 839367 Overlapping slice assign fails 828941 Numarray: determinant returns scalar or array 820122 Linearalgebra2.determinant problem 817343 Sub-classing of NumArray inhibited by complex values 793336 crash in _sort.pyd 772548 Reference counting errors 683957 Adding certain arrays fails in Numarray III. CAUTIONS 1. numarray extension writers should note that the documented use of PyArray_INCREF and PyArray_XDECREF (in numarray) has been found to be incompatible with Numeric and has therefore been deprecated. numarray wrapper functions using PyArray_INCREF and PyArray_XDECREF should switch to ordinary Py_INCREF and Py_XDECREF. WHERE ----------- Numarray-0.9 windows executable installers, source code, and manual is here: http://sourceforge.net/project/showfiles.php?group_id=1369 Numarray is hosted by Source Forge in the same project which hosts Numeric: http://sourceforge.net/projects/numpy/ The web page for Numarray information is at: http://stsdas.stsci.edu/numarray/index.html Trackers for Numarray Bugs, Feature Requests, Support, and Patches are at the Source Forge project for NumPy at: http://sourceforge.net/tracker/?group_id=1369 REQUIREMENTS ------------------------------ numarray-0.9 requires Python 2.2.2 or greater. AUTHORS, LICENSE ------------------------------ Numarray was written by Perry Greenfield, Rick White, Todd Miller, JC Hsu, Paul Barrett, Phil Hodge at the Space Telescope Science Institute. We'd like to acknowledge the assitance of Francesc Alted, Paul Dubois, Sebastian Haase, Tim Hochberg, Nadav Horesh, Edward C. Jones, Eric Jones, Jochen Küpper, Travis Oliphant, Pearu Peterson, Peter Verveer, Colin Williams, and everyone else who has contributed with comments and feedback. Numarray is made available under a BSD-style License. See LICENSE.txt in the source distribution for details. -- Todd Miller jmiller(a)stsci.edu

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A simple question about type().
by Andrea Riciputi March 11, 2004

March 11, 2004

Hi, it is surely a silly question, but I'm quite new to Python and Numpy. So I hope you can help me, I've searched Numpy's manual without successs. Suppose I want to test if a paremeter inside a function is an array or not. How can I test this? type(my_array) is array returns 'False'!! Thanks in advance, Andrea. --- Andrea Riciputi "Science is like sex: sometimes something useful comes out, but that is not the reason we are doing it" -- (Richard Feynman)

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by Derrick Meyer March 11, 2004

March 11, 2004

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numarray: shape of scalar
by Sebastian Haase March 10, 2004

March 10, 2004

Hi, Is this intended: >>> na.array([1,2,4]).shape (3,) >>> na.array([1,2]).shape (2,) >>> na.array([1]).shape (1,) >>> na.array(1).shape () Why is na.array([1]).shape not equal to na.array(1).shape ? Did I miss the respective section in the manual ? Thanks, Sebastian Haase

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[ANN] PyTables 0.8
by Francesc Alted March 3, 2004

March 3, 2004

I'm happy to announce the availability of PyTables 0.8. PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the numarray package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy-to-use tool for interactively saving and retrieving very large amounts of data. It also provides flexible indexed access on disk to anywhere in the data. PyTables is not designed to work as a relational database competitor, but rather as a teammate. If you want to work with large datasets of multidimensional data (for example, for multidimensional analysis), or just provide a categorized structure for some portions of your cluttered RDBS, then give PyTables a try. It works well for storing data from data acquisition systems (DAS), simulation software, network data monitoring systems (for example, traffic measurements of IP packets on routers), working with very large XML files or as a centralized repository for system logs, to name only a few possible uses. In this release you will find: - Variable Length Arrays (VLA's) for saving a collection of variable length of elements in each row of an array. - Extensible Arrays (EA's) for extending homogeneous datasets on disk. - Powerful replication capabilities, ranging from single leaves up to complete hierarchies. - With the introduction of the UnImplemented class, greatly improved HDF5 native file import capabilities. - Two new useful utilities: ptdump & ptrepack. - Improved documentation (with the help of Scott Prater). - New record on data size achieved: 5.6 TB (!) in one single file. - Enhanced platform support. New platforms: MacOSX, FreeBSD, Linux64, IRIX64 (yes, a clean 64-bit port is there) and probably more. - More tests units (now exceeding 800). - Many other minor improvements. More in detail: What's new ----------- - The new VLArray class enables you to store large lists of rows containing variable numbers of elements. The elements can be scalars or fully multimensional objects, in the PyTables tradition. This class supports two special objects as rows: Unicode strings (UTF-8 codification is used internally) and generic Python objects (through the use of cPickle). - The new EArray class allows you to enlarge already existing multidimensional homogeneous data objects. Consider it an extension of the already existing Array class, but with more functionality. Online compression or other filters can be applied to EArray instances, for example. Another nice feature of EA's is their support for fully multidimensional data selection with extended slices. You can write "earray[1,2:3,...,4:200]", for example, to get the desired dataset slice from the disk. This is implemented using the powerful selection capabilities of the HDF5 library, which results in very highly efficient I/O operations. The same functionality has been added to Array objects as well. - New UnImplemented class. If a dataset contains unsupported datatypes, it will be associated with an UnImplemented instance, then inserted into to the object tree as usual. This allows you to continue to work with supported objects while retaining access to attributes of unsupported datasets. This has changed from previous versions, where a RuntimeError occurred when an unsupported object was encountered. The combination of the new UnImplemented class with the support for new datatypes will enable PyTables to greatly increase the number of types of native HDF5 files that can be read and modified. - Boolean support has been added for all the Leaf objects. - The Table class has now an append() method that allows you to save large buffers of data in one go (i.e. bypassing the Row accessor). This can greatly improve data gathering speed. - The standard HDF5 shuffle filter (to further enhance the compression level) is supported. - The standard HDF5 fletcher32 checksum filter is supported. - As the supported number of filters is growing (and may be further increased in the future), a Filters() class has been introduced to handle filters more easily. In order to add support for this class, it was necessary to make a change in the createTable() method that is not backwards compatible: the "compress" and "complib" parameters are deprecated now and the "filters" parameter should be used in their place. You will be able to continue using the old parameters (only a Deprecation warning will be issued) for the next few releases, but you should migrate to the new version as soon as possible. In general, you can easily migrate old code by substituting code in its place: table = fileh.createTable(group, 'table', Test, '', complevel, complib) should be replaced by table = fileh.createTable(group, 'table', Test, '', Filters(complevel, complib)) - A copy() method that supports slicing and modification of filtering capabilities has been added for all the Leaf objects. See the User's Manual for more information. - A couple of new methods, namely copyFile() and copyChilds(), have been added to File class, to permit easy replication of complete hierarchies or sub-hierarchies, even to other files. You can change filters during the copy process as well. - Two new utilities has been added: ptdump and ptrepack. The utility ptdump allows the user to examine the contents of PyTables files (both metadata and actual data). The powerful ptrepack utility lets you selectively copy (portions of) hierarchies to specific locations in other files. It can be also used as an importer for generic HDF5 files. - The meaning of the stop parameter in read() methods has changed. Now a value of 'None' means the last row, and a value of 0 (zero) means the first row. This is more consistent with the range() function in python and the __getitem__() special method in numarray. - The method Table.removeRows() is no longer limited by table size. You can now delete rows regardless of the size of the table. - The "numarray" value has been added to the flavor parameter in the Table.read() method for completeness. - The attributes (.attr instance variable) are Python properties now. Access to their values is no longer lazy, i.e. you will be able to see both system or user attributes from the command line using the tab-completion capability of your python console (if enabled). - Documentation has been greatly improved to explain all the new functionality. In particular, the internal format of PyTables is now fully described. You can now build "native" PyTables files using any generic HDF5 software by just duplicating their format. - Many new tests have been added, not only to check new functionality but also to more stringently check existing functionality. There are more than 800 different tests now (and the number is increasing :). - PyTables has a new record in the data size that fits in one single file: more than 5 TB (yeah, more than 5000 GB), that accounts for 11 GB compressed, has been created on an AMD Opteron machine running Linux-64 (the 64 bits version of the Linux kernel). See the gory details in: http://pytables.sf.net/html/HowFast.html. - New platforms supported: PyTables has been compiled and tested under Linux32 (Intel), Linux64 (AMD Opteron and Alpha), Win32 (Intel), MacOSX (PowerPC), FreeBSD (Intel), Solaris (6, 7, 8 and 9 with UltraSparc), IRIX64 (IRIX 6.5 with R12000) and it probably works in many more architectures. In particular, release 0.8 is the first one that provides a relatively clean porting to 64-bit platforms. - As always, some bugs have been solved (especially bugs that occur when deleting and/or overwriting attributes). - And last, but definitely not least, a new donations section has been added to the PyTables web site (http://sourceforge.net/projects/pytables, then follow the "Donations" tag). If you like PyTables and want this effort to continue, please, donate! What is a table? ---------------- A table is defined as a collection of records whose values are stored in fixed-length fields. All records have the same structure and all values in each field have the same data type. The terms "fixed-length" and "strict data types" seem to be quite a strange requirement for an language like Python that supports dynamic data types, but they serve a useful function if the goal is to save very large quantities of data (such as is generated by many scientific applications, for example) in an efficient manner that reduces demand on CPU time and I/O resources. What is HDF5? ------------- For those people who know nothing about HDF5, it is is a general purpose library and file format for storing scientific data made at NCSA. HDF5 can store two primary objects: datasets and groups. A dataset is essentially a multidimensional array of data elements, and a group is a structure for organizing objects in an HDF5 file. Using these two basic constructs, one can create and store almost any kind of scientific data structure, such as images, arrays of vectors, and structured and unstructured grids. You can also mix and match them in HDF5 files according to your needs. Platforms --------- I'm using Linux (Intel 32-bit) as the main development platform, but PyTables should be easy to compile/install on many other UNIX machines. This package has also passed all the tests on a UltraSparc platform with Solaris 7 and Solaris 8. It also compiles and passes all the tests on a SGI Origin2000 with MIPS R12000 processors, with the MIPSPro compiler and running IRIX 6.5. It also runs fine on Linux 64-bit platforms, like an AMD Opteron running SuSe Linux Enterprise Server. It has also been tested in MacOSX platforms (10.2 but should also work on newer versions). Regarding Windows platforms, PyTables has been tested with Windows 2000 and Windows XP (using the Microsoft Visual C compiler), but it should also work with other flavors as well. An example? ----------- For online code examples, have a look at http://pytables.sourceforge.net/html/tut/tutorial1-1.html and, for newly introduced Variable Length Arrays: http://pytables.sourceforge.net/html/tut/vlarray2.html Web site -------- Go to the PyTables web site for more details: http://pytables.sourceforge.net/ Share your experience --------------------- Let me know of any bugs, suggestions, gripes, kudos, etc. you may have. Have fun! -- Francesc Alted falted(a)pytables.org -- Francesc Alted

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[Ann] Discretizing with Numarray
by Magnus Lie Hetland March 1, 2004

March 1, 2004

I've just written a small tutorial on discretizing time series with numarray: http://hetland.org/python/disc-with-numarray Just some basic stuff I've used when working with time series in my research, such as mapping integer values to symbols, mapping real values to integers using bins, and extracting features using non-overlapping or overlapping windows (sliding window). It was written in a flash, and I'm no numarray expert, so feedback/corrections would be welcome. -- Magnus Lie Hetland "The mind is not a vessel to be filled, http://hetland.org but a fire to be lighted." [Plutarch]

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