Mailman 3 February 2010 - SciPy-User

monic option of orthogonal polynomials
by Jonathan Stickel 24 Feb '10

24 Feb '10

What does the "monic" option do when calling an orthogonal polynomial function, e.g. special.legendre? It does not seem to be documented, or at least I have not found it. Thanks, Jonathan

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List to record array problems, questions
by Vincent Davis 24 Feb '10

24 Feb '10

I must be missing something simple. I have a list of lists data = "[[' 0', ' 0', '234.0', '24.0', ' 25'], [' 1', ' 0', '22428.0', '2378.1', ' 25'],......" and what to make a record array (I guess I have made a structured array) from it but it gets screwed up or I don't get it, likely both. Notice that at this stage the items are strings, not numbers, and there is whitespace not sure this matters. Here is what is happening, notice that the number get screwed up in the structured array (not sure who to get a record array, I thought this was but was told it is not) adata = numpy.array(data,numpy.float64) >>> adata array([[ 0.00000000e+00, 0.00000000e+00, 2.34000000e+02, 2.40000000e+01, 2.50000000e+01], ..., [ 4.77000000e+02, 4.77000000e+02, 2.07000000e+02, 4.58000000e+01, 2.50000000e+01]]) This is what I would expect except it is not a record array. This is not what I expect. I think I have tried every iteration including using numpy dtaypes numpy.int32 or bdata = numpy.array(data, dtype = [('x', int),('y', int),('mean',float),('stdv',float),('npixcels',int)]) What am I missing? bdata = numpy.array(data, [('x', int),('y', int),('mean',float),('stdv',float),('npixcels',int)]) >>> bdata array([[(3153952, 0, 0.0, 0.0, 0), (3153952, 0, 0.0, 0.0, 0), (206933603122, 0, 0.0, 0.0, 0), (808334386, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], [(3219488, 0, 0.0, 0.0, 0), (3153952, 0, 0.0, 0.0, 0), (13561617777439282, 0, 0.0, 0.0, 0), (54074581398322, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], [(3285024, 0, 0.0, 0.0, 0), (3153952, 0, 0.0, 0.0, 0), (206933931058, 0, 0.0, 0.0, 0), (925775666, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], ..., [(3487540, 0, 0.0, 0.0, 0), (3618612, 0, 0.0, 0.0, 0), (206933602866, 0, 0.0, 0.0, 0), (908996661, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], [(3553076, 0, 0.0, 0.0, 0), (3618612, 0, 0.0, 0.0, 0), (13561596370041137, 0, 0.0, 0.0, 0), (62870573495603, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], [(3618612, 0, 0.0, 0.0, 0), (3618612, 0, 0.0, 0.0, 0), (206933798962, 0, 0.0, 0.0, 0), (942552372, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)]], dtype=[('x', '<i8'), ('y', '<i8'), ('mean', '<f8'), ('stdv', '<f8'), ('npixcels', '<i8')]) *Vincent Davis 720-301-3003 * vincent(a)vincentdavis.net my blog <http://vincentdavis.net> | LinkedIn<http://www.linkedin.com/in/vincentdavis>

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TimeSeries and Milliseconds for aircraft data
by Chris Jesse 24 Feb '10

24 Feb '10

Hi All, I've got lots of timeseries data from aircraft sensors (such as altitude, temperatures, landing gear up/down, airspeed..) which I was hoping to load into a timeseries array. However, I've just read that it only supports time resolutions down to a second. Is there a solution for my data which contains samples from hundreds of different parameters at different frequencies from 8Hz to 1/64Hz over an 8 hour period? I need to be able to interpolate between values, mask values when samples are corrupt / outside of operational boundaries and store values as accurately to the nearest 1/16th of a second if possible. Your help is much appreciated! Thanks, Chris

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Suggest moving curve_fit from minpack to top level
by Pim Schellart 24 Feb '10

24 Feb '10

Dear Scipy users, I would like to suggest moving the curve_fit wrapper function from the minpack module to the top level and possibly renaming it to "fit". So you could access it using: import scipy as sp sp.fit(...) Curve fitting is one of the most important tasks in scientific analysis and most people starting with scipy struggle to find this (undocumented) function, especially when moving to scipy from gnuplot which has a basic fit function that performs the same task. What are your thoughts about this? Kind regards, Pim Schellart

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[ANN] HDF5 for Python (h5py) 1.3.0 beta
by Andrew Collette 23 Feb '10

23 Feb '10

HDF5 for Python (h5py) 1.3.0 BETA ================================= I'm pleased to announce that HDF5 for Python 1.3 is now available! This is a significant release introducing a number of new features, including support for soft/external links as well as object and region references. I encourage all interested HDF5/NumPy/Python users to give the beta a try and to do your best to break it. :) Download, documentation and contact links are below. What is h5py? ------------- HDF5 for Python (h5py) is a general-purpose Python interface to the Hierarchical Data Format library, version 5. HDF5 is a mature scientific software library originally developed at NCSA, designed for the fast, flexible storage of enormous amounts of data. >From a Python programmer's perspective, HDF5 provides a robust way to store data, organized by name in a tree-like fashion. You can create datasets (arrays on disk) hundreds of gigabytes in size, and perform random-access I/O on desired sections. Datasets are organized in a filesystem-like hierarchy using containers called "groups", and accesed using the tradional POSIX /path/to/resource syntax. In addition to providing interoperability with existing HDF5 datasets and platforms, h5py is a convienient way to store and retrieve arbitrary NumPy data and metadata. HDF5 datasets and groups are presented as "array-like" and "dictionary-like" objects in order to make best use of existing experience. For example, dataset I/O is done with NumPy-style slicing, and group access is via indexing with string keys. Standard Python exceptions (KeyError, etc) are raised in response to underlying HDF5 errors. New features in 1.3 ------------------- - Full support for soft and external links - Full support for object and region references, in all contexts (datasets, attributes, etc). Region references can be created using the standard NumPy slicing syntax. - A new get() method for HDF5 groups, which also allows the type of an object or link to be queried without first opening it. - Improved locking system which makes h5py faster in both multi-threaded and single-threaded applications. - Automatic creation of missing intermediate groups (HDF5 1.8) - Anonymous group and dataset creation (HDF5 1.8) - Option to enable cProfile support for the parts of h5py written in Cython - Many bug fixes and performance enhancements Other changes ------------- - Old-style dictionary methods (listobjects, etc) will now issue DeprecationWarning, and will be removed in 1.4. - Dataset .value attribute is deprecated. Use dataset[...] or dataset[()]. - new_vlen(), get_vlen(), new_enum() and get_enum() are deprecated in favor of the functions h5py.special_dtype() and h5py.check_dtype(), which also support reference types. Where to get it --------------- * Main website, documentation: http://h5py.alfven.org * Downloads, bug tracker: http://h5py.googlecode.com * Mailing list (discussion and development): h5py at googlegroups.com * Contact email: h5py at alfven.org Requires -------- * Linux, Mac OS-X or Windows * Python 2.5 or 2.6 * NumPy 1.0.3 or later * HDF5 1.6.5 or later (including 1.8); HDF5 is included with the Windows version.

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test
by Bruce Peterson 23 Feb '10

23 Feb '10

Bruce Peterson 425 466 7344

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list to structured or record array
by Vincent Davis 23 Feb '10

23 Feb '10

I must be missing something simple. I have a list of lists data = "[[' 0', ' 0', '234.0', '24.0', ' 25'], [' 1', ' 0', '22428.0', '2378.1', ' 25'],......" and what to make a record array (I guess I have made a structured array) from it but it gets screwed up or I don't get it, likely both. Notice that at this stage the items are strings, not numbers, and there is whitespace not sure this matters. Here is what is happening, notice that the number get screwed up in the structured array (not sure who to get a record array, I thought this was but was told it is not) adata = numpy.array(data,numpy.float64) >>> adata array([[ 0.00000000e+00, 0.00000000e+00, 2.34000000e+02, 2.40000000e+01, 2.50000000e+01], ..., [ 4.77000000e+02, 4.77000000e+02, 2.07000000e+02, 4.58000000e+01, 2.50000000e+01]]) This is what I would expect except it is not a record array. This is not what I expect. I think I have tried every iteration including using numpy dtaypes numpy.int32 or bdata = numpy.array(data, dtype = [('x', int),('y', int),('mean',float),('stdv',float),('npixcels',int)]) What am I missing? bdata = numpy.array(data, [('x', int),('y', int),('mean',float),('stdv',float),('npixcels',int)]) >>> bdata array([[(3153952, 0, 0.0, 0.0, 0), (3153952, 0, 0.0, 0.0, 0), (206933603122, 0, 0.0, 0.0, 0), (808334386, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], [(3219488, 0, 0.0, 0.0, 0), (3153952, 0, 0.0, 0.0, 0), (13561617777439282, 0, 0.0, 0.0, 0), (54074581398322, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], [(3285024, 0, 0.0, 0.0, 0), (3153952, 0, 0.0, 0.0, 0), (206933931058, 0, 0.0, 0.0, 0), (925775666, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], ..., [(3487540, 0, 0.0, 0.0, 0), (3618612, 0, 0.0, 0.0, 0), (206933602866, 0, 0.0, 0.0, 0), (908996661, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], [(3553076, 0, 0.0, 0.0, 0), (3618612, 0, 0.0, 0.0, 0), (13561596370041137, 0, 0.0, 0.0, 0), (62870573495603, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)], [(3618612, 0, 0.0, 0.0, 0), (3618612, 0, 0.0, 0.0, 0), (206933798962, 0, 0.0, 0.0, 0), (942552372, 0, 0.0, 0.0, 0), (3486240, 0, 0.0, 0.0, 0)]], dtype=[('x', '<i8'), ('y', '<i8'), ('mean', '<f8'), ('stdv', '<f8'), ('npixcels', '<i8')]) *Vincent Davis 720-301-3003 * vincent(a)vincentdavis.net my blog <http://vincentdavis.net> | LinkedIn<http://www.linkedin.com/in/vincentdavis>

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SciPy2010 Call for Papers
by Stéfan van der Walt 23 Feb '10

23 Feb '10

========================== SciPy 2010 Call for Papers ========================== SciPy 2010, the 9th Python in Science Conference, will be held from June 28th - July 3rd, 2010 in Austin, Texas. At this conference, novel applications and breakthroughs made in the pursuit of science using Python are presented. Attended by leading figures from both academia and industry, it is an excellent opportunity to experience the cutting edge of scientific software development. The conference is preceded by two days of paid tutorials, during which community experts provide training on several scientific Python packages. We invite you to take part by submitting a talk abstract on the conference website at: http://conference.scipy.org Talk/Paper Submission ===================== We solicit talks and accompanying papers (either formal academic or magazine-style articles) that discuss topics regarding scientific computing using Python, including applications, teaching, development and research. Papers are included in the peer-reviewed conference proceedings, published online. Please note that submissions primarily aimed at the promotion of a commercial product or service will not be considered. Important dates for authors include: * 11 April: Talk abstracts due * 20 April: Notification of acceptance * 13 June: Papers due * 15 August: Publication of proceedings Further detail will be made available on http://conference.scipy.org Conference Dates ================ * Friday, 10 May: Early registration ends * Monday-Tuesday, 28-29 June: Tutorials * Wednesday-Thursday, June 30-July 1: Conference * Friday-Saturday, July 2-3: Coding Sprints Executive Committee =================== * Conference: Jarrod Millman & Eric Jones * Program: Stefan van der Walt & Ondrej Certik * Student Sponsorship: Travis Oliphant For more information on Python, visit http://www.python.org.

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Reg: google summer of code 2010
by Mohit Goyal 22 Feb '10

22 Feb '10

I am a masters student doing research in data engineering. I have done projects involving web development and data management in python. I want to contribute to scipy via gsoc 2010. I am interested in working on data related projects in scipy like improving data sources and integrate it into all the numpy/scipy , adding data sets and examples to scipy, data analysis etc. Please suggest me how to proceed. Thank You. -- The Game is Not Over Because i haven't Won Yet. -Mohit Goyal

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data smoothing by regularization
by Jonathan Stickel 22 Feb '10

22 Feb '10

There has been some recent discussions on the list about interpolation and smoothing. I have recently implemented a regularization method to smooth 1-dimensional (y vs. x) data. Although the method is analogous to spline-based smoothing that already exists in Scipy and my code is currently limited to 1D, I have implemented some useful features, specifically generalized cross-validation for determining the regularization parameter and the ability to add constraints to the smooth result. Would there be enough interest for me to contribute my code to Scipy? How would I go about doing so? Please know that I am new to the scipy community. Thanks, Jonathan P.S. For further reading, see: http://dx.doi.org/10.1016/j.compchemeng.2009.10.007 If you do not have access to the journal, you may email me offlist.

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