Mailman 3 March 2010 - SciPy-User

SciPy 2010: Vote on Tutorials & Sprints
by Amenity Applewhite 30 Mar '10

30 Mar '10

Email not displaying correctly? View it in your browser. Hello Amenity, Spring is upon us and arrangements for SciPy 2010 are in full swing. We're already nearing on some important deadlines for conference participants: April 11th is the deadline for submitting an abstract for a paper, and April 15th is the deadline for submitting a tutorial proposal. Help choose tutorials for SciPy 2010... We set up a UserVoice page to brainstorm tutorial topics last week and we already have some great ideas. The top ones at the moment are: Effective multi-core programming with Cython and Python Building your own app with Mayavi High performance computing with Python Propose your own or vote on the existing suggestions here. ...Or instruct a tutorial and cover your conference costs. Did you know that we're awarding generous stipends to tutorial instructors this year? So if you believe you could lead a tutorial, by all means submit your proposal — soon! They're due April 15th. Call for Papers Continues Submitting a paper for to present at SciPy 2010 is easy, so remember to prepare one and have your friends and colleagues follow suit. Send us your abstract before April 11th and let us know whether you'd like to speak at the main conference or one of the specialized tracks. Details here. Have you registered? Booking your tickets early should save you money — not to mention the early registration prices you will qualify for if you register before May 10th. Best, The SciPy 2010 Team @SciPy2010 on Twitter You are receiving this email because you have registered for the SciPy 2010 conference in Austin, TX. Unsubscribe amenity(a)enthought.com from this list | Forward to a friend | Update your profile Our mailing address is: Enthought, Inc. 515 Congress Ave. Austin, TX 78701 Add us to your address book Copyright (C) 2010 Enthought, Inc. All rights reserved.

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ANN: visvis 1.2
by Almar Klein 30 Mar '10

30 Mar '10

Hi all, I am pleased to announce version 1.2 of visvis, an object oriented approach to visualization of 1D to 4D data. Website: http://code.google.com/p/visvis/ Discussion group: http://groups.google.com/group/visvis/ Documentation: http://code.google.com/p/visvis/wiki/Visvis_basics Since the last release, much work has been done on the interaction with the backend GUI toolkits, making visvis work (better) on Linux. As a result of these changes, the FLTK backend is now also stable. For more changes see the release notes <http://code.google.com/p/visvis/wiki/releaseNotes>. === Description === Visvis is a pure Python library for visualization of 1D to 4D data in an object oriented way. It can visualize plots (lines with markers), show images, and render 3D volumes. These data can also be moved in time. Visvis is cross-platform, lightweight, and is designed to be easily extendable. Essentially, visvis is an object oriented layer of Python on top of OpenGl, thereby combining the power of OpenGl with the usability of Python. A Matlab-like interface in the form of a set of functions allows easy creation of objects (e.g. plot(), imshow(), volshow()). Visvis can be used in Python scripts, interactive Python shells (as with IPython or IEP) and can be embedded in GUI applications. Regards, Almar

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ANN: Veusz 1.7
by Jeremy Sanders 30 Mar '10

30 Mar '10

Veusz 1.7 --------- Velvet Ember Under Sky Zenith ----------------------------- http://home.gna.org/veusz/ Veusz is Copyright (C) 2003-2010 Jeremy Sanders <jeremy(a)jeremysanders.net> Licenced under the GPL (version 2 or greater). Veusz is a Qt4 based scientific plotting package. It is written in Python, using PyQt4 for display and user-interfaces, and numpy for handling the numeric data. Veusz is designed to produce publication-ready Postscript/PDF/SVG output. The user interface aims to be simple, consistent and powerful. Veusz provides a GUI, command line, embedding and scripting interface (based on Python) to its plotting facilities. It also allows for manipulation and editing of datasets. Data can be captured from external sources such as internet sockets or other programs. Changes in 1.7: * Widgets can be moved by dragged and dropped in the widget tree, or copied by holding down ctrl at the same time * Tick labels are centred if possible at the start and ends of axes * When putting graphs in grid, axis labels and tick labels are placed in much better positions * Embedding module is shipped in binary versions * Grid lines can be drawn on axis minor ticks * Contour widget can draw minor (dotted) contours between main contours * Logarithmic contours have proper logarithmic spacing * Fixes for widget names and dataset names with Unicode characters, including copy and paste * Optional smoothing in the image widget Minor changes: * Errors in evaluating expressions are logged in the console window, and do not show exceptions * Fix problems when importing multiple symbols from python modules in the custom import dialog * Use minus sign for negative numbers in tick labels, rather than hyphens * Contour widget lines can have transparency * Datasets are sorted by name when writing to saved document * Use correct status for paste button when starting application * Add option for extra space between axes and tick labels, and axis labels * Preference added for background color of exported bitmaps * Add IsClosed() and WaitForClose() embedding functions to check whether plot window is closed, or to wait for closing of plot window. Features of package: * X-Y plots (with errorbars) * Line and function plots * Contour plots * Images (with colour mappings and colorbars) * Stepped plots (for histograms) * Bar graphs * Plotting dates * Fitting functions to data * Stacked plots and arrays of plots * Plot keys * Plot labels * Shapes and arrows on plots * LaTeX-like formatting for text * EPS/PDF/PNG/SVG/EMF export * Scripting interface * Dataset creation/manipulation * Embed Veusz within other programs * Text, CSV and FITS importing * Data can be captured from external sources * User defined functions, constants and can import external Python functions Requirements for source install: Python (2.4 or greater required) http://www.python.org/ Qt >= 4.3 (free edition) http://www.trolltech.com/products/qt/ PyQt >= 4.3 (SIP is required to be installed first) http://www.riverbankcomputing.co.uk/pyqt/ http://www.riverbankcomputing.co.uk/sip/ numpy >= 1.0 http://numpy.scipy.org/ Optional: Microsoft Core Fonts (recommended for nice output) http://corefonts.sourceforge.net/ PyFITS >= 1.1 (optional for FITS import) http://www.stsci.edu/resources/software_hardware/pyfits pyemf >= 2.0.0 (optional for EMF export) http://pyemf.sourceforge.net/ For EMF and better SVG export, PyQt >= 4.6 or better is required, to fix a bug in the C++ wrapping For documentation on using Veusz, see the "Documents" directory. The manual is in PDF, HTML and text format (generated from docbook). The examples are also useful documentation. Issues with the current version: * Due to Qt, hatched regions sometimes look rather poor when exported to PostScript, PDF or SVG. * Due to a bug in Qt, some long lines, or using log scales, can lead to very slow plot times under X11. It is fixed by upgrading to Qt-4.5.1 (or using a binary). Switching off antialiasing in the options may help. If you enjoy using Veusz, I would love to hear from you. Please join the mailing lists at https://gna.org/mail/?group=veusz to discuss new features or if you'd like to contribute code. The latest code can always be found in the SVN repository. Jeremy Sanders

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Zero crossing detection
by Moritz Maus 30 Mar '10

30 Mar '10

Is there a way for the ODE-integrators to a.) stop the simulation when a specific event occurred or b.) doing a MatLab (Simulink)-like "zero crossing detection", (when these timepoints are not pre-defined?)

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question about timeseries indexing
by Christian Prinoth 30 Mar '10

30 Mar '10

Hi, I am experimenting with scikits.timeseries, and have found a behaviour that I do not understand. I have the following time series: s2 = ts.time_series(np.arange(10),start_date=ts.Date('B','2010-01-01')) Now, I get the following: In [79]: s2['2010-01-08'] Out[79]: 5 In [80]: s2['2010-01-09'] Out[80]: 6 In [81]: s2['2010-01-10'] Out[81]: 5 In [82]: s2['2010-01-11'] Out[82]: 6 for lines 80 and 81, I would expect either an error message or 5, but not two different results. What is the reason for this behaviour? Also, more generally, is there some way to index a time series with dates so that I get the latest available value? Thanks Chris

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How to estimate error in polynomial coefficients from scipy.polyfit?
by Jeremy Conlin 29 Mar '10

29 Mar '10

I am using scipy.polyfit to fit a curve to my data. Members of this list have been integral in my understanding of how to use this function. Now I would like to know how I can get the uncertainties (standard deviations) of polynomial coefficients from the returned values from scipy.polyfit. If I understand correctly, the residuals are sometimes called the R^2 error, right? That gives an estimate of how well we have fit the data. I don't know how to use rank or any of the other returned values to get the uncertainties. Can someone please help? Thanks, Jeremy

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tests for equality of coefficients across regression or groups
by josef.pktd＠gmail.com 29 Mar '10

29 Mar '10

I wrote a class that is more convenient to test whether all coefficient are the same across different regressions or groups. This can also be used to test for structural breaks at predefined change points, and it is a bit similar to ANCOVA (as far as I understand) however currently all coefficients are tested for changes across groups. (While ANCOVA assumes that some slope coefficients are the same. But I'm not sure about all the ANCOVA, ANOVA and MANCOVA terminology). If the only regressor is a constant, then the result is identical to scipy.stats.f_oneway. The original will be in the statsmodels sandbox, until some missing enhancements are finished. The attached file contains a standalone version (but requires scikits.statsmodels). Several examples are included. Below is par of the example output. 4 groups, groups 0 and 1 and groups 2 and 3 have identical coefficients, but large difference between 0,1 and 2,3 sample size is 100 where the results are pretty clear cut. I haven't thought much about the API for this kind of convenience class. I will finish it up one of the next weekends. Any comments are welcome, in case someone finds this useful. Josef Test for equality of coefficients for all exogenous variables ------------------------------------------------------------- Table of F-tests for overall or pairwise equality of coefficients hypothesis F-statistic p-value df_denom df_num reject ('all', (212.94813033530886, 5.0118071432076266e-082, 192.0, 6)) * ((0, 1), (1.8208707506170163, 0.16466906994879624, 192.0, 2)) ((0, 2), (281.4877465209409, 8.2293196773132505e-058, 192.0, 2)) * ((0, 3), (322.21218144617899, 4.4051148030359891e-062, 192.0, 2)) * ((1, 2), (313.90670953958153, 3.0218699484829141e-061, 192.0, 2)) * ((1, 3), (356.39739587117498, 2.3340444544965008e-065, 192.0, 2)) * ((2, 3), (1.4442741180990379, 0.23846859348160215, 192.0, 2)) Notes: p-values are not corrected for many tests (no Bonferroni correction) * : reject at 5% uncorrected confidence level Null hypothesis: all or pairwise coefficient are the same Alternative hypothesis: all coefficients are different Comparison with stats.f_oneway (5.4853751213497954, 0.0012219835303810941) Likelihood Ratio Test likelihood ratio p-value df (407.06211448271051, 8.4739101836496682e-085, 6.0) Null model: pooled all coefficients are the same across groups, Alternative model: all coefficients are allowed to be different not verified but looks close to f-test result Ols parameters by group from individual, separate ols regressions 0 [ 14.89986056 10.49887409] 1 [ 14.56954028 9.8411222 ] 2 [ 16.95335652 18.83403447] 3 [ 17.03709822 19.43980093] Check for heteroscedasticity, variance and standard deviation for individual regressions group 0 group 1 group 2 group 3 variance [ 2.615224 2.73526096 4.38683135 2.91666872] standard dev [ 1.61716542 1.65386244 2.09447639 1.70782573]

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Again with matrices multiplication
by Graziano Mirata 29 Mar '10

29 Mar '10

Hi guys, it's me again. Still problems with matrices multiplication. So i have used dot(w,R) to multiply two arrays but now i cannot access to elements. I.e In [224]: w.shape Out[224]: (3,) In [226]: R.shape Out[226]: (24000, 3) In [227]: r = dot(R,w) In [228]: type(r) Out[228]: <type 'numpy.ndarray'> In [229]: r.shape Out[229]: (24000,) In [230]: r[0,0] --------------------------------------------------------------------------- IndexError Traceback (most recent call last) /<ipython console> in <module>() IndexError: invalid index In [231]: r[:,0] --------------------------------------------------------------------------- IndexError Traceback (most recent call last) /<ipython console> in <module>() IndexError: invalid index Thanx G

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experiences with Numpy+Scipy+IronPython?
by Tim Michelsen 27 Mar '10

27 Mar '10

Dear all, are there any users which have projects that use the Numpy/Scipy libraries on IronPython? Ironclad (http://code.google.com/p/ironclad/)claims to make bothe accessible under IronPython There exist then a possibility to plug this into Excel... Can I install IronPython alongside normal Python without conflicts? Best regards, Tim

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SciPy 2010 Tutorials: brainstorming and call for proposals
by Brian Granger 27 Mar '10

27 Mar '10

Greetings everyone, This year, there will be two days of tutorials (June 28th and 29th) before the main SciPy 2010 conference. Each of the two tutorial tracks (intro, advanced) will have a 3-4 hour morning and afternoon session both days, for a total of 4 intro sessions and 4 advanced sessions. The main tutorial web page for SciPy 2010 is here: http://conference.scipy.org/scipy2010/tutorials.html We are currently in the process of planning the tutorial sessions. You can help us in two ways: Brainstorm/vote on potential tutorial topics ============================================ To help us plan the tutorials, we have setup a web site that allow everyone in the community to brainstorm and vote on tutorial ideas/topics. The website for brainstorming/voting is here: http://conference.scipy.org/scipy2010/tutorialsUV.html The tutorial committee will use this information to help select the tutorials. Please jump in and let us know what tutorial topics you would like to see. Tutorial proposal submissions ============================= We are now accepting tutorial proposals from individuals or teams that would like to present a tutorial. Tutorials should be focused on covering a well defined topic in a hands on manner. We want to see tutorial attendees coding! We are pleased to offer tutorial presenters stipends this year for the first time: * 1 Session: $1,000 (half day) * 2 Sessions: $1,500 (full day) Optionally, part of this stipend can be applied to the presenter's registration costs. To submit a tutorial proposal please submit the following materials to 2010tutorials(a)scipy.org by April 15: * A short bio of the presenter or team members. * Which track the tutorial would be in (intro or advanced). * A short description and/or outline of the tutorial content. * A list of Python packages that attendees will need to have installed to follow along.

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