Mailman 3 April 2012 - SciPy-User

n-dimensional sparse array
by Wolfgang Kerzendorf April 5, 2012

April 5, 2012

Is there a scipy or numpy (or any other package) way to store an n-dimensional sparse array? I did only find the sparsematrix which is only for two dimensions. Cheers Wolfgang

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Strange behavior of sph_jn
by Yann ZIEGLER April 5, 2012

April 5, 2012

Hi everybody, I noticed a strange behavior of the sph_jn function (and it really bothers me!). Here is a copy-past of what I get (from scipy.special import sph_jn) in IPython: In [1]: sph_jn(2,3350.506) Out[1]: (array([ 2.98461400e-04, -6.76491641e-07, -2.98462005e-04]), array([ 6.76491641e-07, 2.98461803e-04, -4.09252598e-07])) In [2]: sph_jn(2,3350.507) Out[2]: (array([ inf, -3.78029638e-07, -inf]), array([ 3.78029638e-07, inf, inf])) As you can see, there is a 'jump' (a very big one!) between 3350.506 and 3350.507 for some orders and some derivatives. What happens ? As a point of comparison, Mathematica (http://www.wolframalpha.com/input/?i=SpericalBesselJ[2%2C3350.507]) says: J_2(3350.507) ~= -0.000298462... wich seems to be far more correct. Is there any 'numerical disaster' ? Thanks, Yann

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nan's in stats.spearmanr
by Ben April 5, 2012

April 5, 2012

Apologies if this seems obvious to others, but I'm using both functions from pandas and stats.spearmanr in different bits of my code and noticed something odd. Is the following output expected? from pandas import DataFrame from scipy import stats a = [1, nan, 2] b = [1, 2, 2] df = DataFrame(zip(a,b)) stats.spearmanr(a,b) gives: (0.86602540378443871, 0.3333333333333332) df.corr(method="spearman") 0 1 0 1 1 1 1 1 Removing the nan from a produces identical results. I had expected the first output, but perhaps I'm not understanding how scipy likes to handle nan. Any advice much appreciated. Regards, Ben

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Rician distributions lacks sigma parameter
by Morten Kjeldgaard April 4, 2012

April 4, 2012

Hi, I am a new reader of this list, please forgive me if this issue has already discussed. I have been wanting to use the rician distribution (stats.rice) to analyze some data, but it seems that the implementation in scipy does not take the distribution's sigma parameter into account; rather, it has been set to 1.0. The wikipedia article shows the traditional formulation of the rician distribution [0]. I understand that some distributions, e.g. stats.norm, use the scale parameter to define std, but this does not seem to be the case with stats.rice. Any ideas on how to get around this, without actually modifying distribution.py? I have no experience with the internals of scipy, and wouldn't know how to modify it correctly. Cheers, Morten [0] http://en.wikipedia.org/wiki/Rice_distribution

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ANN: Veusz 1.15
by Jeremy Sanders April 4, 2012

April 4, 2012

For possible interest to members of this mailing list... Veusz 1.15 ---------- http://home.gna.org/veusz/ 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 1.15: * Improved hatching: - More hatch styles - Adjust spacing of hatching - Change hatching line style - Allow hatching background color * Axes will not extend beyond specified min and max values * Add options to extend axes by 2, 5, 10 and 15% of data range * Ctrl+MouseWheel zooms in and out of plot * Full screen graph view mode * New dataset plugins - Linear interpolation - Cumulative value - Rolling average - Subtract mean / minimum * Allow grid widgets to be placed in grid widgets * Catch EnvironmentError exceptions on Windows * Allow multiple datasets to be selected in dataset browser * Allow tagging of datasets and allow datasets be grouped by tags in dataset browser * Allow text to be written as text in SVG, rather than curves * Add DBus interface to program, if DBus is installed * 2D QDP support * Add setup.py options for packagers --veusz-resource-dir : location of data files --disable-install-docs * Add title option for keys Minor changes: * Use / rather than \ for path separator in saved file names for Windows/Unix compatibility * Add diamond fill error bar type * Add \color and \marker commands to text renderer * Support labels on xy datasets if one of x or y datasets missing * Reorganise dataset plugin menu * Fix links in INSTALL/README * Floating point intervals in capture dialog Bug fixes: * Trap case where nan values could be plotted * Fix error if website not accessible in exception dialog * Crash when min and max of axes are too similar * Fix clipping of paths after transform in SVG files * Fix crash in picker * Fix crash if duplication of characters in CSV date format * Fix crash in tool tip in dataset browser * Fix GlobalColor error (on certain dark color sets) * Fix blocked data import if no descriptor * Fix crash if log contours and minimum is zero * Bug fix https://bugzilla.redhat.com/show_bug.cgi?id=800196 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 * Vector field plots * Box plots * Polar plots * Ternary plots * 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, FITS, NPY/NPZ, QDP, binary and user-plugin importing * Data can be captured from external sources * User defined functions, constants and can import external Python functions * Plugin interface to allow user to write or load code to - import data using new formats - make new datasets, optionally linked to existing datasets - arbitrarily manipulate the document * Data picker * Interactive tutorial * Multithreaded rendering Requirements for source install: Python (2.4 or greater required) http://www.python.org/ Qt >= 4.4 (free edition) http://www.trolltech.com/products/qt/ PyQt >= 4.3 (SIP is required to be installed first) http://www.riverbankcomputing.co.uk/software/pyqt/ http://www.riverbankcomputing.co.uk/software/sip/ numpy >= 1.0 http://numpy.scipy.org/ Optional: 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/ PyMinuit >= 1.1.2 (optional improved fitting) http://code.google.com/p/pyminuit/ For EMF and better SVG export, PyQt >= 4.6 or better is required, to fix a bug in the C++ wrapping dbus-python, for dbus interface http://dbus.freedesktop.org/doc/dbus-python/ 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. Please also see and contribute to the Veusz wiki: http://barmag.net/veusz-wiki/ Issues with the current version: * Some recent versions of PyQt/SIP will causes crashes when exporting SVG files. Update to 4.7.4 (if released) or a recent snapshot to solve this problem. If you enjoy using Veusz, we 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 Git repository at https://github.com/jeremysanders/veusz.git.

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Problems with dopri45
by L. Barrott April 3, 2012

April 3, 2012

>On Tue, Apr 3, 2012 at 5:54 AM, L. Barrott <lb489(a)cam.ac.uk> wrote: > >> Hello, >> >> I have been trying to get scipy to solve a set of coupled odes and in >> particular I want to use the dopri 45 method as I want to compare the >> results to the ode45 method in MATLAB. The code runs along the lines of: >> >> def func (t, Y, params): >> ... >> return (Ydot) >> >> with Y a vector. The other ode methods (except dop853 of course) solve >> this fine but even if I use the example code on the documentation page >> the dopri method returns the following error >> >> create_cb_arglist: Failed to build argument list (siz) with enough >> arguments (tot-opt) required by user-supplied function >> (siz,tot,opt=2,3,0). ...(traceback stuff) _dop.error: failed in >> processing argument list for call-back fcn. >> >> Any ideas where I am going wrong? >> >> Many thanks >> LB >> > > >It would help to see more of your code. Could you include a complete, >self-contained script that demonstrates the error? > >Warren Even something as simple as; from scipy.integrate import ode y0, t0 = [0, 1], 0 def func (t, y, x): return [x, y[0]] r = ode(func).set_integrator ('dopri5') r.set_initial_value(y0, t0).set_f_params(1) t1 = 10 dt = 0.1 while r.successful() and r.t < t1: r.integrate(r.t+dt) Will fail and this is lifted straight from the documentation as far as I can see. The full error message is create_cb_arglist: Failed to build argument list (siz) with enough arguments (tot-opt) required by user-supplied function (siz,tot,opt=2,3,0). Traceback (most recent call last): File "<stdin>", line 2, in <module> File "/usr/lib/python2.7/dist-packages/scipy/integrate/ode.py", line 326, in integrate self.f_params,self.jac_params) File "/usr/lib/python2.7/dist-packages/scipy/integrate/ode.py", line 745, in run x,y,iwork,idid = self.runner(*((f,t0,y0,t1) + tuple(self.call_args))) _dop.error: failed in processing argument list for call-back fcn. LB

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Problems with dopri45
by L. Barrott April 3, 2012

April 3, 2012

Hello, I have been trying to get scipy to solve a set of coupled odes and in particular I want to use the dopri 45 method as I want to compare the results to the ode45 method in MATLAB. The code runs along the lines of: def func (t, Y, params): ... return (Ydot) with Y a vector. The other ode methods (except dop853 of course) solve this fine but even if I use the example code on the documentation page the dopri method returns the following error create_cb_arglist: Failed to build argument list (siz) with enough arguments (tot-opt) required by user-supplied function (siz,tot,opt=2,3,0). ...(traceback stuff) _dop.error: failed in processing argument list for call-back fcn. Any ideas where I am going wrong? Many thanks LB

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ANN: Spyder v2.1.9
by Pierre Raybaut April 1, 2012

April 1, 2012

Hi all, On the behalf of Spyder's development team (http://code.google.com/p/spyderlib/people/list), I'm pleased to announce that Spyder v2.1.9 has been released and is available for Windows XP/Vista/7, GNU/Linux and MacOS X: http://code.google.com/p/spyderlib/ This is a pure maintenance release -- a lot of bugs were fixed since v2.1.8: http://code.google.com/p/spyderlib/wiki/ChangeLog Spyder is a free, open-source (MIT license) interactive development environment for the Python language with advanced editing, interactive testing, debugging and introspection features. Originally designed to provide MATLAB-like features (integrated help, interactive console, variable explorer with GUI-based editors for dictionaries, NumPy arrays, ...), it is strongly oriented towards scientific computing and software development. Thanks to the `spyderlib` library, Spyder also provides powerful ready-to-use widgets: embedded Python console (example: http://packages.python.org/guiqwt/_images/sift3.png), NumPy array editor (example: http://packages.python.org/guiqwt/_images/sift2.png), dictionary editor, source code editor, etc. Description of key features with tasty screenshots can be found at: http://code.google.com/p/spyderlib/wiki/Features On Windows platforms, Spyder is also available as a stand-alone executable (don't forget to disable UAC on Vista/7). This all-in-one portable version is still experimental (for example, it does not embed sphinx -- meaning no rich text mode for the object inspector) but it should provide a working version of Spyder for Windows platforms without having to install anything else (except Python 2.x itself, of course). Don't forget to follow Spyder updates/news: * on the project website: http://code.google.com/p/spyderlib/ * and on our official blog: http://spyder-ide.blogspot.com/ Last, but not least, we welcome any contribution that helps making Spyder an efficient scientific development/computing environment. Join us to help creating your favourite environment! (http://code.google.com/p/spyderlib/wiki/NoteForContributors) Enjoy! -Pierre

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