Mailman 3 July 2011 - SciPy-User

Time Series using 15 minute intervals using scikits.timeseries
by David Montgomery 04 Jul '11

04 Jul '11

Hi, Using scikits timeseries I can create daily and hourly time series....no prob But.... I have time series at 15 minutes intervals...this I dont know how to do... Can a timeseries array handle 15 min intervals? Do I use a minute intervals and use mask arrays for the missing minutes? Also..I can figure out how to create a array at minute intervals. So..what is best practice? Any examples? Thanks st = ts.Date('H', year=ts_start_date.year,month=ts_start_date.month,day=ts_start_date.day,hour=ts_start_hour) ed = ts.Date('H', year=ts_end_date.year,month=ts_end_date.month,day=ts_end_date.day,hour=ts_end_hour) st_beg = st.asfreq('H', relation='START') ed_end = ed.asfreq('H', relation='END')

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64 bit compilation
by Jaime Lozano 03 Jul '11

03 Jul '11

Hello scipy users, I've installed 64 bit python from source (CC=gcc -m64 ...). Now I'm trying to install scipy python setup.py install but it doesn't work. I know there are 64 bit binaries, but I want to compile it myself. What I am doing wrong? Do I need a special configuration in setup.py? Have a nice day Jaime

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[scikits.timeseries] Custom xticks labels
by Paweł 02 Jul '11

02 Jul '11

Hello, I have a problem with xticks while using scikits.timeseries. While plotting long series of data, the default labels of xticks are month names, and year numbers every 12th tick. I want to change this to something like mm.yy (%m.%y) under every tick. Up to now I experimented with xaxis.set_major_formatter() and TimeSeries_DateFormatter, but none of my methods work. I can do what I want in 'pure' matplotlib (using set_major_formatter and matplotlib.dates.DateFormatter), but I'm unable to achieve this while plotting timeseries using scikits.timeseries module. Can anyone help me? Any working example or a clue? greetings, Paweł Rumian

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ANN: Veusz 1.12 - a python-based GUI/scripted scientific plotting package
by Jeremy Sanders 02 Jul '11

02 Jul '11

I am pleased to announce Veusz 1.12, a python-based GUI/command line/scripted plotting package. This release has some new features and a large number of bug fixes (see below). Jeremy Veusz 1.12 ---------- Velvet Ember Under Sky Zenith ----------------------------- http://home.gna.org/veusz/ Copyright (C) 2003-2011 Jeremy Sanders <jeremy(a)jeremysanders.net> and contributors. 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 1.12: * Multiple widgets can now be selected for editing properties * Add Edit->Select menu and context menu for above * Added context menu on dataset browser for filenames to reload, delete or unlink all associated datasets * New tree-like dataset browsing widget is shown in data edit dialog * Importing 1D fits images is now supported * Date / time data has its own dataset type * The data edit dialog box can create or edit date/time data in human-readable form Minor improvements: * Add LaTeX commands \cdot, \nabla, \overline plus some arrows * Inform user in exception dialog if a new version is available * Add linevertbar and linehorzbar error bar styles Bug fixes: * Fix crash on filling filled error regions if no error bars * Remove grouping separator to numbers in locale as it creates ambiguous lists of numbers * Undo works properly for boolean and integer settings * Prevent widgets getting the same names when dragging and dropping * Hidden plot widgets are ignored when calculating axis ranges * Combo boxes are now case sensitive when displaying matches with previous text * Fix errors if plotting DatasetRange or Dataset1DPlugin datasets against data with nan values * Fix division by zero in dataset preview * Do not leave settings pointing to deleted widgets after an undo * Fix errors when using super/subscripts of super/subscripts * Fix crash when giving positions of bar plot and labels * Do not allow dataset names to be invalid after remaining * Several EMF format bug fixes, including not showing hidden lines and not connecting points making curves * Stop crash when contouring zero-sized datasets 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 * 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 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 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/ 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 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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PyCon DE 2011 - Call for Proposals extended to July 15, 2011
by Mike Müller 02 Jul '11

02 Jul '11

PyCon DE 2011 - Deadline for Proposals extended to July 15, 2011 ================================================================ The deadline for talk proposals is extended to July 15, 2011. You would like to talk about your Python project to the German-speaking Python community? Just submit your proposal within the next two weeks: http://de.pycon.org/2011/speaker/ About PyCon DE 2011 ------------------- The first PyCon DE will be held October 4-9, 2011 in Leipzig, Germany. The conference language will be German. Talks in English are possible. Please contact us for details. The call for proposals is now open. Please submit your talk by June 30, 2011 online. There are two types of talks: standard talks (20 minutes + 5 minutes Q&A) and long talks (45 minutes + 10 minutes Q&A). More details about the call can be found on the PyCon DE website: http://de.pycon.org/2011/Call_for_Papers/ Since the conference language will be German, the call is in German too. PyCon DE 2011 - Neuer Einsendeschluss für Vortragsvorschläge 15.07.2011 ======================================================================= Noch bis zum 15.7.2011 kann jeder, der sich für Python interessiert, einen Vortragsvorschlag für die PyCon DE 2011 einreichen. Es gibt nur zwei Bedingungen: das Thema sollte interessant sein und etwas mit Python zu tun haben. Für die erste deutsche Python-Konferenz sind wir an einer breiten Themenpalette interessiert, die das ganze Spektrum der Entwicklung, Nutzung und Wirkung von Python zeigt. Mögliche Themen sind zum Beispiel: * Webanwendungen mit Python * Contentmanagement mit Python * Datenbankanwendungen mit Python * Testen mit Python * Systemintegration mit Python * Python für große Systeme * Python im Unternehmensumfeld * Pythonimplementierungen (Jython, IronPython, PyPy, Unladen Swallow und andere) * Python als erste Programmiersprache * Grafische Nutzerschnittstellen (GUIs) * Parallele Programmierung mit Python * Python im wissenschaftlichen Bereich (Bioinformatik, Numerik, Visualisierung und anderes) * Embedded Python * Marketing für Python * Python, Open Source und Entwickler-Gemeinschaft * Zukünftige Entwicklungen * mehr ... Ihr Themenbereich ist nicht aufgelistet, wäre aber aus Ihrer Sicht für die PyCon DE interessant? Kein Problem. Reichen Sie Ihren Vortragsvorschlag einfach ein. Auch wir können nicht alle Anwendungsbereiche von Python überschauen. Vortragstage sind vom 5. bis 7. Oktober 2011. Es gibt zwei Vortragsformate: * Standard-Vortrag -- 20 Minuten Vortrag + 5 Minuten Diskussion * Lang-Vortrag -- 45 Minuten Vortrag + 10 Minuten Diskussion Die Vortragszeit wird strikt eingehalten. Bitte testen Sie die Länge Ihres Vortrags. Lassen Sie gegebenenfalls ein paar Folien weg. Die Vortragsprache ist Deutsch. In begründeten Ausnahmefällen können Vorträge auch auf Englisch gehalten werden. Bitte fragen Sie uns dazu. Bitte reichen Sie Ihren Vortrag auf der Konferenz-Webseite http://de.pycon.org bis zum 15.07.2011 ein. Wir entscheiden bis zum 31. Juli 2011 über die Annahme des Vortrags.

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Question: scipy.stats.gamma.fit
by Ning Guo 02 Jul '11

02 Jul '11

Dear scipy-users, I'm using scipy.stats.gamma.fit to fit a set of random variables for gamma distribution. And to validate the results I also use the fitdistr function in R. However the results generated by these two packages are different, i.e. shape parameter and scale parameter for the gamma pdf are different. Though the difference is not large, I'm wondering what causes this difference. I think both of them are using maximum likelihood estimation to fit the function. Best regards! Ning

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Weird error in fmin_l_bfgs_b
by Jose Gomez-Dans 01 Jul '11

01 Jul '11

Hi, I'm getting an error in scipy.optimize.fmin_l_bfgs_b, apparently related to the fortran wrapper. This is strange, because exactly the same problem works well with the TNC solver. I have a function that returns both a scalar value (that will be minimised) and the derivative of the function at that point. The error in the L-BFG-S solver is File "/usr/lib/python2.7/dist-packages/scipy/optimize/lbfgsb.py", line 181, in fmin_l_bfgs_b isave, dsave) ValueError: failed to initialize intent(inout) array -- input not fortran contiguous My code looks like this: # x0 is the starting point, a 1d array >>> solution, x, info = scipy.optimize.fmin_tnc( cost_function, x0, args=([operators]), bounds=bounds ) # Using fmin_tnc works well, solution is what I expect it to be >> solution, cost, information = scipy.optimize.fmin_l_bfgs_b ( cost_function, solution, bounds=bounds, args=[ operators ], iprint=101 ) 2011-07-01 11:34:24,703 - eoldas.Model - INFO - 46 days, 46 quantised days tnc: Version 1.3, (c) 2002-2003, Jean-Sebastien Roy (js(a)jeannot.org) tnc: RCS ID: @(#) $Jeannot: tnc.c,v 1.205 2005/01/28 18:27:31 js Exp $ NIT NF F GTG 0 1 1.988301629303336E+02 8.17118991E+06 tnc: fscale = 0.000249879 1 5 1.338514420154698E+01 1.82689516E+04 tnc: fscale = 0.00528464 2 9 9.476573219561992E+00 2.21390020E+04 3 19 6.684083971679802E+00 3.88897225E+03 4 69 6.274247682836059E+00 2.43671753E+03 tnc: |fn-fn-1] = 4.5037e-13 -> convergence 5 120 6.274247682835608E+00 2.43671753E+03 tnc: Converged (|f_n-f_(n-1)| ~= 0) RUNNING THE L-BFGS-B CODE * * * Machine precision = 1.084D-19 N = 46 M = 10 L = -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 -2.0000D-01 X0 = 5.6013D-02 1.1717D-01 1.9201D-01 2.7557D-01 3.7013D-01 4.5702D-01 5.3491D-01 6.0661D-01 6.7624D-01 7.4649D-01 8.0318D-01 8.5203D-01 8.8633D-01 9.0102D-01 8.9914D-01 8.7521D-01 8.2816D-01 7.6529D-01 7.0559D-01 6.5371D-01 6.0520D-01 5.5814D-01 5.0991D-01 4.4783D-01 3.7790D-01 3.0041D-01 2.1894D-01 1.5147D-01 1.0832D-01 8.3926D-02 6.6473D-02 4.8621D-02 3.2567D-02 2.0086D-02 1.0881D-02 2.4890D-03 8.8000D-04 -4.2729D-03 -4.6658D-03 -5.5940D-03 -4.1690D-03 -1.2577D-02 -2.2529D-02 -2.9114D-02 -1.5938D-02 1.9755D-02 U = 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 1.2000D+00 At X0 0 variables are exactly at the bounds Traceback (most recent call last): File "example_identity.py", line 199, in <module> main ( sys.argv ) File "example_identity.py", line 166, in main solution, cost, information = scipy.optimize.fmin_l_bfgs_b ( cost_function, solution, bounds=bounds, args=[ operators ], iprint=101 ) File "/usr/lib/python2.7/dist-packages/scipy/optimize/lbfgsb.py", line 181, in fmin_l_bfgs_b isave, dsave) ValueError: failed to initialize intent(inout) array -- input not fortran contiguous Any clues of where to look for issues? Thanks! jose

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ANN: NumPy 1.6.1 release candidate 2
by Ralf Gommers 01 Jul '11

01 Jul '11

Hi, I am pleased to announce the availability (only a little later than planned) of the second release candidate of NumPy 1.6.1. This is a bugfix release, list of fixed bugs: #1834 einsum fails for specific shapes #1837 einsum throws nan or freezes python for specific array shapes #1838 object <-> structured type arrays regression #1851 regression for SWIG based code in 1.6.0 #1863 Buggy results when operating on array copied with astype() #1870 Fix corner case of object array assignment #1843 Py3k: fix error with recarray #1885 nditer: Error in detecting double reduction loop #1874 f2py: fix --include_paths bug #1749 Fix ctypes.load_library() If no new problems are reported, the final release will be in one week. Sources and binaries can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.6.1rc2/ Enjoy, Ralf

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Fitting procedure to take advantage of cluster
by J. David Lee 01 Jul '11

01 Jul '11

Hello, I'm attempting to perform a fit of a model function's output to some measured data. The model has around 12 parameters, and takes tens of minutes to run. I have access to a cluster with several thousand processors that can run the simulations in parallel, so I'm wondering if there are any algorithms out there that I can use to leverage this computing power to efficiently solve my problem - that is, besides grid searches or Monte-Carlo methods. Thanks for your help, David

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