Mailman 3 September 2010 - SciPy-User

help with leastsq and fmin
by Joses Ho Sept. 9, 2010

Sept. 9, 2010

Hi, I am trying to fit to my qPCR data an exponential function where each successive y-value depends on the previous v-value in the following fashion: y[n] = y[n-1] + k ln (1 + (y[n-1]/k) + b where y[n] = current y-value being evaluated and y[n-1] = previous y-value I therefore have three values to fit - y0 (starting value of y), k, and b. I define a residuals function where I compute 20 values of y, given an initial estimate of y0, k and b, and then subtract these from my 20 measured values of y. I have tried to use scipy.optimize.leastsq on the above residuals function, but it doesn't seem to be converging on the correct values of y0,k and b. I have tried scipy.optimize.fmin on the same function, but I get the following error: ...fsim[0] = func(x0) ...ValueError: setting an array element with a sequence. which seems to me that it refuses to accept the x0 array I give to it, consisting of [y0,k,b]. Basically, I am trying to implement the recent method of Boggy and Woolf (PLoS 2010) [doi:10.1371/ journal.pone.0012355] in Python. I am using SciPy 0.8.0b1 and NumPy 1.4.0 on Python 2.6.5 of the Enthought Python Distribution 6.2-2. Any help here would be very much appreciated! Thanks Joses

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numpy.diff of sparse matrix
by Jonathan Stickel Sept. 8, 2010

Sept. 8, 2010

I'd like to use numpy.diff on a sparse matrix, but I get the following: In [76]: np.diff(np.identity(3),axis=0) Out[76]: array([[-1., 1., 0.], [ 0., -1., 1.]]) In [77]: np.diff(sparse.identity(3),axis=0) --------------------------------------------------------------------------- IndexError Traceback (most recent call last) /Users/jstickel/temp/<ipython console> in <module>() /opt/local/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/site-packages/numpy/lib/function_base.pyc in diff(a, n, axis) 961 slice1 = [slice(None)]*nd 962 slice2 = [slice(None)]*nd --> 963 slice1[axis] = slice(1, None) 964 slice2[axis] = slice(None, -1) 965 slice1 = tuple(slice1) IndexError: list assignment index out of range I suppose numpy.diff doesn't know how to handle a sparse matrix object. Converting to dense would work, but that would defeat the purpose of using sparse matrices. Any quick solutions? I don't have time now, but if I were to implement a sparse diff, should it go within numpy.diff or be a separate function (maybe scipy.sparse.spdiff)? Thanks, Jonathan

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MannWhitneyU test critical values
by kossnair＠web.de Sept. 8, 2010

Sept. 8, 2010

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Gaussian Beam propagation
by Daniele Nicolodi Sept. 8, 2010

Sept. 8, 2010

Hello. Sorry for the slightly off topic question. I'm looking for a Gaussian beam propagation software able to propagate a Gaussian beam trough a simple paraxial lens setup. Of course I would prefer a python one, or one with python binding. Does someone know any such package? Writing the code by myself would not be too difficult, and will probably be a nice learning experience, and I would be glad to release it with an open source license. But I do not have the time to write the code right now. Thanks. Cheers, -- Daniele

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Images as markers in matplotlib?
by Joshua Holbrook Sept. 7, 2010

Sept. 7, 2010

Hey y'all, I recently read about Chernoff faces (http://en.wikipedia.org/wiki/Chernoff_face) in one of Edward Tufte's books (great read btw) and would like to mess around with them in matplotlib. My current approach is to generate the faces as images, and then use them as markers on an x-y plot (like this example I found). I just realized, though, that I have no idea how to incorporate images as position markers in matplotlib, or if it's even possible. My search of the mpl docs didn't turn up much. Any ideas? (I know this isn't matplotlib-users, but I was having trouble figuring out where to sign up for that list. If anyone wants would rather I ask them, feel free to point out the sign-up page---I'd appreciate it.) Thanks! --Joshua holbrook

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EPD Webinar: How to solve ODEs with SciPy
by Amenity Applewhite Sept. 7, 2010

Sept. 7, 2010

Enthought Python Distribution Webinar September 10 This Friday,Warren Weckesser will host the first of three webinars in a series on solving differential equations in Python. We will take a close look at the two tools available for solving ordinary differential equations in SciPy: the "odeint" function and the "ode" class. Two examples will be discussed: (1) the famous Lorenz equations that exhibit chaos, and (2) the Gray-Scott reaction-diffusion equations in 1D, from which we will obtain a system of ordinary differential equations by using the "Method of Lines". The demonstrations also include 3D plots and animation using Matplotlib. Enthought Python Distribution Webinar How do I...solve differential equations with Python? Part I: SciPy Tools Friday, September 10: 1pm CST/6pm UTC Wait list (for non EPD subscribers): email:amenity@enthought.com Early in 2011, Warren will host Part II: boundary value problems, and in the spring he'll follow up with a third installment to the series. Have a fantastic September, The Enthought Team

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Re: [SciPy-User] [ANN] guiqwt v2.0.3
by Pierre.RAYBAUT＠CEA.FR Sept. 6, 2010

Sept. 6, 2010

Hi Stéfan, That's an interesting question indeed (I'm sending this message to the scipy-user mailing list too, as I told you in private). Actually it was a hard choice to make. The Enthought Tool Suite has wonderful similar features thanks to Traits and Chaco but -especially at the time I made this decision- this is a too big package with too much dependencies, not as much documentation as needed (this has changed since then I think) and far too much updates and API compatibility breaks. In other words, after a careful analysis in 2008, ETS was not mature enough for our applications. We needed (and we still do) to rely on stable libraries for which major API changes do not occur too frequently. From a certain point of view, perhaps ETS is too ambitious with respect to the number of developers behind it, hence its lack of stability. So the idea was to develop a library much more simple and low-level than Traits and Chaco (less ambitious). The result is two tiny libraries with powerful features (tiny = easy to maintain = durability) and a low number of dependencies. Note that `guiqwt` has exclusive features like an image display engine (written in C++) which is very efficient (CPU + memory): real-time affine transforms (translation, rotation, resize, etc.) of possibly very large images on an average computer configuration *without* making any copy of the displayed array in memory. Cheers, Pierre > -----Message d'origine----- > De : sjvdwalt(a)gmail.com [mailto:sjvdwalt@gmail.com] De la part de Stéfan > van der Walt > Envoyé : samedi 4 septembre 2010 11:58 > À : RAYBAUT Pierre 602007 > Objet : Re: [SciPy-User] [ANN] guiqwt v2.0.3 > > Hi Pierre > > Wow, this is a massive amount of work! Out of curiosity, why did you > choose not leverage Traits and Chaco? Thank you for taking the > trouble to open source your work; the scientific community is indebted > (already for Python(x,y) that, e.g., our university uses, but also for > this). > > Kind regards > Stéfan > > On Fri, Sep 3, 2010 at 6:03 PM, <Pierre.RAYBAUT(a)cea.fr> wrote: > > Hi all, > > > > I am pleased to announce that `guiqwt` v2.0.3 has been released. This > library was initially written to help me to develop signal/image > processing software for internal use only, but after a long process I've > just been able to share this work with the scientific Python community. > > > > Based on PyQwt (plot widgets for PyQt4 graphical user interfaces) and on > scientific modules NumPy and SciPy, guiqwt is a Python library for > efficient 2-D data plotting (curves, 1-D and 2-D histograms, images) and > signal/image processing application development. > > > > As you certainly know, the most popular Python module for data plotting > is currently matplotlib, an open-source library providing a lot of plot > types and an API (the pylab interface) which is very close to MATLAB's > plotting interface. > > > > guiqwt plotting features are quite limited in terms of plot types > compared to matplotlib. However the currently implemented plot types are > much more efficient. For example, the guiqwt image showing function > (imshow) do not make any copy of the displayed data, hence allowing to > show images much larger than with its matplotlib's counterpart. In other > terms, when showing a 30-MB image (16-bits unsigned integers for example) > with guiqwt, no additional memory is wasted to display the image (except > for the offscreen image of course which depends on the window size) > whereas matplotlib takes more than 600-MB of additional memory (the > original array is duplicated four times using 64-bits float data types). > > > > guiqwt also provides the following features: > > > > guiqwt.pyplot: equivalent to matplotlib's pyplot module (pylab) > > > > supported plot items: > > > > * curves, error bar curves and 1-D histograms > > * images (RGB images are not supported), images with non-linear > x/y scales, images with specified pixel size (e.g. loaded from DICOM > files), 2-D histograms, pseudo-color images (pcolor) > > * labels, curve plot legends > > * shapes: polygon, polylines, rectangle, circle, ellipse and > segment > > * annotated shapes (shapes with labels showing position and > dimensions): rectangle with center position and size, circle with center > position and diameter, ellipse with center position and diameters (these > items are very useful to measure things directly on displayed images) > > > > curves, images and shapes: > > > > * multiple object selection for moving objects or editing their > properties through automatically generated dialog boxes (guidata) > > * item list panel: move objects from foreground to background, > show/hide objects, remove objects, ... > > * customizable aspect ratio > > * a lot of ready-to-use tools: plot canvas export to image file, > image snapshot, image rectangular filter, etc. > > > > curves: > > > > * interval selection tools with labels showing results of > computing on selected area > > * curve fitting tool with automatic fit, manual fit with sliders, > ... > > > > images: > > > > * contrast adjustment panel: select the LUT by moving a range > selection object on the image levels histogram, eliminate outliers, ... > > * X-axis and Y-axis cross-sections: support for multiple images, > average cross-section tool on a rectangular area, ... > > * apply any affine transform to displayed images in real-time > (rotation, magnification, translation, horizontal/vertical flip, ...) > > > > application development helpers: > > > > * ready-to-use curve and image plot widgets and dialog boxes > > * load/save graphical objects (curves, images, shapes) > > * a lot of test scripts which demonstrate guiqwt features > > > > > > guiqwt has been successfully tested on GNU/Linux and Windows platforms. > > > > Python package index page: > > http://pypi.python.org/pypi/guiqwt/ > > > > Documentation, screenshots: > > http://packages.python.org/guiqwt/ > > > > Downloads (source + Python(x,y) plugin): > > http://sourceforge.net/projects/guiqwt/ > > > > Cheers, > > Pierre > > > > --- > > > > Dr. Pierre Raybaut > > CEA - Commissariat à l'Energie Atomique et aux Energies Alternatives > > _______________________________________________ > > SciPy-User mailing list > > SciPy-User(a)scipy.org > > http://mail.scipy.org/mailman/listinfo/scipy-user > >

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[ANN] guiqwt v2.0.3
by Pierre.RAYBAUT＠CEA.FR Sept. 3, 2010

Sept. 3, 2010

Hi all, I am pleased to announce that `guiqwt` v2.0.3 has been released. This library was initially written to help me to develop signal/image processing software for internal use only, but after a long process I've just been able to share this work with the scientific Python community. Based on PyQwt (plot widgets for PyQt4 graphical user interfaces) and on scientific modules NumPy and SciPy, guiqwt is a Python library for efficient 2-D data plotting (curves, 1-D and 2-D histograms, images) and signal/image processing application development. As you certainly know, the most popular Python module for data plotting is currently matplotlib, an open-source library providing a lot of plot types and an API (the pylab interface) which is very close to MATLAB's plotting interface. guiqwt plotting features are quite limited in terms of plot types compared to matplotlib. However the currently implemented plot types are much more efficient. For example, the guiqwt image showing function (imshow) do not make any copy of the displayed data, hence allowing to show images much larger than with its matplotlib's counterpart. In other terms, when showing a 30-MB image (16-bits unsigned integers for example) with guiqwt, no additional memory is wasted to display the image (except for the offscreen image of course which depends on the window size) whereas matplotlib takes more than 600-MB of additional memory (the original array is duplicated four times using 64-bits float data types). guiqwt also provides the following features: guiqwt.pyplot: equivalent to matplotlib's pyplot module (pylab) supported plot items: * curves, error bar curves and 1-D histograms * images (RGB images are not supported), images with non-linear x/y scales, images with specified pixel size (e.g. loaded from DICOM files), 2-D histograms, pseudo-color images (pcolor) * labels, curve plot legends * shapes: polygon, polylines, rectangle, circle, ellipse and segment * annotated shapes (shapes with labels showing position and dimensions): rectangle with center position and size, circle with center position and diameter, ellipse with center position and diameters (these items are very useful to measure things directly on displayed images) curves, images and shapes: * multiple object selection for moving objects or editing their properties through automatically generated dialog boxes (guidata) * item list panel: move objects from foreground to background, show/hide objects, remove objects, ... * customizable aspect ratio * a lot of ready-to-use tools: plot canvas export to image file, image snapshot, image rectangular filter, etc. curves: * interval selection tools with labels showing results of computing on selected area * curve fitting tool with automatic fit, manual fit with sliders, ... images: * contrast adjustment panel: select the LUT by moving a range selection object on the image levels histogram, eliminate outliers, ... * X-axis and Y-axis cross-sections: support for multiple images, average cross-section tool on a rectangular area, ... * apply any affine transform to displayed images in real-time (rotation, magnification, translation, horizontal/vertical flip, ...) application development helpers: * ready-to-use curve and image plot widgets and dialog boxes * load/save graphical objects (curves, images, shapes) * a lot of test scripts which demonstrate guiqwt features guiqwt has been successfully tested on GNU/Linux and Windows platforms. Python package index page: http://pypi.python.org/pypi/guiqwt/ Documentation, screenshots: http://packages.python.org/guiqwt/ Downloads (source + Python(x,y) plugin): http://sourceforge.net/projects/guiqwt/ Cheers, Pierre --- Dr. Pierre Raybaut CEA - Commissariat à l'Energie Atomique et aux Energies Alternatives

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[ANN] guidata v1.2.1
by Pierre.RAYBAUT＠CEA.FR Sept. 3, 2010

Sept. 3, 2010

Hi all, I am pleased to announce that `guidata` v1.2.1 has been released. This library was initially written to help me to develop software for internal use only, but after a long process I've just been able to share this work with the Python community. Based on the Qt Python binding module PyQt4, guidata is a Python library for easy data set edition (and display) thanks to automatically generated graphical user interfaces - it also provides helpers and application development tools for PyQt4. guidata also provides the following features: * guidata.qthelpers: PyQt4 helpers * guidata.disthelpers: py2exe helpers * guidata.userconfig: .ini configuration management helpers (based on Python standard module ConfigParser) * guidata.configtools: library/application data management * guidata.gettext_helpers: translation helpers (based on the GNU tool gettext) * guidata.guitest: automatic GUI-based test launcher * guidata.utils: miscelleneous utilities guidata has been successfully tested on GNU/Linux and Windows platforms. Python package index page: http://pypi.python.org/pypi/guidata/ Documentation, screenshots: http://packages.python.org/guidata/ Downloads (source + Python(x,y) plugin): http://sourceforge.net/projects/guidata/ Cheers, Pierre --- Dr. Pierre Raybaut CEA - Commissariat à l'Energie Atomique et aux Energies Alternatives

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Discrete integration with delays
by Thomas Fitzpatrick Sept. 3, 2010

Sept. 3, 2010

Can anybody help me with the following equation? It is similar to a correlation, but I am not sure how this done as scipy's correlation packages are compiled into a pyd sigtools. I would like to get an array that is the same size as the input. Here is the equation I am trying to code: F(T) = integration[{I(t) + I(t - T)}*E(t)*Econj(t - T)]dt from -inf to +inf (The code be for a discrete function) I tried to do it by adapting this autocorrelation function: N = len(x) Rxx = np.zeros(N) for m in range(N): for n in range(N-m): Rxx[m] = Rxx[m] + x[n]*x[n+m-1] Here is my attempt: f1 = np.zeros(N) for m in range(N): for n in range(0, N - m): f1[m] = f1[m] + .5*(it[n] + it[n + m])*et[n]*et[n + m] This works pretty well, but it doesn't return an array of the same size as the input which is an option for the scipy function 'correlate.'

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