Mailman 3 July 2001 - NumPy-Discussion

Kronecker product
by Nils Wagner 18 Jul '01

18 Jul '01

Hi, I would appreciate it, if Numpy could handle the Kronecker-product of two matrices X, Y. kron(X,Y) is the Kronecker tensor product of X and Y. The result is a large matrix formed by taking all possible products between the elements of X and those of Y. If X is m-by-n and Y is p-by-q, then kron(X,Y) is m*p-by-n*q. Nils

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Cygwin
by Jochen Küpper 18 Jul '01

18 Jul '01

Another little patch needed for NumPy as of today to compile on Cygwin: Index: Src/fastumathmodule.c =================================================================== RCS file: /cvsroot/numpy/Numerical/Src/fastumathmodule.c,v retrieving revision 1.1 diff -u -r1.1 fastumathmodule.c --- Src/fastumathmodule.c 2001/07/16 23:19:23 1.1 +++ Src/fastumathmodule.c 2001/07/17 19:16:39 @@ -2123,7 +2123,7 @@ {NULL, NULL, 0} /* sentinel */ }; -void initfastumath() { +DL_EXPORT(void) initfastumath() { PyObject *m, *d, *s; /* Create the module and add the functions */ Greetings, Jochen -- Einigkeit und Recht und Freiheit http://www.Jochen-Kuepper.de Liberté, Égalité, Fraternité GnuPG key: 44BCCD8E Sex, drugs and rock-n-roll

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Wishlist for Numpy
by Nils Wagner 11 Jul '01

11 Jul '01

Hi, Is there any wish-list of projects and feature requests for Numpy ? I would appreciate it, if Numpy could handle the following functions : 1. Fix eig to also be able to solve the generalized eigenvalue problem and polynomial eigenvalue problems 2. Support for matrix functions like logm, expm, sqrtm 3. Wavelet transform 4. Solver for ODE's and DAE's Nils Wagner

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MA : assignment to slices doesn't work like Numeric.
by Joe Van Andel 10 Jul '01

10 Jul '01

I retrieved the latest MA from CVS. I've noticed that assigning to a slice doesn't work the same as Numeric. Here's a simple test program: -------------------------------- from MA import * #from Numeric import * numBeams,numGates = (5,4) result = ones((numBeams, numGates),'f') * -327.68 print 'result = ', result t1 = ones((numGates,),'f') t2 = 2* ones((numGates,),'f') result[0] = t1 result[1][:] = t2 print 'result = ', result ----------------------------------------- Output using 'MA': result = [[-327.68,-327.68,-327.68,-327.68,] [-327.68,-327.68,-327.68,-327.68,] [-327.68,-327.68,-327.68,-327.68,] [-327.68,-327.68,-327.68,-327.68,] [-327.68,-327.68,-327.68,-327.68,]] result = [[ 1. , 1. , 1. , 1. ,] [-327.68,-327.68,-327.68,-327.68,] [-327.68,-327.68,-327.68,-327.68,] [-327.68,-327.68,-327.68,-327.68,] [-327.68,-327.68,-327.68,-327.68,]] However, if I use Numeric, rather than MA, I get: result = [[-327.68 -327.68 -327.68 -327.68] [-327.68 -327.68 -327.68 -327.68] [-327.68 -327.68 -327.68 -327.68] [-327.68 -327.68 -327.68 -327.68] [-327.68 -327.68 -327.68 -327.68]] result = [[ 1. 1. 1. 1. ] [ 2. 2. 2. 2. ] [-327.68 -327.68 -327.68 -327.68] [-327.68 -327.68 -327.68 -327.68] [-327.68 -327.68 -327.68 -327.68]] So, a[x][:] = my_array doesn't seem to work using 'MA'. -- Joe VanAndel National Center for Atmospheric Research http://www.atd.ucar.edu/~vanandel/ Internet: vanandel(a)ucar.edu

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Cannot reply to some questions
by Paul F. Dubois 10 Jul '01

10 Jul '01

I receive a steady stream of questions sent to me about Numerical Python. Frequently, in their zeal to protect themselves from spam, the senders obscure their sending address. This causes my reply to bounce. I sometimes drop the ball and fail to reply promptly due to the volume of letters I receive, advancing senility, or because the question requires research and it goes onto my stack for too long. But before you conclude that I'm not answering you because of ineptitude, please be sure I *can* answer your mail with a simple reply. Thanks, Paul

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Numpy on Cygwin
by Jochen Küpper 03 Jul '01

03 Jul '01

Dear All, I have had some problems with Cannot export _bss_end__: symbol not defined messages and friends again for the cvs Numerical Python package. Although the LAPACK-module uses the DL_EXPORT approach and works fine, the other packages apparently try the .def-approach (which I barely heard about); this packages do not build for me on latest Cygwin. (I have not tried it on older ones.) Putting DL_EXPORTs around all the init-functions fixes this for me: Index: Packages/FFT/Src/fftpackmodule.c =================================================================== RCS file: /cvsroot/numpy/Numerical/Packages/FFT/Src/fftpackmodule.c,v retrieving revision 1.1 diff -u -r1.1 fftpackmodule.c --- Packages/FFT/Src/fftpackmodule.c 2000/07/06 16:54:16 1.1 +++ Packages/FFT/Src/fftpackmodule.c 2001/07/03 14:06:48 @@ -238,7 +238,7 @@ "" ; -void +DL_EXPORT(void) initfftpack() { PyObject *m, *d; Index: Packages/RNG/Src/RNGmodule.c =================================================================== RCS file: /cvsroot/numpy/Numerical/Packages/RNG/Src/RNGmodule.c,v retrieving revision 1.1 diff -u -r1.1 RNGmodule.c --- Packages/RNG/Src/RNGmodule.c 2000/07/06 16:54:17 1.1 +++ Packages/RNG/Src/RNGmodule.c 2001/07/03 14:06:49 @@ -613,7 +613,7 @@ "Random number generator: independent random number streams." ; -void +DL_EXPORT(void) initRNG() { PyObject *m, *d; Index: Packages/kinds/Src/_kindsmodule.c =================================================================== RCS file: /cvsroot/numpy/Numerical/Packages/kinds/Src/_kindsmodule.c,v retrieving revision 1.1 diff -u -r1.1 _kindsmodule.c --- Packages/kinds/Src/_kindsmodule.c 2001/04/17 23:35:10 1.1 +++ Packages/kinds/Src/_kindsmodule.c 2001/07/03 14:06:49 @@ -10,7 +10,7 @@ {NULL, NULL, 0} /* sentinel */ }; -void +DL_EXPORT(void) init_kinds() { PyObject *m, *d; Greetings, Jochen -- Einigkeit und Recht und Freiheit http://www.Jochen-Kuepper.de Liberté, Égalité, Fraternité GnuPG key: 44BCCD8E Sex, drugs and rock-n-roll

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ANNOUNCE: SciGraphica-0.7.0
by Adrian Feiguin 03 Jul '01

03 Jul '01

I'm pleased to announce the new release of Scigraphica with bugfixes and enhancements. Among other things, embedding images is now possible, as well as setting background images for the plots. Clipboard for plots: copy a plot, and paste it on a different plot window. Improved PostScript and WYSIWYG. It includes pysga.py, a Python module for interacting with plots and worksheets from terminal. New scheme for storing numerical data in worksheets. Examples are included. Scigraphica is a powerful tool for scientific graphics and data analysis. It pretends to be a clone of the popular commercial (and expensive) application "Microcal Origin". It fully supplies plotting features for 2D, 3D and polar charts. The aim is to obtain a fully-featured, cross-plattform, user-friendly, self-growing scientific application. It is free and open-source, released under the GPL license. Main features: -You can plot functions and manipulate data in worksheets. -You can open several worksheets and plots and work with them at the same time. -The plots are fully configurable using a control panel dialog. -The look and feel is completely WYSIWYG. -Publication quality PostScript output. -You can interact with the plots double-clicking, dragging and moving objects with the mouse. -Export/Import features in XML format. -You can insert Python expressions in the worksheets. -Terminal with command-line Python interface for interacting with plots and worksheets URL: http://scigraphica.sourceforge.net Enjoy! The SciGraphica Team.- ------------------------------------------------------------------------ TODO for sg-0.8.0: sg-0.8.0 will have a completely different structure, more modular, built ontop of reusable libraries. SGplot, SGworksheet, SGlayer, and SGdataset will be GtkWidgets and part of a shareable library, that could be used by other applications, and from Python.

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gtkLeastSquares.py
by Adrian Feiguin 03 Jul '01

03 Jul '01

Hi everybody, I'm the author of SciGraphica (http://scigraphica.sourceforge.net), an application for scientific graphics and data analysis that uses Python and Numpy. Among other things, it features spreadheets that can contain Python data, like formulas. The Python stuff was mainly done by Conrad Steenberg, because I don't have much experience, specially embedding Python into C. However, I decided to get started this weekend because we needed badly a GUI for non-linear least squares fits. The result is gtkLeastSquares.py, which is attached below. It's going to be distributed with SG, but it works independently and needs gtk.py and Scientific. Basically it is a gtk GUI for Scientific.Functions.LeastSquares. I'm quite satisfied with the result, considering that I'm a newbie ;-) I'm writting because I'd like to have some feedback, and people interested in contributing. The code is still immature, and needs enhancements, like user defined functions and parameters (either editing the existing in the GUI, or adding new ones interactively). I still have to learn. It is very simple, and easy to use. You'll find the code below, with an example at the bottom of the file. I hope you like it. All comments, and suggestions are very welcome (as well as contributors ;-) Thank you, <ADRIAN> -------------- Cut here ---------------------- # # gtkLeastSquares.py : GUI for the module Scientific.Functions.LeastSquares # Implementation of the Levenberg-Marquardt algorithm for general # non-linear least-squares fits. # # Copyright (C) 2001 Adrian E. Feiguin <feiguin(a)ifir.edu.ar> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. # from string import * from gtk import * from Numeric import * from Scientific.Functions.LeastSquares import * from Scientific.Functions.FirstDerivatives import * def fit_linear(parameters, values): a, b = parameters x = values return (a + b * x) def fit_cuadratic(parameters, values): a, b, c, x0 = parameters x = values return(a + b * (x - x0) + c * (x - x0)**2) return def fit_gauss(parameters, values): y0, x0, a, w = parameters x = values return(y0 + a * exp(-2*(x-x0)**2/w**2)) return def fit_lorentz(parameters, values): x0, y0, a, b = parameters x = values return(y0 + 2*a/pi * w / (4 * (x - x0)**2 + w**2)) return def fit_boltzman(parameters, values): x0, a1, a2, dx = parameters x = values return((a1 - a2)/(1 + exp((x - x0)/dx)) + a2) def fit_logistic(parameters, values): x0, a1, a2, p = parameters x = values return((a1 - a2)/(1 + (x/x0)**p) + a2) def fit_expdecay(parameters, values): x0, y0, a, t = parameters x = values return(y0 + a * exp(-(x - x0)/t)) def fit_expgrow(parameters, values): x0, y0, a, t = parameters x = values return(y0 + a * exp((x - x0)/t)) def fit_expassoc(parameters, values): y0, a1, t1, a2, t2 = parameters x = values return(y0 + a1 * (1 + exp(-x/t1)) + a2 * (1 + exp(-x/t2))) def fit_hyperbl(parameters, values): p1, p2 = parameters x = values return(p1 * x/ (p2 + x)) def fit_pulse(parameters, values): x0, y0, a, t1, t2 = parameters x = values return(y0 + a * (1 + exp(-(x - x0)/t1)) * exp(-(x - x0)/t2)) def fit_rational0(parameters, values): a, b, c = parameters x = values return((b + c*x)/(1 + a*x)) def fit_sine(parameters, values): x0, a, w = parameters x = values return(a * sin(pi*(x - x0)/w)) def fit_gaussamp(parameters, values): x0, y0, a, w = parameters x = values return(y0 + a * exp(-(x - x0)**2/(2*w**2))) def fit_allometric(parameters, values): a, b = parameters x = values return(a * x**b) fit_linear_dic = { "Doc" : "Linear Function", "Exp" : "y = a + b * x", "Par" : ("a", "b"), "NumPar" : 2, "IVar" : "x", "DVar" : "y", "Function" : fit_linear } fit_cuadratic_dic = { "Doc" : "Cuadratic Function", "Exp" : "y = a + b * (x - x0) + c * (x - x0)**2", "Par" : ("a", "b", "c", "x0"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_cuadratic } fit_gauss_dic = { "Doc" : "Amplitude version of Gaussian Function", "Exp" : "y = y0 + a * exp(-2*(x-x0)**2/w**2)", "Par" : ("x0", "y0", "a", "w"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_gauss } fit_lorentz_dic = { "Doc" : "Lorentzian Peak Function", "Exp" : "y = y0 + 2*a/pi * w / (4 * (x - x0)**2 + w**2)", "Par" : ("x0", "y0", "a", "w"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_lorentz } fit_boltzman_dic = { "Doc" : "Boltzman Function: sigmoidal curve", "Exp" : "y = (a1 - a2)/(1 + exp((x - x0)/dx)) + a2", "Par" : ("x0", "a1", "a2", "dx"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_boltzman } fit_logistic_dic = { "Doc" : "Logistic dose/response", "Exp" : "y = (a1 - a2)/(1 + (x/x0)**p) + a2", "Par" : ("x0", "a1", "a2", "p"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_logistic } fit_expdecay_dic = { "Doc" : "Exponential Decay", "Exp" : "y = y0 + a * exp(-(x - x0)/t)", "Par" : ("x0", "y0", "a", "t"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_expdecay } fit_expgrow_dic = { "Doc" : "Exponential Growth", "Exp" : "y = y0 + a * exp((x - x0)/t)", "Par" : ("x0", "y0", "a", "t"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_expgrow } fit_expassoc_dic = { "Doc" : "Exponential Associate", "Exp" : "y = y0 + a1 * (1 + exp(-x/t1)) + a2 * (1 + exp(-x/t2))", "Par" : ("y0", "a1", "t1", "a2", "t2"), "NumPar" : 5, "IVar" : "x", "DVar" : "y", "Function" : fit_expassoc } fit_hyperbl_dic = { "Doc" : "Hyperbola Function", "Exp" : "y = p1 * x/ (p2 + x)", "Par" : ("p1", "p2"), "NumPar" : 2, "IVar" : "x", "DVar" : "y", "Function" : fit_hyperbl } fit_pulse_dic = { "Doc" : "Pulse Function", "Exp" : "y = y0 + a * (1 + exp(-(x - x0)/t1)) * exp(-(x - x0)/t2)", "Par" : ("y0", "a", "t1", "t2"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_pulse } fit_rational0_dic = { "Doc" : "Rational Function , type 0", "Exp" : "y = (b + c*x)/(1 + a*x)", "Par" : ("a", "b", "c"), "NumPar" : 3, "IVar" : "x", "DVar" : "y", "Function" : fit_rational0 } fit_sine_dic = { "Doc" : "Sine Function", "Exp" : "y = a * sin(pi*(x - x0)/w)", "Par" : ("a", "x0", "w"), "NumPar" : 3, "IVar" : "x", "DVar" : "y", "Function" : fit_sine } fit_gaussamp_dic = { "Doc" : "Amplitude version of Gaussian Peak Function", "Exp" : "y = y0 + a * exp(-(x - x0)**2/(2*w**2))", "Par" : ("x0", "y0", "a", "w"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_gaussamp } fit_allometric_dic = { "Doc" : "Classical Freundlich Model", "Exp" : "y = a * x**b", "Par" : ("a", "b"), "NumPar" : 4, "IVar" : "x", "DVar" : "y", "Function" : fit_allometric } functions = { "Linear" : fit_linear_dic, "Cuadratic" : fit_cuadratic_dic, "Gauss" : fit_gauss_dic, "Lorentz" : fit_lorentz_dic, "Boltzman" : fit_boltzman_dic, "Logistic" : fit_logistic_dic, "ExpDecay" : fit_expdecay_dic, "ExpGrow" : fit_expgrow_dic, "ExpAssoc" : fit_expassoc_dic, "Hyperbl" : fit_hyperbl_dic, "Pulse" : fit_pulse_dic, "Rational0" : fit_rational0_dic, "Sine" : fit_sine_dic, "GaussAmp" : fit_gaussamp_dic, "Allometric" : fit_allometric_dic } def fit(data): main_window = GtkWindow() main_window.set_title("Curve Fitting") main_window.set_border_width(5) main_window.connect("destroy", mainquit) main_window.connect("delete_event", mainquit) main_box = GtkVBox(FALSE, 5) main_window.add(main_box) main_frame = GtkFrame("Select Function") main_box.pack_start(main_frame) table = GtkTable(7, 4, FALSE) table.set_col_spacings(10) table.set_row_spacings(5) table.set_border_width(5) main_frame.add(table) swindow = GtkScrolledWindow() swindow.set_policy(POLICY_AUTOMATIC, POLICY_AUTOMATIC) swindow.set_usize(120, 100) table.attach(swindow, 0, 1, 0, 6) clist = GtkCList(1) swindow.add(clist) table.attach(GtkVSeparator(), 1, 2, 0, 6) text = map(lambda i: str(i), range(20)) k = functions.keys() k.sort() for i in k: text[0] = i clist.append(text) label = GtkLabel("Exp:") label.set_alignment(1., .5) table.attach(label, 2, 3, 0, 1) fentry = GtkEntry() # fentry.set_editable(FALSE) table.attach(fentry, 3, 4, 0, 1) label = GtkLabel("Number of Param:") label.set_alignment(1., .5) table.attach(label, 2, 3, 1, 2) nspin = GtkSpinButton(GtkAdjustment(0, 0, 8, 1, 8, 0), 0, 0) nspin.set_editable(FALSE) nspin.set_state(STATE_INSENSITIVE) table.attach(nspin, 3, 4, 1, 2) label = GtkLabel("Param:") label.set_alignment(1., .5) table.attach(label, 2, 3, 2, 3) pentry = GtkEntry() pentry.set_editable(FALSE) pentry.set_state(STATE_INSENSITIVE) table.attach(pentry, 3, 4, 2, 3) label = GtkLabel("Independent Var:") label.set_alignment(1., .5) table.attach(label, 2, 3, 3, 4) iventry = GtkEntry() iventry.set_editable(FALSE) iventry.set_state(STATE_INSENSITIVE) table.attach(iventry, 3, 4, 3, 4) label = GtkLabel("Dependent Var:") label.set_alignment(1., .5) table.attach(label, 2, 3, 4, 5) dventry = GtkEntry() dventry.set_editable(FALSE) dventry.set_state(STATE_INSENSITIVE) table.attach(dventry, 3, 4, 4, 5) action_area = GtkHButtonBox() action_area.set_layout(BUTTONBOX_END) action_area.set_spacing(5) main_box.pack_start(action_area) fit_button = GtkButton("Fit") action_area.pack_start(fit_button) close_button = GtkButton("Close") action_area.pack_start(close_button) lframe = GtkFrame() lframe.set_shadow_type(SHADOW_IN) main_box.pack_start(lframe) explabel = GtkLabel("Choose a Fitting Function") lframe.add(explabel) # CALLBACK FUNCTIONS def select_function(_clist, row, col, event, functions = functions, label = explabel, fentry = fentry, pentry = pentry, nspin = nspin, iventry = iventry, dventry = dventry): k = _clist.get_text(row, col) f = functions[k] label.set_text(f["Doc"]) fentry.set_text(f["Exp"]) nspin.set_value(f["NumPar"]) iventry.set_text(f["IVar"]) dventry.set_text(f["DVar"]) s = "" for i in f["Par"]: s = s + i + ", " pentry.set_text(s[:len(s)-2]) def open_fit_dialog(_button, functions = functions, clist = clist, data = data): a = clist.__getattr__("selection") k = clist.get_text(a[0], 0) f = functions[k] param = (1, 1) fit_dialog(f, data) # CONNECT OBJECTS clist.connect("select_row", select_function) fit_button.connect("clicked", open_fit_dialog) close_button.connect("clicked", main_window.destroy) clist.select_row(0, 0) main_window.show_all() mainloop() def fit_dialog(f, data): main_window = GtkWindow() main_window.set_title("Fit") main_window.set_border_width(5) main_window.connect("destroy", mainquit) main_window.connect("delete_event", mainquit) table = GtkTable(len(f["Par"])+3, 2, FALSE) table.set_col_spacings(10) table.set_row_spacings(5) main_window.add(table) table.attach(GtkLabel("Variable"), 0, 1, 0, 1) table.attach(GtkLabel("Value"), 1, 2, 0, 1) table.attach(GtkHSeparator(), 0, 2, 1, 2) r = 2 entries = [] for i in f["Par"]: # check = GtkCheckButton(i+":") # table.attach(check, 0, 1, r, r+1) table.attach(GtkLabel(i+":"), 0, 1, r, r+1) entry = GtkEntry() entries = entries + [entry] entry.set_text("0.0") table.attach(entry, 1, 2, r, r+1) r = r + 1 table.attach(GtkHSeparator(), 0, 2, r, r + 1) r = r + 1 table.attach(GtkLabel("Chi_Sqr:"), 0, 1, r, r + 1) err_entry = GtkEntry() table.attach(err_entry, 1, 2, r, r + 1) r = r + 1 table.attach(GtkHSeparator(), 0, 2, r, r + 1) def run_fit(_button, f = f, data = data, entries = entries, err_entry = err_entry): n = 0 p = () for i in entries: s = entries[n].get_text() p = p + (atof(s),) n = n + 1 fit, error = leastSquaresFit(f["Function"], p, data) n = 0 for i in entries: entries[n].set_text(str(fit[n])) n = n + 1 err_entry.set_text(str(error)) # print "Fitted parameters: ", fit # print "Fit error: ", error return action_area = GtkHButtonBox() action_area.set_layout(BUTTONBOX_SPREAD) action_area.set_spacing(5) run_button = GtkButton("Run") close_button = GtkButton("Close") action_area.pack_start(run_button) action_area.pack_start(close_button) table.attach(action_area, 0, 2, r + 1, r + 2) # CONNECT OBJECTS run_button.connect("clicked", run_fit) close_button.connect("clicked", main_window.destroy) main_window.show_all() mainloop() # Test for linear fit: # # from gtkLeastSquares import * # data = [ (0., 0.), (1., 1.1), (2., 1.98), (3., 3.05) ] # fit(data)

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segfault in PyArray_FromDims
by Juerg Tschirren 02 Jul '01

02 Jul '01

I did some experimenting with the NumPy C API. I wrote two functions. One for processing a NumPy array in C++ and the other one for generating a NumPy array in C++. The processing function work perfectly fine. But in the array-generating function I get a segmentation fault whenever I call PyArray_FromDims. I used swig for generating the wrapper functions. The two src-files (numPyExt.i and numPyExt.cc): --- begin numPyExt.i ------------------------------------------------- %module numPyExt %{ #include <iostream.h> #include <Python.h> #include <Numeric/arrayobject.h> %} %init %{ import_array(); %} %typemap(python,in) double * { PyArrayObject *py_arr; if(!PyArray_Check($source)) { PyErr_SetString(PyExc_TypeError, "Not a NumPy array"); return NULL; } if (PyArray_ObjectType($source,0) != PyArray_DOUBLE) { PyErr_SetString(PyExc_ValueError, "Array must be of type double"); return NULL; } py_arr = (PyArrayObject*) \ (PyArray_ContiguousFromObject($source, PyArray_DOUBLE, 1, 1)); if (py_arr->nd != 1) { PyErr_SetString(PyExc_TypeError, "Array must be 1D"); return NULL; } $target = (double*)(py_arr->data); } extern PyObject* createArray(); extern void processArray(double* pdInArray); --- end numPyExt.i --------------------------------------------------- --- begin numPyExt.cc ------------------------------------------------ #include <iostream.h> #include <Python.h> #include <Numeric/arrayobject.h> //------ PyObject* createArray() { PyArrayObject* retArray; int iDimensions[3] = {10, 10, 10}; cout << "before PyArray_FromDims" << endl << flush; retArray = (PyArrayObject*)PyArray_FromDims(3, iDimensions, PyArray_INT); cout << "after PyArray_FromDims" << endl << flush; return PyArray_Return(retArray); } //------ void processArray(double* pdInArray) { cout << *pdInArray << " " << *(pdInArray+1) << " " << *(pdInArray+2) << endl; } --- end numPyExt.cc -------------------------------------------------- Compiled with: g++ -c -I/usr/local/include/python2.0 -I/usr/local/lib/python2.0/site-packages -O2 numPyExt.cc swig -python -c++ numPyExt.i g++ -c -O2 numPyExt_wrap.c -DOS_LINUX -DHAVE_CONFIG_H -I. -I/usr/local/include/python2.0 -I/usr/local/lib/python2.0/site-packages -I/usr/local/lib/python2.0/config g++ -W1,--heap,50000,--stack,100000 -O2 -shared numPyExt.o numPyExt_wrap.o -lstdc++ -o numPyExt.so The Python test code I am using: import Numeric, numPyExt vec = Numeric.array((1.23, 4.56, 7.89)) numPyExt.processArray(vec) # works fine a = numPyExt.createArray() # seg fault here print a I am using NumPy v20.0.0, Python 2.1, and gcc 2.95.2 on a Linux 2.2.16 sytem. Does anybody have an idea what's causing this problem? Juerg

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