Mailman 3 July 2010 - SciPy-User

use of solveh_banded
by Giacomo Boffi July 10, 2010

July 10, 2010

i'm trying to use solveh_banded, but to no avail until now from ipython -p scipy: ======================================================================== In [74]: b Out[74]: array([ 1., 2., 3., 4., 5., 6., 7., 8., 9.]) In [75]: m Out[75]: array([[-1., 3.], [-1., 3.], [-1., 3.], [-1., 3.], [-1., 3.], [-1., 3.], [-1., 3.], [-1., 3.], [-1., 3.]]) In [76]: solveh_banded(m,b) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) /home/boffi/<ipython console> in <module>() /usr/lib/python2.6/dist-packages/scipy/linalg/basic.pyc in solveh_banded(ab, b, overwrite_ab, overwrite_b, lower) 254 lower=lower, 255 overwrite_ab=overwrite_ab, --> 256 overwrite_b=overwrite_b) 257 if info==0: 258 return c, x ValueError: On entry to DPBSV parameter number 8 had an illegal value In [77]: pbsv?? Type: fortran String Form: <fortran object at 0xb60d5680> Namespace: Interactive Docstring [source file open failed]: dpbsv - Function signature: c,x,info = dpbsv(ab,b,[lower,ldab,overwrite_ab,overwrite_b]) Required arguments: ab : input rank-2 array('d') with bounds (ldab,n) b : input rank-2 array('d') with bounds (nrhs,ldb) Optional arguments: lower := 0 input int overwrite_ab := 0 input int ldab := shape(ab,0) input int overwrite_b := 0 input int Return objects: c : rank-2 array('d') with bounds (ldab,n) and ab storage x : rank-2 array('d') with bounds (nrhs,ldb) and b storage info : int In [78]: ======================================================================== in particular, i cannot understand the "parameter number 8 had an illegal value" error message thank you in advance, gb -- This message has been scanned for viruses and dangerous content by MailScanner, and is believed to be clean.

2 1

optimize.leastsq and improper input parameters
by ms July 10, 2010

July 10, 2010

Hi, I am stuck with optimize.leastsq. I am writing a quite complicated code that fits data to two variations of the same function, each of those can be with or without some parameters fixed. The concept is: - write the two variations - have a list of what parameters will be fixed and which not - use reasonable starting points of the non-fixed params to fit, and keep the fixed params as fixed within the function - then have a generalized routine that has as input the function to actually fit, among other things - apply leastsq on that function What comes out is similar, in structure, to this simplified example: ----- import scipy as sp import scipy.optimize as opt import numpy as np #Initial data x = np.arange(0,10,1) y = [i**2 for i in x] #Three nice functions to fit def xexponent1(param,i): exp = param[0] return i**exp def xexponent2(param, i): exp_a=param[0] exp_b=param[1] return i**(exp_a) + i**(exp_b) def line(param,i): A = param[0] b = param[1] return i*b + A def f_to_minimize(pars,args): #Generalized function we use to minimize the fit #calculates function and squared residuals function,x,y = args[0],args[1],args[2] y_estimate = [function(pars,xi) for xi in x] #calculate squared residuals resid = [(i-j)**2 for i,j in zip(y,y_estimate)] return sum(resid) def minimize(x,y,func,p0): #calls minimization args = [func,x,y] i = opt.leastsq(f_to_minimize, p0, args) print i minimize(x,y,line,[1,2]) ----- Here you can play with p0 and the function to give to f_to_minimize as an argument. What comes out is that if I give a p0 = [1] and I use a single-variable function, it works. As soon as I try a two-variable function (and thus I need two input parameters), I get: massimo@boltzmann:~/work$ python test_norm_leastsq.py Traceback (most recent call last): File "test_norm_leastsq.py", line 44, in <module> minimize(x,y,[1,2]) File "test_norm_leastsq.py", line 41, in minimize i = opt.leastsq(f_to_minimize, p0, args) File "/usr/lib/python2.6/dist-packages/scipy/optimize/minpack.py", line 300, in leastsq raise errors[info][1], errors[info][0] TypeError: Improper input parameters. The funny thing is that it worked *before* I messed with the thing to simplify the function-choosing mechanism (before I had N different functions for each combination of fixed/nonfixed params, now I just have two and I fix stuff *inside* the function), and I can't see however how can this be different. Also, the example above leaves me perplexed -it seems leastsq simply doesn't want two-variable functions to be minimized in this case. Any hint? Thanks a lot, Massimo

2 4

[ANN] Autumn School "Advanced Scientific Programming in Python" in Trento, Italy
by Tiziano Zito July 9, 2010

July 9, 2010

Advanced Scientific Programming in Python ========================================= an Autumn School by the G-Node, the Center for Mind/Brain Sciences and the Fondazione Bruno Kessler Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists actually use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques with theoretical lectures and practical exercises tailored to the needs of a programming scientist. New skills will be tested in a real programming project: we will team up to develop an entertaining scientific computer game. We'll use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. Clean language design and easy extensibility are driving Python to become a standard tool for scientific computing. Some of the most useful open source libraries for scientific computing and visualization will be presented. This school is targeted at Post-docs and PhD students from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or Mathematica is absolutely required. A basic knowledge of the Python language is assumed. Participants without prior experience with Python should work through the proposed introductory materials. Date and Location ================= October 4th—8th, 2010. Trento, Italy. Preliminary Program =================== Day 0 (Mon Oct 4) — Software Carpentry & Advanced Python • Documenting code and using version control • Object-oriented programming, design patterns, and agile programming • Exception handling, lambdas, decorators, context managers, metaclasses Day 1 (Tue Oct 5) — Software Carpentry • Test-driven development, unit testing & Quality Assurance • Debugging, profiling and benchmarking techniques • Data serialization: from pickle to databases Day 2 (Wed Oct 6) — Scientific Tools for Python • Advanced NumPy • The Quest for Speed (intro): Interfacing to C • Programming project Day 3 (Thu Oct 7) — The Quest for Speed • Writing parallel applications in Python • When parallelization does not help: the starving CPUs problem • Programming project Day 4 (Fri Oct 8) — Practical Software Development • Efficient programming in teams • Programming project • The Pac-Man Tournament Every evening we will have the tutors' consultation hour: Tutors will answer your questions and give suggestions for your own projects Applications ============ You can apply on-line at http://www.g-node.org/python-autumnschool Applications must be submitted before August 31th, 2010. Notifications of acceptance will be sent by September 4th, 2010. No fee is charged but participants should take care of travel, living, and accommodation expenses. Candidates will be selected on the basis of their profile. Places are limited: acceptance rate in past editions was around 30%. Prerequisites ============= You are supposed to know the basics of Python to participate in the lectures! Look on the website for a list of introductory material. Faculty ======= • Francesc Alted, author of PyTables, Castelló de la Plana, Spain • Pietro Berkes, Volen Center for Complex Systems, Brandeis University, USA • Valentin Haenel, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany • Zbigniew Jędrzejewski-Szmek, Faculty of Physics, University of Warsaw, Poland • Eilif Muller, The Blue Brain Project, Ecole Polytechnique Fédérale de Lausanne, Switzerland • Emanuele Olivetti, NeuroInformatics Laboratory, Fondazione Bruno Kessler and University of Trento, Italy • Rike-Benjamin Schuppner, Bernstein Center for Computational Neuroscience Berlin, Germany • Bartosz Teleńczuk, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Bastian Venthur, Berlin Institute of Technology and Bernstein Focus: Neurotechnology, Germany • Stéfan van der Walt, Applied Mathematics, University of Stellenbosch, South Africa • Tiziano Zito, Berlin Institute of Technology and Bernstein Center for Computational Neuroscience Berlin, Germany Organized by Paolo Avesani for the Center for Mind/Brain Sciences and the Fondazione Bruno Kessler , and by Zbigniew JędrzejewscySzmek and Tiziano Zito for the German Neuroinformatics Node of the INCF. Website: http://www.g-node.org/python-autumnschool Contact: python-info(a)g-node.org

1 0

butterworth filter on .WAV file
by Peter Howard July 8, 2010

July 8, 2010

I'm trying to write a very simple example of applying a band pass filter to a .WAV music file. I'm distinctly rusty on DSP and inexperienced with SciPy/NumPy so apologies if I've made a dumb mistake. It executes without errors or warnings. It produces the output file, but this is twice the size of the input file, which is clearly wrong. I'm most uncertain about casting the filtered data back to integers and thus being suitable for writing back out to .WAV. I'm also bit uncertain about my interpretation / understanding of the frequency and gain specifications. Any help and advice very much appreciated. Pete <snip> from scipy.io.wavfile import read, write from scipy.signal.filter_design import butter, buttord from scipy.signal import lfilter from numpy import asarray def convert_hertz(freq): # convert frequency in hz to units of pi rad/sample # (our .WAV is sampled at 44.1KHz) return freq * 2.0 / 44100.0 rate, sound_samples = read('monty.wav') pass_freq = convert_hertz(440.0) # pass up to 'middle C' stop_freq = convert_hertz(440.0 * 4) # max attenuation from 3 octaves higher pass_gain = 3.0 # tolerable loss in passband (dB) stop_gain = 60.0 # required attenuation in stopband (dB) ord, wn = buttord(pass_freq, stop_freq, pass_gain, stop_gain) b, a = butter(ord, wn, btype = 'low') filtered = lfilter(b, a, sound_samples) integerised_filtered = asarray(filtered, int) write('monty-filtered.wav', rate, integerised_filtered)

7 10

many test failures on windows 64
by Robin July 8, 2010

July 8, 2010

Hi, I am using Python.org amd64 python build on windows 7 64 bit. I am using numpy and scipy builds from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/ I get many errors in scipy test (none for numpy). Particularly in scipy.sparse.linalg which I need to use (and in my code it appears spsolve is giving incorrect results). Is there a better 64 bit windows build to use? >>> scipy.test() Running unit tests for scipy NumPy version 1.4.1 NumPy is installed in C:\Python26\lib\site-packages\numpy SciPy version 0.8.0b1 SciPy is installed in C:\Python26\lib\site-packages\scipy Python version 2.6.5 (r265:79096, Mar 19 2010, 18:02:59) [MSC v.1500 64 bit (AMD64)] nose version 0.11.3 ........................................................................................................................ ............................E....................................F...................................................... ..............................C:\Python26\lib\site-packages\scipy\interpolate\fitpack2.py:639: UserWarning: The coefficients of the spline returned have been computed as the minimal norm least-squares solution of a (numerically) rank deficient system (deficiency=7). If deficiency is large, the results may be inaccurate. Deficiency may strongly depend on the value of eps. warnings.warn(message) .....C:\Python26\lib\site-packages\scipy\interpolate\fitpack2.py:580: UserWarning: The required storage space exceeds the available storage space: nxest or nyest too small, or s too small. The weighted least-squares spline corresponds to the current set of knots. warnings.warn(message) ...........................................K..K......................................................................... ........................................................................................................................ ........................................................................................................................ EC:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `write_array` is deprecated! This function is replaced by numpy.savetxt which allows the same functionality through a different syntax. warnings.warn(depdoc, DeprecationWarning) C:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `read_array` is deprecated! The functionality of read_array is in numpy.loadtxt which allows the same functionality using different syntax. warnings.warn(depdoc, DeprecationWarning) ...........................................Exception AttributeError: "'netcdf_file' object has no attribute 'mode'" in < bound method netcdf_file.close of <scipy.io.netcdf.netcdf_file object at 0x000000000C64D6D8>> ignored ............C:\Python26\lib\site-packages\numpy\lib\utils.py:140: DeprecationWarning: `npfile` is deprecated! You can achieve the same effect as using npfile using numpy.save and numpy.load. You can use memory-mapped arrays and data-types to map out a file format for direct manipulation in NumPy. warnings.warn(depdoc, DeprecationWarning) .........C:\Python26\lib\site-packages\scipy\io\wavfile.py:30: WavFileWarning: Unfamiliar format bytes warnings.warn("Unfamiliar format bytes", WavFileWarning) C:\Python26\lib\site-packages\scipy\io\wavfile.py:120: WavFileWarning: chunk not understood warnings.warn("chunk not understood", WavFileWarning) ........................................................................................................................ .......................................................................................................SSSSSS......SSSSS S......SSSS...............................................................S............................................. ........................................................................................................................ ..............................................E......................................................................... ........................................................................................................................ ....SSS.........S....................................................................................................... .............................................................F.......................................................... ........................................................................................................................ .....................................................FFF.....................................................C:\Python26 \lib\site-packages\scipy\signal\filter_design.py:247: BadCoefficients: Badly conditioned filter coefficients (numerator) : the results may be meaningless "results may be meaningless", BadCoefficients) ........................................................................................................................ ................................................................................................E....................... ..........................SSSSSSSSSSS.FE.EE.EE......K.........E.E...................................E................... ....................................................................K..................E................................ ............K..................E.................................................E...................................... ..............................................KK........................E............................................... ........................................................................................................................ ..................................................................................................................F..... ...............................................................................K.K...................................... ........................................................................................................................ ........................................................................................................................ ..........................F...F..............................................................K........K.........SSSSS... ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ ........................................................................................................................ .............................S.......................................................................................... ...........................................................................................C:\Python26\lib\site-packages \scipy\stats\morestats.py:702: UserWarning: Ties preclude use of exact statistic. warnings.warn("Ties preclude use of exact statistic.") ........................................................................................................................ ........................................................................................................................ ....................................................error removing c:\users\robince\appdata\local\temp\tmpr3s_aecat_test : c:\users\robince\appdata\local\temp\tmpr3s_aecat_test: The directory is not empty .................................................................................................. ====================================================================== ERROR: Testing that kmeans2 init methods work. ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\cluster\tests\test_vq.py", line 166, in test_kmeans2_init kmeans2(data, 3, minit = 'points') File "C:\Python26\lib\site-packages\scipy\cluster\vq.py", line 671, in kmeans2 clusters = init(data, k) File "C:\Python26\lib\site-packages\scipy\cluster\vq.py", line 523, in _kpoints p = np.random.permutation(n) File "mtrand.pyx", line 4231, in mtrand.RandomState.permutation (build\scons\numpy\random\mtrand\mtrand.c:18669) File "mtrand.pyx", line 4174, in mtrand.RandomState.shuffle (build\scons\numpy\random\mtrand\mtrand.c:18261) TypeError: len() of unsized object ====================================================================== ERROR: test_basic (test_array_import.TestNumpyio) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\io\tests\test_array_import.py", line 29, in test_basic b = numpyio.fread(fid,1000000,N.Int16,N.Int) MemoryError ====================================================================== ERROR: test_decomp.test_lapack_misaligned(<function solve at 0x0000000006366438>, (array([[ 1.734e-255, 8.189e-217, 4.025e-178, 1.903e-139, 9.344e-101, ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\linalg\tests\test_decomp.py", line 1074, in check_lapack_misaligned func(*a,**kwargs) File "C:\Python26\lib\site-packages\scipy\linalg\basic.py", line 49, in solve a1, b1 = map(asarray_chkfinite,(a,b)) File "C:\Python26\lib\site-packages\numpy\lib\function_base.py", line 586, in asarray_chkfinite raise ValueError, "array must not contain infs or NaNs" ValueError: array must not contain infs or NaNs ====================================================================== ERROR: Regression test for #880: empty array for zi crashes. ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\signal\tests\test_signaltools.py", line 422, in test_empty_zi y, zf = lfilter(b, a, x, zi=zi) File "C:\Python26\lib\site-packages\scipy\signal\signaltools.py", line 610, in lfilter return sigtools._linear_filter(b, a, x, axis, zi) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_linsolve.TestSplu.test_lu_refcount ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 122, in test_lu_refcount lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_linsolve.TestSplu.test_spilu_smoketest ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 60, in test_spilu_smokete st lu = spilu(self.A, drop_tol=1e-2, fill_factor=5) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 245, in spilu ilu=True, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_linsolve.TestSplu.test_splu_basic ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 87, in test_splu_basic lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_linsolve.TestSplu.test_splu_perm ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 100, in test_splu_perm lu = splu(a_) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_linsolve.TestSplu.test_splu_smoketest ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 53, in test_splu_smoketes t lu = splu(self.A) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: Check that QMR works with left and right preconditioners ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_iterative.py", line 161, in test_leftright_p recond L_solver = splu(L) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_preconditioner (test_lgmres.TestLGMRES) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\isolve\tests\test_lgmres.py", line 38, in test_preconditioner pc = splu(Am.tocsc()) File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\linsolve.py", line 173, in splu ilu=False, options=_options) RuntimeError: Factor is exactly singular ====================================================================== ERROR: test_mu (test_base.TestBSR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_mu (test_base.TestCSC) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_mu (test_base.TestCSR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_mu (test_base.TestDIA) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== ERROR: test_mu (test_base.TestLIL) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\tests\test_base.py", line 966, in test_mu D1 = A * B.T File "C:\Python26\lib\site-packages\numpy\matrixlib\defmatrix.py", line 319, in __mul__ return N.dot(self, asmatrix(other)) TypeError: array cannot be safely cast to required type ====================================================================== FAIL: test_complex (test_basic.TestLongDoubleFailure) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\fftpack\tests\test_basic.py", line 527, in test_complex np.longcomplex) AssertionError: Type <type 'numpy.complex128'> not supported but does not fail ====================================================================== FAIL: extrema 3 ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\ndimage\tests\test_ndimage.py", line 3149, in test_extrema03 self.failUnless(numpy.all(output1[2] == output4)) AssertionError ====================================================================== FAIL: test_lorentz (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 292, in test_lorentz 3.7798193600109009e+00]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 1.00000000e+03, 1.00000000e-01, 3.80000000e+00]) y: array([ 1.43067808e+03, 1.33905090e-01, 3.77981936e+00]) ====================================================================== FAIL: test_multi (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 188, in test_multi 0.5101147161764654, 0.5173902330489161]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 4. , 2. , 7. , 0.4, 0.5]) y: array([ 4.37998803, 2.43330576, 8.00288459, 0.51011472, 0.51739023]) ====================================================================== FAIL: test_pearson (test_odr.TestODR) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\odr\tests\test_odr.py", line 235, in test_pearson np.array([ 5.4767400299231674, -0.4796082367610305]), File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 765, in assert_array_almost_equal header='Arrays are not almost equal') File "C:\Python26\lib\site-packages\numpy\testing\utils.py", line 609, in assert_array_compare raise AssertionError(msg) AssertionError: Arrays are not almost equal (mismatch 100.0%) x: array([ 1., 1.]) y: array([ 5.47674003, -0.47960824]) ====================================================================== FAIL: test_twodiags (test_linsolve.TestLinsolve) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\scipy\sparse\linalg\dsolve\tests\test_linsolve.py", line 39, in test_twodiags assert( norm(b - Asp*x) < 10 * cond_A * eps ) AssertionError ====================================================================== FAIL: test_kdtree.test_vectorization.test_single_query ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\spatial\tests\test_kdtree.py", line 154, in test_single_query assert isinstance(i,int) AssertionError ====================================================================== FAIL: test_data.test_boost(<Data for arccosh: acosh_data_ipp-acosh_data>,) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "C:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 187, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 1.77636e-15 Max |rdiff|: 1.09352e-13 Bad results for the following points (in output 0): 1.0000014305114746 => 0.0016914556651294794 != 0.0016914556651292944 (rdiff 1.093 5249113484058e-13) 1.000007152557373 => 0.0037822080446614169 != 0.0037822080446612951 (rdiff 3.222 0418006721235e-14) 1.0000138282775879 => 0.005258943946801071 != 0.0052589439468011014 (rdiff 5.772 5773723182603e-15) 1.0000171661376953 => 0.0058593666181291238 != 0.0058593666181292027 (rdiff 1.347 0725302071254e-14) 1.0000600814819336 => 0.01096183199218881 != 0.010961831992188852 (rdiff 3.798 0296955025714e-15) 1.0001168251037598 => 0.015285472131830317 != 0.015285472131830425 (rdiff 7.036 2795851489781e-15) 1.0001487731933594 => 0.017249319093529933 != 0.017249319093529877 (rdiff 3.218 1647826365358e-15) 1.0003981590270996 => 0.028218171738655599 != 0.028218171738655373 (rdiff 7.991 8023735059643e-15) 1.000638484954834 => 0.035732814682314498 != 0.035732814682314568 (rdiff 1.941 8828227213605e-15) 1.0010714530944824 => 0.046287402472878984 != 0.046287402472878776 (rdiff 4.497 2672043800306e-15) 1.0049939155578613 => 0.099897593086028066 != 0.099897593086027803 (rdiff 2.639 4826962588157e-15) 1.024169921875 => 0.21942279004958387 != 0.21942279004958354 (rdiff 1.517 9230348510424e-15) ====================================================================== FAIL: test_data.test_boost(<Data for arctanh: atanh_data_ipp-atanh_data>,) ---------------------------------------------------------------------- Traceback (most recent call last): File "C:\Python26\lib\site-packages\nose-0.11.3-py2.6.egg\nose\case.py", line 186, in runTest self.test(*self.arg) File "C:\Python26\lib\site-packages\scipy\special\tests\test_data.py", line 205, in _test_factory test.check(dtype=dtype) File "C:\Python26\lib\site-packages\scipy\special\tests\testutils.py", line 187, in check assert False, "\n".join(msg) AssertionError: Max |adiff|: 6.39488e-12 Max |rdiff|: 1.01982e-12 Bad results for the following points (in output 0): -0.99999284744262695 => -6.2705920974721474 != -6.2705920974657525 (rdiff 1.019 8214973073088e-12) -0.99998283386230469 => -5.832855225376532 != -5.832855225378502 (rdiff 3.377 3849320373679e-13) ---------------------------------------------------------------------- Ran 4410 tests in 29.842s FAILED (KNOWNFAIL=11, SKIP=38, errors=16, failures=9) <nose.result.TextTestResult run=4410 errors=16 failures=9> >>>

9 18

[ANN] NLopt, a nonlinear optimization library, now with Python interface
by Steven G. Johnson July 8, 2010

July 8, 2010

The NLopt library, available from http://ab-initio.mit.edu/nlopt provides a common interface for a large number of algorithms for both global and local nonlinear optimizations, both with and without gradient information, and including both bound constraints and nonlinear equality/inequality constraints. NLopt is written in C, but now includes a Python interface (as well as interfaces for C++, Fortran, Matlab, Octave, and Guile). It is free software under the GNU LGPL. Regards, Steven G. Johnson

4 5

scipy.optimize.leastsq question
by Ralph Kube July 7, 2010

July 7, 2010

Hello people, I am having a problem using the leastsq routine. My goal is to determine three parameters r_i, r_s and ppw so that the residuals to a model function a(r_i, r_s, ppw) to a measurement are minimal. When I call the leastsq routine with a good guess of starting values, it iterates 6 times without changing the vales of the initial parameters and then exits without an error. The function a is very complicated and expensive to evaluate. Some evaluation is done by using the subprocess module of python. Can this pose a problem for the leastsq routine? This is in the main routine: import numpy as N for t_idx, t in enumerate(time_var): r_i = 300. r_s = 1.0 ppw=1e-6 sza = 70. wl = N.arange(300., 3001., 1.) albedo_true = compute_albedo(r_i, r_s, ppw, sza, wl) # This emulates the measurement data albedo_meas = albedo_true + 0.01*N.random.randn(len(wl)) print 'Optimizing albedo' p0 = [2.*r_i, 1.4*r_s, 4.*ppw] plsq2 = leastsq(albedo_residual, p0, args=(albedo_meas, sza, wl)) print '... done: ', plsq2[0][0], plsq2[0][1], plsq2[0][2] albedo_model = compute_albedo(plsq2[0][0], plsq2[0][1], plsq2[0][2], sza, wl) The residual function: def albedo_residual(p, y, sza, wvl): r_i, r_s, ppw = p albedo = compute_albedo(r_i, r_s, ppw, sza, wvl) err = albedo - y print 'Albedo for r_i = %4.0f, r_s = %4.2f, ppw = %3.2e \ Residual squared: %5f' % (r_i, r_s, ppw, N.sum(err**2)) return err The definition of the function a(r_i, r_s, ppw) def compute_albedo(radius_ice, radius_soot, ppw, sza, wvl): The output is: Optimizing albedo Albedo for r_i = 600, r_s = 1.40, ppw = 4.00e-06 Residual squared: 0.973819 Albedo for r_i = 600, r_s = 1.40, ppw = 4.00e-06 Residual squared: 0.973819 Albedo for r_i = 600, r_s = 1.40, ppw = 4.00e-06 Residual squared: 0.973819 Albedo for r_i = 600, r_s = 1.40, ppw = 4.00e-06 Residual squared: 0.973819 Albedo for r_i = 600, r_s = 1.40, ppw = 4.00e-06 Residual squared: 0.973819 Albedo for r_i = 600, r_s = 1.40, ppw = 4.00e-06 Residual squared: 0.973819 ... done: 600.0 1.4 4e-06 To check for errors, I implemented the example code from http://www.tau.ac.il/~kineret/amit/scipy_tutorial/ in my code and it runs successfully. I would be glad for any suggestion. Cheers, Ralph

6 13

[ANN] la 0.4, the labeled array
by Keith Goodman July 7, 2010

July 7, 2010

The main class of the la package is a labeled array, larry. A larry consists of data and labels. The data is stored as a NumPy array and the labels as a list of lists (one list per dimension). Alignment by label is automatic when you add (or subtract, multiply, divide) two larrys. The focus of this release was binary operations between unaligned larrys with user control of the join method (five available) and the fill method. A general binary function, la.binaryop(), was added as were the convenience functions add, subtract, multiply, divide. Supporting functions such as la.align(), which aligns two larrys, were also added. download http://pypi.python.org/pypi/la doc http://larry.sourceforge.net code http://github.com/kwgoodman/la list1 http://groups.google.ca/group/pystatsmodels list2 http://groups.google.com/group/labeled-array RELEASE NOTES New larry methods - ismissing: A bool larry with element-wise marking of missing values - take: A copy of the specified elements of a larry along an axis New functions - rand: Random samples from a uniform distribution - randn: Random samples from a Gaussian distribution - missing_marker: Return missing value marker for the given larry - ismissing: A bool Numpy array with element-wise marking of missing values - correlation: Correlation of two Numpy arrays along the specified axis - split: Split into train and test data along given axis - listmap_fill: Index map a list onto another and index of unmappable elements - listmap_fill: Cython version of listmap_fill - align: Align two larrys using one of five join methods - info: la package information such as version number and HDF5 availability - binaryop: Binary operation on two larrys with given function and join method - add: Sum of two larrys using given join and fill methods - subtract: Difference of two larrys using given join and fill methods - multiply: Multiply two larrys element-wise using given join and fill methods - divide: Divide two larrys element-wise using given join and fill methods Enhancements - listmap now has option to ignore unmappable elements instead of KeyError - listmap.pyx now has option to ignore unmappable elements instead of KeyError - larry.morph() is much faster as are methods, such as merge, that use it Breakage from la 0.3 - Development moved from launchpad to github - func.py and afunc.py renamed flarry.py and farray.py to match new flabel.py. Broke: "from la.func import stack"; Did not break: "from la import stack" - Default binary operators (+, -, ...) no longer raise an error when no labels overlap Bug fixes - #590270 Index with 1d array bug: lar[1darray,:] worked; lar[1darray] crashed

1 0

ANN: Visvis 1.3.1 (bugfix release)
by Almar Klein July 6, 2010

July 6, 2010

Hi all, I recently announced Visvis version 1.3. However, there was a bug in the setup script that caused the processing subpackage to not being loaded. Visvis 1.3.1 fixes this issue along with another issue with regard to volume rendering. ===== Below is the announcement of visvis 1.3: ===== I am exited to announce version 1.3 of Visvis, the object oriented approach to visualization. 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 The largest improvement is the Mesh class to represent triangular and quad meshes and surface data. The Axes class got a property to access 8 different light sources. These improvements enable numerous new possibilities to visualize data using Visvis. Further changes include the introduction of polar plotting and 3D bar charts. For a (more) complete list of 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. Essentially, visvis is an object oriented layer of Python on top of OpenGl? <http://code.google.com/p/visvis/w/edit/OpenGl>, thereby combining the power of OpenGl?<http://code.google.com/p/visvis/w/edit/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(), surf()). Regards, Almar

1 0

ANN: scipy 0.8.0 release candidate 1
by Ralf Gommers July 5, 2010

July 5, 2010

I'm pleased to announce the availability of the first release candidate of SciPy 0.8.0. Please try it out and report any problems on the scipy-dev mailing list. SciPy is a package of tools for science and engineering for Python. It includes modules for statistics, optimization, integration, linear algebra, Fourier transforms, signal and image processing, ODE solvers, and more. This release candidate release comes almost one and a half year after the 0.7.0 release and contains many new features, numerous bug-fixes, improved test coverage, and better documentation. Please note that SciPy 0.8.0rc1 requires Python 2.4-2.6 and NumPy 1.4.1 or greater. For more information, please see the release notes: http://sourceforge.net/projects/scipy/files/scipy/0.8.0rc1/NOTES.txt/view You can download the release from here: https://sourceforge.net/projects/scipy/ Python 2.5/2.6 binaries for Windows and OS X are available, as well as source tarballs for other platforms and the documentation in pdf form. Thank you to everybody who contributed to this release. Enjoy, Ralf

1 0