Mailman 3 September 2010 - SciPy-Dev

SLSQP Constrained Optimizer Status
by Rob Falck 25 Nov '13

25 Nov '13

I'm currently implementing the Sequential Least Squares Quadratic Programming (SLSQP) optimizer, by Dieter Kraft, for use in Scipy. The Fortran code being wrapped with F2PY is here: http://www.netlib.org/toms/733 (its use within Scipy has been cleared) SLSQP provides for bounds on the independent variables, as well as equality and inequality constraint functions, which is a capability that doesn't exist in scipy.optimize. Currently the code works, although the constraint normals are being generated by approximation. I'm working on a way to pass these in. I think the most elegant way will be a single function that returns the matrix of constraint normals. For a demonstration of what the code can do, here is an optimization of f(x,y) = 2xy + 2x - x**2 - 2y**2 Example 14.2 in Chapra & Canale gives the maximum as x=2.0, y=1.0. The unbounded optimization tests find this solution. As expected, its faster when derivatives are provided rather than approximated. Unbounded optimization. Derivatives approximated. Elapsed time: 1.45792961121 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] Unbounded optimization. Derivatives provided. Elapsed time: 1.03211402893 ms Results [[1.9999999515712266, 0.99999996181577444], -1.9999999999999984, 4, 0, 'Optimization terminated successfully.'] The following example uses an equality constraint to find the optimal when x=y. Bound optimization. Derivatives approximated. Elapsed time: 1.384973526 ms Results [[0.99999996845920858, 0.99999996845920858], -0.99999999999999889, 4, 0, 'Optimization terminated successfully.'] I've tried to conform to the syntax used by the other optimizers in scipy.optimize. The function definition and doc string are below. If anyone is interested in testing it out, let me know. def fmin_slsqp( func, x0 , eqcons=[], f_eqcons=None, ieqcons=[], f_ieqcons=None, bounds = [], fprime = None, fprime_cons=None,args = (), iter = 100, acc = 1.0E-6, iprint = 1, full_output = 0, epsilon = _epsilon ): """ Minimize a function using Sequential Least SQuares Programming Description: Python interface function for the SLSQP Optimization subroutine originally implemented by Dieter Kraft. Inputs: func - Objective function (in the form func(x, *args)) x0 - Initial guess for the independent variable(s). eqcons - A list of functions of length n such that eqcons[j](x0,*args) == 0.0 in a successfully optimized problem f_eqcons - A function of the form f_eqcons(x, *args) that returns an array in which each element must equal 0.0 in a successfully optimized problem. If f_eqcons is specified, eqcons is ignored. ieqcons - A list of functions of length n such that ieqcons[j](x0,*args) >= 0.0 in a successfully optimized problem f_ieqcons - A function of the form f_ieqcons(x0, *args) that returns an array in which each element must be greater or equal to 0.0 in a successfully optimized problem. If f_ieqcons is specified, ieqcons is ignored. bounds - A list of tuples specifying the lower and upper bound for each independent variable [ (xl0, xu0), (xl1, xu1), ...] fprime - A function that evaluates the partial derivatives of func fprime_cons - A function of the form f(x, *args) that returns the m by n array of constraint normals. If not provided, the normals will be approximated. Equality constraint normals precede inequality constraint normals. args - A sequence of additional arguments passed to func and fprime iter - The maximum number of iterations (int) acc - Requested accuracy (float) iprint - The verbosity of fmin_slsqp. iprint <= 0 : Silent operation iprint == 1 : Print summary upon completion (default) iprint >= 2 : Print status of each iterate and summary full_output - If 0, return only the minimizer of func (default). Otherwise, output final objective function and summary information. epsilon - The step size for finite-difference derivative estimates. Outputs: ( x, { fx, gnorm, its, imode, smode }) x - The final minimizer of func. fx - The final value of the objective function. its - The number of iterations. imode - The exit mode from the optimizer, as an integer. smode - A string describing the exit mode from the optimizer. Exit modes are defined as follows: -1 : Gradient evaluation required (g & a) 0 : Optimization terminated successfully. 1 : Function evaluation required (f & c) 2 : Number of equality constraints larger than number of independent variables 3 : More than 3*n iterations in LSQ subproblem 4 : Inequality constraints incompatible 5 : Singular matrix E in LSQ subproblem 6 : Singular matrix C in LSQ subproblem 7 : Rank-deficient equality constraint subproblem HFTI 8 : Positive directional derivative for linesearch 9 : Iteration limit exceeded -- - Rob Falck

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Subversion scipy.stats irregular problem with source code example
by James Phillips 10 Dec '10

10 Dec '10

Since I observed the following behavior in the SVN respository version of SciPy, it seemed to me proper to post to the dev mailing list. I'm using Ubuntu Lucid Lynx 32 bit and a fresh GIT of Numpy. I am not sure if a Trac bug report needs to be entered. Below is some example code for fitting two statistical distributions. Sometimes the numpy-generated data is fit, as I can see the estimated and fitted parameters are different. Sometimes I receive many messages repeated on the command line like: Warning: invalid value encountered in absolute Warning: invalid value encountered in subtract and the estimated parameters equal the fitted parameter values, indicating no fitting took place. Sometimes I receive on the command line: Traceback (most recent call last): File "/home/zunzun/local/lib/python2.6/site-packages/scipy/stats/distributions.py", line 1987, in func sk = 2*(b-a)*math.sqrt(a + b + 1) / (a + b + 2) / math.sqrt(a*b) ValueError: math domain error Traceback (most recent call last): File "example.py", line 10, in <module> fitStart_beta = scipy.stats.beta._fitstart(data) File "/home/zunzun/local/lib/python2.6/site-packages/scipy/stats/distributions.py", line 1992, in _fitstart a, b = optimize.fsolve(func, (1.0, 1.0)) File "/home/zunzun/local/lib/python2.6/site-packages/scipy/optimize/minpack.py", line 125, in fsolve maxfev, ml, mu, epsfcn, factor, diag) minpack.error: Error occured while calling the Python function named func and program flow is stopped. In summary, three behaviors: (1) Fits OK (2) Many exceptions with no fitting (3) minpack error. Running the program 10 times or so will reproduce these behaviors without fail from the "bleeding-edge" repository code. James Phillips ######################################################## import numpy, scipy, scipy.stats # test uniform distribution fitting data = numpy.random.uniform(2.0, 3.0, size=100) fitStart_uniform = scipy.stats.uniform._fitstart(data) fittedParameters_uniform = scipy.stats.uniform.fit(data) # test beta distribution fitting data = numpy.random.beta(2.0, 3.0, size=100) fitStart_beta = scipy.stats.beta._fitstart(data) fittedParameters_beta = scipy.stats.beta.fit(data) print print 'uniform._fitstart returns', fitStart_uniform print 'fitted parameters for uniform =', fittedParameters_uniform print print 'beta._fitstart returns', fitStart_beta print 'fitted parameters for beta =', fittedParameters_beta print

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Improvements to SciPy.org Server
by Aaron River 30 Sep '10

30 Sep '10

Greetings Gentle Folk, I'm happy to announce that the server hosting SciPy.org, as well as other OSS projects, has been migrated into a cluster assigned to our community support efforts. The base instance itself remains relatively unchanged, but is now running atop a more capable set of servers. (Improvements to the services themselves are next in line.) Yesterday afternoon, I allowed the system to run with the configurations carried over from the old hardware, while watching the services for warning signs of trouble. Then, that evening, I loosened things up and adjusted configurations to better utilize the available resources. Though I always expect there to be "gotchas", I've thus far observed much improved response times and throughput. If you do experience any problems, please do not hesitate to let us know. We hope you enjoy the improvements to SciPy.org. Thanks, -- Aaron River Sr IT Administrator Enthought, Inc. --

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Re: [SciPy-Dev] spatial.test() failure
by Warren Weckesser 29 Sep '10

29 Sep '10

> > Nils Wagner wrote: > >>>> from scipy import spatial > >>>> spatial.test() > >>>> > > Running unit tests for scipy.spatial > > NumPy version 2.0.0.dev8714 > > NumPy is installed in > > /home/nwagner/local/lib64/python2.6/site-packages/numpy > > SciPy version 0.9.0.dev6803 > > SciPy is installed in > > /home/nwagner/local/lib64/python2.6/site-packages/scipy > > Python version 2.6.5 (r265:79063, Jul 5 2010, 11:46:13) > > [GCC 4.5.0 20100604 [gcc-4_5-branch revision 160292]] > > nose version 0.11.2 > > > ............................................................................................................................F.................................................................................................................................................. > > ====================================================================== > > FAIL: Tests pdist(X, 'minkowski') on iris data. (float32) > > ---------------------------------------------------------------------- > > Traceback (most recent call last): > > File > > > "/home/nwagner/local/lib64/python2.6/site-packages/scipy/spatial/tests/test_distance.py", > > line 837, in test_pdist_minkowski_3_2_iris_float32 > > self.failUnless(within_tol(Y_test1, Y_right, eps)) > > AssertionError > > > > ---------------------------------------------------------------------- > > Ran 271 tests in 38.005s > > > > FAILED (failures=1) > > <nose.result.TextTestResult run=271 errors=0 failures=1> > > > > > In r6799, the names of some tests were changed so there are no longer > duplicated test function names. This means tests are now being run that > had previously been shadowed by the duplicate names. I also get the > failure of the test test_pdist_minkowski_3_2_iris_float32(). I created > a ticket for the problem: http://projects.scipy.org/scipy/ticket/1278 > > Warren > If the tolerance in the test is changed from eps=1e-7 to 1e-6, the test passes on my computer. Several other float32 tests using the iris data also use 1e-6. I added a note about the tolerance of similar tests in the ticket. It does not seem unreasonable to simply loosen the tolerance a bit. Any objections? Warren

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scipy.optimize.curve_fit does not work with single parameter function
by Daniele Nicolodi 28 Sep '10

28 Sep '10

Hello, I copied the code of scipy.optimize.curve_fit into my data analysis code to use it on a not so recent versions of scipy available on my systems. However it does not work when I try to fit a simple function that has only one parameter, for example: def func(x, p1): return x * p1 The problem is that scipy.optimize.leastsq() returns a list of parameters values for cases where the function has more than one parameter it returns a tuple of parameters values, while in the case of one single parameters it returns a numpy scalar value. This causes an error in the curve_fit _general_function() and _weighted_general_function() where the target function is called: function(xdata, *params) This works only if params is a python list or tuple. Also the return value suffers from the same problem. If the target function has more than one parameter you get a parameters list, otherwise you get a scalar. It would be more consistent to return always a list, also because the covariance is always returned as a matrix. Please ignore this if that has been resolved in more recent version of scipy, I have acces to 0.7.2 (last available on debian sid). Cheers, -- Daniele

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problems configuring scipy (0.8.0) from numpy (1.5.0) in a itanium cluster
by Miguel Pan Fidalgo 28 Sep '10

28 Sep '10

Technical information ++++++++++++++++++++++ $ python -c 'import sys;print sys.version' 2.4.2 (#1, Apr 13 2007, 16:14:04) [GCC 4.1.0 (SUSE Linux)] $ module load numpy/1.5.0 $ module list Currently Loaded Modulefiles: 1) icc/11.1.056 3) mkl/10.2.2 5) nose/0.11.2 7) numpy/1.5.0 2) ifort/11.1.056 4) fftw/3.2.2 6) SuiteSparse/3.4.0 $ python -c 'import numpy;print numpy.__version__' 1.5.0 $ uname -a Linux cn002 2.6.16.53-0.8_SFS2.3_0-default #1 SMP Mon Nov 5 14:15:47 CET 2007 ia64 ia64 ia64 GNU/Linux numpy-1.5.0 +++++++++++++++++ I have installed numpy-1.5.0 in our Itanium cluster. This is my site.cfg file: $ cat /opt/cesga/numpy-1.5.0/src_numpy-1.5.0/site.cfg [DEFAULT] library_dirs = /opt/cesga/fftw-3.2.2/lib:/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64:/opt/cesga/SuiteSparse-3.4.0/lib include_dirs = /opt/cesga/fftw-3.2.2/include:/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/include:/opt/cesga/SuiteSparse-3.4.0/include # UMFPACK # ------- [amd] amd_libs = amd [umfpack] umfpack_libs = umfpack # FFT libraries # ------------- [fftw] libraries = fftw3 # MKL # ---- [mkl] library_dirs = /opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64 lapack_libs = mkl_lapack mkl_libs = mkl_solver_lp64, mkl_intel_lp64, mkl_intel_thread, mkl_i2p, mkl_core These are the hacks: $ diff system_info.py.original system_info.py 818c818 < _lib_mkl = ['mkl','vml','guide'] --- > _lib_mkl = ['mkl','mkl_lapack','guide'] $ diff intelccompiler.py.original intelccompiler.py 28a29 > cc_exe=cc_exe + ' -liomp5 -lpthread -fPIC' scipy-0.8.0 +++++++++++++++ When I try to install scipy-0.8.0, the configuration process fails: $ tar zxvf scipy-0.8.0.tar.gz $ mv scipy-0.8.0 src $ cp -va run.sh src/ $ cd src/ $ cat run.sh #!/bin/bash module load numpy/1.5.0 python setup.py config_fc --fcompiler=intele #install --prefix=/sfs/home/cesga/mpan/scipy-0.8.0 $ ./run.sh Warning: No configuration returned, assuming unavailable.blas_opt_info: blas_mkl_info: FOUND: libraries = ['mkl_solver_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_i2p', 'mkl_core', 'pthread'] library_dirs = ['/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/cesga/fftw-3.2.2/include', '/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/include', '/opt/cesga/SuiteSparse-3.4.0/include'] FOUND: libraries = ['mkl_solver_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_i2p', 'mkl_core', 'pthread'] library_dirs = ['/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/cesga/fftw-3.2.2/include', '/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/include', '/opt/cesga/SuiteSparse-3.4.0/include'] lapack_opt_info: lapack_mkl_info: mkl_info: FOUND: libraries = ['mkl_solver_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_i2p', 'mkl_core', 'pthread'] library_dirs = ['/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/cesga/fftw-3.2.2/include', '/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/include', '/opt/cesga/SuiteSparse-3.4.0/include'] FOUND: libraries = ['mkl_lapack', 'mkl_solver_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_i2p', 'mkl_core', 'pthread'] library_dirs = ['/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/cesga/fftw-3.2.2/include', '/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/include', '/opt/cesga/SuiteSparse-3.4.0/include'] FOUND: libraries = ['mkl_lapack', 'mkl_solver_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_i2p', 'mkl_core', 'pthread'] library_dirs = ['/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64'] define_macros = [('SCIPY_MKL_H', None)] include_dirs = ['/opt/cesga/fftw-3.2.2/include', '/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/include', '/opt/cesga/SuiteSparse-3.4.0/include'] umfpack_info: libraries umfpack not found in /opt/cesga/fftw-3.2.2/lib libraries umfpack not found in /opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64 amd_info: libraries amd not found in /opt/cesga/fftw-3.2.2/lib libraries amd not found in /opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64 FOUND: libraries = ['amd'] library_dirs = ['/opt/cesga/SuiteSparse-3.4.0/lib'] swig_opts = ['-I/opt/cesga/SuiteSparse-3.4.0/include'] define_macros = [('SCIPY_AMD_H', None)] include_dirs = ['/opt/cesga/SuiteSparse-3.4.0/include'] FOUND: libraries = ['umfpack', 'amd'] library_dirs = ['/opt/cesga/SuiteSparse-3.4.0/lib'] swig_opts = ['-I/opt/cesga/SuiteSparse-3.4.0/include', '- I/opt/cesga/SuiteSparse-3.4.0/include'] define_macros = [('SCIPY_UMFPACK_H', None), ('SCIPY_AMD_H', None)] include_dirs = ['/opt/cesga/SuiteSparse-3.4.0/include'] running config_fc unifing config_fc, config, build_clib, build_ext, build commands --fcompiler options the warning (first line) implies that BLAS primitives have to be recompiled. At the end the installation ends properly... this is not the point, but scipy doesn't use MKL BLAS primitives. BUT according to numpy... blas_mkl and blas_opt should be the same (like lapack_mkl and lapack_opt): $ module load numpy/1.5.0 $ python Python 2.4.2 (#1, Apr 13 2007, 16:14:04) [GCC 4.1.0 (SUSE Linux)] on linux2 Type "help", "copyright", "credits" or "license" for more information. >>> from numpy.distutils.system_info import get_info >>> get_info('blas_mkl') {'libraries': ['mkl_solver_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_i2p', 'mkl_core', 'pthread'], 'library_dirs': ['/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64'], 'define_macros': [('SCIPY_MKL_H', None)], 'include_dirs': ['/opt/cesga/fftw-3.2.2/include', '/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/include', '/opt/cesga/SuiteSparse-3.4.0/include']} >>> get_info('blas_opt') {'libraries': ['mkl_solver_lp64', 'mkl_intel_lp64', 'mkl_intel_thread', 'mkl_i2p', 'mkl_core', 'pthread'], 'library_dirs': ['/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/lib/64'], 'define_macros': [('SCIPY_MKL_H', None)], 'include_dirs': ['/opt/cesga/fftw-3.2.2/include', '/opt/cesga/intel/intel11.0/Compiler/11.1/056/mkl/include', '/opt/cesga/SuiteSparse-3.4.0/include']} >>> There is no need to recompile BLAS!!!! any idea or suggestion ? WHAT I'm doing wrong ? -- Atentamente, Miguel Pan Fidalgo (Applications Technician) mail: mpan at cesga.es web: http://www.cesga.es Avda. de Vigo s/n 15705, Santiago de Compostela Telf.: +34 981 569810 - Fax: 981 594616 -------------------------------------------------------------------------

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scipy error compiling csr_wrap under Python 2.7
by Bruce Southey 28 Sep '10

28 Sep '10

Hi, I failed to get the scipy 0.8 rc1 and SVN to build under Python2.7 with numpy '2.0.0.dev8469' and gcc version 4.4.4 20100503 (Red Hat 4.4.4-2) (GCC) . But scipy does compile with Python 2.6. building 'scipy.sparse.sparsetools._csr' extension compiling C++ sources C compiler: g++ -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -fPIC compile options: '-I/usr/local/lib/python2.7/site-packages/numpy/core/include -I/usr/local/include/python2.7 -c' g++: scipy/sparse/sparsetools/csr_wrap.cxx scipy/sparse/sparsetools/csr_wrap.cxx: In function ‘int require_size(PyArrayObject*, npy_intp*, int)’: scipy/sparse/sparsetools/csr_wrap.cxx:2910: error: expected ‘)’ before ‘PRIdPTR’ scipy/sparse/sparsetools/csr_wrap.cxx:2910: warning: spurious trailing ‘%’ in format scipy/sparse/sparsetools/csr_wrap.cxx:2910: warning: too many arguments for format scipy/sparse/sparsetools/csr_wrap.cxx:2917: error: expected ‘)’ before ‘PRIdPTR’ scipy/sparse/sparsetools/csr_wrap.cxx:2917: warning: spurious trailing ‘%’ in format scipy/sparse/sparsetools/csr_wrap.cxx:2917: warning: too many arguments for format scipy/sparse/sparsetools/csr_wrap.cxx: In function ‘int require_size(PyArrayObject*, npy_intp*, int)’: scipy/sparse/sparsetools/csr_wrap.cxx:2910: error: expected ‘)’ before ‘PRIdPTR’ scipy/sparse/sparsetools/csr_wrap.cxx:2910: warning: spurious trailing ‘%’ in format scipy/sparse/sparsetools/csr_wrap.cxx:2910: warning: too many arguments for format scipy/sparse/sparsetools/csr_wrap.cxx:2917: error: expected ‘)’ before ‘PRIdPTR’ scipy/sparse/sparsetools/csr_wrap.cxx:2917: warning: spurious trailing ‘%’ in format scipy/sparse/sparsetools/csr_wrap.cxx:2917: warning: too many arguments for format error: Command "g++ -pthread -fno-strict-aliasing -g -O2 -DNDEBUG -g -fwrapv -O3 -Wall -fPIC -I/usr/local/lib/python2.7/site-packages/numpy/core/include -I/usr/local/include/python2.7 -c scipy/sparse/sparsetools/csr_wrap.cxx -o build/temp.linux-x86_64-2.7/scipy/sparse/sparsetools/csr_wrap.o" failed with exit status 1 The error is possibly related the variable 'NPY_INTP_FMT' (defined in numpy/core/include/numpy/ndarraytypes.h). A grep shows these files: scipy/sparse/sparsetools/coo_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", size[i]); scipy/sparse/sparsetools/coo_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", array_size(ary,i)); scipy/sparse/sparsetools/bsr_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", size[i]); scipy/sparse/sparsetools/bsr_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", array_size(ary,i)); scipy/sparse/sparsetools/numpy.i: sprintf(s,"%" NPY_INTP_FMT ",", size[i]); scipy/sparse/sparsetools/numpy.i: sprintf(s,"%" NPY_INTP_FMT ",", array_size(ary,i)); scipy/sparse/sparsetools/csr_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", size[i]); scipy/sparse/sparsetools/csr_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", array_size(ary,i)); scipy/sparse/sparsetools/csc_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", size[i]); scipy/sparse/sparsetools/csc_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", array_size(ary,i)); scipy/sparse/sparsetools/dia_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", size[i]); scipy/sparse/sparsetools/dia_wrap.cxx: sprintf(s,"%" NPY_INTP_FMT ",", array_size(ary,i)); Note that the scipy/sparse/sparsetools/numpy.i contains tabs in some places instead of spaces. The ndarraytypes.h is present in /usr/local/lib/python2.7/site-packages/numpy/core/include/numpy/ Bruce

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script for trailing whitespace cleanup
by josef.pktd＠gmail.com 24 Sep '10

24 Sep '10

Is the script for trailing whitespace cleanup that is used for numpy, scipy available somewhere? Thanks, Josef

3 3

Problem with N-dimensional interpolation using a new griddata function for N>=3
by Adam Machnik 22 Sep '10

22 Sep '10

Hello , I have downloaded from SVN the latest `griddata` implementation for working in N-D with irregular grids (scipy v0.9.0dev6812). It works great for 2D problems. However for 3D (and more) I have a problem: the NaN values appears always in the same position, and it does not depend on the scalar field being interpolated. Here is a small example of simple 3x3x3 regular grid and a constant scalar field of 1.0 values, that is interpolated at the input data points. Example: ****************************** from numpy import * from scipy.interpolate import * grid_x, grid_y, grid_z = mgrid[0:1:3j, 0:1:3j, 0:1:3j] pts = [[ 0.0 , 0.0 , 0.0 ], [ 0.0 , 0.0 , 0.5 ], [ 0.0 , 0.0 , 1.0 ], [ 0.0 , 0.5 , 0.0 ], [ 0.0 , 0.5 , 0.5 ], [ 0.0 , 0.5 , 1.0 ], [ 0.0 , 1.0 , 0.0 ], [ 0.0 , 1.0 , 0.5 ], [ 0.0 , 1.0 , 1.0 ], [ 0.5 , 0.0 , 0.0 ], [ 0.5 , 0.0 , 0.5 ], [ 0.5 , 0.0 , 1.0 ], [ 0.5 , 0.5 , 0.0 ], [ 0.5 , 0.5 , 0.5 ], [ 0.5 , 0.5 , 1.0 ], [ 0.5 , 1.0 , 0.0 ], [ 0.5 , 1.0 , 0.5 ], [ 0.5 , 1.0 , 1.0 ], [ 1.0 , 0.0 , 0.0 ], [ 1.0 , 0.0 , 0.5 ], [ 1.0 , 0.0 , 1.0 ], [ 1.0 , 0.5 , 0.0 ], [ 1.0 , 0.5 , 0.5 ], [ 1.0 , 0.5 , 1.0 ], [ 1.0 , 1.0 , 0.0 ], [ 1.0 , 1.0 , 0.5 ], [ 1.0 , 1.0 , 1.0 ]] vls = ones(27) points = array(pts).astype('float64') values = array(vls).astype('float64') grid_z1 = griddata(points, values, (grid_x, grid_y, grid_z), method='linear', fill_value=0) print 'grid_z1=', grid_z1 *********************** I obtain the following result: grid_z1= [[[ 1. 1. 1.] [ 1. 1. 1.] [ 1. 1. 1.]] [[ 1. 1. 1.] [ 1. 1. 1.] [ 1. 1. 1.]] [[ 1. 1. 1.] [ 1. 1. nan] [ 1. 1. 1.]]] Thanks, Adam

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Problem with manipulating dates in the time series object
by gharras 22 Sep '10

22 Sep '10

Hi there, I really like the time series object from scipy but I have a problem with manipulating dates. Here is a copy from my ipython session: ----------------------------------------------------------------------------- In [748]: myts=ts.time_series(data=n.arange(20).reshape(2,10).T, dates=n.arange(10)+720000, freq='D') In [749]: myts Out[749]: timeseries( [[ 0 10] [ 1 11] [ 2 12] [ 3 13] [ 4 14] [ 5 15] [ 6 16] [ 7 17] [ 8 18] [ 9 19]], dates = [17-Apr-1972 ... 26-Apr-1972], freq = D) In [750]: myts.dates[myts.weekday==1]+=1 In [751]: myts.dates Out[751]: DateArray([17-Apr-1972, 19-Apr-1972, 19-Apr-1972, 20-Apr-1972, 21-Apr-1972, 22-Apr-1972, 23-Apr-1972, 24-Apr-1972, 26-Apr-1972, 26-Apr-1972], freq='D') In [752]: myts.has_duplicated_dates() Out[752]: False ----------------------------------------------------------------------------- In words: I can change the dates by hand (in line 750), but the object is not aware of it, i.e. it still thinks that it does not contain any duplicates but the dates were however affected by the manipulation in line 750, cf. line 751. Is there a work-around? all the best. Cheers, Georges

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