Mailman 3 October 2013 - SciPy-User

How to display a RGB image (MFISH)?
by jp April 3, 2023

April 3, 2023

Hi, I have written a script to try to combine five images into a three channels RGB images<http://dip4fish.blogspot.com/2011/05/trying-to-combine-mfish-images-into-rg…> . The resulting RGB image looks like a grey scale image instead of a color one. I have three np.array Rnorm, Gnorm, Bnorm which are copied into a rgb array: *rgb = np.zeros((shape[0],shape[1],3),dtype=float) mxr=np.max(R) mxg=np.max(G) mxb=np.max(B) Rnorm=np.uint8((255*(R/mxr)))Gnorm=np.uint8((255*(R/mxg)))Bnorm=np.uint8((255*(R/mxb)))#copy each RGB component in an RGB array rgb[:,:,0]=Rnorm rgb[:,:,1]=Gnorm rgb[:,:,2]=Bnorm* *pylab.subplot(224, aspect='equal',frameon=False, xticks=[], yticks=[]) pylab.imshow(rgb) pylab.show()* Any advice? Thank you Jean-Patrick

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Invitation to connect on LinkedIn
by Jelle Feringa via LinkedIn July 28, 2022

July 28, 2022

LinkedIn ------------ Jelle Feringa requested to add you as a connection on LinkedIn: ------------------------------------------ Jose, I'd like to add you to my professional network on LinkedIn. - Jelle Accept invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… View invitation from Jelle Feringa http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/Q6WKH0LACopGJkAw_6fSqajo6R7VM… ------------------------------------------ DID YOU KNOW your LinkedIn profile helps you control your public image when people search for you? Setting your profile as public means your LinkedIn profile will come up when people enter your name in leading search engines. Take control of your image! http://www.linkedin.com/e/-3wy1w2-gpkzwpgn-5n/ewp/inv-22/ -- (c) 2011, LinkedIn Corporation

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Help required regding Eigenvectors
by Sudheer Phani March 14, 2022

March 14, 2022

Hello All, I have been encountering some strange problems with the calculation of eigen vectors using python. The problem is with the sign of the eigen vectors calculated. On Very few occasions the eigen vectors calculated using python have an opposite sign than the actual eigenvectors, though the magnitude is correct. Can any one of you plz help me in this regard and suggest me some solution if you have encoutered similar problems . Any help in this regd is appreciated Thanks in Advace regds Sudheer

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Allan Variance
by Daniele Nicolodi Feb. 4, 2014

Feb. 4, 2014

Hello, does anyone know or have written and is willing to share some code to compute the Allan variance, modified Allan variance and friends, ideally both from frequency than from phase data? I'm writing my own code, it is not that difficult, but I would prefer the time required for testing and validation doing something more fun. If there is no available code implementing Allan variance, would it be interesting to include in in SciPy? Thank you so much. Best, Daniele

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Re: [SciPy-User] SciPy-User Digest, Vol 120, Issue 12
by Michal Romaniuk Jan. 24, 2014

Jan. 24, 2014

Hi, > Hi, > > On Mon, Aug 19, 2013 at 7:44 AM, Michal Romaniuk > <michal.romaniuk06(a)imperial.ac.uk> wrote: >> Hi, >> >> I'm saving a large batch of data using savemat and although I get no >> errors, the files produced are not readable for either matlab or scipy. >> Is there a limit on file size? > > Ah - yes there is - the individual matrices in the mat file cannot be > larger than 4GB. Is it possible you hit this limit? > > Sorry, I only realized this when Richard Llewellyn pointed this out a > couple of weeks ago on the list: > > http://scipy-user.10969.n7.nabble.com/SciPy-User-scipy-io-loadmat-throws-Ty… > > The current scipy code has an error message for matrices that are too large. > > Cheers, > > Matthew Well, I managed to work around the problem to some extent by setting do_compression=True. Now Matlab can read those files (so they must be valid to some extent) but SciPy can't (even though they were written with SciPy). I get this error: PATH/lib/python2.6/site-packages/scipy/io/matlab/mio.pyc in loadmat(file_name, mdict, appendmat, **kwargs) 173 variable_names = kwargs.pop('variable_names', None) 174 MR = mat_reader_factory(file_name, appendmat, **kwargs) --> 175 matfile_dict = MR.get_variables(variable_names) 176 if mdict is not None: 177 mdict.update(matfile_dict) PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5.pyc in get_variables(self, variable_names) 290 continue 291 try: --> 292 res = self.read_var_array(hdr, process) 293 except MatReadError, err: 294 warnings.warn( PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5.pyc in read_var_array(self, header, process) 253 `process`. 254 ''' --> 255 return self._matrix_reader.array_from_header(header, process) 256 257 def get_variables(self, variable_names=None): PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.array_from_header (scipy/io/matlab/mio5_utils.c:5401)() PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.array_from_header (scipy/io/matlab/mio5_utils.c:4849)() PATH/lib/python2.6/site-packages/scipy/io/matlab/mio5_utils.so in scipy.io.matlab.mio5_utils.VarReader5.read_real_complex (scipy/io/matlab/mio5_utils.c:5602)() ValueError: total size of new array must be unchanged The size of the main array is about 9 GB before compression, but the compressed files are less than 500 MB and closer to 400 MB. There are some other arrays in the file too but they are much smaller. Any ideas on how I could get SciPy to read this data back? Right now I can only think of storing the data in single precision format... Thanks, Michal

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SciPy leastsq behavior
by François-Xavier Thomas Oct. 31, 2013

Oct. 31, 2013

Hi all, I am trying to debug precision errors in an optimization process using ScipPy's leastsq method. Attached is a log of the start of the optimization process, showing mean errors and differences between consecutive X values for each f(X) call. * Why is f(X) computed even though X hasn't moved at all? Did I miss something obvious? * I set the "diag" parameter to [0.1, 0.1, 0.1, 1000, 500, 500, 0.1, 0.1, 0.01, 0.01, 0.01, 0.01], which roughly corresponds to the order of magnitude of my X values. Should that help the optimizer? * Can leastsq work well on higher floating point precisions (i.e. numpy.float128)? What is the underlying precision of the MINPACK libraries? I realise you might not know the answers to all of these questions, but any hint or caveat you might have from your own experience would be useful! Thanks! François-Xavier -- François-Xavier Thomas -- Image Processing AIRINOV Incubateur PRINE 110 rue des Poissonniers - 48 voie CI18 75899 Paris Cedex 18 09 72 39 75 82 www.airinov.fr

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ANN: NumPy 1.8.0 release.
by Charles R Harris Oct. 30, 2013

Oct. 30, 2013

I am pleased to announce the availability of NumPy 1.8.0. This release is the culmination of over a years worth of work by the NumPy team and contains many fixes, enhancements, and new features. Highlights are: - New, no 2to3, Python 2 and Python 3 are supported by a common code base. - New, gufuncs for linear algebra, enabling operations on stacked arrays. - New, inplace fancy indexing for ufuncs with the ``.at`` method. - New, ``partition`` function, partial sorting via selection for fast median. - New, ``nanmean``, ``nanvar``, and ``nanstd`` functions skipping NaNs. - New, ``full`` and ``full_like`` functions to create value initialized arrays. - New, ``PyUFunc_RegisterLoopForDescr``, better ufunc support for user dtypes. - Numerous performance improvements in many areas. This release requires Python 2.6, 2.7 or 3.2-3.3, support for Python 2.4 and 2.5 has been dropped. Sources and binaries can be found at http://sourceforge.net/projects/numpy/files/NumPy/1.8.0/. Some 119 people contributed to this release. This is a remarkable increase and shows that there is still life in this venerable code that had its beginning in Numeric some 18 years ago. Many thanks to you all. Enjoy, Chuck NumPy 1.8.0 Release Notes ************************* This release supports Python 2.6 -2.7 and 3.2 - 3.3. Highlights ========== * New, no 2to3, Python 2 and Python 3 are supported by a common code base. * New, gufuncs for linear algebra, enabling operations on stacked arrays. * New, inplace fancy indexing for ufuncs with the ``.at`` method. * New, ``partition`` function, partial sorting via selection for fast median. * New, ``nanmean``, ``nanvar``, and ``nanstd`` functions skipping NaNs. * New, ``full`` and ``full_like`` functions to create value initialized arrays. * New, ``PyUFunc_RegisterLoopForDescr``, better ufunc support for user dtypes. * Numerous performance improvements in many areas. Dropped Support =============== Support for Python versions 2.4 and 2.5 has been dropped, Support for SCons has been removed. Future Changes ============== The Datetime64 type remains experimental in this release. In 1.9 there will probably be some changes to make it more useable. The diagonal method currently returns a new array and raises a FutureWarning. In 1.9 it will return a readonly view. Multiple field selection from a array of structured type currently returns a new array and raises a FutureWarning. In 1.9 it will return a readonly view. The numpy/oldnumeric and numpy/numarray compatibility modules will be removed in 1.9. Compatibility notes =================== The doc/sphinxext content has been moved into its own github repository, and is included in numpy as a submodule. See the instructions in doc/HOWTO_BUILD_DOCS.rst.txt for how to access the content. .. _numpydoc: https://github.com/numpy/numpydoc The hash function of numpy.void scalars has been changed. Previously the pointer to the data was hashed as an integer. Now, the hash function uses the tuple-hash algorithm to combine the hash functions of the elements of the scalar, but only if the scalar is read-only. Numpy has switched its build system to using 'separate compilation' by default. In previous releases this was supported, but not default. This should produce the same results as the old system, but if you're trying to do something complicated like link numpy statically or using an unusual compiler, then it's possible you will encounter problems. If so, please file a bug and as a temporary workaround you can re-enable the old build system by exporting the shell variable NPY_SEPARATE_COMPILATION=0. For the AdvancedNew iterator the ``oa_ndim`` flag should now be -1 to indicate that no ``op_axes`` and ``itershape`` are passed in. The ``oa_ndim == 0`` case, now indicates a 0-D iteration and ``op_axes`` being NULL and the old usage is deprecated. This does not effect the ``NpyIter_New`` or ``NpyIter_MultiNew`` functions. The functions nanargmin and nanargmax now return np.iinfo['intp'].min for the index in all-NaN slices. Previously the functions would raise a ValueError for array returns and NaN for scalar returns. NPY_RELAXED_STRIDES_CHECKING ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ There is a new compile time environment variable ``NPY_RELAXED_STRIDES_CHECKING``. If this variable is set to 1, then numpy will consider more arrays to be C- or F-contiguous -- for example, it becomes possible to have a column vector which is considered both C- and F-contiguous simultaneously. The new definition is more accurate, allows for faster code that makes fewer unnecessary copies, and simplifies numpy's code internally. However, it may also break third-party libraries that make too-strong assumptions about the stride values of C- and F-contiguous arrays. (It is also currently known that this breaks Cython code using memoryviews, which will be fixed in Cython.) THIS WILL BECOME THE DEFAULT IN A FUTURE RELEASE, SO PLEASE TEST YOUR CODE NOW AGAINST NUMPY BUILT WITH:: NPY_RELAXED_STRIDES_CHECKING=1 python setup.py install You can check whether NPY_RELAXED_STRIDES_CHECKING is in effect by running:: np.ones((10, 1), order="C").flags.f_contiguous This will be ``True`` if relaxed strides checking is enabled, and ``False`` otherwise. The typical problem we've seen so far is C code that works with C-contiguous arrays, and assumes that the itemsize can be accessed by looking at the last element in the ``PyArray_STRIDES(arr)`` array. When relaxed strides are in effect, this is not true (and in fact, it never was true in some corner cases). Instead, use ``PyArray_ITEMSIZE(arr)``. For more information check the "Internal memory layout of an ndarray" section in the documentation. Binary operations with non-arrays as second argument ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Binary operations of the form ``<array-or-subclass> * <non-array-subclass>`` where ``<non-array-subclass>`` declares an ``__array_priority__`` higher than that of ``<array-or-subclass>`` will now unconditionally return *NotImplemented*, giving ``<non-array-subclass>`` a chance to handle the operation. Previously, `NotImplemented` would only be returned if ``<non-array-subclass>`` actually implemented the reversed operation, and after a (potentially expensive) array conversion of ``<non-array-subclass>`` had been attempted. (`bug <https://github.com/numpy/numpy/issues/3375>`_, `pull request <https://github.com/numpy/numpy/pull/3501>`_) Function `median` used with `overwrite_input` only partially sorts array ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If `median` is used with `overwrite_input` option the input array will now only be partially sorted instead of fully sorted. Fix to financial.npv ~~~~~~~~~~~~~~~~~~~~ The npv function had a bug. Contrary to what the documentation stated, it summed from indexes ``1`` to ``M`` instead of from ``0`` to ``M - 1``. The fix changes the returned value. The mirr function called the npv function, but worked around the problem, so that was also fixed and the return value of the mirr function remains unchanged. Runtime warnings when comparing NaN numbers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Comparing ``NaN`` floating point numbers now raises the ``invalid`` runtime warning. If a ``NaN`` is expected the warning can be ignored using np.errstate. E.g.:: with np.errstate(invalid='ignore'): operation() New Features ============ Support for linear algebra on stacked arrays ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The gufunc machinery is now used for np.linalg, allowing operations on stacked arrays and vectors. For example:: >>> a array([[[ 1., 1.], [ 0., 1.]], [[ 1., 1.], [ 0., 1.]]]) >>> np.linalg.inv(a) array([[[ 1., -1.], [ 0., 1.]], [[ 1., -1.], [ 0., 1.]]]) In place fancy indexing for ufuncs ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The function ``at`` has been added to ufunc objects to allow in place ufuncs with no buffering when fancy indexing is used. For example, the following will increment the first and second items in the array, and will increment the third item twice: ``numpy.add.at(arr, [0, 1, 2, 2], 1)`` This is what many have mistakenly thought ``arr[[0, 1, 2, 2]] += 1`` would do, but that does not work as the incremented value of ``arr[2]`` is simply copied into the third slot in ``arr`` twice, not incremented twice. New functions `partition` and `argpartition` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ New functions to partially sort arrays via a selection algorithm. A ``partition`` by index ``k`` moves the ``k`` smallest element to the front of an array. All elements before ``k`` are then smaller or equal than the value in position ``k`` and all elements following ``k`` are then greater or equal than the value in position ``k``. The ordering of the values within these bounds is undefined. A sequence of indices can be provided to sort all of them into their sorted position at once iterative partitioning. This can be used to efficiently obtain order statistics like median or percentiles of samples. ``partition`` has a linear time complexity of ``O(n)`` while a full sort has ``O(n log(n))``. New functions `nanmean`, `nanvar` and `nanstd` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ New nan aware statistical functions are added. In these functions the results are what would be obtained if nan values were ommited from all computations. New functions `full` and `full_like` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ New convenience functions to create arrays filled with a specific value; complementary to the existing `zeros` and `zeros_like` functions. IO compatibility with large files ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Large NPZ files >2GB can be loaded on 64-bit systems. Building against OpenBLAS ~~~~~~~~~~~~~~~~~~~~~~~~~ It is now possible to build numpy against OpenBLAS by editing site.cfg. New constant ~~~~~~~~~~~~ Euler's constant is now exposed in numpy as euler_gamma. New modes for qr ~~~~~~~~~~~~~~~~ New modes 'complete', 'reduced', and 'raw' have been added to the qr factorization and the old 'full' and 'economic' modes are deprecated. The 'reduced' mode replaces the old 'full' mode and is the default as was the 'full' mode, so backward compatibility can be maintained by not specifying the mode. The 'complete' mode returns a full dimensional factorization, which can be useful for obtaining a basis for the orthogonal complement of the range space. The 'raw' mode returns arrays that contain the Householder reflectors and scaling factors that can be used in the future to apply q without needing to convert to a matrix. The 'economic' mode is simply deprecated, there isn't much use for it and it isn't any more efficient than the 'raw' mode. New `invert` argument to `in1d` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The function `in1d` now accepts a `invert` argument which, when `True`, causes the returned array to be inverted. Advanced indexing using `np.newaxis` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ It is now possible to use `np.newaxis`/`None` together with index arrays instead of only in simple indices. This means that ``array[np.newaxis, [0, 1]]`` will now work as expected and select the first two rows while prepending a new axis to the array. C-API ~~~~~ New ufuncs can now be registered with builtin input types and a custom output type. Before this change, NumPy wouldn't be able to find the right ufunc loop function when the ufunc was called from Python, because the ufunc loop signature matching logic wasn't looking at the output operand type. Now the correct ufunc loop is found, as long as the user provides an output argument with the correct output type. runtests.py ~~~~~~~~~~~ A simple test runner script ``runtests.py`` was added. It also builds Numpy via ``setup.py build`` and can be used to run tests easily during development. Improvements ============ IO performance improvements ~~~~~~~~~~~~~~~~~~~~~~~~~~~ Performance in reading large files was improved by chunking (see also IO compatibility). Performance improvements to `pad` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The `pad` function has a new implementation, greatly improving performance for all inputs except `mode=<function>` (retained for backwards compatibility). Scaling with dimensionality is dramatically improved for rank >= 4. Performance improvements to `isnan`, `isinf`, `isfinite` and `byteswap` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ `isnan`, `isinf`, `isfinite` and `byteswap` have been improved to take advantage of compiler builtins to avoid expensive calls to libc. This improves performance of these operations by about a factor of two on gnu libc systems. Performance improvements via SSE2 vectorization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Several functions have been optimized to make use of SSE2 CPU SIMD instructions. * Float32 and float64: * base math (`add`, `subtract`, `divide`, `multiply`) * `sqrt` * `minimum/maximum` * `absolute` * Bool: * `logical_or` * `logical_and` * `logical_not` This improves performance of these operations up to 4x/2x for float32/float64 and up to 10x for bool depending on the location of the data in the CPU caches. The performance gain is greatest for in-place operations. In order to use the improved functions the SSE2 instruction set must be enabled at compile time. It is enabled by default on x86_64 systems. On x86_32 with a capable CPU it must be enabled by passing the appropriate flag to the CFLAGS build variable (-msse2 with gcc). Performance improvements to `median` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ `median` is now implemented in terms of `partition` instead of `sort` which reduces its time complexity from O(n log(n)) to O(n). If used with the `overwrite_input` option the array will now only be partially sorted instead of fully sorted. Overrideable operand flags in ufunc C-API ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When creating a ufunc, the default ufunc operand flags can be overridden via the new op_flags attribute of the ufunc object. For example, to set the operand flag for the first input to read/write: PyObject \*ufunc = PyUFunc_FromFuncAndData(...); ufunc->op_flags[0] = NPY_ITER_READWRITE; This allows a ufunc to perform an operation in place. Also, global nditer flags can be overridden via the new iter_flags attribute of the ufunc object. For example, to set the reduce flag for a ufunc: ufunc->iter_flags = NPY_ITER_REDUCE_OK; Changes ======= General ~~~~~~~ The function np.take now allows 0-d arrays as indices. The separate compilation mode is now enabled by default. Several changes to np.insert and np.delete: * Previously, negative indices and indices that pointed past the end of the array were simply ignored. Now, this will raise a Future or Deprecation Warning. In the future they will be treated like normal indexing treats them -- negative indices will wrap around, and out-of-bound indices will generate an error. * Previously, boolean indices were treated as if they were integers (always referring to either the 0th or 1st item in the array). In the future, they will be treated as masks. In this release, they raise a FutureWarning warning of this coming change. * In Numpy 1.7. np.insert already allowed the syntax `np.insert(arr, 3, [1,2,3])` to insert multiple items at a single position. In Numpy 1.8. this is also possible for `np.insert(arr, [3], [1, 2, 3])`. Padded regions from np.pad are now correctly rounded, not truncated. C-API Array Additions ~~~~~~~~~~~~~~~~~~~~~ Four new functions have been added to the array C-API. * PyArray_Partition * PyArray_ArgPartition * PyArray_SelectkindConverter * PyDataMem_NEW_ZEROED C-API Ufunc Additions ~~~~~~~~~~~~~~~~~~~~~ One new function has been added to the ufunc C-API that allows to register an inner loop for user types using the descr. * PyUFunc_RegisterLoopForDescr Deprecations ============ The 'full' and 'economic' modes of qr factorization are deprecated. General ~~~~~~~ The use of non-integer for indices and most integer arguments has been deprecated. Previously float indices and function arguments such as axes or shapes were truncated to integers without warning. For example `arr.reshape(3., -1)` or `arr[0.]` will trigger a deprecation warning in NumPy 1.8., and in some future version of NumPy they will raise an error. Authors ======= This release contains work by the following people who contributed at least one patch to this release. The names are in alphabetical order by first name: * 87 * Adam Ginsburg + * Adam Griffiths + * Alexander Belopolsky + * Alex Barth + * Alex Ford + * Andreas Hilboll + * Andreas Kloeckner + * Andreas Schwab + * Andrew Horton + * argriffing + * Arink Verma + * Bago Amirbekian + * Bartosz Telenczuk + * bebert218 + * Benjamin Root + * Bill Spotz + * Bradley M. Froehle * Carwyn Pelley + * Charles Harris * Chris * Christian Brueffer + * Christoph Dann + * Christoph Gohlke * Dan Hipschman + * Daniel + * Dan Miller + * daveydave400 + * David Cournapeau * David Warde-Farley * Denis Laxalde * dmuellner + * Edward Catmur + * Egor Zindy + * endolith * Eric Firing * Eric Fode * Eric Moore + * Eric Price + * Fazlul Shahriar + * Félix Hartmann + * Fernando Perez * Frank B + * Frank Breitling + * Frederic * Gabriel * GaelVaroquaux * Guillaume Gay + * Han Genuit * HaroldMills + * hklemm + * jamestwebber + * Jason Madden + * Jay Bourque * jeromekelleher + * Jesús Gómez + * jmozmoz + * jnothman + * Johannes Schönberger + * John Benediktsson + * John Salvatier + * John Stechschulte + * Jonathan Waltman + * Joon Ro + * Jos de Kloe + * Joseph Martinot-Lagarde + * Josh Warner (Mac) + * Jostein Bø Fløystad + * Juan Luis Cano Rodríguez + * Julian Taylor + * Julien Phalip + * K.-Michael Aye + * Kumar Appaiah + * Lars Buitinck * Leon Weber + * Luis Pedro Coelho * Marcin Juszkiewicz * Mark Wiebe * Marten van Kerkwijk + * Martin Baeuml + * Martin Spacek * Martin Teichmann + * Matt Davis + * Matthew Brett * Maximilian Albert + * m-d-w + * Michael Droettboom * mwtoews + * Nathaniel J. Smith * Nicolas Scheffer + * Nils Werner + * ochoadavid + * Ondřej Čertík * ovillellas + * Paul Ivanov * Pauli Virtanen * peterjc * Ralf Gommers * Raul Cota + * Richard Hattersley + * Robert Costa + * Robert Kern * Rob Ruana + * Ronan Lamy * Sandro Tosi * Sascha Peilicke + * Sebastian Berg * Skipper Seabold * Stefan van der Walt * Steve + * Takafumi Arakaki + * Thomas Robitaille + * Tomas Tomecek + * Travis E. Oliphant * Valentin Haenel * Vladimir Rutsky + * Warren Weckesser * Yaroslav Halchenko * Yury V. Zaytsev + A total of 119 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

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ANN: IEP 3.3 - the Interactive Editor for Python
by Almar Klein Oct. 29, 2013

Oct. 29, 2013

Dear all, We're pleased to announce version 3.3 of the Interactive Editor for Python. IEP <http://www.iep-project.org> is a cross-platform Python IDE aimed at interactivity and introspection. Its practical design is aimed at simplicity and efficiency. IEP consists of an *editor*, a *shell*, and a set of *tools* to help the programmer in various ways. Binaries are available for Windows, Linux, and Mac. A selection of the changes since the last release: - Debugging with breakpoints! - New magic commands for package management: pip and conda - The binaries for Linux are now build without -gtkstyle, making them look better or worse, depending on your OS. See release notes for more info. - Qt backend now runs in native event loop - Translations for Russian (in addition to Spanish, Catalan, French and Dutch) - We have a new website and logo Website: http;//iep-project.org Discussion group: http://groups.google.com/group/iep_ Release notes: https://bitbucket.org/iep-project/iep/wiki/Release%20notes Happy coding! - Almar

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Constrained optimization algorithm for layout?
by raghavan Oct. 28, 2013

Oct. 28, 2013

Hi, I'm trying to create an arrangement of several UI objects in a layout, each having their own constraints in terms of how they need to be placed and aligned. What optimization algorithm would be useful for an application like this? Thanks, Raghavan.

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ANN: Visvis 1.9 - The object oriented approach to visualization
by Almar Klein Oct. 26, 2013

Oct. 26, 2013

On behalf of the Visvis development team, I'm pleased to announce version 1.9 of Visvis - the object oriented approach to visualization. Essentially, visvis is an object oriented layer of Python on top of OpenGl, thereby combining the power of 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()). This is mostly a maintenance release. Note that development of Visvis will be limited to basic maintenance, as I now focus my time on Vispy<http://vispy.org> . Website: https://code.google.com/p/visvis/ Discussion group: http://groups.google.com/group/visvis Release notes: https://code.google.com/p/visvis/wiki/releaseNotes Regards, Almar

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