I was looking for some days in SWIG documentation how I could release the GIL (Global Interpreter Lock) with SWIG. There were some macros defined in the generated code, but none was used in any place.
In fact, I just had to enable the thread support with an additional argument (-threads) and now every wrapped function releases the GIL before it is called, but that does not satisfy me. Indeed, some of my wrappers must retain the GIL while they are used (see this item). So here are the features that can be used :
- nothread enables or disables the whole thread lock for a function :
- %nothread activates the nothread feature
- %thread disables the feature
- %clearnothread clears the feature
- nothreadblock enables or disables the block thread lock for a function :
- %nothreadblock activates the nothreadblock feature
- %threadblock disables the feature
- %clearnothreadblock clears the feature
- nothreadallow enables or disables the allow thread lock for a function :
- %nothreadallow activates the nothreadallow feature
- %threadallow disables the feature
- %clearnothreadallow clears the feature
When the whole thread lock is enabled, the GIL is locked when entering the C function (with the macro SWIG_PYTHON_THREAD_BEGIN_BLOCK). Then it is released before the call to the function (with SWIG_PYTHON_THREAD_BEGIN_ALLOW), retained after the end (SWIG_PYTHON_THREAD_END_ALLOW) and finally it is released when exiting the function (SWIG_PYTHON_THREAD_END_BLOCK), after all Python result variables are created and/or modified.
When moving to Python, the real big problem that arises is the transformation of a Python array into the C++ container the team used for years.
Let’s set some hypothesis :
- there is a separation between the class containing the data and the class that uses the data (iterators, …)
- the containing class can be changed (policy or strategy pattern)
The first hypothesis is derived from the responsibility principle, the two classes have two distinct responsibilities, the first allocates the data space and allows simple access to it, the second allows usual operations (assignation, comparison tests or iterations for instance).
The second one will be the heart of the wrapper. It allows to change the way data is stored and accessed in a simple way.
Continue reading Wrapping a C++ container in Python
For my research, I had to create a set of smooth deformation fields where I knew which points were moved and by which amount.
I tried to find a script, but I couldn’t find an appropriate one, not even talking about one in Python. So here I propose my own version, allowing to interpolate a 1D, 2D or 3D deformation field based on some points.
How does it work ? It is based on Bookstein’s algorithm. The first step is the computation of the coefficients of the smooth deformation field and then they are used to compute the values on the deformation field on a grid and this grid is returned.
The function to use is denseDeformationFieldFromSparse(), the arguments being size, the size of the desired grid, points, the locations where the deformation field is known, and displacements, the amount of displacement for each previously given point.
This code is given as is, but feel free to comment so that bugs can be ironed out (if there are bugs). It was tested with 1D, 2D and 3D test cases which can also be found on the gist.
Thanks to Bill Baxter for the distance function that was proposed on the numpy discussion list.
I now regularly use Scons as a cross-platform software construction tool. It is easy, written in Python, and I know Python, so no problem learning a new language as for CMake. In some cases when I use SWIG, the target platform does not have the SWIG executable. But when compiling a module, Scons must use this executable, whatever you try to do. In this case, one need to create a new SharedLibrary builder, so that this attribute will determine if SWIG is present or if the generated .c or .cpp files must be used instead.
Continue reading Building with Scons and an optional SWIG