I got accepted to Google Summer of Code! Thank you everyone who has helped me get this far, and Amber for making me a congratulatory google cake!  On my proposal timeline I gave myself 2 weeks to add support to sparse matrices for `dtype=bool`. So far, `dtype=bool` works implementation was not too hard, and most of my time was spent figuring out how [SWIG](http://swig.org/) works. However there is still a lot of work to do in testing bool support. This will be more time consuming than it should be because the sparse test suite is messy. # bool dtype implementation ## sparsetools The `sparsetools/` directory contains several `*.cxx`, `*.h`, `*.i`, and `*.py` files. The header files (`*.h`) contain C++ routines these are wrapped by SWIG according to instructions in the `*.i` interface files. SWIG uses the header and interface files to generate python `*.py` files C++ code in `*.cxx` files. Once these `*.cxx` files are compiled and linked the functions they define can be called by the generated `*.py` files. ## Where do I add bool support? Contained in `sparsetools/` is a file called `complex_ops.h` which translates numpy's complex types to C++ classes. Well, that is what I want to do, except for numpy's bool types instead! My new file `bool_ops.h` is much simpler though. It basically does typedef npy_int8 npy_bool_wrapper; Yep that's it, (for now anyway). This file basically says to treat the `npy_bool_wrapper` type like the `npy_int8`. Because boolean algebra is *almost* integer algebra. `npy_bool_wrapper` is used in `numpy.i` to define a [typemap](http://www.swig.org/Doc1.3/Python.html#Python_nn53) from numpy's `npy_bool` type to our `npy_bool_wrapper`. In `sparsetools.i` we have to declare the new data type and add it to the `INSTANTIATE_ALL` macro which is used in all the \*.i files to call the functions we want from the \*.h files. # What else? Adding `bool` to `supported_dtypes` in `sputils.py` prevents it from upcast. Then tests need to be added, but this isn't too easy. The test suite does not have a way to take some general data or dtype. So I am going through adding tests one-by-one where it seems appropriate. I'm also trying to add tests for other dtypes because there don't seem to be any tests for anything other than float64 and int64. While I was doing this I discovered a few [bugs](https://github.com/scipy/scipy/issues/2534) relating to how sparse handles `uint` dtypes. Once I'm done adding tests and fixing what ever does not pass, I'll move on to the next stage in my proposal. Adding support for boolean operations.