How to implement fixed-point binary support in numpy

Question

I have a homebrew binary fixed-point arithmetic support library and would like to add numpy array support. Specifically I would like to be able to pass around 2D arrays of fixed-point binary numbers and do various operations on them such as addition, subtraction, multiplication, rounding, changing of fixed point format, etc.

The fixed-point support under the hood works on integers, and separate tracking of fixed-point format data (number of integer and fractional bits) for range checking and type conversion.

I have been reading the numpy documentation on ndarray subclassing and dtype, it seems like I might want at least a custom dtype, or separate dtype object for every unique range/precision configuration of fixed-point numbers. I tried subclassing numpy.dtype in Python but that is not allowed.

I'm not sure if I can write something to interoperate with numpy in the way I want without writing C level code - everything so far is pure Python, I have avoided looking under the covers at how to work on the C-based layer of numpy.

Answer 1

For anyone interested, this turned out to be too hard to do in Python extended Numpy, or just didn't fit the data model. I ended up writing a separate Python library of types implementing the behaviours I wanted, that use Numpy arrays of integers under the hood for speed. It works OK and does the strict binary range calculation and checking that I wanted, but suffers Python code speed overhead especially with small arrays. If I had time I'm sure it could be done much better/faster as a C library.