Stream Conventions

Most (all?) functions in npstreams are designed to work on streams, or iterables of NumPy arrays. These iterables can be infinite. The quintessential example is a stream of images progressively read from disk. These streams of arrays must contain arrays that all have the same shape and data-type, unless specified otherwise.

An example of a function that operates on a stream of arrays of different shapes is ieinsum()

A single NumPy array can be passed where a stream is expected; the array will be repackaged into a stream of a single array.

Naming Conventions

In order to facilitate documentation, functions in npstreams follow the following conventions:

  • Routines are named after their closest equivalent in numpy and scipy.

  • Routines with names starting with ‘i’ (e.g. iprod()) are generator functions; they yield running results as they are being computer. Usually, these functions have a non-generator equivalent that consumes the entire stream (e.g. iaverage() vs. average()).

  • Routines with names starting with ‘c’ (e.g. csum()) are CUDA-enabled (requires pycuda)

  • Routines with names starting with ‘p’ (e.g. pmap()) can be parallelized. The default behavior is always to not use multiple cores. For example, the default behavior of pmap() is to behave like map().

Axis Conventions

NumPy arrays provide operations along axes. Similarly, npstreams also exposes the axis keyword in some (most?) reduction functions like isum() and iprod().

The convention for specification of the axis parameter is as follows:

  • If axis = None, arrays are flattened before being combined. The result will be a scalar of a 0d array.

  • The default (axis = -1) always corresponds to combining arrays along a new axis. For example, summing images together along axis = -1 is equivalent to stacking images along a new axis, then averaging along this new axis

  • if axis is an int, then arrays are reduced according to this axis, and then combined.

CUDA-enabled functions

Some functions are implemented using CUDA