Snuggs are s-expressions for Numpy
Python

README.rst

snuggs

https://travis-ci.org/mapbox/snuggs.svg?branch=master

Snuggs are s-expressions for Numpy

>>> snuggs.eval("(+ (asarray 1 1) (asarray 2 2))")
array([3, 3])

Syntax

Snuggs wraps Numpy in expressions with the following syntax:

expression = "(" (operator | function) *arg ")"
arg = expression | name | number | string

Examples

Addition of two numbers

import snuggs
snuggs.eval('(+ 1 2)')
# 3

Multiplication of a number and an array

Arrays can be created using asarray.

snuggs.eval("(* 3.5 (asarray 1 1))")
# array([ 3.5,  3.5])

Evaluation context

Expressions can also refer by name to arrays in a local context.

snuggs.eval("(+ (asarray 1 1) b)", b=np.array([2, 2]))
# array([3, 3])

This local context may be provided using keyword arguments (e.g., b=np.array([2, 2])), or by passing a dictionary that stores the keys and associated array values. Passing a dictionary, specifically an OrderedDict, is important when using a function or operator that references the order in which values have been provided. For example, the read function will lookup the i-th value passed:

ctx = OrderedDict((
    ('a', np.array([5, 5])),
    ('b', np.array([2, 2]))
))
snuggs.eval("(- (read 1) (read 2))", ctx)
# array([3, 3])

Functions and operators

Arithmetic (* + / -) and logical (< <= == != >= > & |) operators are available. Members of the numpy module such as asarray(), mean(), and where() are also available.

snuggs.eval("(mean (asarray 1 2 4))")
# 2.3333333333333335
snuggs.eval("(where (& tt tf) 1 0)",
    tt=numpy.array([True, True]),
    tf=numpy.array([True, False]))
# array([1, 0])

Higher-order functions

New in snuggs 1.1 are higher-order functions map and partial.

snuggs.eval("((partial * 2) 2)")
# 4

snuggs.eval('(asarray (map (partial * 2) (asarray 1 2 3)))')
# array([2, 4, 6])

Performance notes

Snuggs makes simple calculator programs possible. None of the optimizations of, e.g., numexpr (multithreading, elimination of temporary data, etc) are currently available.

If you're looking to combine Numpy with a more complete Lisp, see Hy:

=> (import numpy)
=> (* 2 (.asarray numpy [1 2 3]))
array([2, 4, 6])