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Snuggs are s-expressions for Numpy

Project description

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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])

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])

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