colss 0.3.1

Fast Mathematical exp evaluator for numpy

colss

Fast expression evaluator for NumPy.

GitHub: https://github.com/SivaPA08/colss

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Features

Category	Support
Arithmetic	+ - * / %
Bitwise	& | ~ << >> ^
Logical	&& || !
Comparison	== != < <= > >=
Ternary	?:
Trigonometric	sin cos tan asin acos atan
Logarithmic	log log10
Exponential	exp
Root	sqrt
Rounding	floor ceil round
Utility	abs min max

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Installation

pip install colss

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Usage

import numpy as np
import colss as cs

a = np.array([1.0, 2.0, 3.0])
b = np.array([4.0, 5.0, 6.0])

print(cs.query("a + b", a=a, b=b))
print(cs.query("sqrt(a) + sin(b)", a=a, b=b))
print(cs.query("a > b ? a : b", a=a, b=b))
print(cs.query("max(1,1,3,1,1)"))

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Syntax

a + b
a - b
a * b
a / b
a % b
a ^ b

(a + b) * c

a > b
a <= b
a == b

(a > 0) && (b > 0)
(a > 0) || (b > 0)
!(a > 0)

a > b ? a : b

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Functions

abs(x)
sqrt(x)
log(x)
log10(x)
exp(x)
sin(x)
cos(x)
tan(x)
asin(x)
acos(x)
atan(x)
floor(x)
ceil(x)
round(x)
min(...)
max(...)

min() and max() support multiple arguments.

cs.query("max(1,1,3,1,1)")
cs.query("min(a,b,c,d)")

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Multidimensional Arrays

colss supports multidimensional arrays directly.

import numpy as np
import colss as cs

a = np.random.rand(2, 3, 4)
b = np.random.rand(2, 3, 4)

res = cs.query("a + b", a=a, b=b)
print(res.shape)

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Notes

• Arrays in the same expression must have identical shapes.
• query() returns a NumPy array.
• For best performance, use float64 and C-contiguous arrays.
• No constants are built in.