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Perform a numpy array transformation by giving examples.

Project description

np-xarr

Perform a numpy array transformation intuitively by giving simple patterns.

Install

$ pip install np-xarr

Usage

>> from npxarr import X
>> import numpy as np

>> a = X('[1, 2, 3, ...]', '[[1, 2], [2, 3], ...]')(np.r_[0, 1, 2, 3, 4, 5]) # sliding window

[[0, 1], [1, 2], [2, 3], [3, 4], [4, 5]]

>> a = X('[[1, 2, ...], 
    [3, 4, ...], ...]', '[[1, 3, ...], [2, 4, ...], ...]') # transpose
>> a(np.array([[0, 1], [2, 3], [4, 5]])

[[0 2 4]
 [1 3 5]]

or a simpler form:

>> a = X('[[a0, a1], [a1, a2], ...]')
>> a = X('[[a00, a10, ...], [a01, a11, ...], ...]')

where a denotes the first input array, the number behind it denotes the index of the item. For example, a01 means the item in the first input array with index (0, 1).

Multiple inputs or outputs are supported.

>> a = X(['[1, 2, ...]', '[a, b, ...]'],  # multiple input in a list
         '[1, a, 2, b, ...]; [[a, 1], [b, 2], ...]') # or seperate by ;
# or >> a = X('[a0, b0, a1, b1, ...]; [[b0, a0], [b1, a1], ...]')
>> a([np.r_[1, 2, 3, 4, 5], np.r_[10, 20, 30]]) # for incompatible input shapes, it can figure out the maximum valid output shape

(array([ 1, 10,  2, 20,  3, 30,  4], dtype=int32), 
 array([[10,  1], [20,  2], [30,  3]], dtype=int32))

>> a[1]([np.r_[1, 2], np.r_[10, 20, 30]) # or just get the transformation for second output

[[10  1], [20  2]]

Functions can be applied.

>> a = X('[1, 2, 3, 4, ...]', '[times(2), neg(1), times(4), neg(3), ...]', 
         f={'neg': lambda x: -x, 'times': lambda x: 10*x})
# a = X('[times(a1), neg(a0), times(a3), neg(a2), ...]')

notice here the output with sequence [2, 1, 4, 3, ...]

>> a(np.r_[0, 1, 2, 3, 4, 5])

[10, 0, 30, -2, 50, -4]

and unpacking

>> a = X('1; 2', '[*1, *2, *1]')([np.r_[1, 2],  np.r_[10, 20]])
# a = X('[*a, *b, *a]')

[ 1  2 10 20  1  2]

You can provide output shape by hand

>> a = X('[1, 2, ...]', '[[1, 1, ...], [2, 2, ...], ...]')
# a = X('[[a0, a0, ...], [a1, a1, ...], ...]')
>> a(np.arange(6), outShapes=(-1, 3)) # or outShapes=[(-1, 3)], 

[[0 0 0]
 [1 1 1]
 [2 2 2]
 [3 3 3]
 [4 4 4]
 [5 5 5]]

And by providing parameter extraShapes...

>> a = X('[1, 2, 3, ...]', '[[1, 2], [2, 3], ...]')
# a = X('[[a0, a1], [a1, a2], ...]')
>> a(np.r_[0, 1, 2, 3], extraShapes=(1, 0)))

[[0 1]
 [1 2]
 [2 3]
 [3 0]]

How np-xarr does

When the pattern is given, e.g.,

>> a = X('[a, b, c, ...]', '[[a, b], [b, c], ...]')

X will deduce the transformation equation between the input and output, and can be seen by

>> a

y0 = |_x0_| + |_x1_|

where |_x0_| means floor(x0). The equation y0 = |_x0_| + |_x1_| build the relation between the output index (x0, x1) and the input index (y0,) as follows:

output index (x0, x1) item equation (x0, x1) -> (y0, ) index (y0, ) input item
(0, 0) a 0 + 0 = 0 (0, ) a
(0, 1) b 0 + 1 = 1 (1, ) b
(1, 0) b 1 + 0 = 1 (1, ) b
(1, 1) c 1 + 1 = 2 (2, ) c

Another example:

>> a = X('[a, b, ...]', '[a, a, b, b, ...]')
>> a

y0 = |_0.50*x0_|
output index (x0, ) item equation (x0, ) -> (y0, ) input index (y0, ) item
(0, ) a floor(0.5*0) = 0 (0, ) a
(1, ) a floor(0.5*1) = 0 (0, ) a
(2, ) b floor(0.5*2) = 1 (1, ) b
(3, ) b floor(0.5*3) = 2 (1, ) b

Notes:

  • It is recommended to write patterns with at least two periods, e.g. [1, 2, ...] -> [[1, 2], ...] will be inferred as [1, 2, 3, ...] -> [[1, 2], [2, 3], ...] rather than [[1, 2], [3, 4], ...]

  • Inefficient for large array

    The output array is built by code like np.array([inArrays[indexConverter(index)] for index <= outShape])

  • Only support transformation with formula $y_j = floor(a_ij*x_i) + b_j + floor(c_ij*mod(x_i, d_ij))$

Todo

  • [ ] Improve exception system
  • [ ] Try to deduce possible transformation using native numpy function from calculated equation

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