pyailib.base package

Submodules

pyailib.base.arrayops module

pyailib.base.arrayops.arraycomb(arrays, out=None)

compute the elemnts combination of several lists.

Parameters

  • arrays (list or numpy array) – The lists or arrays.

  • out (numpy array, optional) – The combination results (defaults is None).

Returns

The combination results.

Return type

numpy array

Examples:

Compute the combination of three lists: \([1,2,3]\), \([4, 5]\), \([6,7]\), this will produce a \(12\times 3\) array.

x = arraycomb(([1, 2, 3], [4, 5], [6, 7]))
print(x, x.shape)

# output:
[[1 4 6]
[1 4 7]
[1 5 6]
[1 5 7]
[2 4 6]
[2 4 7]
[2 5 6]
[2 5 7]
[3 4 6]
[3 4 7]
[3 5 6]
[3 5 7]] (12, 3)
pyailib.base.arrayops.cat(arrays, axis=None, out=None)
pyailib.base.arrayops.cut(x, pos, axis=None)

Cut array at given position.

Cut array at given position.

Parameters

  • x (numpy array) – an array to be cutted.

  • pos (tulpe or list) – cut positions: ((cpstart, cpend), (cpstart, cpend), …)

  • axis (int, tulpe or list, optional) – cut axis (the default is None, which means nothing)

pyailib.base.arrayops.sl(dims, axis, idx=None)

slice any axis

generates slice of specified axis.

Parameters

  • dims (int) – total dimensions

  • axis (int or list) – select axis list.

  • idx (list or None, optional) – slice lists of the specified axis, if None, does nothing (the default)

Returns

slice for specified axis elements.

Return type

tuple of slice

Examples

import numpy as np

np.random.seed(2020)
X = np.random.randint(0, 100, (9, 10))
print(X, 'X)
print(X[sl(2, -1, [0, 1])], 'Xsl')

# output:

[[96  8 67 67 91  3 71 56 29 48]
[32 24 74  9 51 11 55 62 67 69]
[48 28 20  8 38 84 65  1 79 69]
[74 73 62 21 29 90  6 38 22 63]
[21 68  6 98  3 20 55  1 52  9]
[83 82 65 42 66 55 33 80 82 72]
[94 91 14 14 75  5 38 83 99 10]
[80 64 79 30 84 22 46 26 60 13]
[24 63 25 89  9 69 47 89 55 75]] X
[[96  8]
[32 24]
[48 28]
[74 73]
[21 68]
[83 82]
[94 91]
[80 64]
[24 63]] Xsl

pyailib.base.baseops module

pyailib.base.baseops.dmka(D, Ds)

Multi-key value assign

Multi-key value assign

Parameters

  • D (dict) – main dict.

  • Ds (dict) – sub dict

pyailib.base.baseops.dreplace(d, fv=None, rv='None', new=False)

pyailib.base.mathops module

pyailib.base.mathops.abs(X, caxis=None, keepdims=False)

obtain amplitude of a array

Both complex and real representation are supported.

\[{\rm abs}({\bf X}) = |{\bf x}| = \sqrt{u^2 + v^2}, x\in {\bf X} \]

where, \(u, v\) are the real and imaginary part of x, respectively.

Parameters

  • X (array) – input

  • caxis (int or None) – If X is complex-valued, cdim is ignored. If X is real-valued and cdim is integer then X will be treated as complex-valued, in this case, cdim specifies the complex axis; otherwise (None), X will be treated as real-valued

  • keepdims (bool, optional) – keep dimensions?

Returns

the inputs’s amplitude.

Return type

array

Examples

np.random.seed(2020)
X = np.random.rand(2, 3, 3)

print('---abs')
print(abs(X, caxis=0))
print(abs(X[0] + 1j * X[1]))

# ---output
---abs
[[0.99864747 0.88468226 0.91269439]
[0.78490066 0.48990863 0.40424448]
[0.72184896 0.40619981 1.02884318]]
[[0.99864747 0.88468226 0.91269439]
[0.78490066 0.48990863 0.40424448]
[0.72184896 0.40619981 1.02884318]]
pyailib.base.mathops.c2r(X, caxis=- 1)

convert complex-valued array to real-valued array

Parameters

  • X (numpy array) – complex-valued array

  • caxis (int, optional) – complex axis for real-valued array. Defaults to -1.

Returns

real-valued array

Return type

numpy array

Examples

import numpy as np

np.random.seed(2020)

Xreal = np.random.randint(0, 30, (3, 2, 4))
Xcplx = r2c(Xreal, caxis=1)
Yreal = c2r(Xcplx, caxis=0, keepdims=True)

print(Xreal, Xreal.shape, 'Xreal')
print(Xcplx, Xcplx.shape, 'Xcplx')
print(Yreal, Yreal.shape, 'Yreal')
print(np.sum(Yreal[0] - Xcplx.real), np.sum(Yreal[1] - Xcplx.imag), 'Error')

# output
[[[ 0  8  3 22]
[ 3 27 29  3]]

[[ 7 24 29 16]
[ 0 24 10  9]]

[[19 11 23 18]
[ 3  6  5 16]]] (3, 2, 4) Xreal

[[[ 0. +3.j  8.+27.j  3.+29.j 22. +3.j]]

[[ 7. +0.j 24.+24.j 29.+10.j 16. +9.j]]

[[19. +3.j 11. +6.j 23. +5.j 18.+16.j]]] (3, 1, 4) Xcplx

[[[[ 0.  8.  3. 22.]]

[[ 7. 24. 29. 16.]]

[[19. 11. 23. 18.]]]


[[[ 3. 27. 29.  3.]]

[[ 0. 24. 10.  9.]]

[[ 3.  6.  5. 16.]]]] (2, 3, 1, 4) Yreal

0.0 0.0, Error
pyailib.base.mathops.conj(X, caxis=None)

conjugates a array

Both complex and real representation are supported.

Parameters

  • X (array) – input

  • caxis (int or None) – If X is complex-valued, cdim is ignored. If X is real-valued and cdim is integer then X will be treated as complex-valued, in this case, cdim specifies the complex axis; otherwise (None), X will be treated as real-valued

Returns

the inputs’s conjugate matrix.

Return type

array

Examples

np.random.seed(2020)
X = np.random.rand(2, 3, 3)

print('---conj')
print(conj(X, caxis=0))
print(conj(X[0] + 1j * X[1]))

# ---output
---conj
[[[ 0.98627683  0.87339195  0.50974552]
[ 0.27183571  0.33691873  0.21695427]
[ 0.27647714  0.34331559  0.86215894]]

[[-0.15669967 -0.14088724 -0.75708028]
[-0.73632492 -0.35566309 -0.34109302]
[-0.66680305 -0.21710064 -0.56142698]]]
[[0.98627683-0.15669967j 0.87339195-0.14088724j 0.50974552-0.75708028j]
[0.27183571-0.73632492j 0.33691873-0.35566309j 0.21695427-0.34109302j]
[0.27647714-0.66680305j 0.34331559-0.21710064j 0.86215894-0.56142698j]]
pyailib.base.mathops.ebeo(a, b, op='+')

element by element operation

Element by element operation.

Parameters

  • a (list, tuple or ndarray) – The first list/tuple/ndarray.

  • b (list, tuple or ndarray) – The second list/tuple/ndarray.

  • op (str, optional) – Supported operations are: - '+' or 'add' for addition (default) - '-' or 'sub' for substraction - '*' or 'mul' for multiplication - '/' or 'div' for division - '**' or pow for power - '<', or 'lt' for less than - '<=', or 'le' for less than or equal to - '>', or 'gt' for greater than - '>=', or 'ge' for greater than or equal to - '&' for bitwise and - '|' for bitwise or - '^' for bitwise xor - function for custom operation.

Raises

TypeError – If the specified operator not in the above list, raise a TypeError.

pyailib.base.mathops.imag(X, caxis=None, keepdims=False)

obtain imaginary part of a array

Both complex and real representation are supported.

Parameters

  • X (array) – input

  • caxis (int or None) – If X is complex-valued, cdim is ignored. If X is real-valued and cdim is integer then X will be treated as complex-valued, in this case, cdim specifies the complex axis; otherwise (None), X will be treated as real-valued

  • keepdims (bool, optional) – keep dimensions?

Returns

the inputs’s imaginary part array.

Return type

array

Examples

np.random.seed(2020)
X = np.random.rand(2, 3, 3)

print('---imag')
print(imag(X, caxis=0))
print(imag(X[0] + 1j * X[1]))

# ---output
---imag
[[0.15669967 0.14088724 0.75708028]
[0.73632492 0.35566309 0.34109302]
[0.66680305 0.21710064 0.56142698]]
[[0.15669967 0.14088724 0.75708028]
[0.73632492 0.35566309 0.34109302]
[0.66680305 0.21710064 0.56142698]]
pyailib.base.mathops.nextpow2(x)

get the next higher power of 2.

Given an number \(x\), returns the first p such that \(2^p >=|x|\).

Parameters

x (int or float) – an number.

Returns

Next higher power of 2.

Return type

int

Examples

print(prevpow2(-5), nextpow2(-5))
print(prevpow2(5), nextpow2(5))
print(prevpow2(0.3), nextpow2(0.3))
print(prevpow2(7.3), nextpow2(7.3))
print(prevpow2(-3.5), nextpow2(-3.5))

# output
2 3
2 3
-2 -1
2 3
1 2
pyailib.base.mathops.pow(X, caxis=None, keepdims=False)

obtain power of a array

Both complex and real representation are supported.

\[{\rm pow}({\bf X}) = |{\bf x}| = u^2 + v^2, x\in {\bf X} \]

where, \(u, v\) are the real and imaginary part of x, respectively.

Parameters

  • X (array) – input

  • caxis (int or None) – If X is complex-valued, cdim is ignored. If X is real-valued and cdim is integer then X will be treated as complex-valued, in this case, cdim specifies the complex axis; otherwise (None), X will be treated as real-valued

  • keepdims (bool, optional) – keep dimensions?

Returns

the inputs’s power.

Return type

array

Examples

np.random.seed(2020)
X = np.random.rand(2, 3, 3)

print('---pow')
print(pow(X, caxis=0))
print(pow(X[0] + 1j * X[1]))

# ---output
---pow
[[0.99729677 0.78266271 0.83301105]
[0.61606904 0.24001046 0.1634136 ]
[0.52106592 0.16499828 1.05851829]]
[[0.99729677 0.78266271 0.83301105]
[0.61606904 0.24001046 0.1634136 ]
[0.52106592 0.16499828 1.05851829]]
pyailib.base.mathops.prevpow2(x)

get the previous lower power of 2.

Given an number \(x\), returns the first p such that \(2^p <=|x|\).

Parameters

x (int or float) – an number.

Returns

Next higher power of 2.

Return type

int

Examples

print(prevpow2(-5), nextpow2(-5))
print(prevpow2(5), nextpow2(5))
print(prevpow2(0.3), nextpow2(0.3))
print(prevpow2(7.3), nextpow2(7.3))
print(prevpow2(-3.5), nextpow2(-3.5))

# output
2 3
2 3
-2 -1
2 3
1 2
pyailib.base.mathops.r2c(X, caxis=- 1, keepdims=False)

convert real-valued array to complex-valued array

Convert real-valued array (the size of axis -th dimension is 2) to complex-valued array

Parameters

  • X (numpy array) – real-valued array.

  • caxis (int, optional) – the complex axis. Defaults to -1.

  • keepdims (bool, optional) – keepdims? default is False.

Returns

complex-valued array

Return type

numpy array

Examples

import numpy as np

np.random.seed(2020)

Xreal = np.random.randint(0, 30, (3, 2, 4))
Xcplx = r2c(Xreal, caxis=1)
Yreal = c2r(Xcplx, caxis=0, keepdims=True)

print(Xreal, Xreal.shape, 'Xreal')
print(Xcplx, Xcplx.shape, 'Xcplx')
print(Yreal, Yreal.shape, 'Yreal')
print(np.sum(Yreal[0] - Xcplx.real), np.sum(Yreal[1] - Xcplx.imag), 'Error')

# output
[[[ 0  8  3 22]
[ 3 27 29  3]]

[[ 7 24 29 16]
[ 0 24 10  9]]

[[19 11 23 18]
[ 3  6  5 16]]] (3, 2, 4) Xreal

[[[ 0. +3.j  8.+27.j  3.+29.j 22. +3.j]]

[[ 7. +0.j 24.+24.j 29.+10.j 16. +9.j]]

[[19. +3.j 11. +6.j 23. +5.j 18.+16.j]]] (3, 1, 4) Xcplx

[[[[ 0.  8.  3. 22.]]

[[ 7. 24. 29. 16.]]

[[19. 11. 23. 18.]]]


[[[ 3. 27. 29.  3.]]

[[ 0. 24. 10.  9.]]

[[ 3.  6.  5. 16.]]]] (2, 3, 1, 4) Yreal

0.0 0.0, Error
pyailib.base.mathops.real(X, caxis=None, keepdims=False)

obtain real part of a array

Both complex and real representation are supported.

Parameters

  • X (array) – input

  • caxis (int or None) – If X is complex-valued, cdim is ignored. If X is real-valued and cdim is integer then X will be treated as complex-valued, in this case, cdim specifies the complex axis; otherwise (None), X will be treated as real-valued

  • keepdims (bool, optional) – keep dimensions?

Returns

the inputs’s real part array.

Return type

array

Examples

np.random.seed(2020)
X = np.random.rand(2, 3, 3)

print('---real')
print(real(X, caxis=0))
print(real(X[0] + 1j * X[1]))

# ---output
---real
[[0.98627683 0.87339195 0.50974552]
[0.27183571 0.33691873 0.21695427]
[0.27647714 0.34331559 0.86215894]]
[[0.98627683 0.87339195 0.50974552]
[0.27183571 0.33691873 0.21695427]
[0.27647714 0.34331559 0.86215894]]

pyailib.base.randomfunc module

pyailib.base.randomfunc.randgrid(start, stop, step, shake=0, n=None)

generates non-repeated uniform stepped random integers

Generates n non-repeated random integers from start to stop with step size step.

When step is 1 and shake is 0, it works similar to randperm,

Parameters

  • start (int or list) – start sampling point

  • stop (int or list) – stop sampling point

  • step (int or list) – sampling stepsize

  • shake (float) – the shake rate, if shake is 0, no shake, (default), if positive, add a positive shake, if negative, add a negative.

  • n (int or None) – the number of samples (default None, int((stop0 - start0) / step0) * int((stop1 - start1) / step1)…).

Returns

Return type

for multi-dimension, return a list of lists, for 1-dimension, return a list of numbers.

see randperm().

Example

Plot sampled randperm and randgrid point.

_images/demo_randgrid.png

The results shown in the above figure can be obtained by the following codes.

import matplotlib.pyplot as plt

setseed(2021)
print(randperm(2, 40, 8), ", randperm(2, 40, 8)")
print(randgrid(2, 40, 1, -1., 8), ", randgrid(2, 40, 1, 8, -1.)")
print(randgrid(2, 40, 6, -1, 8), ", randgrid(2, 40, 6, 8)")
print(randgrid(2, 40, 6, 0.5, 8), ", randgrid(2, 40, 6, 8, 0.5)")
print(randgrid(2, 40, 6, -1, 12), ", randgrid(2, 40, 6, 12)")
print(randgrid(2, 40, 6, 0.5, 12), ", randgrid(2, 40, 6, 12, 0.5)")

mask = np.zeros((5, 6))
mask[3, 4] = 0
mask[2, 5] = 0

Rh, Rw = randperm2d(5, 6, 4, mask=mask)

print(Rh)
print(Rw)

N, H, W = 32, 512, 512

y1 = pl.randperm(0, H, N)
x1 = pl.randperm(0, W, N)
print(len(y1), len(x1))

y2 = pl.randgrid(0, H, 32, 0., N)
x2 = pl.randgrid(0, W, 32, 0., N)
print(len(y2), len(x2))
print(y2, x2)

y3, x3 = pl.randperm([0, 0], [H, W], N)
print(len(y3), len(x3))

y4, x4 = pl.randgrid([0, 0], [H, W], [32, 32], [0.25, 0.25], N)
print(len(y4), len(x4))

plt.figure()
plt.subplot(221)
plt.grid()
plt.plot(x1, y1, '*')
plt.subplot(222)
plt.grid()
plt.plot(x2, y2, '*')
plt.subplot(223)
plt.grid()
plt.plot(x3, y3, '*')
plt.subplot(224)
plt.grid()
plt.plot(x4, y4, '*')
plt.show()
pyailib.base.randomfunc.randperm(start, stop, n)

randperm function like matlab

genarates diffrent random interges in range [start, stop)

Parameters

  • start (int or list) – start sampling point

  • stop (int or list) – stop sampling point

  • n (int, list or None) – the number of samples (default None, (stop - start))

Returns

pyailib.base.randomfunc.randperm2d(H, W, number, population=None, mask=None)

randperm 2d function

genarates diffrent random interges in range [start, end)

Parameters

  • H (int) – height

  • W (int) – width

  • number (int) – random numbers

  • population ({list or numpy array(1d or 2d)}) – part of population in range(0, H*W)

Returns

  • Ph (list) – the randomly permuted intergers in height direction.

  • Pw (list) – the randomly permuted intergers in width direction.

see randgrid(), randperm().

pyailib.base.randomfunc.setseed(seed=None, target='numpy')

set seed

Set numpy / random seed.

Parameters

  • seed (int or None, optional) – seed for random number generator (the default is None)

  • target (str, optional) –

    • 'numpy': np.random.seed(seed) (the default)

    • 'random': random(seed)

Module contents