便捷函式

1 常用函式

import numpy as np

a = np.arange(-5,5)

signs = np.sign(a)

piecewises = np.piecewise(a,[a>0,a<0],[1,-1])

np.array_equal(signs,piecewises)

True

2 建立矩陣

A = np.mat('1 2 3; 4 5 6; 7 8 9')

A

matrix([[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]])
 

A.T

matrix([[1, 4, 7],
        [2, 5, 8],
        [3, 6, 9]])

A = np.eye(2)

A

array([[1., 0.],
       [0., 1.]])

B = 2*A

B

array([[2., 0.],
       [0., 2.]])

np.bmat('A B;A B')

matrix([[1., 0., 2., 0.],
        [0., 1., 0., 2.],
        [1., 0., 2., 0.],
        [0., 1., 0., 2.]])
 

3 通用函式

通用函式的輸入是一組標量,輸出也是一組標量,他們通常可以對應於基本數學運算,如加減乘除

def ultimate_answer(a):
    return np.zeros_like(a)

a = np.eye(3)

ultimate_answer(a)

array([[0., 0., 0.],
       [0., 0., 0.],
       [0., 0., 0.]])

a.flat

<numpy.flatiter at 0x2e247c0>

list(a.flat)

[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
 

a = np.arange(9)

np.add.reduce(a)

36

np.add.accumulate(a)

array([ 0,  1,  3,  6, 10, 15, 21, 28, 36])

4 算術運算

a = np.array([2,6,5])
b = np.array([1,2,3])

np.divide(a,b)

array([2, 3, 1])

np.divide(b,a)

array([0, 0, 0])

np.true_divide(a,b)

array([2.        , 3.        , 1.66666667])

np.true_divide(b,a)

array([0.5       , 0.33333333, 0.6       ])

from __future__ import division

a/b

array([2.        , 3.        , 1.66666667])

a//b

array([2, 3, 1])

a = np.arange(-4,4)

a

array([-4, -3, -2, -1,  0,  1,  2,  3])

np.remainder(a,2)

array([0, 1, 0, 1, 0, 1, 0, 1])

np.mod(a,2)

array([0, 1, 0, 1, 0, 1, 0, 1])

a%2

array([0, 1, 0, 1, 0, 1, 0, 1])

np.fmod(a,2)

array([ 0, -1,  0, -1,  0,  1,  0,  1])

from matplotlib.pyplot import plot
from matplotlib.pyplot import show

a = 1
b =2

t = np.linspace(-np.pi,np.pi,201)

x = np.sin(a*t +np.pi/2)
y = np.sin(b*t)

plot(x)

[<matplotlib.lines.Line2D at 0x7f6c02908f50>]

show()