NumPy中掩码数组的操作

import numpy as np
import numpy.ma as ma

import numpy as np
import numpy.ma as ma

a = ma.array([0, 1, 2, 3], mask=[0, 0, 1, 0]) # 指定第3个元素无效
print(a)
print(type(a))
print(a.min(), a.max(), a.mean(), a.var())

import numpy as np
import numpy.ma as ma

a = np.arange(5)
print(ma.asarray(a))

a = np.array([1, np.nan, 2, np.inf, 3]) # 包含特殊值的数组
print(ma.asarray(a))

import numpy as np
import numpy.ma as ma

a = np.array([1, np.nan, 2, np.inf, 3])
print(ma.masked_invalid(a))

import numpy as np
import numpy.ma as ma

a = np.arange(3).repeat(2)
print(ma.masked_equal(a, 1)) # 对数组元素1做掩码

import numpy as np
import numpy.ma as ma

a = np.arange(8)
print(ma.masked_greater(a, 4)) # 掩码大于4的元素
print(ma.masked_greater_equal(a, 4)) # 掩码大于等于4的元素
print( ma.masked_less(a, 4)) # 掩码小于4的元素
print(ma.masked_less_equal(a, 4)) # 掩码小于等于4的元素
print(ma.masked_inside(a, 2, 5)) # 掩码 [2,5]之间的元素
print(ma.masked_outside(a, 2, 5)) # 掩码 [2,5]之外的元素

import numpy as np
import numpy.ma as ma

a = np.arange(8)
b = np.random.random(8)
print( ma.masked_where(a>5, b)) # 用a>5的条件掩码数组b 

import numpy as np
import numpy.ma as ma

a = np.array([1, np.nan, 2, np.inf, 3])
a = ma.masked_invalid(a)
print(a[0], a[1], a[-1])
print(a[1:-1])

import numpy as np
import numpy.ma as ma

a = np.array([1, np.nan, 2, np.inf, 3])
a = ma.masked_invalid(a)
print(a.min(), a.max(), a.mean(), a.var())
#print(np.sin(a)) # 虽然可以执行，但会弹出警告
print(ma.sin(a)) # 这才是正确的用法

import numpy as np
import numpy.ma as ma

a = ma.array([1, np.nan, 2, np.inf, 3])
print(a)

x = a.data
y = a.__array__()
z = ma.getdata(a)
w = np.copy(a.__array__()) # 复制数据视图

print(x)
print(y)
print(z)
print(w)

a[-1] = 9
print(x)
print(y)
print(z)
print(w)

import numpy as np
import numpy.ma as ma

a = ma.masked_invalid(np.array([1, np.nan, 2, np.inf, 3]))
print(a.mask)

import numpy as np
import numpy.ma as ma

a = ma.masked_invalid(np.array([1, np.nan, 2, np.inf, 3]))
print(a.mask)
print(a)

a[:2] = ma.masked
print(a)

import numpy as np
import numpy.ma as ma

a = ma.masked_invalid(np.array([1, np.nan, 2, np.inf, 3]))
a[1] = 1.5
a[2:4] = 5
print(a)