Python Numpy MaskedArray.atleast_1d()函数
numpy.MaskedArray.atleast_1d()函数用于将输入转换为至少有一维的掩码数组。
语法: numpy.ma.atleast_1d(*arys)
参数:
arys: [ array_like] 一个或多个输入数组。
返回 : [ ndarray] 一个数组,或数组列表,每个数组都有 arr.ndim >= 1。
代码#1:
# Python program explaining
# numpy.MaskedArray.atleast_1d() method
# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
# creating input arrays
in_arr1 = geek.array([[1, 2], [ 3, -1], [ 5, -3]])
print ("1st Input array : ", in_arr1)
in_arr2 = geek.array(2)
print ("2nd Input array : ", in_arr2)
# Now we are creating masked array.
# by making entry as invalid.
mask_arr1 = ma.masked_array(in_arr1, mask =[[ 1, 0], [ 0, 1], [ 0, 0]])
print ("1st Masked array : ", mask_arr1)
mask_arr2 = ma.masked_array(in_arr2, mask = 0)
print ("2nd Masked array : ", mask_arr2)
# applying MaskedArray.atleast_1d methods
# to masked array
out_arr = ma.atleast_1d(mask_arr1, mask_arr2)
print ("Output masked array : ", out_arr)
输出:
1st Input array : [[ 1 2]
[ 3 -1]
[ 5 -3]]
2nd Input array : 2
1st Masked array : [[-- 2]
[3 --]
[5 -3]]
2nd Masked array : 2
Output masked array : [masked_array(
data=[[--, 2],
[3, --],
[5, -3]],
mask=[[ True, False],
[False, True],
[False, False]],
fill_value=999999), masked_array(data=[2],
mask=[False],
fill_value=999999)]
代码#2:
# Python program explaining
# numpy.MaskedArray.atleast_1d() method
# importing numpy as geek
# and numpy.ma module as ma
import numpy as geek
import numpy.ma as ma
# creating input array
in_arr = geek.array([[[ 2e8, 3e-5]], [[ -45.0, 2e5]]])
print ("Input array : ", in_arr)
# Now we are creating a masked array.
# by making one entry as invalid.
mask_arr = ma.masked_array(in_arr, mask =[[[ 1, 0]], [[ 0, 0]]])
print ("3D Masked array : ", mask_arr)
# applying MaskedArray.atleast_1d methods
# to masked array
out_arr = ma.atleast_1d(mask_arr)
print ("Output masked array : ", out_arr)
输出:
Input array : [[[ 2.0e+08 3.0e-05]]
[[-4.5e+01 2.0e+05]]]
3D Masked array : [[[-- 3e-05]]
[[-45.0 200000.0]]]
Output masked array : [[[-- 3e-05]]
[[-45.0 200000.0]]]