理解numpy中的切片访问

一维数组的切片访问

numpy 中的一维数组的切片方法与 python 内置的list 切片类似.

Details：

    1. ndarray[start:stop:step]     # means start from start, stop at stop, step by step
    2. ndarray[start:stop]          # means start from start, stop at stop, step by 1
    3. ndarray[start:]              # means start from start, stop at the end, step by 1
    4. ndarray[:stop]               # means start from the beginning, stop at stop, step by 1
    5. ndarray[:]                   # means start from the beginning, stop at the end, step by 1                   
    6. ndarray[start:stop:step, start:stop:step]                  # means 2-dimensional slicing

    Note:
    start is from 0
    start is inclusive, stop is exclusive
    step could be negative, which means reverse order

下面是例子
先构建1个1维数组：

    arr = np.arange(10) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    logger.info(f"arr: {
     arr}") # [0 1 2 3 4 5 6 7 8 9]

1. 如果stop index > 数组长度， 则只会返回 start: last index + 1

logger.info(f"arr[1:100]: {
     arr[1:100]}") # [1 2 3 4 5 6 7 8 9] even stop > len(arr)

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