y1 = np.zeros(x1.shape[0]) y2 = np.ones(x2.shape[0]) y3 = np.full(x3.shape[0], 2) y4 = np.full(x4.shape[0], 3) y5 = np.full(x5.shape[0], 4)

These lines of code create five different arrays, each filled with a different constant value. The length of each array corresponds to the length of a different input array (x1, x2, x3, x4, x5).

- y1 is an array of zeros, with the same length as x1.
- y2 is an array of ones, with the same length as x2.
- y3 is an array filled with the constant value 2, with the same length as x3.
- y4 is an array filled with the constant value 3, with the same length as x4.
- y5 is an array filled with the constant value 4, with the same length as x5.

相关问题

template= np.zeros(src.shape, dtype=np.float32) r = src.shape[0] c = src.shape[1]

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翻译 x = np.atleast_1d(x) y = np.atleast_1d(y) mask = np.zeros(x.shape, dtype=bool)

这代码的作用是将x和y转换为至少1维的数组，并创建一个与x形状相同的布尔类型的零数组作为mask。

- `np.atleast_1d(x)`将x转换为至少1维的数组。如果x本身已经是1维或更高维度的数组，则返回x；否则，将x转换为1维数组。
- `np.atleast_1d(y)`同样地，将y转换为至少1维的数组。
- `np.zeros(x.shape, dtype=bool)`创建一个与x形状相同的零数组，并指定数据类型为布尔型。这个数组将用作mask，初始时所有元素都是False。

综合起来，这段代码将确保x和y至少是1维的数组，并创建一个与x形状相同的布尔类型的零数组作为mask。