首页 if not np.isnan(ls).all(): m = np.append(ma, ls) ma = m else: x = np.append(xc, i) xc = x

if not np.isnan(ls).all(): m = np.append(ma, ls) ma = m else: x = np.append(xc, i) xc = x

时间: 2024-05-12 07:21:02 浏览: 110

# As an AI language model, I cannot determine the context and purpose of the code. However, I can explain what this code block does. This code block contains an if-else statement. It checks if all the values in the numpy array 'ls' are NaN (Not a Number). If they are not all NaN, it appends the values of 'ls' to another numpy array 'ma'. If all the values in 'ls' are NaN, it appends a variable 'i' to another numpy array 'xc'. Here's a breakdown of the code: ``` if not np.isnan(ls).all(): ``` This line checks if all the values in the numpy array 'ls' are NaN. The 'np.isnan' function returns a boolean array of the same shape as 'ls' with True for NaN values and False for non-NaN values. The 'all' function returns True if all the values in the boolean array are True, which means all the values in 'ls' are NaN. The 'not' keyword negates this result, so if any value in 'ls' is not NaN, the condition is True. ``` m = np.append(ma, ls) ma = m ``` If the condition is True, this block of code appends the values of 'ls' to the numpy array 'ma' using the 'np.append' function. The result is stored in a new numpy array 'm', which is then assigned to 'ma'. ``` else: ``` If the condition is False, this block of code is executed. ``` x = np.append(xc, i) xc = x ``` This block of code appends a variable 'i' to the numpy array 'xc' using the 'np.append' function. The result is stored in a new numpy array 'x', which is then assigned to 'xc'.

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