Numpy - 用 NaN 替换数字 [英] Numpy - Replace a number with NaN

问题描述

我想用 numpy 中的 NaN 替换一个数字，并且正在寻找一个类似于 numpy.nan_to_num 的函数，但反向除外.

I am looking to replace a number with NaN in numpy and am looking for a function like numpy.nan_to_num, except in reverse.

随着处理不同的数组，该数字可能会发生变化，因为每个数组都可以有一个唯一定义的 NoDataValue.我看到有人使用字典，但数组很大并且充满了正负浮点数.我怀疑尝试将所有这些加载到任何东西中以创建密钥效率不高.

The number is likely to change as different arrays are processed because each can have a uniquely define NoDataValue. I have see people using dictionaries, but the arrays are large and filled with both positive and negative floats. I suspect that it is not efficient to try to load all of these into anything to create keys.

我尝试使用以下和 numpy 要求我使用 any() 或 all().我意识到我需要明智地迭代元素，但希望内置函数可以实现这一点.

I tried using the following and numpy requiring that I use any() or all(). I realize that I need to iterate element wise, but hope that a built-in function can achieve this.

def replaceNoData(scanBlock, NDV):
    for n, i in enumerate(array):
        if i == NDV:
            scanBlock[n] = numpy.nan

NDV 是 GDAL 的无数据值，数组是一个 numpy 数组.

NDV is GDAL's no data value and array is a numpy array.

掩码数组是否可行?

推荐答案

A[A==NDV]=numpy.nan

A==NDV 将产生一个布尔数组，可用作 A 的索引

A==NDV will produce a boolean array that can be used as an index for A

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