numpy的 - 更换楠号 [英] Numpy - Replace a number with NaN

问题描述

我期待在numpy的与NaN的更换号码，我期待像numpy.nan_to_num功能，除了在反向。

的数量很可能会改变，因为不同的阵列处理，因为每个都可以具有一个唯一地定义NoDataValue。我看到有使用字典的人，但阵列都很大，充满了积极和消极的浮动。我怀疑这是不是有效的尝试所有这些加载到任何创建密钥。

我试着用以下和numpy的要求，我用任何（）或全部（）。我意识到我需要明智的重复元素，而是希望一个内置功能，可以实现这一点。

 高清replaceNoData（scanBlock，NDV）：
    对于n，我历数（阵列）：
        如果我== NDV：
            scanBlock [N] = numpy.nan
 

NDV是GDAL的任何数据值和数组是一个numpy的数组。

是一个蒙面的数组去也许是这样？

解决方案

  A [A == NDV] = numpy.nan
 

A == NDV会产生可以用作指数

一个布尔数组

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.

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.

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

Is a masked array the way to go perhaps?

解决方案

A[A==NDV]=numpy.nan

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

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