如何使用numpy.savetxt保存和加载复数数组? [英] How to save and load an array of complex numbers using numpy.savetxt?

问题描述

我想使用 numpy.savetxt() 来将复数数组保存到文本文件.问题:

I want to use numpy.savetxt() to save an array of complex numbers to a text file. Problems:

• 如果使用默认格式字符串保存复杂数组，则虚部将被丢弃.
• 如果使用fmt='%s'，那么除非指定dtype=complex, converters={0: lambda s: complex(s)}，否则numpy.loadtxt()无法加载它.即使那样，即使数组中存在NaN，加载仍然会失败.

• If you save the complex array with the default format string, the imaginary part is discarded.
• If you use fmt='%s', then numpy.loadtxt() can't load it unless you specify dtype=complex, converters={0: lambda s: complex(s)}. Even then, if there are NaN's in the array, loading still fails.

好像有人问过这个多个在Numpy邮件列表上时间甚至提交了错误，但没有得到回应.在我自己整理东西之前，有没有一种规范的方法可以做到这一点?

It looks like someone has inquired about this multiple times on the Numpy mailing list and even filed a bug, but has not gotten a response. Before I put something together myself, is there a canonical way to do this?

推荐答案

它更加简单，并保存了一些临时数组，以将数组重新解释为真实数组.

It's easier and saves a few temporary arrays to just reinterpret the array as a real array.

保存:

numpy.savetxt('outfile.txt', array.view(float))

正在加载:

array = numpy.loadtxt('outfile.txt').view(complex)

如果您希望在文件的同一行上包含实部和虚部，则可以使用

If you prefer to have real and imaginary part on the same line in the file, you can use

numpy.savetxt('outfile.txt', array.view(float).reshape(-1, 2))

或

array = numpy.loadtxt('outfile.txt').view(complex).reshape(-1)

分别.

(请注意，view()和reshape()均不会复制该数组，它只会以不同的方式重新解释相同的数据.)

(Note that neither view() nor reshape() copies the array -- it will just reinterpret the same data in a different way.)

提问者的附录:

如果要在同一文件中保存多个复杂数组，可以这样做:

If you want to save more than one complex array in the same file, you can do it like so:

numpy.savetxt('outfile.txt', numpy.column_stack([
    array1.view(float).reshape(-1, 2),
    array2.view(float).reshape(-1, 2),
]))

array1, array2 = numpy.loadtxt('outfile.txt', unpack=True).view(complex)

重塑是必要的，因为numpy.view()不适用于跨步数组.

The reshaping is necessary because numpy.view() doesn't operate on strided arrays.

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