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

  • 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.

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?

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2 Answers 2

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20

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

Saving:

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

Loading:

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

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))

or

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

respectively.

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

Addendum from the question asker:

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)

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

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1 Comment

Excellent! Short and elegant.
6

Here's my solution, in case anybody hits this question from Google.

Saving:

numpy.savetxt('outfile.txt', numpy.column_stack([array.real, array.imag]))

Loading:

array_real, array_imag = numpy.loadtxt('outfile.txt', unpack=True) array = array_real + 1j * array_imag

I will still award the checkmark to a better solution!

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