Numpy Creating a complex array from 2 real ones

Question

I want to combine 2 parts of the same array to make a complex array  Data       0    Data       1   These don t work  x   np complex Data       0   Data       1   x   complex Data       0   Data       1    Am I missing something  Does numpy not like performing array functions on complex numbers  Here s the error  TypeError  only length-1 arrays can be converted to Python scalars

User · Answer

import numpy as np  n   51  number of data points   Suppose the real and imaginary parts are created independently real part   np random normal size n  imag part   np random normal size n     Create a complex array - the imaginary part will be equal to zero z   np array real part  dtype complex    Now define the imaginary part  z imag   imag part print z

User · Answer

I am python novice so this may not be the most efficient method but  if I understand the intent of the question correctly  steps listed below worked for me    gt  gt  gt  import numpy as np  gt  gt  gt  Data   np random random  100  100  1000  2    gt  gt  gt  result   np empty Data shape  -1   dtype complex   gt  gt  gt  result real   Data     0   result imag   Data     1   gt  gt  gt  print Data 0 0 0 0   Data 0 0 0 1   result 0 0 0  0 0782889873474 0 156087854837  0 0782889873474 0 156087854837j

User · Answer

This is what your are looking for   from numpy import array  a array  1 2 3   b array  4 5 6    a   1j b  - gt array   1  4 j   2  5 j   3  6 j

User · Answer

If your real and imaginary parts are the slices along the last dimension and your array is contiguous along the last dimension  you can just do  A view dtype np complex128    If you are using single precision floats  this would be  A view dtype np complex64    Here is a fuller example  import numpy as np from numpy random import rand   Randomly choose real and imaginary parts    Treat last axis as the real and imaginary parts  A   rand 100  2    Cast the array as a complex array   Note that this will now be a 100x1 array A comp   A view dtype np complex128    To get the original array A back from the complex version A   A view dtype np float64    If you want to get rid of the extra dimension that stays around from the casting  you could do something like  A comp   A view dtype np complex128      0    This works because  in memory  a complex number is really just two floating point numbers  The first represents the real part  and the second represents the imaginary part  The view method of the array changes the dtype of the array to reflect that you want to treat two adjacent floating point values as a single complex number and updates the dimension accordingly   This method does not copy any values in the array or perform any new computations  all it does is create a new array object that views the same block of memory differently  That makes it so that this operation can be performed much faster than anything that involves copying values  It also means that any changes made in the complex-valued array will be reflected in the array with the real and imaginary parts   It may also be a little trickier to recover the original array if you remove the extra axis that is there immediately after the type cast  Things like A comp     np newaxis  view np float64  do not currently work because  as of this writing  NumPy doesn t detect that the array is still C-contiguous when the new axis is added  See this issue  A comp view np float64  reshape A shape  seems to work in most cases though

User · Answer

I use the following method  import numpy as np  real   np ones  2  3   imag   2 np ones  2  3    complex   np vectorize complex  real  imag    OR complex   real   1j imag

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That worked for me   input    complex a b  for a b in zip  1 2 3   1 2 3      output     1 4j    2 5j    3 6j

User · Answer

This seems to do what you want   numpy apply along axis lambda args   complex  args    3  Data    Here is another solution     The ellipsis is equivalent here to            numpy vectorize complex  Data     0   Data     1     And yet another simpler solution   Data     0    1j   Data     1    PS  If you want to save memory  no intermediate array    result   1j Data     1   result    Data     0    devS  solution below is also fast

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If you really want to eke out performance  with big arrays   numexpr can be used  which takes advantage of multiple cores   Setup    gt  gt  gt  import numpy as np  gt  gt  gt  Data   np random randn 64  64  64  2   gt  gt  gt  x  y   Data     0   Data     1    With numexpr    gt  gt  gt  import numexpr as ne  gt  gt  gt   timeit result   ne evaluate  complex x  y    573   s    21 1   s per loop  mean    std  dev  of 7 runs  1000 loops each    Compared to fast numpy method    gt  gt  gt   timeit result   np empty x shape  dtype complex   result real   x  result imag   y 1 39 ms    5 74   s per loop  mean    std  dev  of 7 runs  1000 loops each

User · Answer

There s of course the rather obvious   Data     0    1j   Data     1

[python] Numpy: Creating a complex array from 2 real ones?

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