How to convert complex matrix R to numpy array using rpy2
it is clear to me how to convert a float / double R-matrix to a numpy array, but I get an error if the matrix is complex .
Example:
import numpy as np
import rpy2.robjects as robjects
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()
m1=robjects.IntVector(range(10))
m2 = robjects.r.matrix(robjects.r['as.complex'](m1), nrow=5)
tmp=np.array(m2, dtype=complex) #ValueError: invalid __array_struct__
The problem persists with the following line of code:
tmp=np.array(m2)
Everything works fine if the matrix is not complicated :
m2 = robjects.r.matrix(m1, nrow=5)
tmp=np.array(m2)
Thanks for any help!
PS: Note that the following dirty trick solves the problem, but doesn't really answer the question:
tmp=np.array(robjects.r.Re(m2))+1j*np.array(robjects.r.Im(m2))
PS2: nobody seems to be able to answer this question, should we conclude that there is a bug in rpy2?
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Sometimes it is difficult to convert objects rpy
to numpy
, but it is much more reliable to convert them to objects python
( list
, tuple
etc.) and build array
later. Decision:
In [33]:
import numpy as np
import rpy2.robjects as robjects
robjects.reval('m1 <- c(1:10)')
robjects.reval("m2 <- matrix(as.complex(m1), nrow=5)")
np.array(list(robjects.r.m2)).reshape(robjects.r.m2.dim)
Out[33]:
array([[ 1.+0.j, 2.+0.j],
[ 3.+0.j, 4.+0.j],
[ 5.+0.j, 6.+0.j],
[ 7.+0.j, 8.+0.j],
[ 9.+0.j, 10.+0.j]])
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