Convert structured array to regular NumPy array

Question

The answer will be very obvious I think, but I don't see it at the moment.

How can I convert a record array back to a regular ndarray?

Suppose I have following simple structured array:

x = np.array([(1.0, 4.0,), (2.0, -1.0)], dtype=[('f0', '<f8'), ('f1', '<f8')])

then I want to convert it to:

array([[ 1.,  4.],
       [ 2., -1.]])

I tried asarray and astype, but that didn't work.

UPDATE (solved: float32 (f4) instead of float64 (f8))

OK, I tried the solution of Robert (x.view(np.float64).reshape(x.shape + (-1,)) ), and with a simple array it works perfectly. But with the array I wanted to convert it gives a strange outcome:

data = np.array([ (0.014793682843446732, 0.006681123282760382, 0.0, 0.0, 0.0, 0.0008984912419691682, 0.0, 0.013475529849529266, 0.0, 0.0),
       (0.014793682843446732, 0.006681123282760382, 0.0, 0.0, 0.0, 0.0008984912419691682, 0.0, 0.013475529849529266, 0.0, 0.0),
       (0.014776384457945824, 0.006656022742390633, 0.0, 0.0, 0.0, 0.0008901208057068288, 0.0, 0.013350814580917358, 0.0, 0.0),
       (0.011928378604352474, 0.002819152781739831, 0.0, 0.0, 0.0, 0.0012627150863409042, 0.0, 0.018906937912106514, 0.0, 0.0),
       (0.011928378604352474, 0.002819152781739831, 0.0, 0.0, 0.0, 0.001259754877537489, 0.0, 0.01886274479329586, 0.0, 0.0),
       (0.011969991959631443, 0.0028706740122288465, 0.0, 0.0, 0.0, 0.0007433745195157826, 0.0, 0.011164642870426178, 0.0, 0.0)], 
      dtype=[('a_soil', '<f4'), ('b_soil', '<f4'), ('Ea_V', '<f4'), ('Kcc', '<f4'), ('Koc', '<f4'), ('Lmax', '<f4'), ('malfarquhar', '<f4'), ('MRN', '<f4'), ('TCc', '<f4'), ('Vcmax_3', '<f4')])

and then:

data_array = data.view(np.float).reshape(data.shape + (-1,))

gives:

In [8]: data_array
Out[8]: 
array([[  2.28080997e-20,   0.00000000e+00,   2.78023241e-27,
          6.24133580e-18,   0.00000000e+00],
       [  2.28080997e-20,   0.00000000e+00,   2.78023241e-27,
          6.24133580e-18,   0.00000000e+00],
       [  2.21114197e-20,   0.00000000e+00,   2.55866881e-27,
          5.79825816e-18,   0.00000000e+00],
       [  2.04776835e-23,   0.00000000e+00,   3.47457730e-26,
          9.32782857e-17,   0.00000000e+00],
       [  2.04776835e-23,   0.00000000e+00,   3.41189244e-26,
          9.20222417e-17,   0.00000000e+00],
       [  2.32706550e-23,   0.00000000e+00,   4.76375305e-28,
          1.24257748e-18,   0.00000000e+00]])

which is an array with other numbers and another shape. What did I do wrong?

Answer 1

The simplest method is probably

x.view((float, len(x.dtype.names))) 

(float must generally be replaced by the type of the elements in x: x.dtype[0]). This assumes that all the elements have the same type.

This method gives you the regular numpy.ndarray version in a single step (as opposed to the two steps required by the view(…).reshape(…) method.

Answer 2

[~] |5> x = np.array([(1.0, 4.0,), (2.0, -1.0)], dtype=[('f0', '<f8'), ('f1', '<f8')])  [~] |6> x.view(np.float64).reshape(x.shape + (-1,)) array([[ 1.,  4.],        [ 2., -1.]]) 

Answer 3

np.array(x.tolist())
array([[ 1.,  4.],
      [ 2., -1.]])

but maybe there is a better method...