3

I read in a sequence of numbers with 

np.array(f.read().split(),dtype=np.float64)

Then I convert this to a 2-D array using np.reshape().

After this, how do to convert arr to a record array? I've tried (something like) the following:

filename = 'unstructured-file.txt'
nfields = 3
names = ('r','g','b')
with open(filename,'r') as f:
    arr = np.array(f.read().split(),dtype=np.float64)
    arr = arr.reshape(-1,nfields)
    out = np.array(arr,dtype=zip(names,['float64']*length(names))

but says TypeError: expected a readable buffer object

Any suggestions?

Edit: The main thing I want to do is to name my columns.

Instead of 

out = np.array(arr,dtype=zip(names,['float64']*length(names))

If I use this,

out = np.core.records.fromrecords(arr.reshape(-1,nfields),names=','.join(names))

I can use out['r'] and so on, but out.dtype.names is None`. What is going on?

Edit2

The unstructured file looks like 

 Some text
 More text
       100  1.000000E-01        46
 -1.891701E+04  1.702921E+02 -2.323660E+04  4.547841E+03 -2.778444E+04
  0.000000E+00  0.000000E+00  0.000000E+00  0.000000E+00 -2.149862E+04
  1.753467E+02  3.410277E+03 -1.034898E+05  2.778692E+04  0.000000E+00
  0.000000E+00  0.000000E+00  0.000000E+00  1.492281E+04  0.000000E+00
  0.000000E+00  0.000000E+00  9.000000E+01  9.000000E+01  9.000000E+01
  0.000000E+00 -4.774939E-01  0.000000E+00  0.000000E+00  0.000000E+00
 -2.243495E-01  3.513048E-01 -2.678782E-01  3.513048E-01 -7.155493E-01
  5.690034E-01 -2.678782E-01  5.690034E-01 -4.783123E-01  2.461974E+01
  0.000000E+00  0.000000E+00  0.000000E+00  2.461974E+01  0.000000E+00
  0.000000E+00  0.000000E+00  2.461974E+01
       200  2.000000E-01        46
 -1.891815E+04  1.421984E+02 -2.424678E+04  5.199451E+03 -2.944623E+04
  0.000000E+00  0.000000E+00  0.000000E+00  0.000000E+00 -2.174561E+04
  1.274613E+02 -6.004790E+01 -1.139308E+05  2.944807E+04  0.000000E+00
  0.000000E+00  0.000000E+00  0.000000E+00  1.445855E+04  0.000000E+00
  0.000000E+00  0.000000E+00  9.000000E+01  9.000000E+01  9.000000E+01
  0.000000E+00  7.785923E-01  0.000000E+00  0.000000E+00  0.000000E+00
  8.123304E-01  3.023486E-01 -5.891595E-01  3.023486E-01 -8.560144E-02
 -3.830618E-01 -5.891595E-01 -3.830618E-01  1.608437E+00  2.436174E+01
  0.000000E+00  0.000000E+00  0.000000E+00  2.436174E+01  0.000000E+00
  0.000000E+00  0.000000E+00  2.436174E+01
hatmatrix
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1 Answers1

5

To convert a plain numpy array to a structured array, use view:

import numpy as np

filename = 'unstructured-file.txt'
nfields = 3
names = ('r','g','b')
with open(filename,'r') as f:
    arr = np.array(f.read().split(),dtype=np.float64)
    arr = arr.reshape(-1,nfields)
    out = arr.view(dtype=zip(names,['float64']*len(names))).copy()
unutbu
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  • Thanks, but I meant for the 'unstructured-file.txt' designation to indicate that it's not in a table and so does not work for this. – hatmatrix Oct 11 '11 at 11:03
  • Can you give an example of what `unstructured-file.txt` looks like? – unutbu Oct 11 '11 at 11:35
  • brilliant -- what happens if you don't use the .copy() method? Seems to still work? – hatmatrix Oct 11 '11 at 11:54
  • 2
    `out=arr.view(...)` makes `out` a view of `arr`. So modifying `out` would also modify `arr`. They share the same underlying data. I added `copy()` so that `out` would be an independent array. Both are useful; it just depends on what you want to do. – unutbu Oct 11 '11 at 11:58