Numpy record array - field names for all dimensions

ShanMayne shangach at
Wed Dec 3 21:15:11 CET 2008

Greetings All

I am seeking to represent datasets where each data element is the
calculated result from several (4 for now) other data types. A matrix-
like (in the general mathematical sense) seems logical, where the
intersection of each of the 4 values (from different data sets) holds
the value derived from those 4 values here serving as indexes.

So, each matrix/array element is associated with 4 fields.
matrix element/output value =  24.235  -->
     'Formula' = 'C12H24O2N2'
     'Solvent' =  'Acetonitrile'
     'fragmentation_method' = 'CID'
     'resolution' = 'unit'

ideally I would like to call the output value by indexing the matrix
with the input information. eg:

matrix['C12H24O2N2']['Acetonitrile']['CID']['unit'] = 24.235

Numpy's record arrays seemingly don't allow all dimensions to carry
field names. ie. each column/row carrying a label. Instead fieldname
usage appears to create a "new dimension" as denoted by square

pixel_matrix = array([[(1,2,3), (4,5,6)], [(7,8,9), (10,11,12)]],

Can anyone tell me if the sort of data structuring I seek can be done
with Numpy record arrays or, if not, can you recommend a more suitable

Great & Glowing Thanks!

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