numpy_dtypes.lyx

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Extract from:
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The NumPy Array---A Structure for
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Efficient Numerical Computation
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\begin_layout Author
Stéfan van der Walt
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UC Berkeley
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October 2012
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http://www.ctan.org/tex-archive/macros/latex/contrib/powerdot/doc/powerdot.pdf
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.
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IMPORTANT NOTE TO SELF: This file must be compiled using dvipdfm or ps2pdf,
 
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Structured arrays
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method=direct,toc=Reading/writing data
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Reading data from file
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Reading this kind of data can be somewhat troublesome:
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begin{lstlisting}[language=matlab]
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while ((count > 0) && (n <= NumPoints))
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  % get time - I8 [ms]
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  [lw, count] = fread(fid, 1, 'uint32');
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  if (count > 0) % then carry on
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    uw = fread(fid, 1, 'int32');
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    t(1,n) = (lw+uw*2^32)/1000;
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    % get number of bytes of data
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    numbytes = fread(fid, 1, 'uint32');
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    % read sMEASUREMENTPOSITIONINFO (11 bytes)
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    m(1,n) = fread(fid, 1, 'float32'); % az [rad]
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    m(2,n) = fread(fid, 1, 'float32'); % el [rad]
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    m(3,n) = fread(fid, 1, 'uint8');   % region type
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    m(4,n) = fread(fid, 1, 'uint16');  % region ID
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    g(1,n) = fread(fid, 1, 'uint8');
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    numsamples = (numbytes-12)/2; % 2 byte integers
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    a(:,n) = fread(fid, numsamples, 'int16');
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Reading data from file
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The NumPy solution:
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dt = np.dtype([('time', np.uint64),
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               ('size', np.uint32),
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               ('position', [('az', np.float32),
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                             ('el', np.float32),
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                             ('region_type', np.uint8),
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                             ('region_ID', np.uint16)]),
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               ('gain', np.uint8),
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               ('samples', (np.int16, 2048))])
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data = np.fromfile(f, dtype=dt)
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We can then access this structured array as before:
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data['position']['az']
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Example
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We want to describe the pressure, temperature and velocity of a fluid in
 a 2-dimensional simulation or sampling of experimental data.
 You could create separate arrays for each variable, but one alternative
 is to store all the data into a single structured array:
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dt = np.dtype([
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                ('p', float),
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                ('T', float),
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                ('v', (float, 2))
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              ])
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x = np.array([
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               (100, 20, (0, 0.5)),
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               (200, 10, (5, 5.4))
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             ], dtype=dt)
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x['p']
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x.shape
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x[x['p'] < 150]
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