[Numpy-discussion] Example code for Numpy C preprocessor 'repeat' directive?
Stephen Simmons
mail@stevesimmons....
Wed Mar 4 18:54:23 CST 2009
Hi,
Please can someone suggest resources for learning how to use the
'repeat' macros in numpy C code to avoid repeating sections of
type-specific code for each data type? Ideally there would be two types
of resources: (i) a description of how the repeat macros are meant to be
used/compiled; and (ii) suggestion for a numpy source file that best
illustrates this.
Thanks in advance!
Stephen
P.S. Motivation is this is I'm trying to write an optimised numpy
implementation of SQL-style aggregation operators for an OLAP data
analysis project (using PyTables to store large numpy data sets).
bincount() is being used to implement "SELECT SUM(x) FROM TBL WHERE y
GROUP BY fn(z)". My modified bincount code can handle a wider variety of
index, weight and output array data types. It also supports passing in
the output array as a parameter, allowing multipass aggregation routines.
I got the code working for a small number of data type combinations, but
now I'm drowning in an exponential explosion of manually maintained data
type combinations
---snip----
} else if ((weight_type==NPY_FLOAT)&&(out_type==PyArray_DOUBLE)) {
...
} else if (bin_type==PyArray_INTP) {
for (i=0; i<bin_len; i++) {
bin = ((npy_intp *) bin_data)[i];
if (bin>=0 && bin<=max_bin)
((double*)out_data)[bin] += *((float *)(weights_data +
i*wt_stride));
}
} else if (bin_type==PyArray_UINT8) {
for (i=0; i<bin_len; i++) {
bin = ((npy_uint8 *) bin_data)[i];
if (bin>=0 && bin<=max_bin)
((double*)out_data)[bin] += *((float *)(weights_data +
i*wt_stride));
}
---snip----
'repeat' directives in C comments are obviously the proper way to avoid
manual generating all this boilerplate code. Unfortunately I haven't yet
understood how to make the autogenerated type-specific code link back
into a comment function entry point. Either there is some
compiler/distutils magic going on, or it's explained in a different
numpy source file from where I'm looking right now...
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