# Implementing Custom Weight Functions¶

`Corrfunc`

supports custom weight functions. On this page we describe
the recommended procedure for writing your own. When in doubt, follow
the example of `pair_product`

.

First, see Computing Weighted Correlation Functions for basic usage of `Corrfunc`

’s weight features.

The steps are:

- Add a type to the
`weight_method_t`

enum in`utils/defs.h`

(something like`MY_WEIGHT_SCHEME=1`

). - Determine how many weights per particle your scheme needs, and add a case to the switch-case block in
`get_num_weights_by_method()`

in`utils/defs.h`

.`Corrfunc`

supports up to`MAX_NUM_WEIGHTS=10`

weights per particle; most schemes will simply need 1. To provide multiple weights per particle via the Python interface, simply pass a`weights`

array of shape`(N_WEIGHTS_PER_PARTICLE, N_PARTICLES)`

. - Add an
`if`

statement that maps a string name (like “my_weight_scheme”) to the`weight_method_t`

(which you created above) in`get_weight_method_by_name()`

in`utils/defs.h`

. - Write a function in
`utils/weight_functions.h.src`

that returns the weight for a particle pair, given the weights for the two particles. The weights for each particle are packed in a`const pair_struct_DOUBLE`

struct, which also contains the pair separation. You must write one function for every instruction set you wish to support. This can be quite easy for simple weight schemes; the three functions for`pair_product`

are:

```
/*
* The pair weight is the product of the particle weights
*/
static inline DOUBLE pair_product_DOUBLE(const pair_struct_DOUBLE *pair){
return pair->weights0[0].d*pair->weights1[0].d;
}
#ifdef __AVX__
static inline AVX_FLOATS avx_pair_product_DOUBLE(const pair_struct_DOUBLE *pair){
return AVX_MULTIPLY_FLOATS(pair->weights0[0].a, pair->weights1[0].a);
}
#endif
#ifdef __SSE4_2__
static inline SSE_FLOATS sse_pair_product_DOUBLE(const pair_struct_DOUBLE *pair){
return SSE_MULTIPLY_FLOATS(pair->weights0[0].s, pair->weights1[0].s);
}
#endif
```

See `utils/avx_calls.h`

and `utils/sse_calls.h`

for the lists of available vector instructions.

- For each function you wrote in the last step, add a case to the switch-case block in the appropriate dispatch function in
`utils/weight_functions.h.src`

. If you wrote a weighting function for all three instruction sets, then you’ll need to add the corresponding function to`get_weight_func_by_method_DOUBLE()`

,`get_avx_weight_func_by_method_DOUBLE()`

, and`get_sse_weight_func_by_method_DOUBLE()`

. - Done! Your weight scheme should now be accessible through the Python and C interfaces via the name (“my_weight_scheme”) that you specified above. The output will be accessible in the
`weightavg`

field of the`results`

array.

Pair counts (i.e. the `npairs`

field in the `results`

array)
are never affected by weights. For theory functions like `Corrfunc.theory.xi`

and `Corrfunc.theory.wp`

that actually return a clustering statistic, the statistic is weighted.
For `pair_product`

, the random distribution used to compute the
expected bin weight from an unclustered particle set (the `RR`

term)
is taken to be a spatially uniform particle set where every particle
has the mean weight. See RR in Weighted Clustering Statistics for more discussion.
This behavior (automatically returning weighted clustering statistics)
is only implemented for `pair_product`

, since that is the only weighting
method for which we know the desired equivalent random distribution.
Custom weighting methods can implement similar behavior by modifying
`countpairs_xi_DOUBLE()`

in `theory/xi/countpairs_xi_impl.c.src`

and
`countpairs_wp_DOUBLE()`

in `theory/wp/countpairs_wp_impl.c.src`

.