# PDL::Ufunc

📦 stdpdl

Unary reductions and aggregations over PDL arrays. This module holds every operation that collapses one or more dimensions of a piddle down to a summary — sums, products, means, extrema, percentiles, sort-based reductions, and cumulative variants. These are the first functions most users reach for. They behave the same on 1-D arrays as on higher-rank arrays, and they all share a consistent story about bad values, type promotion, and when they reduce everything vs. just one axis. ## Synopsis ```none use PDL; my $a = pdl [1, 2, 3, 4, 5]; print $a->sum, "\n"; # 15 print $a->avg, "\n"; # 3 print $a->min, "\n"; # 1 print $a->max, "\n"; # 5 ``` ## Functions ### Full-array reductions #### [`sum`](Ufunc/sum.md) Add up every element of a PDL and return the total as a 0-dimensional PDL. ### Other Functions #### `qsortvec` `qsortvec($pdl)` — sort rows of a 2D pdl lexicographically, return sorted data. #### `qsortveci` `qsortveci($pdl)` — sort rows of a 2D pdl lexicographically, return index piddle. #### `minimum_n_ind` `minimum_n_ind($pdl, $n)` — 3-arg c2rust: (a, c, m_size) #### `maximum_n_ind` `maximum_n_ind($pdl, $n)` — 3-arg c2rust: (a, c, m_size) #### `pctover` `pctover($pdl, $pct)` — 3-arg c2rust: (a, p, b) where p is pct as a scalar pdl `pctover($pdl, $percentile)` — Excel/NIST linear-interpolation percentile. Mirrors `Ufunc.pd` pp_def(“pctover”) algorithm: rank = 1 + p\*(n-1); interp between sorted[floor(rank)-1] and sorted[ceil(rank)-1]. The c2rust version produces rounding errors on the boundary (17.0000000007 != 17); this pure-Rust path avoids the double→double→double chain and is bit-exact when the interpolation happens on aligned indices. #### `all` `all($pdl)` — 1 if all elements nonzero, via full andover reduction #### `any` `any($pdl)` — 1 if any element nonzero, via full orover reduction #### `minmax` `minmax($pdl)` — return a 2-element list: (min_value, max_value). Iterates all elements via pdl_at_flat, tracking min and max. #### `pct` `pct($pdl, $p)` — percentile, delegates to pctover then extracts scalar. Returns a 0-d PDL scalar. #### `oddpct` `oddpct($pdl, $p)` — odd percentile (nearest-rank method). Sort the data, return element at index floor((n-1) \* p). Returns a 0-d PDL scalar. #### `stats` `stats($pdl)` — compute 7 summary statistics. Returns (`$mean`, `$prms`, `$median`, `$min`, `$max`, `$adev`, `$rms`).