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<h1 id="obicompactvec-complete-reference">obicompactvec — Complete Reference</h1>
<h2 id="module-structure">Module structure</h2>
<div class="highlight"><pre><span></span><code>src/obicompactvec/src/
lib.rs public re-exports
views.rs BitSliceView&lt;'a&gt;, IntSliceView&lt;'a&gt; — zero-copy read views
traits.rs ColumnWeights, CountPartials, BitPartials (matrix aggregation)
bitvec.rs PersistentBitVec, PersistentBitVecBuilder, BitIter
reader.rs PersistentCompactIntVec (read-only)
builder.rs PersistentCompactIntVecBuilder (read-write)
tempintvec.rs TempCompactIntVec, TempCompactIntVecBuilder (temp-file-backed)
tempbitvec.rs TempBitVec, TempBitVecBuilder (temp-file-backed)
bitmatrix.rs PersistentBitMatrix, PersistentBitMatrixBuilder
intmatrix.rs PersistentCompactIntMatrix, PersistentCompactIntMatrixBuilder
colgroup.rs ColGroup, MatrixGroupOps trait
format.rs file format constants, encode/decode helpers
layer_meta.rs LayerMeta (column metadata)
meta.rs matrix metadata
</code></pre></div>
<pre class="mermaid"><code>graph TD
views --&gt; bitvec
views --&gt; builder
views --&gt; tempbitvec
views --&gt; tempintvec
views --&gt; bitmatrix
views --&gt; intmatrix
format --&gt; reader
format --&gt; builder
reader --&gt; intmatrix
reader --&gt; tempintvec
builder --&gt; intmatrix
builder --&gt; tempintvec
bitvec --&gt; tempbitvec
bitvec --&gt; bitmatrix
tempintvec --&gt; intmatrix
tempintvec --&gt; bitmatrix
tempbitvec --&gt; intmatrix
tempbitvec --&gt; bitmatrix
colgroup --&gt; intmatrix
colgroup --&gt; bitmatrix
layer_meta --&gt; bitmatrix
layer_meta --&gt; intmatrix
meta --&gt; bitmatrix
meta --&gt; intmatrix</code></pre>
<hr/>
<h2 id="compact-int-encoding">Compact int encoding</h2>
<p>All integer vectors use the same two-tier encoding regardless of storage backend.</p>
<p><strong>Primary array</strong> — one <code>u8</code> per slot:</p>
<ul>
<li>Values <strong>0–254</strong> are stored directly. No overhead.</li>
<li>Value <strong>255 is a sentinel</strong>: the slot's actual value is ≥ 255 and lives in the overflow store.</li>
</ul>
<p><strong>Overflow store</strong> — maps slot index to a <code>u32</code> value ≥ 255:</p>
<ul>
<li>In <code>PersistentCompactIntVecBuilder</code>: a <code>HashMap&lt;usize, u32&gt;</code> in RAM.</li>
<li>In <code>PersistentCompactIntVec</code> (reader): a sorted <code>[(slot: u64, value: u32)]</code> array in the mmap, with a sparse L1-resident index for binary search.</li>
</ul>
<pre class="mermaid"><code>flowchart LR
slot --&gt; P["primary[slot]: u8"]
P --&gt;|"&lt; 255"| V["value = byte (0–254)"]
P --&gt;|"= 255 sentinel"| OV["overflow store"]
OV --&gt;|"Builder"| HM["HashMap&amp;lt;usize, u32&amp;gt;\nin RAM"]
OV --&gt;|"PersistentCompactIntVec"| SA["sorted [(slot,value)] in mmap\n+ sparse L1 index"]</code></pre>
<p><strong>Key property — sentinel 255 = +∞ on <code>u8</code>:</strong></p>
<ul>
<li><code>min(a, 255) = a</code> for all <code>a ≤ 254</code> → correct when only one side is overflow</li>
<li><code>max(a, 255) = 255</code> → correct sentinel when either side is overflow</li>
<li>Only the <strong>both-overflow</strong> case requires reading actual values from the overflow store.</li>
</ul>
<p>In practice, k (overflow count) ≪ n (total slots). Observed genomic data: ~0.07% of kmer slots are in overflow.</p>
<hr/>
<h2 id="view-types">View types</h2>
<p>The previous trait hierarchy (<code>BitSlice</code>, <code>BitSliceMut</code>, <code>IntSlice</code>, <code>IntSliceMut</code>) has been replaced by two concrete zero-copy view structs with inherent methods. Views are <strong><code>Copy</code></strong> — passing them is free. All read operations live on these two types.</p>
<h3 id="bitsliceviewa"><code>BitSliceView&lt;'a&gt;</code></h3>
<div class="highlight"><pre><span></span><code><span class="cp">#[derive(Clone, Copy)]</span>
<span class="k">pub</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">BitSliceView</span><span class="o">&lt;'</span><span class="na">a</span><span class="o">&gt;</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">words</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="o">'</span><span class="na">a</span><span class="w"> </span><span class="p">[</span><span class="kt">u64</span><span class="p">],</span><span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">n</span><span class="p">:</span><span class="w"> </span><span class="kt">usize</span><span class="w"> </span><span class="p">}</span>
</code></pre></div>
<p>Bit <code>i</code> is at <code>words[i &gt;&gt; 6]</code> bit <code>i &amp; 63</code> (LSB-first). Padding bits in the last word are zero.</p>
<table>
<thead>
<tr>
<th>Method</th>
<th>Cost</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>len()</code>, <code>is_empty()</code></td>
<td>O(1)</td>
</tr>
<tr>
<td><code>get(slot)</code></td>
<td>O(1)</td>
</tr>
<tr>
<td><code>count_ones()</code></td>
<td>POPCNT per word, O(n/64)</td>
</tr>
<tr>
<td><code>count_zeros()</code></td>
<td><code>n − count_ones()</code>, O(n/64)</td>
</tr>
<tr>
<td><code>iter() -&gt; BitSliceIter&lt;'a&gt;</code></td>
<td>O(1) setup, O(n) iteration</td>
</tr>
<tr>
<td><code>partial_jaccard_dist(other: BitSliceView)</code></td>
<td><code>(a&amp;b).popcount</code>, <code>(a\|b).popcount</code> per word, O(n/64)</td>
</tr>
<tr>
<td><code>jaccard_dist(other: BitSliceView)</code></td>
<td>from partial, O(n/64)</td>
</tr>
<tr>
<td><code>hamming_dist(other: BitSliceView)</code></td>
<td><code>(a^b).popcount</code> per word, O(n/64)</td>
</tr>
</tbody>
</table>
<p><code>BitSliceIter&lt;'a&gt;</code>: word-level scan; one word per 64 iterations.</p>
<h3 id="intsliceviewa"><code>IntSliceView&lt;'a&gt;</code></h3>
<div class="highlight"><pre><span></span><code><span class="cp">#[derive(Clone, Copy)]</span>
<span class="k">pub</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">IntSliceView</span><span class="o">&lt;'</span><span class="na">a</span><span class="o">&gt;</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">primary</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="o">'</span><span class="na">a</span><span class="w"> </span><span class="p">[</span><span class="kt">u8</span><span class="p">],</span>
<span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">overflow_raw</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="o">'</span><span class="na">a</span><span class="w"> </span><span class="p">[</span><span class="kt">u8</span><span class="p">],</span><span class="w"> </span><span class="c1">// sorted [(slot:u64, value:u32)] entries</span>
<span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">n_overflow</span><span class="p">:</span><span class="w"> </span><span class="kt">usize</span><span class="p">,</span>
<span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">n</span><span class="p">:</span><span class="w"> </span><span class="kt">usize</span><span class="p">,</span>
<span class="p">}</span>
</code></pre></div>
<p><code>overflow_raw</code> contains <code>n_overflow</code> entries of <code>OVERFLOW_ENTRY_SIZE</code> bytes each, sorted by slot. The sort invariant is established at <code>close()</code>/<code>freeze()</code> time.</p>
<table>
<thead>
<tr>
<th>Method</th>
<th>Cost</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>len()</code>, <code>is_empty()</code></td>
<td>O(1)</td>
</tr>
<tr>
<td><code>primary_bytes()</code></td>
<td>O(1)</td>
</tr>
<tr>
<td><code>overflow_entries() -&gt; impl Iterator&lt;(usize,u32)&gt;</code></td>
<td>O(n_overflow) iteration</td>
</tr>
<tr>
<td><code>get(slot)</code></td>
<td>O(1) primary; binary search O(log k) for overflow slots</td>
</tr>
<tr>
<td><code>iter() -&gt; IntSliceViewIter&lt;'a&gt;</code></td>
<td>merge scan, O(n + k)</td>
</tr>
<tr>
<td><code>sum()</code></td>
<td>byte scan + overflow, O(n + k)</td>
</tr>
<tr>
<td><code>count_nonzero()</code></td>
<td>byte scan, O(n)</td>
</tr>
<tr>
<td>Distance methods (<code>bray_dist</code>, <code>euclidean_dist</code>, <code>jaccard_dist</code>, …)</td>
<td>O(n + k)</td>
</tr>
</tbody>
</table>
<p><code>IntSliceViewIter&lt;'a&gt;</code>: merge scan using <code>overflow_pos</code> index. Requires sorted overflow — guaranteed by the construction lifecycle.</p>
<p><strong>Builder <code>view()</code> vs reader <code>view()</code>:</strong> <code>PersistentCompactIntVecBuilder</code> stores overflow as an unsorted <code>HashMap</code>, not raw bytes. Its <code>view()</code> returns an <code>IntSliceView</code> with <code>overflow_raw = &amp;[]</code> and <code>n_overflow = 0</code>. This is intentional — the view is primarily useful after <code>freeze()</code>. During building, callers that need overflow use <code>overflow_entries()</code> directly.</p>
<hr/>
<h2 id="concrete-types">Concrete types</h2>
<pre class="mermaid"><code>classDiagram
class BitSliceView {
+words: &amp;[u64]
+n: usize
+get(slot) bool
+count_ones() u64
+iter() BitSliceIter
+jaccard_dist/hamming_dist(other: BitSliceView)
}
class IntSliceView {
+primary: &amp;[u8]
+overflow_raw: &amp;[u8]
+n_overflow: usize
+n: usize
+get(slot) u32
+iter() IntSliceViewIter
+overflow_entries() Iterator
+bray_dist/euclidean_dist/…(other: IntSliceView)
}
class PersistentBitVec {
-mmap: Mmap
-n: usize
+view() BitSliceView
+get(slot) bool
+count_ones/zeros() u64
+iter() BitIter
+partial_jaccard_dist(&amp;Self) (u64,u64)
+jaccard_dist/hamming_dist(&amp;Self) …
}
class PersistentBitVecBuilder {
-mmap: MmapMut
-n: usize
+view() BitSliceView
+set(slot, bool)
+or/and/xor/not(BitSliceView)
+copy_from(BitSliceView)
+close() / finish() → PersistentBitVec
}
class PersistentCompactIntVec {
-mmap: Mmap
-n: usize
-n_overflow: usize
-step: usize
-index: Vec~(usize,usize)~
+view() IntSliceView
+get(slot) u32
+iter() Iter
+sum/count_nonzero() u64
+bray_dist/euclidean_dist/… (&amp;Self)
}
class PersistentCompactIntVecBuilder {
-mmap: MmapMut
-n: usize
-overflow: HashMap~usize,u32~
+view() IntSliceView
+set(slot, u32) / get(slot) u32
+inc / inc_present / inc_present_fast
+inc_predicate / inc_predicate_fast
+add/min/max/diff/mask_with(…View)
+primary_bytes/primary_bytes_mut()
+close() / finish() → PersistentCompactIntVec
}
PersistentBitVec --&gt; BitSliceView : view()
PersistentBitVecBuilder --&gt; BitSliceView : view()
PersistentCompactIntVec --&gt; IntSliceView : view()
PersistentCompactIntVecBuilder --&gt; IntSliceView : view() (primary only)
PersistentBitVecBuilder --&gt; PersistentBitVec : close() then open()
PersistentCompactIntVecBuilder --&gt; PersistentCompactIntVec : close() then open()</code></pre>
<h3 id="persistentbitvec-persistentbitvecbuilder"><code>PersistentBitVec</code> / <code>PersistentBitVecBuilder</code></h3>
<p><code>PersistentBitVec</code> is the read-only type. <code>view()</code> returns a <code>BitSliceView&lt;'_&gt;</code> over the mmap word array. Direct inherent methods delegate to the view: <code>count_ones()</code>, <code>count_zeros()</code>, <code>partial_jaccard_dist(&amp;Self)</code>, <code>jaccard_dist(&amp;Self)</code>, <code>hamming_dist(&amp;Self)</code>.</p>
<p><code>BitIter&lt;'a&gt;</code> — exported iterator for <code>PersistentBitVec::iter()</code>:</p>
<div class="highlight"><pre><span></span><code><span class="k">pub</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">BitIter</span><span class="o">&lt;'</span><span class="na">a</span><span class="o">&gt;</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">words</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="o">'</span><span class="na">a</span><span class="w"> </span><span class="p">[</span><span class="kt">u64</span><span class="p">],</span><span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">slot</span><span class="p">:</span><span class="w"> </span><span class="kt">usize</span><span class="p">,</span><span class="w"> </span><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="n">n</span><span class="p">:</span><span class="w"> </span><span class="kt">usize</span><span class="w"> </span><span class="p">}</span>
</code></pre></div>
<p><code>PersistentBitVecBuilder</code> is the read-write type. Mutation operations accept <code>BitSliceView&lt;'_&gt;</code>:</p>
<table>
<thead>
<tr>
<th>Method</th>
<th>Cost</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>set(slot, bool)</code></td>
<td>O(1)</td>
</tr>
<tr>
<td><code>view() -&gt; BitSliceView&lt;'_&gt;</code></td>
<td>O(1)</td>
</tr>
<tr>
<td><code>or/and/xor(BitSliceView)</code></td>
<td>word-level, O(n/64), SIMD-friendly</td>
</tr>
<tr>
<td><code>not()</code></td>
<td><code>w ^= u64::MAX</code> per word, re-masks last word</td>
</tr>
<tr>
<td><code>copy_from(BitSliceView)</code></td>
<td><code>copy_from_slice</code></td>
</tr>
</tbody>
</table>
<h3 id="persistentcompactintvec-persistentcompactintvecbuilder"><code>PersistentCompactIntVec</code> / <code>PersistentCompactIntVecBuilder</code></h3>
<p><code>PersistentCompactIntVec</code> is the read-only type. <code>view()</code> returns an <code>IntSliceView&lt;'_&gt;</code> over the mmap primary and overflow arrays. Inherent <code>iter()</code> is a merge scan (<code>Iter</code> struct). Inherent <code>sum()</code> and <code>count_nonzero()</code> use fast byte-scan helpers.</p>
<p><code>PersistentCompactIntVecBuilder</code> is the read-write type. Mutation methods on the builder fall into two categories:</p>
<p><strong>Point mutations:</strong></p>
<table>
<thead>
<tr>
<th>Method</th>
<th>Note</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>set(slot, u32)</code></td>
<td>writes primary[slot] or 255+overflow</td>
</tr>
<tr>
<td><code>get(slot) -&gt; u32</code></td>
<td>reads primary byte or HashMap</td>
</tr>
<tr>
<td><code>inc(slot)</code></td>
<td><code>get</code> + <code>set</code>, O(1)</td>
</tr>
</tbody>
</table>
<p><strong>Bulk computation methods</strong> — accept view arguments:</p>
<table>
<thead>
<tr>
<th>Method</th>
<th>Semantics</th>
<th>Overflow</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>inc_present(BitSliceView)</code></td>
<td><code>+= 1</code> at each 1-bit</td>
<td>via <code>inc</code>, safe for any group size</td>
</tr>
<tr>
<td><code>inc_present_fast(BitSliceView)</code></td>
<td>same, raw u8 <code>+= 1</code></td>
<td><code>debug_assert</code> no 255 reached</td>
</tr>
<tr>
<td><code>inc_predicate(IntSliceView, pred)</code></td>
<td><code>+= 1</code> where <code>pred(col[s])</code></td>
<td>two-pass, safe</td>
</tr>
<tr>
<td><code>inc_predicate_fast(IntSliceView, pred)</code></td>
<td>same, raw u8</td>
<td><code>debug_assert</code> no 255 reached</td>
</tr>
<tr>
<td><code>add(IntSliceView)</code></td>
<td><code>self[s] += other[s]</code></td>
<td>primary fast path + overflow fallback</td>
</tr>
<tr>
<td><code>min(IntSliceView)</code></td>
<td>byte min + both-overflow fixup</td>
<td>see algorithm below</td>
</tr>
<tr>
<td><code>max(IntSliceView)</code></td>
<td>pre-pass + byte max</td>
<td>see algorithm below</td>
</tr>
<tr>
<td><code>diff(IntSliceView)</code></td>
<td>saturating sub</td>
<td>self&lt;255 hot path</td>
</tr>
<tr>
<td><code>mask_with(BitSliceView)</code></td>
<td>zeros slots where mask bit = 0</td>
<td>O(n_zeros)</td>
</tr>
</tbody>
</table>
<p><strong><code>inc_present_fast</code> / <code>inc_predicate_fast</code> invariant:</strong> caller guarantees no counter reaches 255 during the operation (group size &lt; 255 for <code>inc_present_fast</code>, or chunk size &lt; 255 for <code>inc_predicate_fast</code>). Violation is caught by <code>debug_assert</code> in dev builds.</p>
<p><strong><code>min</code> algorithm:</strong></p>
<p>Exploits 255 = +∞: byte-level min is correct unless both sides are overflow.</p>
<div class="highlight"><pre><span></span><code>snapshot self_ov: Vec&lt;(slot,val)&gt;
snapshot other_ov: HashMap&lt;slot,val&gt;
clear_overflow()
Pass 1 — byte min, SIMD-vectorizable, O(n)
Pass 2 — both-overflow fixup, O(k_self):
for (slot, self_val) in self_ov:
if slot ∈ other_ov: set(slot, min(self_val, other_ov[slot]))
</code></pre></div>
<p><strong><code>max</code> algorithm:</strong></p>
<p>Cannot do byte max first — <code>max(255, b&lt;255)=255</code> overwrites self's original overflow value. Pre-pass reads self's value at other's overflow slots before the byte pass.</p>
<div class="highlight"><pre><span></span><code>Pre-pass O(k_other): for (slot, other_val) in other.overflow_entries():
set(slot, max(self.get(slot), other_val))
Pass 1 — byte max, SIMD-vectorizable, O(n)
</code></pre></div>
<hr/>
<h2 id="matrix-types">Matrix types</h2>
<p>Four matrix types, two encodings × two formats:</p>
<table>
<thead>
<tr>
<th></th>
<th>Columnar format</th>
<th>Packed format</th>
</tr>
</thead>
<tbody>
<tr>
<td><strong>Bit</strong></td>
<td><code>PersistentBitMatrix</code> (Columnar variant)</td>
<td><code>PersistentBitMatrix</code> (Packed variant)</td>
</tr>
<tr>
<td><strong>Int</strong></td>
<td><code>PersistentCompactIntMatrix</code> (Columnar variant)</td>
<td><code>PersistentCompactIntMatrix</code> (Packed variant)</td>
</tr>
</tbody>
</table>
<p>Both matrix types are enums (<code>Columnar</code> / <code>Packed</code> / <code>Implicit</code> for bit) behind a transparent API. <code>col_view(c)</code> returns the appropriate view directly:</p>
<div class="highlight"><pre><span></span><code><span class="c1">// PersistentBitMatrix</span>
<span class="k">pub</span><span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">col_view</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">c</span><span class="p">:</span><span class="w"> </span><span class="kt">usize</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">BitSliceView</span><span class="o">&lt;'</span><span class="nb">_</span><span class="o">&gt;</span>
<span class="c1">// PersistentCompactIntMatrix</span>
<span class="k">pub</span><span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">col_view</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">c</span><span class="p">:</span><span class="w"> </span><span class="kt">usize</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">IntSliceView</span><span class="o">&lt;'</span><span class="nb">_</span><span class="o">&gt;</span>
</code></pre></div>
<p>No wrapper enums (<code>BitColView</code>, <code>IntColView</code>): the caller receives a <code>Copy</code> view struct immediately usable with any view method or bulk builder method.</p>
<p><code>pack_compact_int_matrix</code> and <code>pack_bit_matrix</code> convert columnar → packed format.</p>
<hr/>
<h2 id="aggregation-traits-matrix-level">Aggregation traits (matrix level)</h2>
<h3 id="columnweights">ColumnWeights</h3>
<div class="highlight"><pre><span></span><code><span class="k">trait</span><span class="w"> </span><span class="n">ColumnWeights</span><span class="p">:</span><span class="w"> </span><span class="nb">Send</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="nb">Sync</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">col_weights</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">Array1</span><span class="o">&lt;</span><span class="kt">u64</span><span class="o">&gt;</span><span class="p">;</span><span class="w"> </span><span class="c1">// sum per column</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">partial_kmer_counts</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">Array1</span><span class="o">&lt;</span><span class="kt">u64</span><span class="o">&gt;</span><span class="p">;</span><span class="w"> </span><span class="c1">// default = col_weights()</span>
<span class="p">}</span>
</code></pre></div>
<p><code>partial_kmer_counts</code> is overridden for count matrices to return <code>count_nonzero</code> per column (distinct kmers) rather than total count.</p>
<h3 id="countpartials">CountPartials</h3>
<p>Abstract required methods: <code>partial_bray</code>, <code>partial_euclidean</code>, <code>partial_threshold_jaccard</code>, <code>partial_relfreq_bray</code>, <code>partial_relfreq_euclidean</code>, <code>partial_hellinger</code>.</p>
<p><strong>Additivity rule:</strong> self-contained partials (<code>partial_bray</code>, <code>partial_euclidean</code>, <code>partial_threshold_jaccard</code>) can be element-wise summed across all <code>(partition, layer)</code> pairs. Normalised partials (<code>partial_relfreq_*</code>, <code>partial_hellinger</code>) require the <strong>global</strong> <code>col_weights</code> (accumulated across all layers and all partitions) as parameter.</p>
<p><strong><code>partial_threshold_jaccard</code> returns <code>(inter, union)</code></strong> because <code>union[i,j]</code> depends on both columns simultaneously.</p>
<p>Provided finalisations:</p>
<table>
<thead>
<tr>
<th>Finalisation</th>
<th>Formula</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>bray_dist_matrix()</code></td>
<td><code>1 − 2·partial_bray[i,j] / (w[i] + w[j])</code></td>
</tr>
<tr>
<td><code>euclidean_dist_matrix()</code></td>
<td><code>√partial_euclidean[i,j]</code></td>
</tr>
<tr>
<td><code>threshold_jaccard_dist_matrix(t)</code></td>
<td><code>1 − inter[i,j] / union[i,j]</code></td>
</tr>
<tr>
<td><code>relfreq_bray_dist_matrix()</code></td>
<td><code>1 − partial_relfreq_bray[i,j]</code></td>
</tr>
<tr>
<td><code>relfreq_euclidean_dist_matrix()</code></td>
<td><code>√partial_relfreq_euclidean[i,j]</code></td>
</tr>
<tr>
<td><code>hellinger_dist_matrix()</code></td>
<td><code>√partial_hellinger[i,j] / √2</code></td>
</tr>
<tr>
<td><code>hellinger_euclidean_dist_matrix()</code></td>
<td><code>√partial_hellinger[i,j]</code></td>
</tr>
<tr>
<td><code>threshold_mash_dist_matrix(k, t)</code></td>
<td>Mash distance, derived from <code>threshold_jaccard_dist_matrix(t)</code> — no separate partial</td>
</tr>
</tbody>
</table>
<h3 id="bitpartials">BitPartials</h3>
<p>Required: <code>partial_jaccard() -&gt; (Array2&lt;u64&gt;, Array2&lt;u64&gt;)</code>, <code>partial_hamming() -&gt; Array2&lt;u64&gt;</code>. Both additive across layers and partitions.</p>
<p>Provided finalisations also include <code>jaccard_dist_matrix()</code>, <code>hamming_dist_matrix()</code>, and <code>mash_dist_matrix(k)</code>.</p>
<h3 id="mash-distance">Mash distance</h3>
<p><code>mash_dist_matrix</code>/<code>threshold_mash_dist_matrix</code> add no new additive primitive: both are a pointwise transform of the existing Jaccard distance matrix, per the Mash mutation-rate estimator (Fan <em>et al.</em> 2015; Marbl Lab 2026)<sup id="fnref:Mash-distances-doc"><a class="footnote-ref" href="#fn:Mash-distances-doc">1</a></sup> <sup id="fnref:Fan2015-mash-formula"><a class="footnote-ref" href="#fn:Fan2015-mash-formula">2</a></sup>:</p>
<div class="highlight"><pre><span></span><code>D = -1/k · ln(2J / (1+J)), J = 1 - d_jaccard
</code></pre></div>
<p><code>J ≤ 0</code> (i.e. <code>d_jaccard ≥ 1</code>, no shared k-mers) maps to <code>D = 1</code> (maximal distance) rather than the <code>ln</code> singularity at <code>J = 0</code>.</p>
<hr/>
<h2 id="temp-file-backed-types">Temp-file-backed types</h2>
<p><strong>All inter-function results use temp-file-backed types</strong> so the OS can page them out under memory pressure. This matters in practice: processing dozens of layers × hundreds of partitions in parallel would otherwise accumulate gigabytes of live anonymous memory.</p>
<h3 id="lifecycle">Lifecycle</h3>
<div class="highlight"><pre><span></span><code>TempCompactIntVecBuilder::new(n) → writable mmap in TempDir
↓ (inc_present_fast / inc_predicate_fast / add / mask_with / …)
.freeze() → TempCompactIntVec (read-only mmap + TempDir)
↓ (optional)
.make_persistent(path) → PersistentCompactIntVec (permanent file)
</code></pre></div>
<p>Same pattern for <code>TempBitVecBuilder</code> → <code>TempBitVec</code> → <code>PersistentBitVec</code>.</p>
<p><strong>Drop order</strong>: <code>TempCompactIntVec { vec: PersistentCompactIntVec, _temp: TempDir }</code> — Rust drops fields in declaration order. <code>vec</code> (mmap) released before <code>_temp</code> (directory deleted). No explicit <code>drop()</code> needed.</p>
<h3 id="tempcompactintvec-tempcompactintvecbuilder">TempCompactIntVec / TempCompactIntVecBuilder</h3>
<div class="highlight"><pre><span></span><code><span class="k">pub</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">TempCompactIntVec</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">vec</span><span class="p">:</span><span class="w"> </span><span class="nc">PersistentCompactIntVec</span><span class="p">,</span>
<span class="w"> </span><span class="n">_temp</span><span class="p">:</span><span class="w"> </span><span class="nc">TempDir</span><span class="p">,</span><span class="w"> </span><span class="c1">// dropped after vec</span>
<span class="p">}</span>
<span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">TempCompactIntVecBuilder</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">builder</span><span class="p">:</span><span class="w"> </span><span class="nc">PersistentCompactIntVecBuilder</span><span class="p">,</span>
<span class="w"> </span><span class="n">temp</span><span class="p">:</span><span class="w"> </span><span class="nc">TempDir</span><span class="p">,</span>
<span class="p">}</span>
</code></pre></div>
<p><code>TempCompactIntVec</code>: read access via <code>get(slot)</code>, <code>sum()</code>, <code>iter()</code>, <code>view() -&gt; IntSliceView&lt;'_&gt;</code>.</p>
<p><code>TempCompactIntVecBuilder</code>: full delegation to inner <code>PersistentCompactIntVecBuilder</code> — all bulk computation methods (<code>inc_present_fast</code>, <code>inc_predicate_fast</code>, <code>add</code>, <code>min</code>, <code>max</code>, <code>diff</code>, <code>mask_with</code>) are exposed as <code>pub(crate)</code>.</p>
<h3 id="tempbitvec-tempbitvecbuilder">TempBitVec / TempBitVecBuilder</h3>
<div class="highlight"><pre><span></span><code><span class="k">pub</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">TempBitVec</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">vec</span><span class="p">:</span><span class="w"> </span><span class="nc">PersistentBitVec</span><span class="p">,</span>
<span class="w"> </span><span class="n">_temp</span><span class="p">:</span><span class="w"> </span><span class="nc">TempDir</span><span class="p">,</span>
<span class="p">}</span>
<span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">TempBitVecBuilder</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">builder</span><span class="p">:</span><span class="w"> </span><span class="nc">PersistentBitVecBuilder</span><span class="p">,</span>
<span class="w"> </span><span class="n">temp</span><span class="p">:</span><span class="w"> </span><span class="nc">TempDir</span><span class="p">,</span>
<span class="p">}</span>
</code></pre></div>
<p><code>TempBitVec</code>: read access via <code>get(slot)</code>, <code>count_ones()</code>, <code>view() -&gt; BitSliceView&lt;'_&gt;</code>, <code>iter()</code>.</p>
<p><code>TempBitVecBuilder</code>: exposes <code>set(slot, bool)</code>, <code>or(BitSliceView)</code>, and:</p>
<div class="highlight"><pre><span></span><code><span class="k">pub</span><span class="p">(</span><span class="k">crate</span><span class="p">)</span><span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">or_where</span><span class="p">(</span><span class="o">&amp;</span><span class="k">mut</span><span class="w"> </span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">col</span><span class="p">:</span><span class="w"> </span><span class="nc">IntSliceView</span><span class="o">&lt;'</span><span class="nb">_</span><span class="o">&gt;</span><span class="p">,</span><span class="w"> </span><span class="n">pred</span><span class="p">:</span><span class="w"> </span><span class="nc">impl</span><span class="w"> </span><span class="nb">Fn</span><span class="p">(</span><span class="kt">u32</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="kt">bool</span><span class="p">)</span>
</code></pre></div>
<p><code>or_where</code> — two passes, no intermediate allocation:</p>
<div class="highlight"><pre><span></span><code>Pass 1 — primary bytes, O(n):
for slot in 0..n:
b = col.primary_bytes()[slot]
if b &lt; 255 AND pred(b as u32): self.set(slot, true)
Pass 2 — overflow, O(k):
for (slot, val) in col.overflow_entries():
if pred(val): self.set(slot, true)
</code></pre></div>
<hr/>
<h2 id="filter-select-api">Filter / Select API</h2>
<h3 id="colgroup">ColGroup</h3>
<div class="highlight"><pre><span></span><code><span class="k">pub</span><span class="w"> </span><span class="k">struct</span><span class="w"> </span><span class="nc">ColGroup</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">pub</span><span class="w"> </span><span class="n">name</span><span class="p">:</span><span class="w"> </span><span class="nb">String</span><span class="p">,</span><span class="w"> </span><span class="k">pub</span><span class="w"> </span><span class="n">indices</span><span class="p">:</span><span class="w"> </span><span class="nb">Vec</span><span class="o">&lt;</span><span class="kt">usize</span><span class="o">&gt;</span><span class="w"> </span><span class="p">}</span>
</code></pre></div>
<p>Defined <strong>once at the index level</strong> from column metadata. Valid in all matrices of all layers and partitions — column structure is identical across the entire hierarchy; only rows (kmer slots) are partitioned.</p>
<h3 id="composition-axis">Composition axis</h3>
<ul>
<li><strong>Across partitions</strong>: kmer space is partitioned → partial results <strong>concatenated</strong> (disjoint kmer ranges).</li>
<li><strong>Across layers</strong>: same kmer space, different counts → partial results <strong>aggregated</strong> (add, OR, etc.).</li>
</ul>
<h3 id="matrixgroupops">MatrixGroupOps</h3>
<p>Five required primitives + two default methods derived from them. All return temp-file-backed types.</p>
<div class="highlight"><pre><span></span><code><span class="k">pub</span><span class="w"> </span><span class="k">trait</span><span class="w"> </span><span class="n">MatrixGroupOps</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="c1">// required</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">partial_group_presence_count</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">ColGroup</span><span class="p">,</span><span class="w"> </span><span class="n">threshold</span><span class="p">:</span><span class="w"> </span><span class="kt">u32</span><span class="p">)</span>
<span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="n">TempCompactIntVec</span><span class="o">&gt;</span><span class="p">;</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">partial_group_sum</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">ColGroup</span><span class="p">)</span>
<span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="n">TempCompactIntVec</span><span class="o">&gt;</span><span class="p">;</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">partial_group_any</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">ColGroup</span><span class="p">,</span><span class="w"> </span><span class="n">threshold</span><span class="p">:</span><span class="w"> </span><span class="kt">u32</span><span class="p">)</span>
<span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="n">TempBitVec</span><span class="o">&gt;</span><span class="p">;</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">partial_group_min</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">ColGroup</span><span class="p">)</span>
<span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="n">TempCompactIntVec</span><span class="o">&gt;</span><span class="p">;</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">partial_group_max</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">ColGroup</span><span class="p">)</span>
<span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="n">TempCompactIntVec</span><span class="o">&gt;</span><span class="p">;</span>
<span class="w"> </span><span class="c1">// defaults derived from partial_group_presence_count</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">partial_group_all</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">ColGroup</span><span class="p">,</span><span class="w"> </span><span class="n">threshold</span><span class="p">:</span><span class="w"> </span><span class="kt">u32</span><span class="p">)</span>
<span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="n">TempBitVec</span><span class="o">&gt;</span><span class="p">;</span><span class="w"> </span><span class="c1">// slot=1 iff count == g.indices.len()</span>
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">partial_group_none</span><span class="p">(</span><span class="o">&amp;</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">g</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">ColGroup</span><span class="p">,</span><span class="w"> </span><span class="n">threshold</span><span class="p">:</span><span class="w"> </span><span class="kt">u32</span><span class="p">)</span>
<span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="n">TempBitVec</span><span class="o">&gt;</span><span class="p">;</span><span class="w"> </span><span class="c1">// slot=1 iff count == 0</span>
<span class="p">}</span>
</code></pre></div>
<p>Implemented for both <code>PersistentCompactIntMatrix</code> and <code>PersistentBitMatrix</code>.</p>
<p>For <strong>bit matrices</strong>: values are 0/1, so <code>partial_group_sum</code> = <code>partial_group_presence_count(g, 1)</code>; <code>partial_group_min</code> is AND (set first column then mask-with remaining); <code>partial_group_max</code> is OR via <code>partial_group_any</code> + <code>inc_present</code>.</p>
<p><strong><code>partial_group_presence_count</code> — chunking for large groups:</strong></p>
<p>When <code>g.indices.len() &lt; 255</code>: per-slot counts stay within <code>u8</code> range. Use <code>inc_present_fast</code> (bit) or <code>inc_predicate_fast(col_view(c), |v| v &gt;= threshold)</code> (int) — raw u8 increment, no overflow entry written.</p>
<p>When <code>g.indices.len() ≥ 255</code>: process in chunks of 254 columns, accumulate via <code>.add(chunk_frozen.view())</code>.</p>
<p><strong><code>partial_group_min</code> (int matrix)</strong>: copy first column via <code>.add(col_view(first))</code> (start from 0 ⇒ copy), then <code>.min(col_view(c))</code> for remaining.</p>
<p><strong><code>partial_group_max</code> (int matrix)</strong>: <code>.max(col_view(c))</code> for all columns (start from 0 ⇒ first column acts as copy).</p>
<p><strong><code>partial_group_any</code></strong> uses <code>or_where</code> on <code>TempBitVecBuilder</code> (two-pass: primary bytes then overflow entries).</p>
<p><strong><code>partial_group_all</code> / <code>partial_group_none</code></strong> (default): call <code>partial_group_presence_count</code>, then iterate slots to produce the bit result. O(n) extra pass, not chunked.</p>
<h3 id="add_col_from-matrix-builder-integration">add_col_from — matrix builder integration</h3>
<p>Both matrix builders accept temp-file results directly:</p>
<div class="highlight"><pre><span></span><code><span class="c1">// PersistentBitMatrixBuilder</span>
<span class="k">fn</span><span class="w"> </span><span class="nf">add_col_from</span><span class="p">(</span><span class="o">&amp;</span><span class="k">mut</span><span class="w"> </span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">TempBitVec</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="p">()</span><span class="o">&gt;</span>
<span class="k">fn</span><span class="w"> </span><span class="nf">add_col_from_int</span><span class="p">(</span><span class="o">&amp;</span><span class="k">mut</span><span class="w"> </span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">TempCompactIntVec</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="p">()</span><span class="o">&gt;</span><span class="w"> </span><span class="c1">// nonzero → 1</span>
<span class="c1">// PersistentCompactIntMatrixBuilder</span>
<span class="k">fn</span><span class="w"> </span><span class="nf">add_col_from</span><span class="p">(</span><span class="o">&amp;</span><span class="k">mut</span><span class="w"> </span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">TempCompactIntVec</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="p">()</span><span class="o">&gt;</span>
<span class="k">fn</span><span class="w"> </span><span class="nf">add_col_from_bit</span><span class="p">(</span><span class="o">&amp;</span><span class="k">mut</span><span class="w"> </span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">src</span><span class="p">:</span><span class="w"> </span><span class="kp">&amp;</span><span class="nc">TempBitVec</span><span class="p">)</span><span class="w"> </span><span class="p">-&gt;</span><span class="w"> </span><span class="nc">io</span><span class="p">::</span><span class="nb">Result</span><span class="o">&lt;</span><span class="p">()</span><span class="o">&gt;</span><span class="w"> </span><span class="c1">// bit → 0/1 u32</span>
</code></pre></div>
<p><code>add_col_from</code> copies the temp file to the matrix directory and increments <code>n_cols</code>; <code>close()</code> writes <code>meta.json</code> with the final column count. No separate <code>write_meta</code> step needed.</p>
<h3 id="mask_with">mask_with</h3>
<p>Direct method on <code>PersistentCompactIntVecBuilder</code> (and delegation via <code>TempCompactIntVecBuilder</code>). Zeros every slot where the corresponding mask bit is 0. Iterates only zero bits — O(n_zeros), O(1) when mask is all-ones.</p>
<div class="highlight"><pre><span></span><code>for (w_idx, word) in mask.words():
if word == u64::MAX: continue // skip all-ones words
zeros = !word
while zeros != 0:
bit = trailing_zeros(zeros)
s = w_idx * 64 + bit
if primary[s] != 0: set(s, 0) // clears overflow entry too
zeros &amp;= zeros − 1
</code></pre></div>
<p>Terminal operation for Filter (retain only selected kmer slots in a count vector) and Select (positional selection without MPHF).</p>
<div class="footnote">
<hr/>
<ol>
<li id="fn:Mash-distances-doc">
<p>Marbl Lab. (2026). <a href="https://mash.readthedocs.io/en/latest/distances.html">Mash distance</a>. <a class="footnote-backref" href="#fnref:Mash-distances-doc" title="Jump back to footnote 1 in the text">↩</a></p>
</li>
<li id="fn:Fan2015-mash-formula">
<p>Fan, H., Ives, A.R., Surget-Groba, Y. &amp; Cannon, C.H. (2015). <a href="https://doi.org/10.1186/s12864-015-1647-5">An assembly and alignment-free method of phylogeny reconstruction from next-generation sequencing data</a>. <em>BMC Genomics</em>, 16. <a class="footnote-backref" href="#fnref:Fan2015-mash-formula" title="Jump back to footnote 2 in the text">↩</a></p>
</li>
</ol>
</div>
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