Eric Coissac
45d49ed501
Add comprehensive documentation for the `obilayeredmap` crate, `PersistentCompactIntVec`, `PersistentBitVec`, and the hierarchical k-mer index architecture, including sidebar navigation updates across all documentation pages. Refactor the Bray-Curtis distance computation in `obicompactvec` to decouple numerator and denominator calculations, replacing direct pairwise calls with explicit loops over precomputed sums. Update tests to verify column sum accuracy and align with the simplified API.
1816 lines
65 KiB
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1816 lines
65 KiB
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obipipeline library
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MPHF selection
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Unitig evidence encoding
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obilayeredmap crate
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PersistentCompactIntVec
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<span class="md-nav__icon md-icon"></span>
|
||
|
||
|
||
Architecture
|
||
|
||
|
||
</label>
|
||
<ul class="md-nav__list" data-md-scrollfix>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item">
|
||
<a href="../sequences/invariant/" class="md-nav__link">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Sequences
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
</li>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<li class="md-nav__item md-nav__item--active">
|
||
|
||
<input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
|
||
|
||
|
||
|
||
|
||
|
||
<label class="md-nav__link md-nav__link--active" for="__toc">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Kmer index
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
<span class="md-nav__icon md-icon"></span>
|
||
</label>
|
||
|
||
<a href="./" class="md-nav__link md-nav__link--active">
|
||
|
||
|
||
|
||
<span class="md-ellipsis">
|
||
|
||
|
||
Kmer index
|
||
|
||
|
||
|
||
</span>
|
||
|
||
|
||
|
||
</a>
|
||
|
||
|
||
|
||
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<label class="md-nav__title" for="__toc">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
Table of contents
|
||
</label>
|
||
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#fundamental-invariant" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Fundamental invariant
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#three-level-hierarchy" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Three-level hierarchy
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#mphflayer-autonomous-mapping-layer" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
MphfLayer — autonomous mapping layer
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#datastore-slot-indexed-data" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
DataStore — slot-indexed data
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#distance-matrix-api-on-datastore-types" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Distance matrix API on DataStore types
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Distance matrix API on DataStore types">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#full-distance-matrices" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Full distance matrices
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#partial-distance-matrices" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Partial distance matrices
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#progressive-aggregation-principle" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Progressive aggregation principle
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#layereddatastore-aggregation-within-one-partition" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
LayeredDataStore — aggregation within one partition
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="LayeredDataStore — aggregation within one partition">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#column-statistics" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Column statistics
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#self-contained-partials" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Self-contained partials
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#normalised-partials-require-global-sums-from-above" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Normalised partials (require global sums from above)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#partitioneddatastore-aggregation-across-all-partitions" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
PartitionedDataStore — aggregation across all partitions
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="PartitionedDataStore — aggregation across all partitions">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#column-statistics_1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Column statistics
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#self-contained-metrics-single-pass" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Self-contained metrics — single pass
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#normalised-metrics-two-passes" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Normalised metrics — two passes
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#parallelism-model" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Parallelism model
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#query-model" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Query model
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Query model">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#point-query-kmer-optionitem" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Point query — kmer → Option<Item>
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#aggregation-result" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Aggregation — → Result
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#datastore-derivation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
DataStore derivation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#relationship-to-current-implementation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Relationship to current implementation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
|
||
|
||
</ul>
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
|
||
<div class="md-sidebar__scrollwrap">
|
||
<div class="md-sidebar__inner">
|
||
|
||
|
||
<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<label class="md-nav__title" for="__toc">
|
||
<span class="md-nav__icon md-icon"></span>
|
||
Table of contents
|
||
</label>
|
||
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#fundamental-invariant" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Fundamental invariant
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#three-level-hierarchy" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Three-level hierarchy
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#mphflayer-autonomous-mapping-layer" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
MphfLayer — autonomous mapping layer
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#datastore-slot-indexed-data" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
DataStore — slot-indexed data
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#distance-matrix-api-on-datastore-types" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Distance matrix API on DataStore types
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Distance matrix API on DataStore types">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#full-distance-matrices" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Full distance matrices
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#partial-distance-matrices" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Partial distance matrices
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#progressive-aggregation-principle" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Progressive aggregation principle
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#layereddatastore-aggregation-within-one-partition" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
LayeredDataStore — aggregation within one partition
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="LayeredDataStore — aggregation within one partition">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#column-statistics" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Column statistics
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#self-contained-partials" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Self-contained partials
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#normalised-partials-require-global-sums-from-above" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Normalised partials (require global sums from above)
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#partitioneddatastore-aggregation-across-all-partitions" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
PartitionedDataStore — aggregation across all partitions
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="PartitionedDataStore — aggregation across all partitions">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#column-statistics_1" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Column statistics
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#self-contained-metrics-single-pass" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Self-contained metrics — single pass
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#normalised-metrics-two-passes" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Normalised metrics — two passes
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#parallelism-model" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Parallelism model
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#query-model" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Query model
|
||
|
||
</span>
|
||
</a>
|
||
|
||
<nav class="md-nav" aria-label="Query model">
|
||
<ul class="md-nav__list">
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#point-query-kmer-optionitem" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Point query — kmer → Option<Item>
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#aggregation-result" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Aggregation — → Result
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
</nav>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#datastore-derivation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
DataStore derivation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
<li class="md-nav__item">
|
||
<a href="#relationship-to-current-implementation" class="md-nav__link">
|
||
<span class="md-ellipsis">
|
||
|
||
Relationship to current implementation
|
||
|
||
</span>
|
||
</a>
|
||
|
||
</li>
|
||
|
||
</ul>
|
||
|
||
</nav>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
|
||
|
||
<div class="md-content" data-md-component="content">
|
||
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
|
||
|
||
<h1 id="kmer-index-architecture">Kmer index architecture</h1>
|
||
<h2 id="fundamental-invariant">Fundamental invariant</h2>
|
||
<p>A given canonical kmer belongs to <strong>exactly one partition</strong> and <strong>exactly one layer</strong> within that partition. This is the property that makes all aggregation operations decomposable and parallelisable without coordination.</p>
|
||
<hr />
|
||
<h2 id="three-level-hierarchy">Three-level hierarchy</h2>
|
||
<div class="highlight"><pre><span></span><code>PartitionedIndex
|
||
├── LayeredPartition (one per minimiser bucket)
|
||
│ ├── MphfLayer 0 kmer → slot (immutable bijection)
|
||
│ │ ├── DataStore A slot → T (e.g. counts)
|
||
│ │ └── DataStore B slot → T (e.g. presence/absence, derived)
|
||
│ ├── MphfLayer 1
|
||
│ │ └── DataStore A
|
||
│ └── ...
|
||
├── LayeredPartition
|
||
│ └── ...
|
||
</code></pre></div>
|
||
<p><strong>PartitionedIndex</strong>: routes queries to partitions via canonical minimiser hash. Owns the partition count and routing scheme (fixed at creation). Dispatches aggregations across partitions in parallel.</p>
|
||
<p><strong>LayeredPartition</strong>: one directory per minimiser bucket. Holds a <code>Vec<MphfLayer></code>. Each layer covers a disjoint kmer set — layer 0 is built from dataset A; layer 1 covers kmers in B absent from layer 0; and so on. Layers within a partition are always disjoint.</p>
|
||
<p><strong>MphfLayer</strong>: the MPHF + evidence + unitig spine. Maps <code>kmer → slot</code> for its disjoint kmer set. Immutable once built. Independent of any data attached to it.</p>
|
||
<p><strong>DataStore</strong>: a slot-indexed data array (e.g. <code>PersistentCompactIntMatrix</code>, <code>PersistentBitMatrix</code>). Attached to a <code>MphfLayer</code> externally. Multiple stores of different types can coexist on the same <code>MphfLayer</code>.</p>
|
||
<hr />
|
||
<h2 id="mphflayer-autonomous-mapping-layer">MphfLayer — autonomous mapping layer</h2>
|
||
<div class="highlight"><pre><span></span><code><span class="n">MphfLayer</span><span class="p">::</span><span class="n">find</span><span class="p">(</span><span class="n">kmer</span><span class="p">:</span><span class="w"> </span><span class="nc">CanonicalKmer</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nb">Option</span><span class="o"><</span><span class="kt">usize</span><span class="o">></span><span class="w"> </span><span class="c1">// slot, or None if absent</span>
|
||
<span class="n">MphfLayer</span><span class="p">::</span><span class="n">n</span><span class="p">()</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="kt">usize</span><span class="w"> </span><span class="c1">// number of slots</span>
|
||
<span class="n">MphfLayer</span><span class="p">::</span><span class="n">build</span><span class="p">(</span><span class="n">dir</span><span class="p">:</span><span class="w"> </span><span class="kp">&</span><span class="nc">Path</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">OLMResult</span><span class="o"><</span><span class="p">(</span><span class="bp">Self</span><span class="p">,</span><span class="w"> </span><span class="kt">usize</span><span class="p">)</span><span class="o">></span><span class="w"> </span><span class="c1">// from unitigs.bin</span>
|
||
<span class="n">MphfLayer</span><span class="p">::</span><span class="n">open</span><span class="p">(</span><span class="n">dir</span><span class="p">:</span><span class="w"> </span><span class="kp">&</span><span class="nc">Path</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">OLMResult</span><span class="o"><</span><span class="bp">Self</span><span class="o">></span>
|
||
</code></pre></div>
|
||
<p><code>find</code> returns <code>Some(slot)</code> only if the kmer is actually in this layer (evidence check included). Returns <code>None</code> for kmers present in other layers or absent from the index.</p>
|
||
<p>The MPHF (<code>mphf.bin</code>, <code>evidence.bin</code>, <code>unitigs.bin</code>) is built once and never rebuilt. All data derivation operations (count → presence, thresholding, merging) reuse the same <code>MphfLayer</code>.</p>
|
||
<hr />
|
||
<h2 id="datastore-slot-indexed-data">DataStore — slot-indexed data</h2>
|
||
<div class="highlight"><pre><span></span><code><span class="k">trait</span><span class="w"> </span><span class="n">DataStore</span><span class="w"> </span><span class="p">{</span>
|
||
<span class="w"> </span><span class="k">type</span><span class="w"> </span><span class="nc">Item</span><span class="p">;</span>
|
||
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">get</span><span class="p">(</span><span class="o">&</span><span class="bp">self</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="p">-></span><span class="w"> </span><span class="nc">Self</span><span class="p">::</span><span class="n">Item</span><span class="p">;</span>
|
||
<span class="w"> </span><span class="k">fn</span><span class="w"> </span><span class="nf">n</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </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>Concrete types from <code>obicompactvec</code>:</p>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>Type</th>
|
||
<th><code>Item</code></th>
|
||
<th>Column stats</th>
|
||
<th>Use</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td><code>PersistentCompactIntMatrix</code></td>
|
||
<td><code>Box<[u32]></code></td>
|
||
<td><code>sum() -> Array1<u64></code></td>
|
||
<td>count per sample per slot</td>
|
||
</tr>
|
||
<tr>
|
||
<td><code>PersistentBitMatrix</code></td>
|
||
<td><code>Box<[bool]></code></td>
|
||
<td><code>count_ones() -> Array1<u64></code></td>
|
||
<td>presence per sample per slot</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p><code>sum()</code> and <code>count_ones()</code> are the bridge between the per-matrix level and cross-layer aggregation: they give the total weight of each column within one (partition, layer) pair, which can be summed to get global column weights.</p>
|
||
<p>A <code>DataStore</code> knows nothing about kmers or MPHFs. It is indexed by <code>usize</code> slot only.</p>
|
||
<hr />
|
||
<h2 id="distance-matrix-api-on-datastore-types">Distance matrix API on DataStore types</h2>
|
||
<p>Both <code>PersistentCompactIntMatrix</code> and <code>PersistentBitMatrix</code> expose two families of distance matrix methods.</p>
|
||
<h3 id="full-distance-matrices">Full distance matrices</h3>
|
||
<p>Compute the final <code>n_cols × n_cols</code> distance matrix from data within a single matrix. Internally parallelised over the upper triangle via rayon.</p>
|
||
<div class="highlight"><pre><span></span><code><span class="c1">// PersistentCompactIntMatrix</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">bray_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">relfreq_bray_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">euclidean_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">relfreq_euclidean_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">hellinger_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">jaccard_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">threshold_jaccard_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</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">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
|
||
<span class="c1">// PersistentBitMatrix</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">jaccard_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">hamming_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span>
|
||
</code></pre></div>
|
||
<p>These are convenience methods. For a <code>LayeredDataStore</code> or <code>PartitionedDataStore</code> they cannot be used directly — the partial API is required.</p>
|
||
<h3 id="partial-distance-matrices">Partial distance matrices</h3>
|
||
<p>Return additive components that can be summed element-wise across (partition, layer) pairs before computing the final distance. This is what makes cross-layer and cross-partition aggregation possible.</p>
|
||
<p><strong>Category 1 — self-contained partials</strong>: additive without any external parameter.</p>
|
||
<div class="highlight"><pre><span></span><code><span class="c1">// PersistentCompactIntMatrix</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_bray_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span>
|
||
<span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="p">(</span><span class="n">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">,</span><span class="w"> </span><span class="c1">// sum_min[i,j]</span>
|
||
<span class="w"> </span><span class="n">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span><span class="w"> </span><span class="c1">// col_sums[k]</span>
|
||
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_euclidean_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span><span class="w"> </span><span class="c1">// sum of squared diffs</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_threshold_jaccard_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</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">-></span><span class="w"> </span><span class="p">(</span><span class="n">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">,</span><span class="w"> </span><span class="c1">// inter[i,j]</span>
|
||
<span class="w"> </span><span class="n">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span><span class="w"> </span><span class="c1">// union[i,j]</span>
|
||
|
||
<span class="c1">// PersistentBitMatrix</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_jaccard_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span>
|
||
<span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="p">(</span><span class="n">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">,</span><span class="w"> </span><span class="c1">// inter[i,j]</span>
|
||
<span class="w"> </span><span class="n">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span><span class="w"> </span><span class="c1">// union[i,j]</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_hamming_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="w"> </span><span class="c1">// differing bits</span>
|
||
</code></pre></div>
|
||
<p><strong>Category 2 — normalised partials</strong>: require global column sums as input, computed beforehand across all (partition, layer) pairs.</p>
|
||
<div class="highlight"><pre><span></span><code><span class="c1">// PersistentCompactIntMatrix only</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_relfreq_bray_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">col_sums</span><span class="p">:</span><span class="w"> </span><span class="kp">&</span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span>
|
||
<span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span><span class="w"> </span><span class="c1">// Σ_slot min(a_slot/sum_i, b_slot/sum_j)</span>
|
||
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_relfreq_euclidean_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">col_sums</span><span class="p">:</span><span class="w"> </span><span class="kp">&</span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span>
|
||
<span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span><span class="w"> </span><span class="c1">// Σ_slot (a_slot/sum_i - b_slot/sum_j)²</span>
|
||
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_hellinger_euclidean_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">col_sums</span><span class="p">:</span><span class="w"> </span><span class="kp">&</span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span>
|
||
<span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span><span class="w"> </span><span class="c1">// Σ_slot (√(a/sum_i) - √(b/sum_j))²</span>
|
||
</code></pre></div>
|
||
<p>The <code>col_sums</code> parameter must reflect the GLOBAL count across all layers and all partitions — passing a per-layer sum would give a wrong result. This constraint drives the two-pass algorithm described below.</p>
|
||
<hr />
|
||
<h2 id="progressive-aggregation-principle">Progressive aggregation principle</h2>
|
||
<p>Aggregation is <strong>hierarchical</strong>: each level computes its contribution by aggregating from the level immediately below it. No level skips a level or collects raw data from two levels down.</p>
|
||
<div class="highlight"><pre><span></span><code>PersistentCompactIntMatrix::sum() — column sums for one (partition, layer) matrix
|
||
↓ Σ across layers
|
||
LayeredCompactIntMatrix::sum() — column sums for one partition
|
||
↓ Σ across partitions
|
||
PartitionedCompactIntMatrix::sum() — global column sums
|
||
</code></pre></div>
|
||
<p>The same cascade applies to every partial computation:</p>
|
||
<div class="highlight"><pre><span></span><code>PersistentCompactIntMatrix::partial_bray_dist_matrix() — one (partition, layer)
|
||
↓ element-wise Σ across layers
|
||
LayeredCompactIntMatrix::partial_bray() — one partition
|
||
↓ element-wise Σ across partitions
|
||
PartitionedCompactIntMatrix::partial_bray() — global partial → final dist
|
||
</code></pre></div>
|
||
<p>This means <code>LayeredCompactIntMatrix</code> never inspects individual <code>PersistentCompactIntVec</code> columns directly, and <code>PartitionedCompactIntMatrix</code> never inspects individual layers. Each level presents a stable API surface to the level above.</p>
|
||
<hr />
|
||
<h2 id="layereddatastore-aggregation-within-one-partition">LayeredDataStore — aggregation within one partition</h2>
|
||
<p>A <code>LayeredDataStore</code> holds one <code>DataStore</code> per layer within a single partition:</p>
|
||
<div class="highlight"><pre><span></span><code><span class="k">struct</span><span class="w"> </span><span class="nc">LayeredCompactIntMatrix</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">layers</span><span class="p">:</span><span class="w"> </span><span class="nb">Vec</span><span class="o"><</span><span class="n">PersistentCompactIntMatrix</span><span class="o">></span><span class="w"> </span><span class="p">}</span>
|
||
<span class="k">struct</span><span class="w"> </span><span class="nc">LayeredBitMatrix</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">layers</span><span class="p">:</span><span class="w"> </span><span class="nb">Vec</span><span class="o"><</span><span class="n">PersistentBitMatrix</span><span class="o">></span><span class="w"> </span><span class="p">}</span>
|
||
</code></pre></div>
|
||
<h3 id="column-statistics">Column statistics</h3>
|
||
<div class="highlight"><pre><span></span><code><span class="c1">// LayeredCompactIntMatrix</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span>
|
||
<span class="w"> </span><span class="c1">// = layers.par_iter().map(|m| m.sum()).reduce(element-wise +)</span>
|
||
|
||
<span class="c1">// LayeredBitMatrix</span>
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">count_ones</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span>
|
||
<span class="w"> </span><span class="c1">// = layers.par_iter().map(|m| m.count_ones()).reduce(element-wise +)</span>
|
||
</code></pre></div>
|
||
<h3 id="self-contained-partials">Self-contained partials</h3>
|
||
<p>Each method reduces across layers by element-wise addition of per-layer matrices:</p>
|
||
<div class="highlight"><pre><span></span><code><span class="k">fn</span><span class="w"> </span><span class="nf">partial_bray</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="p">(</span><span class="n">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">,</span><span class="w"> </span><span class="n">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span>
|
||
<span class="w"> </span><span class="c1">// Σ_l layer_l.partial_bray_dist_matrix()</span>
|
||
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_euclidean</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="w"> </span><span class="c1">// Σ_l layer_l.partial_euclidean_dist_matrix()</span>
|
||
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_jaccard</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="p">(</span><span class="n">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">,</span><span class="w"> </span><span class="n">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span>
|
||
<span class="w"> </span><span class="c1">// Σ_l layer_l.partial_jaccard_dist_matrix() [bit matrix]</span>
|
||
<span class="w"> </span><span class="c1">// Σ_l layer_l.partial_threshold_jaccard_dist_matrix() [int matrix]</span>
|
||
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_hamming</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span>
|
||
<span class="w"> </span><span class="c1">// Σ_l layer_l.partial_hamming_dist_matrix() [bit matrix]</span>
|
||
</code></pre></div>
|
||
<h3 id="normalised-partials-require-global-sums-from-above">Normalised partials (require global sums from above)</h3>
|
||
<div class="highlight"><pre><span></span><code><span class="k">fn</span><span class="w"> </span><span class="nf">partial_relfreq_bray</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">global_sums</span><span class="p">:</span><span class="w"> </span><span class="kp">&</span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="w"> </span><span class="c1">// Σ_l layer_l.partial_relfreq_bray_dist_matrix(global_sums)</span>
|
||
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_relfreq_euclidean</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">global_sums</span><span class="p">:</span><span class="w"> </span><span class="kp">&</span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="w"> </span><span class="c1">// Σ_l layer_l.partial_relfreq_euclidean_dist_matrix(global_sums)</span>
|
||
|
||
<span class="k">fn</span><span class="w"> </span><span class="nf">partial_hellinger</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">,</span><span class="w"> </span><span class="n">global_sums</span><span class="p">:</span><span class="w"> </span><span class="kp">&</span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span>
|
||
<span class="w"> </span><span class="c1">// Σ_l layer_l.partial_hellinger_euclidean_dist_matrix(global_sums)</span>
|
||
</code></pre></div>
|
||
<p><code>global_sums</code> is provided by the <code>PartitionedDataStore</code>; this level does not compute it.</p>
|
||
<hr />
|
||
<h2 id="partitioneddatastore-aggregation-across-all-partitions">PartitionedDataStore — aggregation across all partitions</h2>
|
||
<p>A <code>PartitionedDataStore</code> holds one <code>LayeredDataStore</code> per partition:</p>
|
||
<div class="highlight"><pre><span></span><code><span class="k">struct</span><span class="w"> </span><span class="nc">PartitionedCompactIntMatrix</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">partitions</span><span class="p">:</span><span class="w"> </span><span class="nb">Vec</span><span class="o"><</span><span class="n">LayeredCompactIntMatrix</span><span class="o">></span><span class="w"> </span><span class="p">}</span>
|
||
<span class="k">struct</span><span class="w"> </span><span class="nc">PartitionedBitMatrix</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">partitions</span><span class="p">:</span><span class="w"> </span><span class="nb">Vec</span><span class="o"><</span><span class="n">LayeredBitMatrix</span><span class="o">></span><span class="w"> </span><span class="p">}</span>
|
||
</code></pre></div>
|
||
<h3 id="column-statistics_1">Column statistics</h3>
|
||
<div class="highlight"><pre><span></span><code><span class="k">fn</span><span class="w"> </span><span class="nf">sum</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array1</span><span class="o"><</span><span class="kt">u64</span><span class="o">></span>
|
||
<span class="w"> </span><span class="c1">// = partitions.par_iter().map(|p| p.sum()).reduce(element-wise +)</span>
|
||
</code></pre></div>
|
||
<p><code>p.sum()</code> is itself a reduction across layers (see above) — the cascade is preserved.</p>
|
||
<h3 id="self-contained-metrics-single-pass">Self-contained metrics — single pass</h3>
|
||
<div class="highlight"><pre><span></span><code><span class="k">fn</span><span class="w"> </span><span class="nf">bray_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span><span class="w"> </span><span class="p">{</span>
|
||
<span class="w"> </span><span class="kd">let</span><span class="w"> </span><span class="p">(</span><span class="n">sum_min</span><span class="p">,</span><span class="w"> </span><span class="n">col_sums</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">partitions</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">par_iter</span><span class="p">()</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">map</span><span class="p">(</span><span class="o">|</span><span class="n">p</span><span class="o">|</span><span class="w"> </span><span class="n">p</span><span class="p">.</span><span class="n">partial_bray</span><span class="p">())</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">element</span><span class="o">-</span><span class="n">wise</span><span class="w"> </span><span class="o">+</span><span class="p">);</span>
|
||
<span class="w"> </span><span class="c1">// finalise</span>
|
||
<span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">):</span><span class="w"> </span><span class="nc">dist</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="mi">2</span><span class="err">·</span><span class="n">sum_min</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="p">(</span><span class="n">col_sums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">col_sums</span><span class="p">[</span><span class="n">j</span><span class="p">])</span>
|
||
<span class="p">}</span>
|
||
</code></pre></div>
|
||
<h3 id="normalised-metrics-two-passes">Normalised metrics — two passes</h3>
|
||
<div class="highlight"><pre><span></span><code><span class="k">fn</span><span class="w"> </span><span class="nf">relfreq_bray_dist_matrix</span><span class="p">(</span><span class="o">&</span><span class="bp">self</span><span class="p">)</span><span class="w"> </span><span class="p">-></span><span class="w"> </span><span class="nc">Array2</span><span class="o"><</span><span class="kt">f64</span><span class="o">></span><span class="w"> </span><span class="p">{</span>
|
||
<span class="w"> </span><span class="c1">// pass 1 — progressive: PartitionedDataStore::sum()</span>
|
||
<span class="w"> </span><span class="c1">// calls LayeredDataStore::sum() per partition (parallel)</span>
|
||
<span class="w"> </span><span class="c1">// calls PersistentCompactIntMatrix::sum() per layer (parallel)</span>
|
||
<span class="w"> </span><span class="kd">let</span><span class="w"> </span><span class="n">global_sums</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="bp">self</span><span class="p">.</span><span class="n">sum</span><span class="p">();</span>
|
||
|
||
<span class="w"> </span><span class="c1">// pass 2 — per-partition partial using global_sums (parallel)</span>
|
||
<span class="w"> </span><span class="kd">let</span><span class="w"> </span><span class="n">matrix</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">partitions</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">par_iter</span><span class="p">()</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">map</span><span class="p">(</span><span class="o">|</span><span class="n">p</span><span class="o">|</span><span class="w"> </span><span class="n">p</span><span class="p">.</span><span class="n">partial_relfreq_bray</span><span class="p">(</span><span class="o">&</span><span class="n">global_sums</span><span class="p">))</span>
|
||
<span class="w"> </span><span class="p">.</span><span class="n">reduce</span><span class="p">(</span><span class="n">element</span><span class="o">-</span><span class="n">wise</span><span class="w"> </span><span class="o">+</span><span class="p">);</span>
|
||
<span class="w"> </span><span class="c1">// finalise</span>
|
||
<span class="w"> </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">):</span><span class="w"> </span><span class="nc">dist</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">matrix</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span>
|
||
<span class="p">}</span>
|
||
</code></pre></div>
|
||
<p><code>global_sums</code> is exact because each kmer belongs to exactly one (partition, layer) pair — no double-counting. Pass 1 is itself fully parallel at every level of the hierarchy.</p>
|
||
<hr />
|
||
<h2 id="parallelism-model">Parallelism model</h2>
|
||
<table>
|
||
<thead>
|
||
<tr>
|
||
<th>Level</th>
|
||
<th>Unit</th>
|
||
<th>Coordination</th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr>
|
||
<td>Across partitions</td>
|
||
<td><code>LayeredDataStore</code></td>
|
||
<td>none — fully independent</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Across layers (self-contained)</td>
|
||
<td><code>(partition, layer)</code> pair</td>
|
||
<td>none — disjoint kmer sets</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Across layers (normalised, pass 1)</td>
|
||
<td><code>(partition, layer)</code> pair</td>
|
||
<td>none — sums are additive</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Across layers (normalised, pass 2)</td>
|
||
<td><code>(partition, layer)</code> pair</td>
|
||
<td>global_sums broadcast read-only</td>
|
||
</tr>
|
||
<tr>
|
||
<td>Within a DataStore (distance matrix)</td>
|
||
<td>upper-triangle pair <code>(i,j)</code></td>
|
||
<td>none — rayon par_iter</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<hr />
|
||
<h2 id="query-model">Query model</h2>
|
||
<h3 id="point-query-kmer-optionitem">Point query — <code>kmer → Option<Item></code></h3>
|
||
<div class="highlight"><pre><span></span><code>minimiser(kmer) → partition p
|
||
for each layer l in p:
|
||
slot = MphfLayer_l.find(kmer)
|
||
if slot is Some:
|
||
return DataStore_l.get(slot)
|
||
return None
|
||
</code></pre></div>
|
||
<p>O(n_layers) MPHF probes worst case; O(1) expected. No cross-layer fusion — the result comes from exactly one (partition, layer).</p>
|
||
<h3 id="aggregation-result">Aggregation — <code>→ Result</code></h3>
|
||
<div class="highlight"><pre><span></span><code>result = reduce(
|
||
for p in partitions: // parallel
|
||
for l in layers(p): // parallel
|
||
partial(DataStore_p_l)
|
||
)
|
||
</code></pre></div>
|
||
<p>For normalised metrics replace with the two-pass scheme above.</p>
|
||
<hr />
|
||
<h2 id="datastore-derivation">DataStore derivation</h2>
|
||
<p>Because the <code>MphfLayer</code> is independent of its data stores, new stores can be derived from existing ones without rebuilding the MPHF:</p>
|
||
<div class="highlight"><pre><span></span><code>// count → presence/absence, parallel across (partition, layer)
|
||
for (p, l) in all_partition_layer_pairs().par_iter():
|
||
count_store = open PersistentCompactIntMatrix at (p, l)
|
||
presence_store = PersistentBitMatrix::from_count_matrix(count_store, threshold, dir)
|
||
</code></pre></div>
|
||
<p>Other derivations: threshold a count matrix → binary presence matrix; union two presence matrices; merge two count matrices (saturating add, column-wise). All are local to one <code>(partition, layer)</code> pair.</p>
|
||
<hr />
|
||
<h2 id="relationship-to-current-implementation">Relationship to current implementation</h2>
|
||
<p>The current <code>obilayeredmap</code> crate implements a subset of this architecture. Key divergences:</p>
|
||
<ul>
|
||
<li><code>Layer<D: LayerData></code> fuses <code>MphfLayer</code> and one <code>DataStore</code> into a single generic type. Multiple data stores on the same MPHF are not supported.</li>
|
||
<li><code>LayerData::open(dir)</code> embeds the path convention (<code>counts/</code>, <code>presence/</code>) inside the store type, preventing the <code>PartitionedIndex</code> from managing paths externally.</li>
|
||
<li><code>LayeredDataStore</code> and <code>PartitionedDataStore</code> do not yet exist; <code>LayeredMap</code> is a single-partition structure without a distance matrix API.</li>
|
||
<li>The partial distance methods exist on <code>PersistentCompactIntMatrix</code> and <code>PersistentBitMatrix</code> and are tested; they are not yet composed across layers and partitions.</li>
|
||
</ul>
|
||
<p>Planned refactoring:
|
||
1. Extract <code>MphfLayer</code> from <code>Layer<D></code> as an autonomous type.
|
||
2. Replace <code>LayerData</code> trait with <code>DataStore</code> trait (no path knowledge).
|
||
3. Implement <code>LayeredCompactIntMatrix</code> / <code>LayeredBitMatrix</code> with the partial + full distance APIs described above.
|
||
4. Implement <code>PartitionedCompactIntMatrix</code> / <code>PartitionedBitMatrix</code> with two-pass support for normalised metrics.
|
||
5. Implement <code>PartitionedIndex</code> for point queries with parallel dispatch.</p>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
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