* Add temporal graph evolution & RuVector integration research GOAP Agent 8 output: 1,528-line SOTA research document covering temporal graph models (TGN, JODIE, DyRep), RuVector graph memory design, mincut trajectory tracking with Kalman filtering, event detection pipelines, compressed temporal storage, cross-room transition graphs, and a 5-phase integration roadmap. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add transformer architectures for graph sensing research GOAP Agent 4 output: 896-line SOTA document covering Graph Transformers (Graphormer, SAN, GPS, TokenGT), Temporal Graph Transformers (TGN, TGAT, DyRep), ViT for RF spectrograms, transformer-based mincut prediction, positional encoding for RF graphs, foundation models for RF sensing, and efficient edge deployment with INT8 quantization. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add attention mechanisms for RF sensing research GOAP Agent 3 output: 1,110-line document covering GAT for RF graphs, self-attention for CSI sequences, cross-attention multi-link fusion, attention-weighted differentiable mincut, spatial node attention, antenna-level subcarrier attention, and efficient attention variants (linear, sparse, LSH, S4/Mamba). 8 ASCII architecture diagrams. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add sublinear mincut algorithms research GOAP Agent 5 output: 698-line document covering classical mincut complexity, sublinear approximation (sampling, sparsifiers), dynamic mincut with lazy recomputation hybrid, streaming sketch algorithms, Benczur-Karger sparsification, local partitioning (PageRank-guided cuts), randomized methods reliability analysis, and Rust implementation with const-generic RfGraph, zero-alloc Stoer-Wagner, SIMD batch updates. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add CSI edge weight computation research GOAP Agent 2 output: ~700-line document covering CSI feature extraction, coherence metrics (cross-correlation, mutual information, phasor coherence), multipath stability scoring (MUSIC, ESPRIT, ISTA), temporal windowing (EMA, Welford, Kalman), noise robustness (phase noise, AGC, clock drift), edge weight normalization, and implementation architecture showing 32KB memory for 120 edges within ESP32-S3 capability. Part of RF Topological Sensing research swarm (10 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add contrastive learning for RF coherence research GOAP Agent 7 output: 1,226-line document covering SimCLR/MoCo/BYOL for CSI, AETHER-Topo dual-head extension, coherence boundary detection with multi-scale analysis, delta-driven updates (2-12x efficiency), self-supervised pre-training protocol, triplet networks for 5-state edge classification, and MERIDIAN cross-environment transfer with EWC continual learning. Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add resolution and spatial granularity analysis research GOAP Agent 9 output: 1,383-line document covering Fresnel zone analysis, node density vs resolution (16-node/5m room → 30-60cm), Cramer-Rao lower bounds with Fisher Information Matrix, graph cut resolution theory, multi-frequency enhancement (6cm coherent dual-band limit), RF tomography comparison, experimental validation protocols, and resolution scaling laws (8.8cm theoretical limit). Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add RF graph theory and minimum cut foundations research GOAP Agent 1 output: Graph-theoretic foundations covering max-flow/min-cut for RF (Ford-Fulkerson, Stoer-Wagner, Karger), RF as dynamic graph with CSI coherence weights, topological change detection via Fiedler vector and Cheeger inequality, dynamic graph algorithms, comparison to classical RF sensing, formal mathematical framework, and 9 open research questions. Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add ESP32 mesh hardware constraints research GOAP Agent 6 output: ESP32 CSI capabilities (52/114 subcarriers), 16-node mesh topology with 120 edges, TDM synchronized sensing (3ms slots), computational budget (Stoer-Wagner uses 0.07% of one core), channel hopping, power analysis (0.44W/node), dual-core firmware architecture, and edge vs server computing with 100x data reduction on-device. Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add system architecture and prototype design research GOAP Agent 10 output: End-to-end architecture with pipeline diagrams, existing crate integration mapping, new rf_topology module design (DDD aggregate roots), 100ms latency budget breakdown, 3-phase prototype plan (4-node POC → 16-node room → 72-node multi-room), benchmark design with 8 metrics, ADR-044 draft, and Rust trait definitions (EdgeWeightComputer, TopologyGraph, MinCutSolver, BoundaryInterpolator). Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add quantum sensing and quantum biomedical research documents Agent 11: Quantum-level sensors (729 lines) — NV centers, SQUIDs, Rydberg atoms, quantum illumination, quantum graph theory (walks, spectral, QAOA), hybrid classical-quantum architecture, quantum ML (VQC, kernels, reservoir computing), NISQ applications (D-Wave, VQE), hardware roadmap. Agent 12: Quantum biomedical sensing (827 lines) — whole body biomagnetic mapping, neural field imaging without electrodes, circulation sensing, cellular EM signaling, non-contact diagnostics, coherence-based diagnostics (disease as coherence breakdown), neural interfaces, multimodal observatory, room-scale ambient health monitoring, graph-based biomedical analysis. Part of RF Topological Sensing research swarm (12 agents). https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add research index synthesizing all 12 documents (14,322 lines) Master index for RF Topological Sensing research compendium covering: graph theory foundations, CSI edge weights, attention mechanisms, transformers, sublinear algorithms, ESP32 hardware, contrastive learning, temporal graphs, resolution analysis, system architecture, quantum sensors, and quantum biomedical sensing. Includes key findings, proposed ADRs (044, 045), and 5-phase implementation roadmap. https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add SOTA neural decoding landscape and 10 application domains research - Doc 21: Comprehensive SOTA map (2023-2026) of brain sensors, decoders, and visualization systems with RuVector/mincut positioning analysis - Doc 22: Ten application domains for brain state observatory including disease detection, BCI, cognitive monitoring, mental health diagnostics, neurofeedback, dream reconstruction, cognitive research, HCI, wearables, and brain network digital twins with strategic roadmap https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add NV diamond neural magnetometry research document (13/22) Comprehensive 600+ line document covering NV center physics, neural magnetic field sources, sensor architecture, SQUID comparison, signal processing pipeline, RuVector integration, and development roadmap. https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add ruv-neural workspace Cargo.toml with 12 crate definitions Workspace structure for the rUv Neural brain topology analysis system. 12 mix-and-match crates with shared dependencies including RuVector integration, petgraph, rustfft, and WASM/ESP32 support. https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add ruv-neural crate ecosystem — 12 mix-and-match crates (WIP) Initial implementation of the rUv Neural brain topology analysis system: - ruv-neural-core: Core types, traits, errors, RVF format (compiles) - ruv-neural-sensor: NV diamond, OPM, EEG sensor interfaces (in progress) - ruv-neural-signal: DSP, filtering, spectral, connectivity (in progress) - ruv-neural-graph: Brain connectivity graph construction (in progress) - ruv-neural-mincut: Dynamic minimum cut topology analysis (in progress) - ruv-neural-embed: RuVector graph embeddings (in progress) - ruv-neural-memory: Persistent neural state memory + HNSW (compiles) - ruv-neural-decoder: Cognitive state classification + BCI (in progress) - ruv-neural-esp32: ESP32 edge sensor integration (compiles) - ruv-neural-wasm: WebAssembly browser bindings (in progress) - ruv-neural-viz: Visualization + ASCII rendering (in progress) - ruv-neural-cli: CLI tool (in progress) Agents still writing remaining modules. Next: fix compilation, tests, push. https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Fix ruv-neural crate compilation: all 12 crates build and 1200+ tests pass - Fix node2vec.rs type inference error (Vec<_> → Vec<Vec<f64>>) - Fix artifact.rs with full filter-based detection implementations - Fix signal crate ConnectivityMetric re-export and trait method names - Fix embed crate EmbeddingGenerator trait implementations - Complete spectral, topology, and node2vec embedders with tests - Complete preprocessing pipeline with sequential stage processing - All workspace crates compile cleanly, 0 test failures https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * Add ruv-neural-cli README https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv * fix: convert desktop icons from RGB to RGBA for Tauri build Tauri's generate_context!() macro requires RGBA PNG icons. All 5 icon files (32x32.png, 128x128.png, 128x128@2x.png, icon.icns, icon.ico) were RGB-only, causing a proc macro panic on Linux builds. Fixes #200 Co-Authored-By: claude-flow <ruv@ruv.net> * Add Subcarrier Manifold and Vitals Oracle modules for 3D visualizations - Implemented Subcarrier Manifold to visualize amplitude data as a 3D surface with height and age attributes. - Created Vitals Oracle to represent vital signs using toroidal rings and particle trails, incorporating breathing and heart rate dynamics. - Both modules utilize Three.js for rendering and include custom shaders for visual effects. * feat: complete ruv-neural implementation — physics models, security, witness verification Replace all stubs/mocks with production physics-based signal models: - NV Diamond: ODMR Lorentzian dip, 1/f pink noise (Voss-McCartney), brain oscillations - OPM: SERF-mode, 50/60Hz powerline harmonics, full cross-talk compensation via Gaussian elimination with partial pivoting - EEG: 5 frequency bands, eye blink artifacts (Fp1/Fp2), muscle artifacts, impedance-based thermal noise floor - ESP32 ADC: ring-buffer reader with calibration signal generator, i16 clamp Security hardening (SEC-001 through SEC-005): - RVF bounded allocation (16MB metadata, 256MB payload) - sample_rate validation (>0, finite) - Signal NaN/Inf rejection - ADC resolution_bits overflow clamp - HNSW HashSet visited tracking + bounds checks Performance optimizations (PERF-001 through PERF-005): - 67x fewer FFTs via pre-computed analytic signals - VecDeque O(1) eviction in memory store - Thread-local FFT planner caching - BrainGraph::validate() for edge/weight integrity - Eigenvalue convergence early termination Ed25519 witness verification system: - 41 capability attestations across all 12 crates - SHA-256 digest + Ed25519 signature - CLI commands: `witness --output` and `witness --verify` README: ethics warning, hardware parts list (AliExpress), assembly instructions Co-Authored-By: claude-flow <ruv@ruv.net> * docs: add crates.io badges and install instructions to ruv-neural README Add version badges linking to each published crate on crates.io, cargo add instructions, and crate search link in the Crate Map table. Co-Authored-By: claude-flow <ruv@ruv.net> --------- Co-authored-by: Claude <noreply@anthropic.com> |
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| LICENSE | ||
| README.md | ||
| package.json | ||
| range.bnf | ||
| semver.js | ||
README.md
semver(1) -- The semantic versioner for npm
Install
npm install semver
Usage
As a node module:
const semver = require('semver')
semver.valid('1.2.3') // '1.2.3'
semver.valid('a.b.c') // null
semver.clean(' =v1.2.3 ') // '1.2.3'
semver.satisfies('1.2.3', '1.x || >=2.5.0 || 5.0.0 - 7.2.3') // true
semver.gt('1.2.3', '9.8.7') // false
semver.lt('1.2.3', '9.8.7') // true
semver.minVersion('>=1.0.0') // '1.0.0'
semver.valid(semver.coerce('v2')) // '2.0.0'
semver.valid(semver.coerce('42.6.7.9.3-alpha')) // '42.6.7'
As a command-line utility:
$ semver -h
A JavaScript implementation of the https://semver.org/ specification
Copyright Isaac Z. Schlueter
Usage: semver [options] <version> [<version> [...]]
Prints valid versions sorted by SemVer precedence
Options:
-r --range <range>
Print versions that match the specified range.
-i --increment [<level>]
Increment a version by the specified level. Level can
be one of: major, minor, patch, premajor, preminor,
prepatch, or prerelease. Default level is 'patch'.
Only one version may be specified.
--preid <identifier>
Identifier to be used to prefix premajor, preminor,
prepatch or prerelease version increments.
-l --loose
Interpret versions and ranges loosely
-p --include-prerelease
Always include prerelease versions in range matching
-c --coerce
Coerce a string into SemVer if possible
(does not imply --loose)
--rtl
Coerce version strings right to left
--ltr
Coerce version strings left to right (default)
Program exits successfully if any valid version satisfies
all supplied ranges, and prints all satisfying versions.
If no satisfying versions are found, then exits failure.
Versions are printed in ascending order, so supplying
multiple versions to the utility will just sort them.
Versions
A "version" is described by the v2.0.0 specification found at
https://semver.org/.
A leading "=" or "v" character is stripped off and ignored.
Ranges
A version range is a set of comparators which specify versions
that satisfy the range.
A comparator is composed of an operator and a version. The set
of primitive operators is:
<Less than<=Less than or equal to>Greater than>=Greater than or equal to=Equal. If no operator is specified, then equality is assumed, so this operator is optional, but MAY be included.
For example, the comparator >=1.2.7 would match the versions
1.2.7, 1.2.8, 2.5.3, and 1.3.9, but not the versions 1.2.6
or 1.1.0.
Comparators can be joined by whitespace to form a comparator set,
which is satisfied by the intersection of all of the comparators
it includes.
A range is composed of one or more comparator sets, joined by ||. A
version matches a range if and only if every comparator in at least
one of the ||-separated comparator sets is satisfied by the version.
For example, the range >=1.2.7 <1.3.0 would match the versions
1.2.7, 1.2.8, and 1.2.99, but not the versions 1.2.6, 1.3.0,
or 1.1.0.
The range 1.2.7 || >=1.2.9 <2.0.0 would match the versions 1.2.7,
1.2.9, and 1.4.6, but not the versions 1.2.8 or 2.0.0.
Prerelease Tags
If a version has a prerelease tag (for example, 1.2.3-alpha.3) then
it will only be allowed to satisfy comparator sets if at least one
comparator with the same [major, minor, patch] tuple also has a
prerelease tag.
For example, the range >1.2.3-alpha.3 would be allowed to match the
version 1.2.3-alpha.7, but it would not be satisfied by
3.4.5-alpha.9, even though 3.4.5-alpha.9 is technically "greater
than" 1.2.3-alpha.3 according to the SemVer sort rules. The version
range only accepts prerelease tags on the 1.2.3 version. The
version 3.4.5 would satisfy the range, because it does not have a
prerelease flag, and 3.4.5 is greater than 1.2.3-alpha.7.
The purpose for this behavior is twofold. First, prerelease versions frequently are updated very quickly, and contain many breaking changes that are (by the author's design) not yet fit for public consumption. Therefore, by default, they are excluded from range matching semantics.
Second, a user who has opted into using a prerelease version has clearly indicated the intent to use that specific set of alpha/beta/rc versions. By including a prerelease tag in the range, the user is indicating that they are aware of the risk. However, it is still not appropriate to assume that they have opted into taking a similar risk on the next set of prerelease versions.
Note that this behavior can be suppressed (treating all prerelease
versions as if they were normal versions, for the purpose of range
matching) by setting the includePrerelease flag on the options
object to any
functions that do
range matching.
Prerelease Identifiers
The method .inc takes an additional identifier string argument that
will append the value of the string as a prerelease identifier:
semver.inc('1.2.3', 'prerelease', 'beta')
// '1.2.4-beta.0'
command-line example:
$ semver 1.2.3 -i prerelease --preid beta
1.2.4-beta.0
Which then can be used to increment further:
$ semver 1.2.4-beta.0 -i prerelease
1.2.4-beta.1
Advanced Range Syntax
Advanced range syntax desugars to primitive comparators in deterministic ways.
Advanced ranges may be combined in the same way as primitive
comparators using white space or ||.
Hyphen Ranges X.Y.Z - A.B.C
Specifies an inclusive set.
1.2.3 - 2.3.4:=>=1.2.3 <=2.3.4
If a partial version is provided as the first version in the inclusive range, then the missing pieces are replaced with zeroes.
1.2 - 2.3.4:=>=1.2.0 <=2.3.4
If a partial version is provided as the second version in the inclusive range, then all versions that start with the supplied parts of the tuple are accepted, but nothing that would be greater than the provided tuple parts.
1.2.3 - 2.3:=>=1.2.3 <2.4.01.2.3 - 2:=>=1.2.3 <3.0.0
X-Ranges 1.2.x 1.X 1.2.* *
Any of X, x, or * may be used to "stand in" for one of the
numeric values in the [major, minor, patch] tuple.
*:=>=0.0.0(Any version satisfies)1.x:=>=1.0.0 <2.0.0(Matching major version)1.2.x:=>=1.2.0 <1.3.0(Matching major and minor versions)
A partial version range is treated as an X-Range, so the special character is in fact optional.
""(empty string) :=*:=>=0.0.01:=1.x.x:=>=1.0.0 <2.0.01.2:=1.2.x:=>=1.2.0 <1.3.0
Tilde Ranges ~1.2.3 ~1.2 ~1
Allows patch-level changes if a minor version is specified on the comparator. Allows minor-level changes if not.
~1.2.3:=>=1.2.3 <1.(2+1).0:=>=1.2.3 <1.3.0~1.2:=>=1.2.0 <1.(2+1).0:=>=1.2.0 <1.3.0(Same as1.2.x)~1:=>=1.0.0 <(1+1).0.0:=>=1.0.0 <2.0.0(Same as1.x)~0.2.3:=>=0.2.3 <0.(2+1).0:=>=0.2.3 <0.3.0~0.2:=>=0.2.0 <0.(2+1).0:=>=0.2.0 <0.3.0(Same as0.2.x)~0:=>=0.0.0 <(0+1).0.0:=>=0.0.0 <1.0.0(Same as0.x)~1.2.3-beta.2:=>=1.2.3-beta.2 <1.3.0Note that prereleases in the1.2.3version will be allowed, if they are greater than or equal tobeta.2. So,1.2.3-beta.4would be allowed, but1.2.4-beta.2would not, because it is a prerelease of a different[major, minor, patch]tuple.
Caret Ranges ^1.2.3 ^0.2.5 ^0.0.4
Allows changes that do not modify the left-most non-zero element in the
[major, minor, patch] tuple. In other words, this allows patch and
minor updates for versions 1.0.0 and above, patch updates for
versions 0.X >=0.1.0, and no updates for versions 0.0.X.
Many authors treat a 0.x version as if the x were the major
"breaking-change" indicator.
Caret ranges are ideal when an author may make breaking changes
between 0.2.4 and 0.3.0 releases, which is a common practice.
However, it presumes that there will not be breaking changes between
0.2.4 and 0.2.5. It allows for changes that are presumed to be
additive (but non-breaking), according to commonly observed practices.
^1.2.3:=>=1.2.3 <2.0.0^0.2.3:=>=0.2.3 <0.3.0^0.0.3:=>=0.0.3 <0.0.4^1.2.3-beta.2:=>=1.2.3-beta.2 <2.0.0Note that prereleases in the1.2.3version will be allowed, if they are greater than or equal tobeta.2. So,1.2.3-beta.4would be allowed, but1.2.4-beta.2would not, because it is a prerelease of a different[major, minor, patch]tuple.^0.0.3-beta:=>=0.0.3-beta <0.0.4Note that prereleases in the0.0.3version only will be allowed, if they are greater than or equal tobeta. So,0.0.3-pr.2would be allowed.
When parsing caret ranges, a missing patch value desugars to the
number 0, but will allow flexibility within that value, even if the
major and minor versions are both 0.
^1.2.x:=>=1.2.0 <2.0.0^0.0.x:=>=0.0.0 <0.1.0^0.0:=>=0.0.0 <0.1.0
A missing minor and patch values will desugar to zero, but also
allow flexibility within those values, even if the major version is
zero.
^1.x:=>=1.0.0 <2.0.0^0.x:=>=0.0.0 <1.0.0
Range Grammar
Putting all this together, here is a Backus-Naur grammar for ranges, for the benefit of parser authors:
range-set ::= range ( logical-or range ) *
logical-or ::= ( ' ' ) * '||' ( ' ' ) *
range ::= hyphen | simple ( ' ' simple ) * | ''
hyphen ::= partial ' - ' partial
simple ::= primitive | partial | tilde | caret
primitive ::= ( '<' | '>' | '>=' | '<=' | '=' ) partial
partial ::= xr ( '.' xr ( '.' xr qualifier ? )? )?
xr ::= 'x' | 'X' | '*' | nr
nr ::= '0' | ['1'-'9'] ( ['0'-'9'] ) *
tilde ::= '~' partial
caret ::= '^' partial
qualifier ::= ( '-' pre )? ( '+' build )?
pre ::= parts
build ::= parts
parts ::= part ( '.' part ) *
part ::= nr | [-0-9A-Za-z]+
Functions
All methods and classes take a final options object argument. All
options in this object are false by default. The options supported
are:
looseBe more forgiving about not-quite-valid semver strings. (Any resulting output will always be 100% strict compliant, of course.) For backwards compatibility reasons, if theoptionsargument is a boolean value instead of an object, it is interpreted to be thelooseparam.includePrereleaseSet to suppress the default behavior of excluding prerelease tagged versions from ranges unless they are explicitly opted into.
Strict-mode Comparators and Ranges will be strict about the SemVer strings that they parse.
valid(v): Return the parsed version, or null if it's not valid.inc(v, release): Return the version incremented by the release type (major,premajor,minor,preminor,patch,prepatch, orprerelease), or null if it's not validpremajorin one call will bump the version up to the next major version and down to a prerelease of that major version.preminor, andprepatchwork the same way.- If called from a non-prerelease version, the
prereleasewill work the same asprepatch. It increments the patch version, then makes a prerelease. If the input version is already a prerelease it simply increments it.
prerelease(v): Returns an array of prerelease components, or null if none exist. Example:prerelease('1.2.3-alpha.1') -> ['alpha', 1]major(v): Return the major version number.minor(v): Return the minor version number.patch(v): Return the patch version number.intersects(r1, r2, loose): Return true if the two supplied ranges or comparators intersect.parse(v): Attempt to parse a string as a semantic version, returning either aSemVerobject ornull.
Comparison
gt(v1, v2):v1 > v2gte(v1, v2):v1 >= v2lt(v1, v2):v1 < v2lte(v1, v2):v1 <= v2eq(v1, v2):v1 == v2This is true if they're logically equivalent, even if they're not the exact same string. You already know how to compare strings.neq(v1, v2):v1 != v2The opposite ofeq.cmp(v1, comparator, v2): Pass in a comparison string, and it'll call the corresponding function above."==="and"!=="do simple string comparison, but are included for completeness. Throws if an invalid comparison string is provided.compare(v1, v2): Return0ifv1 == v2, or1ifv1is greater, or-1ifv2is greater. Sorts in ascending order if passed toArray.sort().rcompare(v1, v2): The reverse of compare. Sorts an array of versions in descending order when passed toArray.sort().compareBuild(v1, v2): The same ascomparebut considersbuildwhen two versions are equal. Sorts in ascending order if passed toArray.sort().v2is greater. Sorts in ascending order if passed toArray.sort().diff(v1, v2): Returns difference between two versions by the release type (major,premajor,minor,preminor,patch,prepatch, orprerelease), or null if the versions are the same.
Comparators
intersects(comparator): Return true if the comparators intersect
Ranges
validRange(range): Return the valid range or null if it's not validsatisfies(version, range): Return true if the version satisfies the range.maxSatisfying(versions, range): Return the highest version in the list that satisfies the range, ornullif none of them do.minSatisfying(versions, range): Return the lowest version in the list that satisfies the range, ornullif none of them do.minVersion(range): Return the lowest version that can possibly match the given range.gtr(version, range): Returntrueif version is greater than all the versions possible in the range.ltr(version, range): Returntrueif version is less than all the versions possible in the range.outside(version, range, hilo): Return true if the version is outside the bounds of the range in either the high or low direction. Thehiloargument must be either the string'>'or'<'. (This is the function called bygtrandltr.)intersects(range): Return true if any of the ranges comparators intersect
Note that, since ranges may be non-contiguous, a version might not be
greater than a range, less than a range, or satisfy a range! For
example, the range 1.2 <1.2.9 || >2.0.0 would have a hole from 1.2.9
until 2.0.0, so the version 1.2.10 would not be greater than the
range (because 2.0.1 satisfies, which is higher), nor less than the
range (since 1.2.8 satisfies, which is lower), and it also does not
satisfy the range.
If you want to know if a version satisfies or does not satisfy a
range, use the satisfies(version, range) function.
Coercion
coerce(version, options): Coerces a string to semver if possible
This aims to provide a very forgiving translation of a non-semver string to
semver. It looks for the first digit in a string, and consumes all
remaining characters which satisfy at least a partial semver (e.g., 1,
1.2, 1.2.3) up to the max permitted length (256 characters). Longer
versions are simply truncated (4.6.3.9.2-alpha2 becomes 4.6.3). All
surrounding text is simply ignored (v3.4 replaces v3.3.1 becomes
3.4.0). Only text which lacks digits will fail coercion (version one
is not valid). The maximum length for any semver component considered for
coercion is 16 characters; longer components will be ignored
(10000000000000000.4.7.4 becomes 4.7.4). The maximum value for any
semver component is Integer.MAX_SAFE_INTEGER || (2**53 - 1); higher value
components are invalid (9999999999999999.4.7.4 is likely invalid).
If the options.rtl flag is set, then coerce will return the right-most
coercible tuple that does not share an ending index with a longer coercible
tuple. For example, 1.2.3.4 will return 2.3.4 in rtl mode, not
4.0.0. 1.2.3/4 will return 4.0.0, because the 4 is not a part of
any other overlapping SemVer tuple.
Clean
clean(version): Clean a string to be a valid semver if possible
This will return a cleaned and trimmed semver version. If the provided version is not valid a null will be returned. This does not work for ranges.
ex.
s.clean(' = v 2.1.5foo'):nulls.clean(' = v 2.1.5foo', { loose: true }):'2.1.5-foo's.clean(' = v 2.1.5-foo'):nulls.clean(' = v 2.1.5-foo', { loose: true }):'2.1.5-foo's.clean('=v2.1.5'):'2.1.5's.clean(' =v2.1.5'):2.1.5s.clean(' 2.1.5 '):'2.1.5's.clean('~1.0.0'):null