Research-Stack/0-Core-Formalism/lean/Semantics/Semantics/StreamCompression.lean
allaun 8845c9c347 fix(lean): complete projectionOrdering proof in GeometricCompressionWorkspace
Replace the TODO(lean-port) sorry with a complete proof of the
projectionOrdering theorem: for positive SourceValue pairs s1 < s2
with s2 ≤ maxExpected, projectToCoding preserves strict ordering
of the Q0_64 values.

The proof uses Nat-only arithmetic (no Float) and handles two cases:
  - a2 < d: both values fit in Q0_64 range, ordering follows from
    monotonicity of integer division
  - a2 = d: a2*s/d = s clamped to q0_64MaxRaw; a1*s/d < q0_64MaxRaw
    via the key inequality (d-1)*s < (s-1)*d

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/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Research Stack Team
StreamCompression.lean — DSP-Aware Stream Compression with Spectral Analysis
This module formalizes DSP-aware compression for streaming data:
- Sample-block processing (DSP standard)
- Spectral redundancy detection (FFT-based)
- Q16_16 fixed-point for hardware extraction
- FPGA DSP slice integration (TSM opcodes 0x14, 0x42)
- Energy-aware decompression scheduling
Key insight:
DSP compression treats data as continuous signals, not discrete bytes.
Spectral analysis identifies redundancy in frequency domain, not just spatial.
Per AGENTS.md §1.4: Q16_16 fixed-point for hardware extraction.
Per AGENTS.md §2: PascalCase types, camelCase functions.
Per AGENTS.md §4: Every def has eval witness or theorem.
NOTE(lean-port): Connect to FPGA Warden Node AMMR accumulator
NOTE(lean-port): Integrate PhiRedundancy 3-stream scheme as erasure coding
NOTE(lean-port): Integrate swarm design review
-/
import Mathlib.Data.Nat.Basic
import Mathlib.Data.Real.Basic
import Mathlib.Tactic
import Semantics.FixedPoint
import Semantics.SwarmDesignReview
import Semantics.Timing
namespace Semantics.StreamCompression
open Semantics.Q16_16
open Semantics.SwarmDesignReview
open Semantics.Timing
-- ═══════════════════════════════════════════════════════════════════════════
-- §0 DSP Sample Types
-- ═══════════════════════════════════════════════════════════════════════════
/-- DSP sample in Q16.16 fixed-point format. -/
abbrev DspSample := Q16_16
/-- Sample block (DSP standard processing unit). -/
structure SampleBlock where
samples : Array DspSample
sampleRate : Nat -- Hz
deriving Repr, Inhabited
/-- Frequency domain representation (FFT output). -/
structure FrequencyDomain where
real : Array DspSample -- Real components
imag : Array DspSample -- Imaginary components
deriving Repr, Inhabited
/-- Spectral band for compression decisions. -/
structure SpectralBand where
lowFreq : Nat -- Lower bound (Hz)
highFreq : Nat -- Upper bound (Hz)
energy : Q16_16 -- Band energy (Q16.16)
redundancy : Q16_16 -- Redundancy factor (0 = unique, 1 = fully redundant)
deriving Repr, Inhabited
-- ═══════════════════════════════════════════════════════════════════════════
-- §1 DSP Primitives (Fixed-Point)
-- ═══════════════════════════════════════════════════════════════════════════
/-- Compute energy of sample block: E = Σ x². -/
def computeEnergy (block : SampleBlock) : Q16_16 :=
block.samples.foldl (fun acc s => acc + s * s) zero
/-- Compute mean of sample block. -/
def computeMean (block : SampleBlock) : Q16_16 :=
if block.samples.isEmpty then zero
else
let sum := block.samples.foldl (fun acc s => acc + s) zero
div sum (ofNat block.samples.size)
/-- Compute variance: Var = E[x²] - E[x]². -/
def computeVariance (block : SampleBlock) : Q16_16 :=
let mean := computeMean block
let meanSq := mean * mean
let energy := computeEnergy block
let energyPerSample := div energy (ofNat block.samples.size)
sub energyPerSample meanSq
/-- Simple FIR filter: y[n] = Σ h[k] * x[n-k]. Stub: returns block unchanged. -/
def firFilter (coeffs : Array Q16_16) (block : SampleBlock) : SampleBlock :=
block
-- ═══════════════════════════════════════════════════════════════════════════
-- §2 Spectral Analysis (FFT Approximation)
-- ═══════════════════════════════════════════════════════════════════════════
/-- Power-of-2 check for FFT. -/
def isPowerOfTwo (n : Nat) : Bool :=
n > 0 && (n &&& (n - 1)) = 0
/-- Next power of 2 ≥ n (iterative, no recursion). -/
def nextPowerOfTwo (n : Nat) : Nat :=
if n = 0 then 1 else
let n1 := n - 1
let n2 := n1 ||| (n1 >>> 1)
let n3 := n2 ||| (n2 >>> 2)
let n4 := n3 ||| (n3 >>> 4)
let n5 := n4 ||| (n4 >>> 8)
let n6 := n5 ||| (n5 >>> 16)
n6 + 1
/-- Cooley-Tukey FFT (simplified, fixed-point).
Note: Full FFT implementation requires complex arithmetic.
This is a placeholder for spectral energy estimation. -/
def computeFFT (block : SampleBlock) : FrequencyDomain :=
let n := block.samples.size
if ¬isPowerOfTwo n then
-- Zero-pad to next power of 2
let paddedSize := nextPowerOfTwo n
let padded := Array.mk (List.replicate paddedSize zero)
let filled := padded.foldl (fun arr _ => arr.push zero) block.samples
{ real := filled, imag := Array.mk (List.replicate paddedSize zero) }
else
{ real := block.samples, imag := Array.mk (List.replicate n zero) }
/-- Compute spectral energy in frequency band. -/
def bandEnergy (freq : FrequencyDomain) (low high : Nat) (sampleRate : Nat) : Q16_16 :=
let n := freq.real.size
if n = 0 then zero else
let binWidth := sampleRate / n
let lowBin := low / binWidth
let highBin := high / binWidth
let indices := List.range n |>.filter (fun i => i ≥ lowBin && i ≤ highBin)
indices.foldl (fun acc i =>
if i < freq.real.size && i < freq.imag.size then
let magSq := freq.real[i]! * freq.real[i]! + freq.imag[i]! * freq.imag[i]!
acc + magSq
else acc) zero
/-- Detect spectral redundancy: compare band energy to total. -/
def spectralRedundancy (bandEnergy totalEnergy : Q16_16) : Q16_16 :=
if totalEnergy = zero then zero
else div bandEnergy totalEnergy
-- ═══════════════════════════════════════════════════════════════════════════
-- §3 DSP-Aware Compression
-- ═══════════════════════════════════════════════════════════════════════════
/-- Compression decision based on spectral analysis. -/
inductive CompressionMode where
| lossless -- No compression (unique spectral content)
| lossy8 -- 8-bit quantization
| lossy4 -- 4-bit quantization
| spectral -- Spectral redundancy coding
| genetic -- Genetic compression (DNA/Protein field-guided encoding)
deriving Repr, DecidableEq
/-- DSP compression parameters with curvature coupling from self-compression and genomic field. -/
structure DspCompressionParams where
sampleRate : Nat
blocksize : Nat
quantizationBits : Nat -- 8, 4, or 1
spectralThreshold : Q16_16 -- Redundancy threshold
kappaSquared : Q16_16 -- Curvature coupling κ² from self-compression (arXiv:2301.13142)
-- Genomic field parameters for genetic compression
rhoSeq : Q16_16 -- ρ_seq²: sequence alignment accuracy
vEpigenetic : Q16_16 -- v_epigenetic²: methylation dynamics
tauStructure : Q16_16 -- τ_structure²: 3D folding tension
sigmaEntropy : Q16_16 -- σ_entropy²: nucleotide diversity
qConservation : Q16_16 -- q_conservation²: evolutionary constraint
kappaHierarchy : Q16_16 -- κ_hierarchy²: chromatin levels
epsilonMutation : Q16_16 -- ε_mutation: mutation rate
deriving Repr
/-- Analyze spectral bands and select compression mode with curvature and genomic awareness.
κ² modulates the spectral threshold: higher curvature = more aggressive compression.
Genomic field parameters enable genetic compression when data shows sequence-like structure.
This connects self-compression geometric structure and genomic field to DSP spectral decisions. -/
def selectCompressionMode
(block : SampleBlock)
(params : DspCompressionParams) : CompressionMode :=
let freq := computeFFT block
let totalEnergy := bandEnergy freq 0 (params.sampleRate / 2) params.sampleRate
let lowBandEnergy := bandEnergy freq 0 (params.sampleRate / 4) params.sampleRate
let redundancy := spectralRedundancy lowBandEnergy totalEnergy
-- Curvature-aware threshold: κ² increases effective threshold (more compression)
let curvatureFactor := Q16_16.one + params.kappaSquared
let adjustedThreshold := mul params.spectralThreshold curvatureFactor
-- Genomic field strength: sum of genomic parameters
let genomicStrength := params.rhoSeq + params.vEpigenetic + params.tauStructure +
params.sigmaEntropy + params.qConservation
let hierarchyFactor := Q16_16.one + params.kappaHierarchy
let genomicWeight := mul genomicStrength hierarchyFactor
-- Genetic compression enabled when genomic field is strong enough
if genomicWeight > (ofNat 100) then
CompressionMode.genetic
else if redundancy > adjustedThreshold then
CompressionMode.spectral
else if totalEnergy < (ofNat 100) then -- Low energy = simple signal
CompressionMode.lossy4
else
CompressionMode.lossless
/-- Compress sample block using selected mode with curvature and genomic-aware compression ratio.
κ² increases compression ratio for curved manifolds (quantization structure).
Genomic field parameters enable DNA/Protein-like compression with hierarchy-aware encoding. -/
def compressBlock
(block : SampleBlock)
(params : DspCompressionParams)
(mode : CompressionMode) : Nat × Q16_16 :=
let originalSize := block.samples.size * 2 -- 2 bytes per Q16_16
-- Curvature increases compression ratio for spectral mode
let curvatureBoost := Q16_16.one + params.kappaSquared
-- Genomic field denominator for genetic compression
let kappaSq := params.kappaHierarchy * params.kappaHierarchy
let geomTerm := Q16_16.one + kappaSq
let mutTerm := Q16_16.one + params.epsilonMutation
let genomicDenom := mul geomTerm mutTerm
let genomicNumerator := params.rhoSeq + params.vEpigenetic + params.tauStructure +
params.sigmaEntropy + params.qConservation
let genomicWeight := div genomicNumerator genomicDenom
match mode with
| CompressionMode.lossless => (originalSize, Q16_16.one)
| CompressionMode.lossy8 => (originalSize / 2, ofNat 2)
| CompressionMode.lossy4 => (originalSize / 4, ofNat 4)
| CompressionMode.spectral =>
let baseRatio := ofNat 8
let adjustedRatio := mul baseRatio curvatureBoost
(originalSize / 8, adjustedRatio)
| CompressionMode.genetic =>
let baseRatio := ofNat 12 -- Higher base ratio for genetic compression
let genomicBoost := Q16_16.one + genomicWeight
let adjustedRatio := mul baseRatio genomicBoost
(originalSize / 12, adjustedRatio)
-- ═══════════════════════════════════════════════════════════════════════════
-- §4 FPGA DSP Slice Integration
-- ═══════════════════════════════════════════════════════════════════════════
/-- FPGA DSP opcode mapping (from substrate_isa_spec.md). -/
inductive FpgaDspOpcode where
| resonate -- 0x14: TSM_RESONATE / PHONON_LOCK (Phi=1.618)
| mergeModes -- 0x42: TSM_MERGE_MODES
| ingestVib -- 0x47: TSM_INGEST_VIBRATION
deriving Repr, DecidableEq
/-- DSP slice configuration for compression. -/
structure DspSliceConfig where
opcode : FpgaDspOpcode
phi : Q16_16 -- Resonance parameter (1.618 for golden ratio)
clockCycles : Nat -- Estimated cycles
deriving Repr
/-- Map compression mode to FPGA DSP opcode. -/
def modeToOpcode (mode : CompressionMode) : FpgaDspOpcode :=
match mode with
| CompressionMode.spectral => FpgaDspOpcode.resonate
| CompressionMode.genetic => FpgaDspOpcode.resonate -- Genetic compression uses resonance (phi-encoding)
| CompressionMode.lossless => FpgaDspOpcode.mergeModes
| _ => FpgaDspOpcode.ingestVib
/-- Estimate DSP slice energy cost (cycles * power). -/
def dspEnergyCost (config : DspSliceConfig) : Q16_16 :=
let baseCost := ofNat config.clockCycles
let phiWeight := config.phi
mul baseCost phiWeight
-- ═══════════════════════════════════════════════════════════════════════════
-- §5 Energy-Aware Decompression Scheduling
-- ═══════════════════════════════════════════════════════════════════════════
/-- Decompression task with energy cost and self-compression curvature. -/
structure DecompressionTask where
blockId : Nat
mode : CompressionMode
energyCost : Q16_16 -- DSP energy cost
kappaSquared : Q16_16 -- Curvature coupling from self-compression (arXiv:2301.13142)
priority : Nat -- Lower = higher priority
deriving Repr
/-- Combined cost: DSP energy + curvature penalty from self-compression.
Higher κ² = higher structural complexity = higher scheduling priority. -/
def combinedCost (task : DecompressionTask) : Q16_16 :=
let curvaturePenalty := mul task.kappaSquared (ofNat 1000) -- Scale κ² to energy units
task.energyCost + curvaturePenalty
/-- Energy-aware scheduler: high-cost (energy + curvature) blocks first.
This integrates self-compression geometric structure into scheduling decisions. -/
def scheduleDecompression (tasks : Array DecompressionTask) : Array DecompressionTask :=
tasks.qsort (fun t1 t2 => combinedCost t1 > combinedCost t2)
-- ═══════════════════════════════════════════════════════════════════════════
-- §6 Theorems
-- ═══════════════════════════════════════════════════════════════════════════
/-- Claim boundary: computed sample energy should be non-negative. -/
def energyNonneg (block : SampleBlock) : Prop :=
computeEnergy block ≥ zero
/-- Claim boundary: computed sample variance should be non-negative. -/
def varianceNonneg (block : SampleBlock) : Prop :=
computeVariance block ≥ zero
/-- Claim boundary: spectral redundancy should stay in [0, 1]. -/
def redundancyBounded (band total : Q16_16) (_hPos : total > zero) : Prop :=
zero ≤ spectralRedundancy band total ∧ spectralRedundancy band total ≤ Q16_16.one
/-- Claim boundary: compression ratio should be ≥ 1 (no expansion). -/
def compressionRatioAtLeastOne
(block : SampleBlock)
(params : DspCompressionParams)
(mode : CompressionMode) : Prop :=
Q16_16.one ≤ (compressBlock block params mode).2
/-- Claim boundary: combined cost should be non-negative (energy + curvature penalty). -/
def combinedCostNonneg (task : DecompressionTask) : Prop :=
combinedCost task ≥ zero
/-- Claim boundary: higher κ² should increase scheduling priority (combined cost). -/
def curvatureIncreasesPriority
(task : DecompressionTask)
(kappa1 kappa2 : Q16_16)
(_h : kappa1 > kappa2) : Prop :=
combinedCost { task with kappaSquared := kappa1 } >
combinedCost { task with kappaSquared := kappa2 }
-- ═══════════════════════════════════════════════════════════════════════════
-- §8 Swarm Design Review Integration
-- ═══════════════════════════════════════════════════════════════════════════
/-- Extract geometric parameters from DSP compression params for swarm review. -/
def extractGeometricParams (params : DspCompressionParams) : GeometricParameters :=
{ kappaSquared := params.kappaSquared,
rhoSeq := params.rhoSeq,
vEpigenetic := params.vEpigenetic,
tauStructure := params.tauStructure,
sigmaEntropy := params.sigmaEntropy,
qConservation := params.qConservation,
kappaHierarchy := params.kappaHierarchy,
epsilonMutation := params.epsilonMutation }
/-- Run swarm design review on compression parameters.
Returns swarm analysis with consensus and recommendations for improvement. -/
def runSwarmDesignReview (params : DspCompressionParams) : SwarmState :=
let geomParams := extractGeometricParams params
let swarm := initializeSwarm
runSwarmAnalysis swarm geomParams
/-- Apply swarm recommendations to improve compression parameters.
This function interprets swarm consensus and adjusts parameters accordingly. -/
def applySwarmRecommendations (params : DspCompressionParams) (swarm : SwarmState) : DspCompressionParams :=
-- If consensus is low (< 0.5), increase geometric parameters
if swarm.consensus < (ofNat 32768) then -- 0.5 in Q16.16
-- Boost all geometric parameters to improve utilization
{ params with
kappaSquared := mul params.kappaSquared (ofNat 150) -- 1.5x boost
kappaHierarchy := mul params.kappaHierarchy (ofNat 150)
epsilonMutation := mul params.epsilonMutation (ofNat 150)
rhoSeq := mul params.rhoSeq (ofNat 120)
vEpigenetic := mul params.vEpigenetic (ofNat 120)
tauStructure := mul params.tauStructure (ofNat 120)
sigmaEntropy := mul params.sigmaEntropy (ofNat 120)
qConservation := mul params.qConservation (ofNat 120)
}
else
-- Parameters are well-tuned, keep as-is
params
-- ═══════════════════════════════════════════════════════════════════════════
-- §9 FAMM Integration (Frustration-Aware Manifold Memory)
-- ═══════════════════════════════════════════════════════════════════════════
/-- FAMM-aware compression parameters with frustration-based timing. -/
structure FammCompressionParams where
baseParams : DspCompressionParams
-- FAMM timing parameters
torsionalStress : Q16_16 -- Σ²: torsional stress from manifold state
interlockingEnergy : Q16_16 -- I_lock: interlocking energy
laplacianEnergy : Q16_16 -- Δϕ: Hodge-Laplacian vibration energy
deriving Repr
/-- Derive FAMM timing from compression geometric parameters.
Maps κ² to torsional stress and κ_hierarchy² to interlocking energy. -/
def deriveFammTiming (params : DspCompressionParams) : FammCompressionParams :=
-- Map curvature κ² to torsional stress (higher curvature = more stress)
let torsionalStress := params.kappaSquared
-- Map hierarchy κ² to interlocking energy (hierarchy depth = lock strength)
let kappaSq := params.kappaHierarchy * params.kappaHierarchy
let interlockingEnergy := div kappaSq (Q16_16.one + kappaSq)
-- Map spectral redundancy to laplacian energy (redundancy = neighbor vibration)
let laplacianEnergy := params.spectralThreshold
{
baseParams := params,
torsionalStress := torsionalStress,
interlockingEnergy := interlockingEnergy,
laplacianEnergy := laplacianEnergy
}
/-- Compress with FAMM-aware timing adjustments.
Uses FAMM timing to modulate compression mode selection and ratios. -/
def compressBlockFamm
(block : SampleBlock)
(fammParams : FammCompressionParams) : Nat × Q16_16 × ManifoldTiming :=
let timing := deriveTiming
{ phi := zero,
x_pos := { x := fammParams.interlockingEnergy, y := zero },
x0_pos := { x := zero, y := zero },
g := { xx := zero, xy := zero, yy := zero },
t := { t1_12 := fammParams.torsionalStress, t2_12 := zero },
a := { xx := Q16_16.one, xy := zero, yy := zero }
} fammParams.laplacianEnergy
let params := fammParams.baseParams
let originalSize := block.samples.size * 2
-- FAMM-aware compression: adjust parameters based on timing
let adjustedParams :=
-- If tTCL is high (high stress), use more aggressive compression
if timing.tcl > (ofNat 22) then -- Above baseline
{ params with
kappaSquared := mul params.kappaSquared (ofNat 120), -- Boost κ²
spectralThreshold := mul params.spectralThreshold (ofNat 110) -- Tighten threshold
}
-- If tMRE is high (slipping manifold), refresh more aggressively
else if timing.mre > (ofNat 131072) then -- Above baseline
{ params with
kappaHierarchy := mul params.kappaHierarchy (ofNat 120) -- Boost hierarchy
}
else
params
let mode := selectCompressionMode block adjustedParams
let (compressedSize, ratio) := compressBlock block adjustedParams mode
(compressedSize, ratio, timing)
-- ═══════════════════════════════════════════════════════════════════════════
-- §7 Verification Examples
-- ═══════════════════════════════════════════════════════════════════════════
def exampleBlock : SampleBlock :=
{ samples := #[Q16_16.one, two, Q16_16.one, two, Q16_16.one, two, Q16_16.one, two],
sampleRate := 48000 }
def exampleParams : DspCompressionParams :=
{ sampleRate := 48000,
blocksize := 8,
quantizationBits := 8,
spectralThreshold := ofNat 50, -- 50% redundancy threshold
kappaSquared := zero,
rhoSeq := zero,
vEpigenetic := zero,
tauStructure := zero,
sigmaEntropy := zero,
qConservation := zero,
kappaHierarchy := zero,
epsilonMutation := zero }
#eval computeEnergy exampleBlock
-- Expected: 8 * (1² + 2²) = 8 * 5 = 40 (in Q16.16)
#eval! computeMean exampleBlock
-- Expected: (1+2+1+2+1+2+1+2)/8 = 12/8 = 1.5
#eval! computeVariance exampleBlock
-- Expected: E[x²] - E[x]² = 5 - 2.25 = 2.75
#eval! selectCompressionMode exampleBlock exampleParams
-- Expected: spectral (high redundancy in repeating pattern)
#eval! compressBlock exampleBlock exampleParams CompressionMode.lossy4
-- Expected: (16/4 = 4 bytes, 4.0 ratio)
#eval modeToOpcode CompressionMode.spectral
-- Expected: resonate
#eval dspEnergyCost { opcode := FpgaDspOpcode.resonate, phi := ofNat 16180, clockCycles := 100 }
-- Expected: 100 * 1.618 ≈ 161.8
end Semantics.StreamCompression