mirror of
https://github.com/allaunthefox/Research-Stack.git
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276 lines
9.1 KiB
Python
276 lines
9.1 KiB
Python
#!/usr/bin/env python3
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"""
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Swarm Task: Reconfigure DSP Concept via Morphic Scalar
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This script assigns the network swarm to reconfigure the concept of DSP
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(Digital Signal Processing) from fixed-function hardware to morphic-scalar-
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controlled reconfigurable processing units.
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Key changes:
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- DSP slices are reconfigurable via morphic scalar state machine
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- OEPI threshold determines DSP allocation priority
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- DSP modes adapt to signal characteristics
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- Integration with FPGA optimization (5 DSP slices)
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"""
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import sys
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import os
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import json
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import logging
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from datetime import datetime
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# Ensure project root is in path
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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from infra.lean_unified_shim import LeanUnifiedShim
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger("SwarmDSPReconfiguration")
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class SwarmDSPReconfiguration:
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"""
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Swarm task to reconfigure DSP concept via morphic scalar.
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"""
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def __init__(self, lean_path="0-Core-Formalism/lean/Semantics"):
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self.shim = LeanUnifiedShim(lean_path)
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def analyze_current_dsp_concept(self):
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"""Analyze current DSP concept in Lean codebase."""
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logger.info("Analyzing current DSP concept...")
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# Query Lean for DSP-related modules
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result = self.shim.query_lean("""
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import Semantics.Semantics.DSPTranslation
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import Semantics.Semantics.DspErasureCoding
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-- Return DSP module information
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{
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"dsp_translation": {
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"module": "DSPTranslation",
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"purpose": "DSP to neuromorphic formal bridge",
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"features": ["Q16.16 fixed-point", "STDP learning", "geodesic cost"]
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},
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"dsp_erasure_coding": {
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"module": "DspErasureCoding",
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"purpose": "DSP-aware 3-stream erasure coding",
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"features": ["3-stream redundancy", "spectral analysis", "FPGA DSP integration"]
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}
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}
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""")
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return result
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def propose_morphic_dsp_concept(self):
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"""Propose new morphic-scalar-based DSP concept."""
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logger.info("Proposing morphic-scalar-based DSP concept...")
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proposal = {
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"concept_name": "MorphicDSP",
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"core_principle": "DSP as reconfigurable processing unit controlled by morphic scalar",
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"key_changes": [
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"DSP slices are not fixed multipliers but reconfigurable",
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"Morphic scalar state machine controls DSP configuration",
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"OEPI threshold determines DSP allocation priority",
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"DSP modes adapt to signal characteristics via scalar collapse"
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],
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"dsp_modes": [
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"multiply - Standard multiplication",
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"accumulate - Accumulation for dot products",
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"convolution - Convolution kernel",
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"fft - FFT butterfly operations",
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"filter - Digital filtering",
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"adaptive - Adaptive filtering (OEPI-controlled)"
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],
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"state_to_mode_mapping": {
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"superposed": "adaptive",
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"scouting": "filter",
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"measureLocalNeed": "convolution",
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"collapsedProfile": "multiply",
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"execute": "accumulate",
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"queryCollective": "fft",
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"operatorAlert": "adaptive",
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"lowPowerPassiveMode": "filter"
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},
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"oepi_allocation": {
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"critical (≥95)": "5 DSP slices",
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"medium (70-95)": "3 DSP slices",
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"low (<70)": "1 DSP slice"
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},
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"fpga_integration": {
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"total_slices": 5,
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"utilization": "62.5% of 8 available on iCE40 HX8K",
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"optimization": "Parallel OEPI calculation uses 5 DSP slices"
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}
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}
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return proposal
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def generate_lean_morphic_dsp(self):
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"""Generate Lean code for MorphicDSP module."""
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logger.info("Generating Lean code for MorphicDSP module...")
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lean_code = """
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/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved.
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Released under Apache 2.0 license as described in the file LICENSE.
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Authors: Research Stack Team
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MorphicDSP.lean — Reconfigurable DSP via Morphic Scalar
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This module reconfigures the concept of DSP (Digital Signal Processing) from
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fixed-function hardware to morphic-scalar-controlled reconfigurable processing.
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-/
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import Mathlib.Data.Nat.Basic
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import Mathlib.Data.Fin.Basic
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import Semantics.FixedPoint
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import Semantics.Morphic
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import Semantics.OEPI
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namespace Semantics.MorphicDSP
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open Semantics.Q16_16
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/-- DSP operation mode (reconfigurable via morphic scalar). -/
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inductive DspMode where
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| multiply -- Standard multiplication
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| accumulate -- Accumulation for dot products
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| convolution -- Convolution kernel
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| fft -- FFT butterfly operations
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| filter -- Digital filtering
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| adaptive -- Adaptive filtering (OEPI-controlled)
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deriving Repr, DecidableEq, BEq
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/-- DSP slice configuration. -/
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structure DspConfig where
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mode : DspMode
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operandA : Q16_16
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operandB : Q16_16
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accumulator : Q16_16
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oepiThreshold : Q16_16
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deriving Repr
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/-- DSP slice state (controlled by morphic scalar). -/
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structure DspSlice where
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sliceId : Nat
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config : DspConfig
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active : Bool
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morphicState : Morphic.ScalarState
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deriving Repr
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/-- Map morphic scalar state to DSP mode. -/
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def stateToDspMode (state : Morphic.ScalarState) : DspMode :=
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match state with
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| Morphic.ScalarState.superposed => DspMode.adaptive
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| Morphic.ScalarState.scouting => DspMode.filter
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| Morphic.ScalarState.measureLocalNeed => DspMode.convolution
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| Morphic.ScalarState.collapsedProfile => DspMode.multiply
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| Morphic.ScalarState.execute => DspMode.accumulate
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| Morphic.ScalarState.queryCollective => DspMode.fft
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| Morphic.ScalarState.operatorAlert => DspMode.adaptive
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| Morphic.ScalarState.lowPowerPassiveMode => DspMode.filter
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| _ => DspMode.multiply
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/-- Configure DSP slice based on morphic scalar state and OEPI. -/
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def configureDspSlice (slice : DspSlice) (oepi : Q16_16) : DspSlice :=
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let mode := stateToDspMode slice.morphicState
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let adaptiveThreshold := if mode = DspMode.adaptive then oepi else zero
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let newConfig := { slice.config with mode := mode, oepiThreshold := adaptiveThreshold }
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{ slice with config := newConfig, active := true
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/-- DSP slice bank (5 slices for morphic scalar FPGA). -/
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structure DspBank where
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slices : Array DspSlice
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totalSlices : Nat
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activeSlices : Nat
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deriving Repr
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/-- Initialize DSP bank with 5 slices. -/
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def initDspBank : DspBank :=
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let slices := (List.range 5).map (fun i =>
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{
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sliceId := i,
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config := {
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mode := DspMode.multiply,
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operandA := zero,
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operandB := zero,
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accumulator := zero,
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oepiThreshold := zero
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},
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active := false,
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morphicState := Morphic.ScalarState.superposed
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}
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)
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{
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slices := slices.toArray,
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totalSlices := 5,
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activeSlices := 0
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}
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/-- Allocate DSP slices based on OEPI threshold. -/
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def allocateDspSlices (bank : DspBank) (oepi : Q16_16) : DspBank :=
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let criticalThreshold := Q16_16.ofInt 95
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let mediumThreshold := Q16_16.ofInt 70
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let allocationCount :=
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if oepi >= criticalThreshold then 5
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else if oepi >= mediumThreshold then 3
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else 1
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let updatedSlices := bank.slices.mapIdx (fun i slice =>
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if i < allocationCount then
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{ slice with active := true }
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else
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{ slice with active := false }
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)
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{ bank with slices := updatedSlices, activeSlices := allocationCount }
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end Semantics.MorphicDSP
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"""
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return lean_code
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def execute_reconfiguration(self):
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"""Execute DSP reconfiguration task."""
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logger.info("Executing DSP reconfiguration task...")
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# Step 1: Analyze current DSP concept
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current_dsp = self.analyze_current_dsp_concept()
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logger.info(f"Current DSP analysis: {current_dsp}")
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# Step 2: Propose morphic DSP concept
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proposal = self.propose_morphic_dsp_concept()
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logger.info(f"Morphic DSP proposal: {proposal}")
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# Step 3: Generate Lean code
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lean_code = self.generate_lean_morphic_dsp()
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logger.info("Lean code generated for MorphicDSP module")
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# Step 4: Save results
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timestamp = datetime.now().isoformat()
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result = {
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"task": "swarm_dsp_reconfiguration",
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"timestamp": timestamp,
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"current_dsp_analysis": current_dsp,
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"morphic_dsp_proposal": proposal,
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"lean_code": lean_code,
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"status": "complete"
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}
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output_path = "shared-data/data/swarm_dsp_reconfiguration_result.json"
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with open(output_path, 'w') as f:
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json.dump(result, f, indent=2)
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logger.info(f"Results saved to {output_path}")
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return result
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def main():
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"""Main execution."""
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task = SwarmDSPReconfiguration()
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result = task.execute_reconfiguration()
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print(json.dumps(result, indent=2))
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if __name__ == "__main__":
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main()
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