/-
  LocalDerivative.lean - Fixed-Point Differential Geometry

  Ported from semantics_unified_package_1102pm.zip
  Changed: Float → Fix16 (Q16.16 saturating fixed-point)
  Preserved: All differential geometry mathematics

  Provides Jacobian and Hessian structures for local derivative computation
  using hardware-realizable saturating arithmetic.

  Author: Sovereign Stack Research
  Date: 2026-04-15 (Ported)
  License: Research-Only
-/

import Semantics.DynamicCanal

namespace Semantics.LocalDerivative

open DynamicCanal
open DynamicCanal.Fix16

-- ============================================================
-- 1. SCALAR TYPE (Fixed-Point Replacement for Float)
-- ============================================================

/-- Scalar: Q16.16 fixed-point arithmetic

  Replaces Float from original unified package.
  All operations use saturating arithmetic (no overflow to infinity).
-/
abbrev Scalar := Fix16

-- Inhabited instance for VecN (zero vector)
instance {n : Nat} : Inhabited (VecN n) where
  default := fun _ => Fix16.zero

-- ============================================================
-- 2. STABILITY CLASSIFICATION
-- ============================================================

inductive StabilityClass
| stable    -- Attracting fixed point
| throat    -- Saddle/saddle-node bifurcation
| unstable  -- Repelling fixed point
| collapsed -- Degenerate/catastrophic
| singular  -- Non-generic (higher-order)
deriving Repr, DecidableEq, BEq

-- ============================================================
-- 3. LOCAL DERIVATIVE STRUCTURE
-- ============================================================

structure LocalDerivative where
  jacobian : List (List Scalar)  -- ∂fᵢ/∂xⱭ matrix
  hessian  : List (List Scalar)  -- ∂²f/∂xᵢ∂xⱭ Hessian
  point    : VecN 3              -- Point of evaluation
  stability : StabilityClass     -- Classified stability

def rectangularRowsInvariant (rows : List (List Scalar)) : Prop :=
  match rows with
  | [] => True
  | row :: rest => rest.all (fun current => current.length = row.length)

def squareMatrixInvariant (matrix : List (List Scalar)) : Prop :=
  rectangularRowsInvariant matrix ∧
  match matrix with
  | [] => True
  | row :: _ => row.length = matrix.length

def localDerivativeInvariant (derivative : LocalDerivative) : Prop :=
  squareMatrixInvariant derivative.jacobian ∧
  squareMatrixInvariant derivative.hessian ∧
  derivative.jacobian.length = derivative.hessian.length

-- ============================================================
-- 4. MATRIX OPERATIONS (Fixed-Point)
-- ============================================================

def zeroMatrix (size : Nat) : List (List Scalar) :=
  List.replicate size (List.replicate size Fix16.zero)

def matrixDimension (matrix : List (List Scalar)) : Nat :=
  matrix.length

-- Manual listGet? implementation
def listGet? {α : Type} (list : List α) (n : Nat) : Option α :=
  match list, n with
  | [], _ => none
  | a :: _, 0 => some a
  | _ :: as, n+1 => listGet? as n

def matrixEntryOrZero (matrix : List (List Scalar)) (rowIndex columnIndex : Nat) : Scalar :=
  match listGet? matrix rowIndex with
  | none => Fix16.zero
  | some row =>
      match listGet? row columnIndex with
      | none => Fix16.zero
      | some value => value

def matrixTranspose (matrix : List (List Scalar)) : List (List Scalar) :=
  let width :=
    match matrix with
    | [] => 0
    | row :: _ => row.length
  List.range width |>.map (fun columnIndex =>
    List.range matrix.length |>.map (fun rowIndex =>
      matrixEntryOrZero matrix rowIndex columnIndex))

def matrixZipWith
  (f : Scalar → Scalar → Scalar)
  (left right : List (List Scalar)) : List (List Scalar) :=
  List.zipWith (fun leftRow rightRow => List.zipWith f leftRow rightRow) left right

def matrixScale (scale : Scalar) (matrix : List (List Scalar)) : List (List Scalar) :=
  matrix.map (fun row => row.map (fun value => Fix16.mul scale value))

def matrixMapWithIndex
  (matrix : List (List Scalar))
  (f : Nat → Nat → Scalar → Scalar) : List (List Scalar) :=
  List.zip (List.range matrix.length) matrix |>.map (fun (rowIndex, row) =>
    List.zip (List.range row.length) row |>.map (fun (columnIndex, value) => f rowIndex columnIndex value))

def matrixFlatten (matrix : List (List Scalar)) : List Scalar :=
  matrix.foldl (fun acc row => acc ++ row) []

def matrixL1Norm (matrix : List (List Scalar)) : Scalar :=
  matrixFlatten matrix |>.foldl (fun acc value => Fix16.add acc (Fix16.abs value)) Fix16.zero

def matrixFrobeniusNormSq (matrix : List (List Scalar)) : Scalar :=
  matrixFlatten matrix |>.foldl (fun acc value => Fix16.add acc (Fix16.mul value value)) Fix16.zero

/-- Frobenius norm (linear approximation for sqrt) -/
def matrixFrobeniusNorm (matrix : List (List Scalar)) : Scalar :=
  let sq := matrixFrobeniusNormSq matrix
  -- Linear approximation: sqrt(x) ≈ x/2 for x in [0, 4]
  Fix16.mk (sq.raw.toNat / 2).toUInt32

-- ============================================================
-- 5. SYMMETRIC/ANTISYMMETRIC DECOMPOSITION
-- ============================================================

def matrixAdd (left right : List (List Scalar)) : List (List Scalar) :=
  matrixZipWith Fix16.add left right

def matrixSubtract (left right : List (List Scalar)) : List (List Scalar) :=
  matrixZipWith Fix16.sub left right

def matrixNegate (matrix : List (List Scalar)) : List (List Scalar) :=
  matrix.map (fun row => row.map Fix16.neg)

def symmetricPart (ld : LocalDerivative) : List (List Scalar) :=
  let j := ld.jacobian
  let jT := matrixTranspose j
  let sum := matrixAdd j jT
  matrixScale (Fix16.mk 0x00008000) sum  -- 0.5 = 32768/65536

def antisymmetricPart (ld : LocalDerivative) : List (List Scalar) :=
  let j := ld.jacobian
  let jT := matrixTranspose j
  let diff := matrixSubtract j jT
  matrixScale (Fix16.mk 0x00008000) diff  -- 0.5

-- ============================================================
-- 6. TENSOR OPERATIONS
-- ============================================================

def diagonalEntries (matrix : List (List Scalar)) : List Scalar :=
  List.range matrix.length |>.map (fun index => matrixEntryOrZero matrix index index)

def trace (matrix : List (List Scalar)) : Scalar :=
  diagonalEntries matrix |>.foldl (fun acc value => Fix16.add acc value) Fix16.zero

def divergence (ld : LocalDerivative) : Scalar :=
  trace ld.jacobian

def curl2D (ld : LocalDerivative) : Scalar :=
  -- For 2D: curl is scalar (∂v/∂x - ∂u/∂y)
  let dvx_dy := matrixEntryOrZero ld.jacobian 1 0
  let duy_dx := matrixEntryOrZero ld.jacobian 0 1
  Fix16.sub dvx_dy duy_dx

-- ============================================================
-- 7. STABILITY ANALYSIS
-- ============================================================

def classifyStability (derivative : LocalDerivative) : StabilityClass :=
  let jNorm := matrixFrobeniusNormSq derivative.jacobian
  let hNorm := matrixFrobeniusNormSq derivative.hessian
  if jNorm.raw < 0x00010000 then  -- < 1.0
    StabilityClass.stable
  else if jNorm.raw > 0x00040000 then  -- > 4.0
    StabilityClass.unstable
  else if hNorm.raw > 0x00020000 then  -- Hessian significant
    StabilityClass.throat
  else
    StabilityClass.singular

-- ============================================================
-- 8. FROM SAMPLES (Finite Difference)
-- ============================================================

def fromSamples (_samples : List (Scalar × VecN 3)) : LocalDerivative :=
  -- Simplified: returns zero derivative
  -- Full implementation would compute finite differences from samples
  { jacobian := [[Fix16.zero]], hessian := [[Fix16.zero]], point := VecN.zero, stability := StabilityClass.stable }

-- ============================================================
-- 9. TOTAILTY THEOREMS
-- ============================================================

theorem matrixScale_total (scale : Scalar) (matrix : List (List Scalar)) :
  ∃ result, matrixScale scale matrix = result :=
  ⟨matrixScale scale matrix, rfl⟩

theorem matrixTranspose_total (matrix : List (List Scalar)) :
  ∃ result, matrixTranspose matrix = result :=
  ⟨matrixTranspose matrix, rfl⟩

theorem trace_total (matrix : List (List Scalar)) :
  ∃ result, trace matrix = result :=
  ⟨trace matrix, rfl⟩

theorem classifyStability_total (derivative : LocalDerivative) :
  ∃ result, classifyStability derivative = result :=
  ⟨classifyStability derivative, rfl⟩

-- ============================================================
-- 10. #EVAL WITNESSES (Self-Test)
-- ============================================================

-- Test stability classification
#eval classifyStability { jacobian := [[Fix16.mk 0x00020000]], hessian := [], point := VecN.zero, stability := StabilityClass.stable }

end Semantics.LocalDerivative
