#!/usr/bin/env python3
"""Independent numerical checks of universal moving-dark-frame synthesis.

All tests concern the specified finite-interval passive variational model.
They are implementation checks, not a proof of the theorem or of novelty.
The full ambient penalty problem is discretized in fixed Cartesian variables;
the target matrix Sturm--Liouville problem is also solved by an independent
fundamental-matrix ODE.
"""
from __future__ import annotations

import argparse
import json
from pathlib import Path

import numpy as np
from numpy.polynomial.legendre import leggauss
from scipy import sparse
from scipy.integrate import solve_ivp, solve_bvp
from scipy.sparse.linalg import splu


LENGTH = 1.4
I2 = np.eye(2, dtype=complex)
SX = np.array([[0, 1], [1, 0]], dtype=complex)
SZ = np.diag([1., -1.]).astype(complex)


def coupling(t):
    """A smooth invertible C; V=C^dagger C is positive and noncommuting."""
    return np.array([[1. + .1*np.cos(1.7*t), .35*np.exp(1.4j*t)],
                     [0., .7 + .2*np.sin(1.1*t)]], dtype=complex)


def potential(t):
    cc = coupling(t)
    return cc.conj().T @ cc


def connection(t):
    return 1j*(.2*SZ + .15*np.cos(2*t)*SX)


def generator(t, include_connection):
    cc = coupling(t)
    aa = connection(t) if include_connection else np.zeros((2,2), complex)
    return np.block([[aa, -cc.conj().T], [cc, np.zeros((2,2), complex)]])


def frame_solution(include_connection=False):
    def rhs(t, flat):
        return (flat.reshape(4,4) @ generator(t, include_connection)).ravel()
    sol = solve_ivp(rhs, (0., LENGTH), np.eye(4, dtype=complex).ravel(),
                    method='DOP853', rtol=2e-12, atol=2e-13, dense_output=True)
    if not sol.success:
        raise RuntimeError(sol.message)
    return sol


def frame_identity_checks():
    sol = frame_solution(True)
    rng = np.random.default_rng(20261020)
    metrics = dict(unitarity=0., projector_spectrum=0., connection=0.,
                   geometric_potential=0., section_energy=0.,
                   ode_finite_difference=0.)
    for t in np.linspace(0., LENGTH, 151):
        uu = sol.sol(t).reshape(4,4)
        nn = uu[:, :2]
        nd = (uu @ generator(t, True))[:, :2]
        mm = np.eye(4)-nn@nn.conj().T
        metrics['unitarity'] = max(metrics['unitarity'], float(np.linalg.norm(uu.conj().T@uu-np.eye(4))))
        metrics['projector_spectrum'] = max(metrics['projector_spectrum'], float(np.max(abs(np.linalg.eigvalsh(mm)-[0,0,1,1]))))
        metrics['connection'] = max(metrics['connection'], float(np.linalg.norm(nn.conj().T@nd-connection(t))))
        metrics['geometric_potential'] = max(metrics['geometric_potential'], float(np.linalg.norm(nd.conj().T@mm@nd-potential(t))))
        xi = rng.normal(size=2)+1j*rng.normal(size=2)
        xid = rng.normal(size=2)+1j*rng.normal(size=2)
        lhs = np.linalg.norm(nd@xi+nn@xid)**2
        rhs = np.linalg.norm(xid+connection(t)@xi)**2+np.vdot(xi,potential(t)@xi).real
        metrics['section_energy'] = max(metrics['section_energy'], float(abs(lhs-rhs)))
        if .001 < t < LENGTH-.001:
            hh = 2e-5
            numerical = (sol.sol(t+hh).reshape(4,4)-sol.sol(t-hh).reshape(4,4))/(2*hh)
            metrics['ode_finite_difference'] = max(metrics['ode_finite_difference'], float(np.linalg.norm(numerical-uu@generator(t,True))))
    vv0,vv1=potential(.2),potential(1.1)
    noncomm = float(np.linalg.norm(vv0@vv1-vv1@vv0))
    assert noncomm > .05
    assert max(metrics[k] for k in metrics if k!='ode_finite_difference') < 1e-8, metrics
    assert metrics['ode_finite_difference'] < 1e-7, metrics
    return dict(maximum_absolute_errors=metrics, noncommuting_potential_commutator_norm=noncomm,
                sample_positions=151, seed=20261020)


def assemble_fem(cells, field, dimension, gauss_order=5):
    """Assemble ∫|P'|²+P†field(t)P for nodal linear Cartesian P."""
    nodes = np.linspace(0., LENGTH, cells+1)
    width = LENGTH/cells
    dim = dimension
    ids = dim*np.arange(cells)[:,None]+np.arange(2*dim)[None,:]
    local = np.zeros((cells,2*dim,2*dim), dtype=complex)
    ident = np.eye(dim)
    local[:,:dim,:dim] += ident/width
    local[:,:dim,dim:] -= ident/width
    local[:,dim:,:dim] -= ident/width
    local[:,dim:,dim:] += ident/width
    points,weights=leggauss(gauss_order)
    for gp,gw in zip(points,weights):
        n1,n2=(1-gp)/2,(1+gp)/2
        locations=nodes[:-1]+width*(gp+1)/2
        vv=np.stack([field(t) for t in locations])
        weight=width*gw/2
        local[:,:dim,:dim]+=weight*n1*n1*vv
        local[:,:dim,dim:]+=weight*n1*n2*vv
        local[:,dim:,:dim]+=weight*n2*n1*vv
        local[:,dim:,dim:]+=weight*n2*n2*vv
    rr=np.broadcast_to(ids[:,:,None],local.shape).ravel()
    cc=np.broadcast_to(ids[:,None,:],local.shape).ravel()
    mat=sparse.coo_matrix((local.ravel(),(rr,cc)),shape=(dim*(cells+1),dim*(cells+1))).tocsc()
    return mat


def inlet_dtn(mat, dim, inlet_dim):
    """P(0)=(z,0), P(L)=0. Return z's inlet energy matrix."""
    boundary=np.arange(inlet_dim)
    free=np.arange(dim,mat.shape[0]-dim)
    mff=mat[free,:][:,free].tocsc()
    mfb=mat[free,:][:,boundary].toarray()
    mbb=mat[boundary,:][:,boundary].toarray()
    factor=splu(mff)
    response=factor.solve(mfb)
    capacity=mbb-mfb.conj().T@response
    residue=np.linalg.norm(mff@response-mfb)/max(np.linalg.norm(mfb),1.)
    return (capacity+capacity.conj().T)/2, float(residue)


def target_dtn_ode():
    def rhs(t,flat):
        state=flat.reshape(4,4)
        op=np.block([[np.zeros((2,2)),I2],[potential(t),np.zeros((2,2))]])
        return (op@state).ravel()
    sol=solve_ivp(rhs,(0.,LENGTH),np.eye(4,dtype=complex).ravel(),
                  method='DOP853',rtol=2e-12,atol=2e-13)
    if not sol.success:raise RuntimeError(sol.message)
    flow=sol.y[:,-1].reshape(4,4)
    response=np.linalg.solve(flow[:2,2:],flow[:2,:2])
    return (response+response.conj().T)/2


def penalty_boundary_checks():
    target=target_dtn_ode()
    target_rows=[]
    for cells in (200,400,800):
        hh,res=inlet_dtn(assemble_fem(cells,potential,2),2,2)
        err=float(np.linalg.norm(hh-target)/np.linalg.norm(target))
        target_rows.append(dict(cells=cells,relative_error_to_ode=err,linear_residual=res))
    assert target_rows[-1]['relative_error_to_ode'] < 2e-6
    sol=frame_solution(False)
    def projector(t):
        uu=sol.sol(t).reshape(4,4)
        bb=uu[:,2:]
        return bb@bb.conj().T
    # Precompute the stiffness and unit-penalty matrices once for each mesh.
    rows=[]
    for cells in (800,1600):
        kk=assemble_fem(cells,lambda t:np.zeros((4,4),complex),4)
        mass=assemble_fem(cells,projector,4)-kk
        last=None
        for penalty in (1.,10.,100.,1000.,10000.,100000.):
            hh,res=inlet_dtn(kk+penalty*mass,4,2)
            err=float(np.linalg.norm(hh-target)/np.linalg.norm(target))
            row=dict(cells=cells,penalty=penalty,relative_error_to_limit=err,
                     linear_residual=res,eigenvalues=np.linalg.eigvalsh(hh).tolist(),
                     real_part=hh.real.tolist(),imaginary_part=hh.imag.tolist())
            if last is not None:
                row['least_eigenvalue_of_capacity_increment']=float(np.min(np.linalg.eigvalsh(hh-last)))
                assert row['least_eigenvalue_of_capacity_increment'] > -1e-8
            last=hh
            rows.append(row)
    finest=[r for r in rows if r['cells']==1600]
    assert finest[-1]['relative_error_to_limit'] < .003
    assert finest[-1]['relative_error_to_limit'] < finest[0]['relative_error_to_limit']/30
    coarse=rows[5]
    fine=rows[-1]
    hco=np.array(coarse['real_part'])+1j*np.array(coarse['imaginary_part'])
    hfi=np.array(fine['real_part'])+1j*np.array(fine['imaginary_part'])
    mesh_error=float(np.linalg.norm(hfi-hco)/np.linalg.norm(hfi))
    assert mesh_error < .001
    return dict(interval=[0,LENGTH],target_method='independent fundamental-matrix ODE',
                target_real_part=target.real.tolist(),target_imaginary_part=target.imag.tolist(),
                target_eigenvalues=np.linalg.eigvalsh(target).tolist(),
                target_fem_cross_checks=target_rows,penalty_rows=rows,
                last_mesh_relative_change=mesh_error,
                full_method='fixed Cartesian four-component linear FEM; five-point potential quadrature',
                scope='finite penalties approximate the proven infinite-penalty limit; finite-element errors are empirical mesh checks')


def residual_gram_correction_checks():
    """Check the sharp 1/rho correction by an independent boundary-value ODE.

    Constant diagonal target potentials reduce to two independent real 2x2
    rotating planes. This avoids finite-element locking at large rho.
    """
    rows=[]
    for vv in (.7,1.8):
        cc=np.sqrt(vv)
        kk=np.array([[0.,-cc],[cc,0.]])
        projection=np.diag([0.,1.])
        target=cc/np.tanh(cc*LENGTH)
        derivative_integral=vv/(np.sinh(cc*LENGTH)**2)*(LENGTH/2+np.sinh(2*cc*LENGTH)/(4*cc))
        gram=4*vv*derivative_integral
        ratios=[]
        for rho in (100.,1000.,10000.,100000.,1000000.):
            # The mesh has endpoint clustering; solve_bvp refines it further.
            tt=np.unique(np.concatenate([np.linspace(0,LENGTH,121),
                 LENGTH*np.linspace(0,1,61)**3,
                 LENGTH-LENGTH*np.linspace(0,1,61)**3]))
            xi=np.sinh(cc*(LENGTH-tt))/np.sinh(cc*LENGTH)
            xid=-cc*np.cosh(cc*(LENGTH-tt))/np.sinh(cc*LENGTH)
            guess=np.vstack([xi,np.zeros_like(tt),xid,np.zeros_like(tt)])
            jac=np.block([[np.zeros((2,2)),np.eye(2)],
                          [vv*np.eye(2)+rho*projection,-2*kk]])
            def rhs(t,y):return jac@y
            def bc(left,right):return np.r_[left[:2]-[1.,0.],right[:2]]
            def fjac(t,y):return np.repeat(jac[:,:,None],t.size,axis=2)
            sol=solve_bvp(rhs,bc,tt,guess,tol=3e-10,max_nodes=12000,fun_jac=fjac)
            assert sol.success, sol.message
            actual=-sol.y[2,0]
            defect=target-actual
            ratio=rho*defect/gram
            assert defect > -2e-9
            assert ratio < 1+2e-6
            assert ratio > 0
            ratios.append(ratio)
            rows.append(dict(target_eigenvalue=vv,penalty=rho,
                 target_admittance=float(target),finite_penalty_admittance=float(actual),
                 residual_gram_coefficient=float(gram),scaled_defect=float(rho*defect),
                 scaled_defect_to_gram_ratio=float(ratio),
                 adaptive_nodes=sol.x.size,maximum_rms_residual=float(np.max(sol.rms_residuals))))
        assert abs(ratios[-1]-1) < .006, ratios
        assert all(b>a for a,b in zip(ratios,ratios[1:])),ratios
    return dict(method='independent adaptive rotating-plane boundary-value ODE',
                target_potential_eigenvalues=[.7,1.8],interval=[0,LENGTH],rows=rows,
                checked_bound='0 <= Y_infinity-Y_rho <= W/rho',
                checked_limit='rho*(Y_infinity-Y_rho) -> W')


def main():
    parser=argparse.ArgumentParser(description=__doc__)
    parser.add_argument('--out',type=Path,default=Path(__file__).resolve().parents[1]/'results/universality.json')
    args=parser.parse_args()
    result=dict(status='PASS',frame_checks=frame_identity_checks(),
                boundary_checks=penalty_boundary_checks(),
                residual_gram_correction=residual_gram_correction_checks(),
                hypotheses='smooth prescribed A skew-Hermitian and V=C^dagger C; fixed finite interval; dark inlet and zero far-end trace')
    args.out.parent.mkdir(parents=True,exist_ok=True)
    args.out.write_text(json.dumps(result,indent=2)+'\n',encoding='utf-8')
    print(json.dumps(dict(status='PASS',output=str(args.out),
                         maximum_frame_error=max(result['frame_checks']['maximum_absolute_errors'].values()),
                         final_penalty_error=result['boundary_checks']['penalty_rows'][-1]['relative_error_to_limit'],
                         last_mesh_relative_change=result['boundary_checks']['last_mesh_relative_change']),indent=2))


if __name__=='__main__':
    main()