"""Continuous Gramian factors, subspace references, and canonical transport.

Analytic statements appear in MANUSCRIPT.md. Numerical rank and blind-locus
tolerances are explicit and are not certificates of exact algebraic rank.
"""
from itertools import combinations
from math import factorial
import numpy as np


def _frame(w, tolerance=1e-10):
    w = np.asarray(w, dtype=complex)
    if w.ndim != 2 or not 0 < w.shape[1] <= w.shape[0]:
        raise ValueError("reference must be an n-by-r frame, 1 <= r <= n")
    if not np.allclose(w.conj().T @ w, np.eye(w.shape[1]),
                       atol=tolerance, rtol=tolerance):
        raise ValueError("reference columns must be orthonormal")
    return w


def _projector(p, tolerance=1e-10):
    p = np.asarray(p, dtype=complex)
    if p.ndim != 2 or p.shape[0] != p.shape[1]:
        raise ValueError("projector must be square")
    if not np.allclose(p, p.conj().T, atol=tolerance, rtol=tolerance):
        raise ValueError("projector must be Hermitian")
    if not np.allclose(p @ p, p, atol=tolerance, rtol=tolerance):
        raise ValueError("projector must be idempotent")
    return p


def _positive_power(a, power, tolerance=1e-12):
    values, vectors = np.linalg.eigh((a + a.conj().T) / 2)
    if len(values) == 0 or values[0] <= tolerance:
        raise ValueError("restricted operator is not positive at the declared tolerance")
    return (vectors * values**power) @ vectors.conj().T


def repair_factor(g, c, rank_tolerance=1e-12):
    """Repair a factor when its Gram error is smaller than G's positive gap.

    Returns (D, diagnostics), with D*D=G up to floating point error and the
    same row count as C. Eigenvalues <= rank_tolerance are treated as zero.
    The implementation uses a support frame; the mathematical formula is
    independent of the choice of that frame.
    """
    g = np.asarray(g, dtype=complex)
    c = np.asarray(c, dtype=complex)
    if g.ndim != 2 or g.shape[0] != g.shape[1]:
        raise ValueError("Gramian must be square")
    if c.ndim != 2 or c.shape[1] != len(g):
        raise ValueError("factor must have n columns")
    if not np.allclose(g, g.conj().T, atol=rank_tolerance, rtol=rank_tolerance):
        raise ValueError("Gramian must be Hermitian")
    values, u = np.linalg.eigh(g)
    if values[0] < -rank_tolerance:
        raise ValueError("Gramian must be positive semidefinite")
    keep = values > rank_tolerance
    error = float(np.linalg.norm(c.conj().T @ c - g, 2))
    if not np.any(keep):
        return np.zeros_like(c), {"rank": 0, "gram_error": error,
                                 "rank_tolerance": rank_tolerance}
    u, values = u[:, keep], values[keep]
    gap = float(values[0])
    if error >= gap:
        raise ValueError("Gram error must be strictly smaller than the positive gap")
    a = c @ u
    b = a.conj().T @ a
    inv_sqrt = _positive_power(b, -.5, rank_tolerance)
    d = ((a @ inv_sqrt) * np.sqrt(values)) @ u.conj().T
    correction_bound = error / (np.sqrt(gap) + np.sqrt(gap-error))
    return d, {"rank": len(values), "gram_error": error, "positive_gap": gap,
               "support_correction_bound": correction_bound,
               "total_correction_bound": np.sqrt(error) + correction_bound,
               "rank_tolerance": rank_tolerance}


def reference_frame(p, w, blind_tolerance=1e-12):
    """Polar frame of P W, with exact-model margin sigma_min(P W)."""
    p, w = _projector(p), _frame(w)
    if p.shape[0] != w.shape[0] or int(round(np.trace(p).real)) != w.shape[1]:
        raise ValueError("projector and reference must have equal rank r")
    left, singular, right = np.linalg.svd(p @ w, full_matrices=False)
    margin = float(singular[-1])
    if margin <= blind_tolerance:
        raise ValueError("reference is blind at the declared numerical tolerance")
    return left @ right, margin


def reference_factor(p, w, weight, blind_tolerance=1e-12):
    if weight < 0:
        raise ValueError("weight must be nonnegative")
    frame, margin = reference_frame(p, w, blind_tolerance)
    return np.sqrt(weight) * frame.conj().T, margin


def nearest_blind_projector(p, w, blind_tolerance=1e-12):
    """Construct a rank-r blind projector at distance sigma_min(P W).

    Requires 1 <= r < n. At numerically zero margin P itself is returned.
    The equal-subspace case (margin one) replaces one vector by a vector
    in the orthogonal complement.
    """
    p, w = _projector(p), _frame(w)
    n, r = w.shape
    if not r < n or int(round(np.trace(p).real)) != r:
        raise ValueError("construction requires equal ranks with 1 <= r < n")
    values, vectors = np.linalg.eigh(w.conj().T @ p @ w)
    delta = float(np.sqrt(max(0., values[0])))
    if delta <= blind_tolerance:
        return p.copy(), delta
    ref = w @ vectors[:, 0]
    e = p @ ref / delta
    if 1-delta**2 <= blind_tolerance:
        evals, evecs = np.linalg.eigh(p)
        z = evecs[:, np.argmin(evals)]
    else:
        complement = (ref-delta*e) / np.sqrt(1-delta**2)
        z = np.sqrt(1-delta**2)*e - delta*complement
    blind = p - np.outer(e, e.conj()) + np.outer(z, z.conj())
    return (blind + blind.conj().T)/2, delta


def jet_reference(n, r, t):
    """Orthonormal derivative-evaluation frame for real t, polynomials deg<n."""
    if not 1 <= r <= n or not np.isfinite(t) or not np.isreal(t):
        raise ValueError("require 1 <= r <= n and a finite real node")
    raw = np.zeros((n, r), dtype=float)
    for j in range(n):
        for k in range(min(j+1, r)):
            raw[j, k] = factorial(j)/factorial(j-k) * float(t)**(j-k)
    left, singular, right = np.linalg.svd(raw, full_matrices=False)
    if singular[-1] <= np.finfo(float).eps * singular[0]:
        raise ValueError("jet frame is numerically rank deficient; rescale the basis/nodes")
    return left @ right


def wronskian_atlas(n, r, nodes=None):
    """Explicit minimal atlas of r(n-r)+1 fixed rank-r reference frames.

    Default nodes are Chebyshev nodes in [-1,1]. Coverage is analytic;
    this choice is not asserted to optimize conditioning.
    """
    if not 1 <= r < n:
        raise ValueError("require 1 <= r < n")
    count = r*(n-r)+1
    if nodes is None:
        nodes = np.cos(np.pi*(np.arange(count)+.5)/count)
    nodes = np.asarray(nodes)
    if nodes.shape != (count,) or len(np.unique(nodes)) != count:
        raise ValueError("provide exactly r(n-r)+1 distinct real nodes")
    return [jet_reference(n, r, t) for t in nodes]


def coordinate_atlas(n, r):
    """All binomial(n,r) coordinate frames; certified margin >= binomial^-1/2."""
    if not 1 <= r < n:
        raise ValueError("require 1 <= r < n")
    eye = np.eye(n, dtype=complex)
    return [eye[:, indices] for indices in combinations(range(n), r)]


def select_reference_frame(p, references, blind_tolerance=1e-12):
    """Select a largest-margin chart; ties can cause a discontinuous switch."""
    p = _projector(p)
    refs = [_frame(w) for w in references]
    if not refs or any(w.shape != refs[0].shape for w in refs):
        raise ValueError("references must be a nonempty list of equal-size frames")
    margins = [np.linalg.svd(p @ w, compute_uv=False)[-1] for w in refs]
    index = int(np.argmax(margins))
    frame, margin = reference_frame(p, refs[index], blind_tolerance)
    return index, frame, margin


def subspace_transport(p, q, blind_tolerance=1e-12):
    """Canonical partial isometry Q -> P for equal-rank transverse subspaces."""
    p, q = _projector(p), _projector(q)
    values, basis = np.linalg.eigh(q)
    u = basis[:, values > .5]
    if len(u.T) != int(round(np.trace(p).real)) or len(u.T) == 0:
        raise ValueError("projectors must have the same positive rank")
    b = u.conj().T @ p @ u
    if np.linalg.eigvalsh(b)[0] <= blind_tolerance**2:
        raise ValueError("orthogonal component prevents invertible comparison")
    return p @ u @ _positive_power(b, -.5, blind_tolerance**2) @ u.conj().T


def subspace_holonomy(projectors, initial_frame, blind_tolerance=1e-12):
    """U(r) matrix for T(P0<-P1)...T(Plast<-P0) in the initial frame."""
    ps = [_projector(p) for p in projectors]
    f = _frame(initial_frame)
    if len(ps) < 2 or not np.allclose(f @ f.conj().T, ps[0]):
        raise ValueError("cycle needs at least two vertices and a frame for P0")
    product = np.eye(len(ps[0]), dtype=complex)
    for j, p in enumerate(ps):
        product = product @ subspace_transport(p, ps[(j+1) % len(ps)], blind_tolerance)
    return f.conj().T @ product @ f