Datasets:

PureOne
/

EVE-AEQIRION

#!/usr/bin/env python3
"""Exact finite checks for marginal kernels and separable coding obstructions.
Only standard-library integers and fractions. These checks do not prove
all-dimension statements or literature priority; proofs are in the manuscript.
"""
from fractions import Fraction as F
from itertools import product, combinations
from math import prod
import json
import random


def points(shape):
    return list(product(*(range(q) for q in shape)))


def dot(a, b):
    return sum((x * y for x, y in zip(a, b)), F(0))


def rank(rows):
    if not rows:
        return 0
    a = [list(map(F, row)) for row in rows]
    nr, nc = len(a), len(a[0])
    pivot = 0
    for j in range(nc):
        found = next((i for i in range(pivot, nr) if a[i][j]), None)
        if found is None:
            continue
        a[pivot], a[found] = a[found], a[pivot]
        value = a[pivot][j]
        a[pivot] = [x / value for x in a[pivot]]
        for i in range(pivot + 1, nr):
            if a[i][j]:
                factor = a[i][j]
                a[i] = [x - factor * y for x, y in zip(a[i], a[pivot])]
        pivot += 1
        if pivot == nr:
            break
    return pivot


def marginal_matrix(shape):
    xs = points(shape)
    rows = []
    descriptors = []
    for i in range(len(shape)):
        other = [j for j in range(len(shape)) if j != i]
        for assignment in product(*(range(shape[j]) for j in other)):
            row = [int(all(x[j] == a for j, a in zip(other, assignment))) for x in xs]
            rows.append(row)
            descriptors.append((i, other, assignment))
    return rows, descriptors


def matvec(a, x):
    return [dot(row, x) for row in a]


def transpose_matvec(a, x):
    return [sum((a[i][j] * x[i] for i in range(len(a))), F(0))
            for j in range(len(a[0]))]


def one_factor_basis(q):
    # Orthogonal integer Helmert contrasts; no square roots are approximated.
    out = [[F(1)] * q]
    for a in range(1, q):
        out.append([F(1 if x < a else -a if x == a else 0) for x in range(q)])
    for i, u in enumerate(out):
        for v in out[:i]:
            assert dot(u, v) == 0
    return out


def tensor_vector(shape, basis, labels):
    return [F(prod(basis[i][labels[i]][x[i]] for i in range(len(shape))))
            for x in points(shape)]


def center_coordinate(shape, values, coordinate):
    xs = points(shape)
    lookup = dict(zip(xs, values))
    out = []
    for x in xs:
        total = F(0)
        for a in range(shape[coordinate]):
            y = list(x)
            y[coordinate] = a
            total += lookup[tuple(y)]
        out.append(total / shape[coordinate])
    return out


def observable_from_marginals(shape, sums):
    """Reconstruct (I-P_K)z using inclusion-exclusion of averaging maps.
    The input consists only of actual codimension-one sums; no original z
    or hidden tensor coefficients are available to this decoder.
    """
    a, descriptions = marginal_matrix(shape)
    xs = points(shape)
    one_coordinate_averages = []
    for i in range(len(shape)):
        entries = {}
        for value, (coord, other, assignment) in zip(sums, descriptions):
            if coord == i:
                entries[assignment] = value / shape[i]
        other = [j for j in range(len(shape)) if j != i]
        one_coordinate_averages.append([entries[tuple(x[j] for j in other)] for x in xs])
    out = [F(0)] * len(xs)
    for size in range(1, len(shape) + 1):
        for subset in combinations(range(len(shape)), size):
            value = one_coordinate_averages[subset[0]]
            for i in subset[1:]:
                value = center_coordinate(shape, value, i)
            sign = 1 if size % 2 else -1
            out = [x + sign * y for x, y in zip(out, value)]
    assert matvec(a, out) == sums
    return out


def check_spectrum():
    reports = []
    count = 0
    for shape in ((2, 2), (3, 3), (4, 4), (2, 3, 4), (3, 3, 3)):
        a, _ = marginal_matrix(shape)
        basis = [one_factor_basis(q) for q in shape]
        null_count = 0
        spectral_multiplicity = {}
        for labels in product(*(range(q) for q in shape)):
            v = tensor_vector(shape, basis, labels)
            expected_eigenvalue = sum(q for q, label in zip(shape, labels) if label == 0)
            lhs = transpose_matvec(a, matvec(a, v))
            assert lhs == [expected_eigenvalue * x for x in v]
            spectral_multiplicity[expected_eigenvalue] = spectral_multiplicity.get(
                expected_eigenvalue, 0) + 1
            null_count += expected_eigenvalue == 0
            count += 1
        expected_nullity = prod(q - 1 for q in shape)
        actual_rank = rank(a)
        assert null_count == expected_nullity
        assert actual_rank == prod(shape) - expected_nullity
        assert min(e for e in spectral_multiplicity if e) == min(shape)
        reports.append({"shape": list(shape), "N": prod(shape),
                        "rank": actual_rank, "kernel_dimension": expected_nullity,
                        "gram_eigenvalues_and_multiplicities": spectral_multiplicity})
    return {"exact_eigenvector_cases": count, "shapes": reports}


def check_separable_channels():
    reports = []
    for shape in ((2, 2), (3, 3), (4, 4), (2, 3, 4), (3, 4, 4), (3, 3, 3)):
        a, _ = marginal_matrix(shape)
        xs = points(shape)
        for m in range(1, min(shape) + 1):
            stacked = []
            coordinate_ranks = []
            for q in shape:
                coordinate_ranks.append(rank([[(x + 1) ** j for x in range(q)]
                                              for j in range(m)]))
            for j in range(m):
                # All coordinate factors are strictly positive Vandermonde rows.
                mask = [prod((x[i] + 1) ** j for i in range(len(shape))) for x in xs]
                assert min(mask) > 0
                stacked.extend([[F(value) * weight for value, weight in zip(row, mask)]
                                for row in a])
            expected_nullity = prod(q - r for q, r in zip(shape, coordinate_ranks))
            actual_nullity = prod(shape) - rank(stacked)
            assert actual_nullity == expected_nullity
            assert expected_nullity == prod(max(q - m, 0) for q in shape)
            reports.append({"shape": list(shape), "channels": m,
                            "coordinate_ranks": coordinate_ranks,
                            "joint_nullity": actual_nullity})
        # Repeating a proportional channel adds no information.
        repeated = a + [[3 * x for x in row] for row in a]
        assert rank(repeated) == rank(a)
    # Highlighted strict obstructions and their exact endpoints.
    summary = {(tuple(r['shape']), r['channels']): r['joint_nullity'] for r in reports}
    assert summary[((3, 3), 2)] == 1 and summary[((3, 3), 3)] == 0
    assert summary[((4, 4), 3)] == 1 and summary[((4, 4), 4)] == 0
    assert summary[((2, 3, 4), 2)] == 0
    return {"stacked_rank_cases": len(reports),
            "proportional_channel_counterexamples": 6, "cases": reports}


def check_scalar_completion():
    rng = random.Random(20102026)
    completed = probe_count = orthogonality_checks = null_probe_tests = 0
    examples = []
    for shape in ((2, 2), (3, 3), (4, 4), (2, 3, 4), (3, 3, 3)):
        a, _ = marginal_matrix(shape)
        basis = [one_factor_basis(q) for q in shape]
        hidden = [tensor_vector(shape, basis, labels)
                  for labels in product(*(range(1, q) for q in shape))]
        expected_d = prod(q - 1 for q in shape)
        assert len(hidden) == expected_d
        for i, u in enumerate(hidden):
            assert matvec(a, u) == [0] * len(a)
            for v in hidden[:i]:
                assert dot(u, v) == 0
                orthogonality_checks += 1
        for repetition in range(4):
            z = [F(rng.randrange(1, 100), rng.randrange(1, 20))
                 for _ in range(prod(shape))]
            sums = matvec(a, z)
            visible = observable_from_marginals(shape, sums)
            for h in hidden:
                assert dot(h, visible) == 0
            total = sum(sums[:prod(shape) // shape[0]], F(0))
            assert total == sum(z, F(0))
            out = list(visible)
            for h in hidden:
                alpha = F(1, 1 + max(map(abs, h)))
                mask = [1 + alpha * x for x in h]
                assert min(mask) > 0 and max(mask) < 2
                assert sum(mask, F(0)) == prod(shape)
                measured = dot(mask, z)
                coefficient = (measured - total) / (alpha * dot(h, h))
                out = [x + coefficient * y for x, y in zip(out, h)]
                probe_count += 1
            assert out == z
            completed += 1
            # Omitting one orthogonal completion leaves a concrete positive
            # indistinguishable pair, illustrating the general rank lower bound.
            omitted = repetition % len(hidden)
            delta = min(z) / (2 * max(map(abs, hidden[omitted])))
            alternative = [x + delta * y for x, y in zip(z, hidden[omitted])]
            assert min(alternative) > 0 and alternative != z
            assert matvec(a, alternative) == sums
            for i, h in enumerate(hidden):
                if i != omitted:
                    assert dot(h, alternative) == dot(h, z)
            null_probe_tests += 1
        examples.append({"shape": list(shape), "necessary_and_sufficient_extra_scalars": expected_d})
    return {"positive_tensor_reconstructions": completed,
            "positive_mask_scalar_probes": probe_count,
            "orthogonality_checks": orthogonality_checks,
            "missing_probe_nonuniqueness_examples": null_probe_tests,
            "dimensions": examples}


def check_bounded_fourier_completion():
    """Exact fourth-root-of-unity cases of the general Fourier construction.
    Shapes use only 2 and 4, so real/imaginary character values are integers;
    no trigonometric approximation or complex floating point is involved.
    """
    reports = []
    noise_equalities = 0
    rho = F(1, 2)
    roots = ((1, 0), (0, 1), (-1, 0), (0, -1))
    for shape in ((2, 2), (4, 4), (2, 4, 4), (4, 4, 4)):
        xs = points(shape)
        a, _ = marginal_matrix(shape)
        contrasts = []
        for frequency in product(*(range(1, q) for q in shape)):
            conjugate = tuple((-k) % q for k, q in zip(frequency, shape))
            if frequency > conjugate:
                continue
            real, imag = [], []
            for x in xs:
                phase = sum((4 // q) * k * xi for q, k, xi in zip(shape, frequency, x)) % 4
                re, im = roots[phase]
                real.append(F(re))
                imag.append(F(im))
            contrasts.append(real)
            if frequency != conjugate:
                contrasts.append(imag)
        assert len(contrasts) == prod(q - 1 for q in shape)
        for i, h in enumerate(contrasts):
            assert matvec(a, h) == [0] * len(a)
            assert dot(h, h) in (prod(shape), F(prod(shape), 2))
            for other in contrasts[:i]:
                assert dot(h, other) == 0
        z = [F(2 + (i * 7) % 23, 5) for i in range(prod(shape))]
        visible = observable_from_marginals(shape, matvec(a, z))
        total = sum(z, F(0))
        reconstructed = list(visible)
        for h in contrasts:
            mask = [1 + rho * x for x in h]
            assert min(mask) >= F(1, 2) and max(mask) <= F(3, 2)
            coefficient = (dot(mask, z) - total) / (rho * dot(h, h))
            reconstructed = [x + coefficient * y for x, y in zip(reconstructed, h)]
        assert reconstructed == z
        # Single-coordinate noise on a non-self-conjugate mode attains the
        # uniform sqrt(2/N)/rho bound. Binary-only shapes have the stronger one.
        target = min(contrasts, key=lambda h: dot(h, h))
        epsilon = F(1, 1000)
        perturbation = [epsilon * x / (rho * dot(target, target)) for x in target]
        actual_error_squared = dot(perturbation, perturbation)
        exact_bound_squared = epsilon ** 2 / (rho ** 2 * dot(target, target))
        assert actual_error_squared == exact_bound_squared
        if dot(target, target) == F(prod(shape), 2):
            assert actual_error_squared == 2 * epsilon ** 2 / (rho ** 2 * prod(shape))
        noise_equalities += 1
        reports.append({"shape": list(shape), "positive_probes": len(contrasts),
                        "minimum_contrast_norm_squared": str(dot(target, target)),
                        "recovered_exactly": True})
    return {"exact_fourier_cases": len(reports),
            "sharp_noise_equalities": noise_equalities, "cases": reports}


def run():
    return {"status": "PASS", "arithmetic": "exact integers/Fractions",
            "spectrum": check_spectrum(),
            "separable_channels": check_separable_channels(),
            "scalar_completion": check_scalar_completion(),
            "bounded_fourier_completion": check_bounded_fourier_completion(),
            "scope": "Finite checks support proofs; no priority or physical-sensor optimality claim."}


if __name__ == '__main__':
    print(json.dumps(run(), indent=2, sort_keys=True))

	#!/usr/bin/env python3
	"""Exact finite checks for marginal kernels and separable coding obstructions.

	Only standard-library integers and fractions. These checks do not prove
	all-dimension statements or literature priority; proofs are in the manuscript.
	"""
	from fractions import Fraction as F
	from itertools import product, combinations
	from math import prod
	import json
	import random


	def points(shape):
	return list(product(*(range(q) for q in shape)))


	def dot(a, b):
	return sum((x * y for x, y in zip(a, b)), F(0))


	def rank(rows):
	if not rows:
	return 0
	a = [list(map(F, row)) for row in rows]
	nr, nc = len(a), len(a[0])
	pivot = 0
	for j in range(nc):
	found = next((i for i in range(pivot, nr) if a[i][j]), None)
	if found is None:
	continue
	a[pivot], a[found] = a[found], a[pivot]
	value = a[pivot][j]
	a[pivot] = [x / value for x in a[pivot]]
	for i in range(pivot + 1, nr):
	if a[i][j]:
	factor = a[i][j]
	a[i] = [x - factor * y for x, y in zip(a[i], a[pivot])]
	pivot += 1
	if pivot == nr:
	break
	return pivot


	def marginal_matrix(shape):
	xs = points(shape)
	rows = []
	descriptors = []
	for i in range(len(shape)):
	other = [j for j in range(len(shape)) if j != i]
	for assignment in product(*(range(shape[j]) for j in other)):
	row = [int(all(x[j] == a for j, a in zip(other, assignment))) for x in xs]
	rows.append(row)
	descriptors.append((i, other, assignment))
	return rows, descriptors


	def matvec(a, x):
	return [dot(row, x) for row in a]


	def transpose_matvec(a, x):
	return [sum((a[i][j] * x[i] for i in range(len(a))), F(0))
	for j in range(len(a[0]))]


	def one_factor_basis(q):
	# Orthogonal integer Helmert contrasts; no square roots are approximated.
	out = [[F(1)] * q]
	for a in range(1, q):
	out.append([F(1 if x < a else -a if x == a else 0) for x in range(q)])
	for i, u in enumerate(out):
	for v in out[:i]:
	assert dot(u, v) == 0
	return out


	def tensor_vector(shape, basis, labels):
	return [F(prod(basis[i][labels[i]][x[i]] for i in range(len(shape))))
	for x in points(shape)]


	def center_coordinate(shape, values, coordinate):
	xs = points(shape)
	lookup = dict(zip(xs, values))
	out = []
	for x in xs:
	total = F(0)
	for a in range(shape[coordinate]):
	y = list(x)
	y[coordinate] = a
	total += lookup[tuple(y)]
	out.append(total / shape[coordinate])
	return out


	def observable_from_marginals(shape, sums):
	"""Reconstruct (I-P_K)z using inclusion-exclusion of averaging maps.

	The input consists only of actual codimension-one sums; no original z
	or hidden tensor coefficients are available to this decoder.
	"""
	a, descriptions = marginal_matrix(shape)
	xs = points(shape)
	one_coordinate_averages = []
	for i in range(len(shape)):
	entries = {}
	for value, (coord, other, assignment) in zip(sums, descriptions):
	if coord == i:
	entries[assignment] = value / shape[i]
	other = [j for j in range(len(shape)) if j != i]
	one_coordinate_averages.append([entries[tuple(x[j] for j in other)] for x in xs])
	out = [F(0)] * len(xs)
	for size in range(1, len(shape) + 1):
	for subset in combinations(range(len(shape)), size):
	value = one_coordinate_averages[subset[0]]
	for i in subset[1:]:
	value = center_coordinate(shape, value, i)
	sign = 1 if size % 2 else -1
	out = [x + sign * y for x, y in zip(out, value)]
	assert matvec(a, out) == sums
	return out


	def check_spectrum():
	reports = []
	count = 0
	for shape in ((2, 2), (3, 3), (4, 4), (2, 3, 4), (3, 3, 3)):
	a, _ = marginal_matrix(shape)
	basis = [one_factor_basis(q) for q in shape]
	null_count = 0
	spectral_multiplicity = {}
	for labels in product(*(range(q) for q in shape)):
	v = tensor_vector(shape, basis, labels)
	expected_eigenvalue = sum(q for q, label in zip(shape, labels) if label == 0)
	lhs = transpose_matvec(a, matvec(a, v))
	assert lhs == [expected_eigenvalue * x for x in v]
	spectral_multiplicity[expected_eigenvalue] = spectral_multiplicity.get(
	expected_eigenvalue, 0) + 1
	null_count += expected_eigenvalue == 0
	count += 1
	expected_nullity = prod(q - 1 for q in shape)
	actual_rank = rank(a)
	assert null_count == expected_nullity
	assert actual_rank == prod(shape) - expected_nullity
	assert min(e for e in spectral_multiplicity if e) == min(shape)
	reports.append({"shape": list(shape), "N": prod(shape),
	"rank": actual_rank, "kernel_dimension": expected_nullity,
	"gram_eigenvalues_and_multiplicities": spectral_multiplicity})
	return {"exact_eigenvector_cases": count, "shapes": reports}


	def check_separable_channels():
	reports = []
	for shape in ((2, 2), (3, 3), (4, 4), (2, 3, 4), (3, 4, 4), (3, 3, 3)):
	a, _ = marginal_matrix(shape)
	xs = points(shape)
	for m in range(1, min(shape) + 1):
	stacked = []
	coordinate_ranks = []
	for q in shape:
	coordinate_ranks.append(rank([[(x + 1) ** j for x in range(q)]
	for j in range(m)]))
	for j in range(m):
	# All coordinate factors are strictly positive Vandermonde rows.
	mask = [prod((x[i] + 1) ** j for i in range(len(shape))) for x in xs]
	assert min(mask) > 0
	stacked.extend([[F(value) * weight for value, weight in zip(row, mask)]
	for row in a])
	expected_nullity = prod(q - r for q, r in zip(shape, coordinate_ranks))
	actual_nullity = prod(shape) - rank(stacked)
	assert actual_nullity == expected_nullity
	assert expected_nullity == prod(max(q - m, 0) for q in shape)
	reports.append({"shape": list(shape), "channels": m,
	"coordinate_ranks": coordinate_ranks,
	"joint_nullity": actual_nullity})
	# Repeating a proportional channel adds no information.
	repeated = a + [[3 * x for x in row] for row in a]
	assert rank(repeated) == rank(a)
	# Highlighted strict obstructions and their exact endpoints.
	summary = {(tuple(r['shape']), r['channels']): r['joint_nullity'] for r in reports}
	assert summary[((3, 3), 2)] == 1 and summary[((3, 3), 3)] == 0
	assert summary[((4, 4), 3)] == 1 and summary[((4, 4), 4)] == 0
	assert summary[((2, 3, 4), 2)] == 0
	return {"stacked_rank_cases": len(reports),
	"proportional_channel_counterexamples": 6, "cases": reports}


	def check_scalar_completion():
	rng = random.Random(20102026)
	completed = probe_count = orthogonality_checks = null_probe_tests = 0
	examples = []
	for shape in ((2, 2), (3, 3), (4, 4), (2, 3, 4), (3, 3, 3)):
	a, _ = marginal_matrix(shape)
	basis = [one_factor_basis(q) for q in shape]
	hidden = [tensor_vector(shape, basis, labels)
	for labels in product(*(range(1, q) for q in shape))]
	expected_d = prod(q - 1 for q in shape)
	assert len(hidden) == expected_d
	for i, u in enumerate(hidden):
	assert matvec(a, u) == [0] * len(a)
	for v in hidden[:i]:
	assert dot(u, v) == 0
	orthogonality_checks += 1
	for repetition in range(4):
	z = [F(rng.randrange(1, 100), rng.randrange(1, 20))
	for _ in range(prod(shape))]
	sums = matvec(a, z)
	visible = observable_from_marginals(shape, sums)
	for h in hidden:
	assert dot(h, visible) == 0
	total = sum(sums[:prod(shape) // shape[0]], F(0))
	assert total == sum(z, F(0))
	out = list(visible)
	for h in hidden:
	alpha = F(1, 1 + max(map(abs, h)))
	mask = [1 + alpha * x for x in h]
	assert min(mask) > 0 and max(mask) < 2
	assert sum(mask, F(0)) == prod(shape)
	measured = dot(mask, z)
	coefficient = (measured - total) / (alpha * dot(h, h))
	out = [x + coefficient * y for x, y in zip(out, h)]
	probe_count += 1
	assert out == z
	completed += 1
	# Omitting one orthogonal completion leaves a concrete positive
	# indistinguishable pair, illustrating the general rank lower bound.
	omitted = repetition % len(hidden)
	delta = min(z) / (2 * max(map(abs, hidden[omitted])))
	alternative = [x + delta * y for x, y in zip(z, hidden[omitted])]
	assert min(alternative) > 0 and alternative != z
	assert matvec(a, alternative) == sums
	for i, h in enumerate(hidden):
	if i != omitted:
	assert dot(h, alternative) == dot(h, z)
	null_probe_tests += 1
	examples.append({"shape": list(shape), "necessary_and_sufficient_extra_scalars": expected_d})
	return {"positive_tensor_reconstructions": completed,
	"positive_mask_scalar_probes": probe_count,
	"orthogonality_checks": orthogonality_checks,
	"missing_probe_nonuniqueness_examples": null_probe_tests,
	"dimensions": examples}


	def check_bounded_fourier_completion():
	"""Exact fourth-root-of-unity cases of the general Fourier construction.

	Shapes use only 2 and 4, so real/imaginary character values are integers;
	no trigonometric approximation or complex floating point is involved.
	"""
	reports = []
	noise_equalities = 0
	rho = F(1, 2)
	roots = ((1, 0), (0, 1), (-1, 0), (0, -1))
	for shape in ((2, 2), (4, 4), (2, 4, 4), (4, 4, 4)):
	xs = points(shape)
	a, _ = marginal_matrix(shape)
	contrasts = []
	for frequency in product(*(range(1, q) for q in shape)):
	conjugate = tuple((-k) % q for k, q in zip(frequency, shape))
	if frequency > conjugate:
	continue
	real, imag = [], []
	for x in xs:
	phase = sum((4 // q) * k * xi for q, k, xi in zip(shape, frequency, x)) % 4
	re, im = roots[phase]
	real.append(F(re))
	imag.append(F(im))
	contrasts.append(real)
	if frequency != conjugate:
	contrasts.append(imag)
	assert len(contrasts) == prod(q - 1 for q in shape)
	for i, h in enumerate(contrasts):
	assert matvec(a, h) == [0] * len(a)
	assert dot(h, h) in (prod(shape), F(prod(shape), 2))
	for other in contrasts[:i]:
	assert dot(h, other) == 0
	z = [F(2 + (i * 7) % 23, 5) for i in range(prod(shape))]
	visible = observable_from_marginals(shape, matvec(a, z))
	total = sum(z, F(0))
	reconstructed = list(visible)
	for h in contrasts:
	mask = [1 + rho * x for x in h]
	assert min(mask) >= F(1, 2) and max(mask) <= F(3, 2)
	coefficient = (dot(mask, z) - total) / (rho * dot(h, h))
	reconstructed = [x + coefficient * y for x, y in zip(reconstructed, h)]
	assert reconstructed == z
	# Single-coordinate noise on a non-self-conjugate mode attains the
	# uniform sqrt(2/N)/rho bound. Binary-only shapes have the stronger one.
	target = min(contrasts, key=lambda h: dot(h, h))
	epsilon = F(1, 1000)
	perturbation = [epsilon * x / (rho * dot(target, target)) for x in target]
	actual_error_squared = dot(perturbation, perturbation)
	exact_bound_squared = epsilon 2 / (rho 2 * dot(target, target))
	assert actual_error_squared == exact_bound_squared
	if dot(target, target) == F(prod(shape), 2):
	assert actual_error_squared == 2 * epsilon 2 / (rho 2 * prod(shape))
	noise_equalities += 1
	reports.append({"shape": list(shape), "positive_probes": len(contrasts),
	"minimum_contrast_norm_squared": str(dot(target, target)),
	"recovered_exactly": True})
	return {"exact_fourier_cases": len(reports),
	"sharp_noise_equalities": noise_equalities, "cases": reports}


	def run():
	return {"status": "PASS", "arithmetic": "exact integers/Fractions",
	"spectrum": check_spectrum(),
	"separable_channels": check_separable_channels(),
	"scalar_completion": check_scalar_completion(),
	"bounded_fourier_completion": check_bounded_fourier_completion(),
	"scope": "Finite checks support proofs; no priority or physical-sensor optimality claim."}


	if __name__ == '__main__':
	print(json.dumps(run(), indent=2, sort_keys=True))