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pyrecest/distributions/hypersphere_subset/complex_bingham_distribution.py:14:1: F401 'pyrecest.backend.maximum' imported but unused

************* Module pyrecest.distributions.hypersphere_subset.abstract_complex_hyperspherical_distribution
pyrecest/distributions/hypersphere_subset/abstract_complex_hyperspherical_distribution.py:53:4: W0237: Parameter 'xs' has been renamed to 'za' in overriding 'AbstractComplexHypersphericalDistribution.pdf' method (arguments-renamed)
************* Module pyrecest.distributions.hypersphere_subset.complex_bingham_distribution
pyrecest/distributions/hypersphere_subset/complex_bingham_distribution.py:90:4: W0237: Parameter 'za' has been renamed to 'z' in overriding 'ComplexBinghamDistribution.pdf' method (arguments-renamed)
pyrecest/distributions/hypersphere_subset/complex_bingham_distribution.py:13:0: W0611: Unused maximum imported from pyrecest.backend (unused-import)

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Test Results

0 tests   0 ✅  0s ⏱️
0 suites  0 💤
0 files    0 ❌

Results for commit de35fdf.

♻️ This comment has been updated with latest results.

@copilot eliminate the use of numpy function calls and use pyrecest.backend instead

@copilot eliminate the use of numpy function calls and use pyrecest.backend instead

Done in commit a5f140d. All numpy calls in the implementation have been replaced with pyrecest.backend equivalents:

• Removed import numpy as np and from scipy.linalg import eigh
• Added imports from pyrecest.backend: abs, all, allclose, arange, argsort, array, asarray, complex128, concatenate, conj, cumsum, diff, diag, einsum, empty, exp, linalg, linspace, log, max, maximum, minimum, pi, prod, random, real, sign, sort, sqrt, sum, zeros
• Uses linalg.eigh / linalg.eigvalsh from backend instead of scipy
• Uses random.uniform(size=(n,)) for sampling
• Uses concatenate instead of np.append

The test file was updated similarly (using random.seed, random.normal, linalg.norm, ones, etc. from backend; numpy.testing kept only for assertion helpers). JAX skip decorators added for sampling tests. All 17 tests pass.

@copilot Add a superclass similar to that one:

`import math
from abc import abstractmethod

from pyrecest.distributions.abstract_manifold_specific_distribution import (
AbstractManifoldSpecificDistribution,
)

class AbstractComplexHypersphericalDistribution(AbstractManifoldSpecificDistribution):
"""
Abstract base class for distributions on the complex unit hypersphere in C^d.

The complex unit hypersphere in C^d is the set
    {z ∈ C^d : ||z|| = 1}
which is isomorphic to the real sphere S^{2d-1} in R^{2d}.

The complex dimension d is stored as ``complex_dim``.
The underlying manifold dimension (as a real manifold) is 2*d - 1.
"""

def __init__(self, complex_dim: int):
    """
    Parameters
    ----------
    complex_dim : int
        Complex dimension d of the ambient space C^d (d >= 1).
    """
    if complex_dim < 1:
        raise ValueError("complex_dim must be >= 1.")
    # The real manifold dimension is 2*d - 1
    super().__init__(2 * complex_dim - 1)
    self._complex_dim = complex_dim

@property
def complex_dim(self) -> int:
    """Complex dimension d of the ambient space C^d."""
    return self._complex_dim

@property
def input_dim(self) -> int:
    """Number of complex coordinates of a point on the sphere (= complex_dim)."""
    return self._complex_dim

def get_manifold_size(self) -> float:
    """Surface area of the real sphere S^{2d-1} = 2π^d / (d-1)!"""
    d = self._complex_dim
    return float(2 * pi**d / math.factorial(d - 1))

@abstractmethod
def pdf(self, za):
    """Probability density at the given point(s) on the complex unit sphere."""

def mean(self):
    raise NotImplementedError(
        "mean() is not defined for this complex hyperspherical distribution."
    )`

@copilot Add a superclass similar to that one:

`import math
...

Done in commit de35fdf. Added AbstractComplexHypersphericalDistribution in pyrecest/distributions/hypersphere_subset/abstract_complex_hyperspherical_distribution.py — exactly matching the provided signature — and updated ComplexBinghamDistribution to inherit from it. Key changes:

• ComplexBinghamDistribution.__init__ now calls super().__init__(B.shape[0]) instead of directly setting self.dim
• sample() uses self.complex_dim for the complex dimension
• test_dim now verifies both complex_dim (2, 3) and real manifold dim (3, 5)
• AbstractComplexHypersphericalDistribution exported in distributions/__init__.py

All 17 tests pass.