enable numpy 2.0 and recent scipy
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@ -155,7 +155,7 @@ def generate_kmn(
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All are given in the xyz basis (e.g. `|k|[0,0,0] = norm(G_matrix @ k0)`).
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All are given in the xyz basis (e.g. `|k|[0,0,0] = norm(G_matrix @ k0)`).
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"""
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"""
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k0 = numpy.array(k0)
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k0 = numpy.array(k0)
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G_matrix = numpy.array(G_matrix, copy=False)
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G_matrix = numpy.asarray(G_matrix)
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Gi_grids = numpy.array(numpy.meshgrid(*(fftfreq(n, 1 / n) for n in shape[:3]), indexing='ij'))
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Gi_grids = numpy.array(numpy.meshgrid(*(fftfreq(n, 1 / n) for n in shape[:3]), indexing='ij'))
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Gi = numpy.moveaxis(Gi_grids, 0, -1)
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Gi = numpy.moveaxis(Gi_grids, 0, -1)
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@ -538,7 +538,7 @@ def eigsolve(
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`(eigenvalues, eigenvectors)` where `eigenvalues[i]` corresponds to the
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`(eigenvalues, eigenvectors)` where `eigenvalues[i]` corresponds to the
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vector `eigenvectors[i, :]`
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vector `eigenvectors[i, :]`
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"""
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"""
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k0 = numpy.array(k0, copy=False)
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k0 = numpy.asarray(k0)
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h_size = 2 * epsilon[0].size
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h_size = 2 * epsilon[0].size
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@ -566,7 +566,7 @@ def eigsolve(
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if y0 is None:
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if y0 is None:
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Z = rng.random(y_shape) + 1j * rng.random(y_shape)
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Z = rng.random(y_shape) + 1j * rng.random(y_shape)
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else:
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else:
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Z = numpy.array(y0, copy=False).T
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Z = numpy.asarray(y0).T
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while True:
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while True:
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Z *= num_modes / norm(Z)
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Z *= num_modes / norm(Z)
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@ -145,7 +145,7 @@ def sim(
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omega=omega,
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omega=omega,
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dxes=dxes,
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dxes=dxes,
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epsilon=eps_vec,
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epsilon=eps_vec,
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matrix_solver_opts={'atol': 1e-15, 'tol': 1e-11},
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matrix_solver_opts={'atol': 1e-15, 'rtol': 1e-11},
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)
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)
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e = unvec(e_vec, shape[1:])
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e = unvec(e_vec, shape[1:])
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@ -162,7 +162,7 @@ def sim(
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omega=omega,
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omega=omega,
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dxes=dxes,
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dxes=dxes,
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epsilon=eps_vec,
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epsilon=eps_vec,
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matrix_solver_opts={'atol': 1e-15, 'tol': 1e-11},
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matrix_solver_opts={'atol': 1e-15, 'rtol': 1e-11},
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)
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)
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e = unvec(e_vec, shape[1:])
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e = unvec(e_vec, shape[1:])
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@ -39,8 +39,8 @@ include = [
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]
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]
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dynamic = ["version"]
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dynamic = ["version"]
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dependencies = [
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dependencies = [
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"numpy~=1.26",
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"numpy>=1.26",
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"scipy",
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"scipy~=1.14",
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]
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]
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