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feat: guards added for functionality that breaks with numpy>=1.25 and
autograd==1.5.
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parent
f14042132f
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3 changed files with 31 additions and 20 deletions
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@ -1,3 +1,4 @@
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from packaging import version
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import numpy as np
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import autograd.numpy as anp # Thinly-wrapped numpy
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from .obs import derived_observable, CObs, Obs, import_jackknife
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@ -260,6 +261,8 @@ def _mat_mat_op(op, obs, **kwargs):
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def eigh(obs, **kwargs):
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"""Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh."""
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if version.parse(np.__version__) >= version.parse("1.25.0"):
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raise NotImplementedError("eigh error propagation is not working with numpy>=1.25 and autograd==1.5.")
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w = derived_observable(lambda x, **kwargs: anp.linalg.eigh(x)[0], obs)
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v = derived_observable(lambda x, **kwargs: anp.linalg.eigh(x)[1], obs)
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return w, v
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@ -278,6 +281,8 @@ def pinv(obs, **kwargs):
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def svd(obs, **kwargs):
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"""Computes the singular value decomposition of a matrix of Obs."""
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if version.parse(np.__version__) >= version.parse("1.25.0"):
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raise NotImplementedError("svd error propagation is not working with numpy>=1.25 and autograd==1.5.")
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u = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[0], obs)
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s = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[1], obs)
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vh = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[2], obs)
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@ -1,3 +1,4 @@
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from packaging import version
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import numpy as np
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import autograd.numpy as anp
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import math
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@ -291,23 +292,26 @@ def test_matrix_functions():
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diff = entry - sym[i, j]
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assert diff.is_zero()
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# Check eigh
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e, v = pe.linalg.eigh(sym)
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for i in range(dim):
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tmp = sym @ v[:, i] - v[:, i] * e[i]
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for j in range(dim):
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assert tmp[j].is_zero()
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# These linalg functions don't work with numpy>=1.25 and autograd==1.5.
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# Remove this guard once this is fixed in autograd.
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if version.parse(np.__version__) < version.parse("1.25.0"):
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# Check eigh
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e, v = pe.linalg.eigh(sym)
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for i in range(dim):
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tmp = sym @ v[:, i] - v[:, i] * e[i]
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for j in range(dim):
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assert tmp[j].is_zero()
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# Check eig function
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e2 = pe.linalg.eig(sym)
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assert np.all(np.sort(e) == np.sort(e2))
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# Check eig function
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e2 = pe.linalg.eig(sym)
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assert np.all(np.sort(e) == np.sort(e2))
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# Check svd
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u, v, vh = pe.linalg.svd(sym)
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diff = sym - u @ np.diag(v) @ vh
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# Check svd
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u, v, vh = pe.linalg.svd(sym)
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diff = sym - u @ np.diag(v) @ vh
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for (i, j), entry in np.ndenumerate(diff):
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assert entry.is_zero()
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for (i, j), entry in np.ndenumerate(diff):
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assert entry.is_zero()
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# Check determinant
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assert pe.linalg.det(np.diag(np.diag(matrix))) == np.prod(np.diag(matrix))
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@ -1,3 +1,4 @@
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from packaging import version
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import numpy as np
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import pyerrors as pe
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import pytest
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@ -5,10 +6,11 @@ import pytest
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np.random.seed(0)
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def test_mpm():
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corr_content = []
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for t in range(8):
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f = 0.8 * np.exp(-0.4 * t)
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corr_content.append(pe.pseudo_Obs(np.random.normal(f, 1e-2 * f), 1e-2 * f, 't'))
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if version.parse(np.__version__) < version.parse("1.25.0"):
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def test_mpm():
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corr_content = []
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for t in range(8):
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f = 0.8 * np.exp(-0.4 * t)
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corr_content.append(pe.pseudo_Obs(np.random.normal(f, 1e-2 * f), 1e-2 * f, 't'))
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res = pe.mpm.matrix_pencil_method(corr_content)
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res = pe.mpm.matrix_pencil_method(corr_content)
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