feat: guards added for functionality that breaks with numpy>=1.25 and

autograd==1.5.
This commit is contained in:
Fabian Joswig 2023-06-19 13:28:30 +01:00
parent f14042132f
commit 13ace62262
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3 changed files with 31 additions and 20 deletions

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@ -1,3 +1,4 @@
from packaging import version
import numpy as np
import autograd.numpy as anp # Thinly-wrapped numpy
from .obs import derived_observable, CObs, Obs, import_jackknife
@ -260,6 +261,8 @@ def _mat_mat_op(op, obs, **kwargs):
def eigh(obs, **kwargs):
"""Computes the eigenvalues and eigenvectors of a given hermitian matrix of Obs according to np.linalg.eigh."""
if version.parse(np.__version__) >= version.parse("1.25.0"):
raise NotImplementedError("eigh error propagation is not working with numpy>=1.25 and autograd==1.5.")
w = derived_observable(lambda x, **kwargs: anp.linalg.eigh(x)[0], obs)
v = derived_observable(lambda x, **kwargs: anp.linalg.eigh(x)[1], obs)
return w, v
@ -278,6 +281,8 @@ def pinv(obs, **kwargs):
def svd(obs, **kwargs):
"""Computes the singular value decomposition of a matrix of Obs."""
if version.parse(np.__version__) >= version.parse("1.25.0"):
raise NotImplementedError("svd error propagation is not working with numpy>=1.25 and autograd==1.5.")
u = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[0], obs)
s = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[1], obs)
vh = derived_observable(lambda x, **kwargs: anp.linalg.svd(x, full_matrices=False)[2], obs)

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@ -1,3 +1,4 @@
from packaging import version
import numpy as np
import autograd.numpy as anp
import math
@ -291,23 +292,26 @@ def test_matrix_functions():
diff = entry - sym[i, j]
assert diff.is_zero()
# Check eigh
e, v = pe.linalg.eigh(sym)
for i in range(dim):
tmp = sym @ v[:, i] - v[:, i] * e[i]
for j in range(dim):
assert tmp[j].is_zero()
# These linalg functions don't work with numpy>=1.25 and autograd==1.5.
# Remove this guard once this is fixed in autograd.
if version.parse(np.__version__) < version.parse("1.25.0"):
# Check eigh
e, v = pe.linalg.eigh(sym)
for i in range(dim):
tmp = sym @ v[:, i] - v[:, i] * e[i]
for j in range(dim):
assert tmp[j].is_zero()
# Check eig function
e2 = pe.linalg.eig(sym)
assert np.all(np.sort(e) == np.sort(e2))
# Check eig function
e2 = pe.linalg.eig(sym)
assert np.all(np.sort(e) == np.sort(e2))
# Check svd
u, v, vh = pe.linalg.svd(sym)
diff = sym - u @ np.diag(v) @ vh
# Check svd
u, v, vh = pe.linalg.svd(sym)
diff = sym - u @ np.diag(v) @ vh
for (i, j), entry in np.ndenumerate(diff):
assert entry.is_zero()
for (i, j), entry in np.ndenumerate(diff):
assert entry.is_zero()
# Check determinant
assert pe.linalg.det(np.diag(np.diag(matrix))) == np.prod(np.diag(matrix))

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@ -1,3 +1,4 @@
from packaging import version
import numpy as np
import pyerrors as pe
import pytest
@ -5,10 +6,11 @@ import pytest
np.random.seed(0)
def test_mpm():
corr_content = []
for t in range(8):
f = 0.8 * np.exp(-0.4 * t)
corr_content.append(pe.pseudo_Obs(np.random.normal(f, 1e-2 * f), 1e-2 * f, 't'))
if version.parse(np.__version__) < version.parse("1.25.0"):
def test_mpm():
corr_content = []
for t in range(8):
f = 0.8 * np.exp(-0.4 * t)
corr_content.append(pe.pseudo_Obs(np.random.normal(f, 1e-2 * f), 1e-2 * f, 't'))
res = pe.mpm.matrix_pencil_method(corr_content)
res = pe.mpm.matrix_pencil_method(corr_content)