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feat: linalg.jack_matmul now also works with non Obs valued matrices.

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Fabian Joswig 2021-11-18 11:49:46 +00:00
parent 28bf0f1701
commit 976d9c3f29
2 changed files with 40 additions and 11 deletions
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10
pyerrors/linalg.py
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@ -205,7 +205,10 @@ def jack_matmul(*operands):
r = _exp_to_jack(operands[0])
for op in operands[1:]:
r = r @ _exp_to_jack(op)
if isinstance(op[0, 0], CObs):
r = r @ _exp_to_jack(op)
else:
r = r @ op
return _imp_from_jack(r)
else:
name = operands[0][0, 0].names[0]
@ -225,7 +228,10 @@ def jack_matmul(*operands):
r = _exp_to_jack(operands[0])
for op in operands[1:]:
r = r @ _exp_to_jack(op)
if isinstance(op[0, 0], Obs):
r = r @ _exp_to_jack(op)
else:
r = r @ op
return _imp_from_jack(r)

41
tests/linalg_test.py
View file

@ -55,23 +55,46 @@ def test_jack_matmul():
check1 = pe.linalg.jack_matmul(tt, tt) - pe.linalg.matmul(tt, tt)
[o.gamma_method() for o in check1.ravel()]
assert np.all([o.is_zero_within_error(0.1) for o in check1.ravel()])
assert np.all([o.dvalue < 0.001 for o in check1.ravel()])
trace1 = np.trace(check1)
trace1.gamma_method()
assert trace1.dvalue < 0.001
tt2 = get_complex_matrix(8)
check2 = pe.linalg.jack_matmul(tt2, tt2) - pe.linalg.matmul(tt2, tt2)
tr = np.random.rand(8, 8)
check2 = pe.linalg.jack_matmul(tt, tr) - pe.linalg.matmul(tt, tr)
[o.gamma_method() for o in check2.ravel()]
assert np.all([o.real.is_zero_within_error(0.1) for o in check2.ravel()])
assert np.all([o.imag.is_zero_within_error(0.1) for o in check2.ravel()])
assert np.all([o.is_zero_within_error(0.1) for o in check2.ravel()])
assert np.all([o.dvalue < 0.001 for o in check2.ravel()])
trace2 = np.trace(check2)
trace2.gamma_method()
assert trace2.real.dvalue < 0.001
assert trace2.imag.dvalue < 0.001
assert trace2.dvalue < 0.001
tt2 = get_complex_matrix(8)
check3 = pe.linalg.jack_matmul(tt2, tt2) - pe.linalg.matmul(tt2, tt2)
[o.gamma_method() for o in check3.ravel()]
assert np.all([o.real.is_zero_within_error(0.1) for o in check3.ravel()])
assert np.all([o.imag.is_zero_within_error(0.1) for o in check3.ravel()])
assert np.all([o.real.dvalue < 0.001 for o in check3.ravel()])
assert np.all([o.imag.dvalue < 0.001 for o in check3.ravel()])
trace3 = np.trace(check3)
trace3.gamma_method()
assert trace3.real.dvalue < 0.001
assert trace3.imag.dvalue < 0.001
tr2 = np.random.rand(8, 8) + 1j * np.random.rand(8, 8)
check4 = pe.linalg.jack_matmul(tt2, tr2) - pe.linalg.matmul(tt2, tr2)
[o.gamma_method() for o in check4.ravel()]
assert np.all([o.real.is_zero_within_error(0.1) for o in check4.ravel()])
assert np.all([o.imag.is_zero_within_error(0.1) for o in check4.ravel()])
assert np.all([o.real.dvalue < 0.001 for o in check4.ravel()])
assert np.all([o.imag.dvalue < 0.001 for o in check4.ravel()])
trace4 = np.trace(check4)
trace4.gamma_method()
assert trace4.real.dvalue < 0.001
assert trace4.imag.dvalue < 0.001
def test_multi_dot():
for dim in [4, 8]:
for dim in [4, 6]:
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
@ -167,8 +190,8 @@ def test_complex_matrix_inverse():
base_matrix = np.empty((dimension, dimension), dtype=object)
matrix = np.empty((dimension, dimension), dtype=complex)
for (n, m), entry in np.ndenumerate(base_matrix):
exponent_real = np.random.normal(3, 5)
exponent_imag = np.random.normal(3, 5)
exponent_real = np.random.normal(2, 3)
exponent_imag = np.random.normal(2, 3)
base_matrix[n, m] = pe.CObs(pe.pseudo_Obs(2 + 10 ** exponent_real, 10 ** (exponent_real - 1), 't'),
pe.pseudo_Obs(2 + 10 ** exponent_imag, 10 ** (exponent_imag - 1), 't'))