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feat: derived observable array mode works now, test added

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Fabian Joswig 2021-12-06 21:59:41 +00:00
parent 52867fb033
commit 02d8f469eb
2 changed files with 19 additions and 18 deletions
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2
pyerrors/obs.py
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@ -1157,7 +1157,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
for name in new_cov_names:
g_extracted[name] = []
for i_dat, dat in enumerate(data):
g_extracted[name].append(np.array([obs.covobs[name]]).reshape(dat.shape + (1, )))
g_extracted[name].append(np.array([o.covobs[name].grad for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (1, )))
for i_val, new_val in np.ndenumerate(new_values):
new_deltas = {}

35
tests/linalg_test.py
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@ -30,24 +30,25 @@ def get_complex_matrix(dimension):
def test_matmul():
for dim in [4, 8]:
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
my_array = np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
for const in [1, pe.cov_Obs(1.0, 0.002, 'cov')]:
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
my_array = const * np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
my_array = np.array(my_list).reshape((dim, dim))
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
my_list = []
length = 1000 + np.random.randint(200)
for i in range(dim ** 2):
my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
my_array = np.array(my_list).reshape((dim, dim)) * const
tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
for t, e in np.ndenumerate(tt):
assert e.is_zero(), t
def test_jack_matmul():