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fix: array mode now works with elements defined on different ensembles

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Fabian Joswig 2021-12-07 07:36:24 +00:00
commit df6b151c13
2 changed files with 7 additions and 8 deletions
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7
pyerrors/obs.py
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@ -1146,14 +1146,13 @@ def derived_observable(func, data, array_mode=False, **kwargs):
final_result = np.zeros(new_values.shape, dtype=object)
# TODO: array mode does not work when matrices are defined on differenet ensembles
if array_mode is True:
class Zero_grad():
class _Zero_grad():
def __init__(self):
self.grad = 0
zero_grad = Zero_grad()
zero_grad = _Zero_grad()
d_extracted = {}
g_extracted = {}
@ -1161,7 +1160,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
d_extracted[name] = []
for i_dat, dat in enumerate(data):
ens_length = len(new_idl_d[name])
d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas[name], o.idl[name], o.shape[name], new_idl_d[name]) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
d_extracted[name].append(np.array([_expand_deltas_for_merge(o.deltas.get(name, np.zeros(ens_length)), o.idl.get(name, new_idl_d[name]), o.shape.get(name, ens_length), new_idl_d[name]) for o in dat.reshape(np.prod(dat.shape))]).reshape(dat.shape + (ens_length, )))
for name in new_cov_names:
g_extracted[name] = []
for i_dat, dat in enumerate(data):

8
tests/linalg_test.py
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@ -29,10 +29,10 @@ def get_complex_matrix(dimension):
def test_matmul():
for dim in [4, 8]:
for dim in [4, 6]:
for const in [1, pe.cov_Obs(1.0, 0.002, 'cov')]:
my_list = []
length = 1000 + np.random.randint(200)
length = 100 + 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))
@ -41,7 +41,7 @@ def test_matmul():
assert e.is_zero(), t
my_list = []
length = 1000 + np.random.randint(200)
length = 100 + 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'])))
@ -189,7 +189,7 @@ def test_matmul_irregular_histories():
standard_array = []
for i in range(dim ** 2):
standard_array.append(pe.Obs([np.random.normal(1.1, 0.2, length)], ['ens1']))
standard_matrix = np.array(standard_array).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') # * pe.pseudo_Obs(0.1, 0.002, 'qr')
standard_matrix = np.array(standard_array).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') * pe.pseudo_Obs(0.1, 0.002, 'qr')
for idl in [range(1, 501, 2), range(250, 273), [2, 8, 19, 20, 78]]:
irregular_array = []