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Merge branch 'develop' into documentation

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fjosw 2021-12-08 16:28:48 +00:00
commit d06629ef27
1 changed files with 23 additions and 37 deletions
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pyerrors/obs.py
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@ -119,9 +119,12 @@ class Obs:
for name, sample in sorted(zip(names, samples)):
self.idl[name] = range(1, len(sample) + 1)
self._value = 0
self.N = 0
if means is not None:
for name, sample, mean in sorted(zip(names, samples, means)):
self.shape[name] = len(self.idl[name])
self.N += self.shape[name]
if len(sample) != self.shape[name]:
raise Exception('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
self.r_values[name] = mean
@ -129,18 +132,16 @@ class Obs:
else:
for name, sample in sorted(zip(names, samples)):
self.shape[name] = len(self.idl[name])
self.N += self.shape[name]
if len(sample) != self.shape[name]:
raise Exception('Incompatible samples and idx for %s: %d vs. %d' % (name, len(sample), self.shape[name]))
self.r_values[name] = np.mean(sample)
self.deltas[name] = sample - self.r_values[name]
self.is_merged = {}
self.N = sum(list(self.shape.values()))
self._value = 0
if means is None:
for name in self.names:
self._value += self.shape[name] * self.r_values[name]
self._value /= self.N
self.is_merged = {}
else:
self._value = 0
self.is_merged = {}
@ -1053,17 +1054,11 @@ def derived_observable(func, data, array_mode=False, **kwargs):
raveled_data = data.ravel()
# Workaround for matrix operations containing non Obs data
# TODO: Find more elegant solution here.
for i_data in raveled_data:
if isinstance(i_data, Obs):
first_name = i_data.names[0]
first_shape = i_data.shape[first_name]
first_idl = i_data.idl[first_name]
break
for i in range(len(raveled_data)):
if isinstance(raveled_data[i], (int, float)):
raveled_data[i] = Obs([raveled_data[i] + np.zeros(first_shape)], [first_name], idl=[first_idl])
if array_mode is True:
if not all(isinstance(x, Obs) for x in raveled_data):
for i in range(len(raveled_data)):
if isinstance(raveled_data[i], (int, float)):
raveled_data[i] = cov_Obs(raveled_data[i], 0.0, "###dummy_covobs###")
allcov = {}
for o in raveled_data:
@ -1081,16 +1076,6 @@ def derived_observable(func, data, array_mode=False, **kwargs):
is_merged = {name: (len(list(filter(lambda o: o.is_merged.get(name, False) is True, raveled_data))) > 0) for name in new_sample_names}
reweighted = len(list(filter(lambda o: o.reweighted is True, raveled_data))) > 0
new_idl_d = {}
for name in new_sample_names:
idl = []
for i_data in raveled_data:
tmp = i_data.idl.get(name)
if tmp is not None:
idl.append(tmp)
new_idl_d[name] = _merge_idx(idl)
if not is_merged[name]:
is_merged[name] = (1 != len(set([len(idx) for idx in [*idl, new_idl_d[name]]])))
if data.ndim == 1:
values = np.array([o.value for o in data])
@ -1099,21 +1084,24 @@ def derived_observable(func, data, array_mode=False, **kwargs):
new_values = func(values, **kwargs)
multi = 0
if isinstance(new_values, np.ndarray):
multi = 1
multi = int(isinstance(new_values, np.ndarray))
new_r_values = {}
new_idl_d = {}
for name in new_sample_names:
idl = []
tmp_values = np.zeros(n_obs)
for i, item in enumerate(raveled_data):
tmp = item.r_values.get(name)
if tmp is None:
tmp = item.value
tmp_values[i] = tmp
tmp_values[i] = item.r_values.get(name, item.value)
tmp_idl = item.idl.get(name)
if tmp_idl is not None:
idl.append(tmp_idl)
if multi > 0:
tmp_values = np.array(tmp_values).reshape(data.shape)
new_r_values[name] = func(tmp_values, **kwargs)
new_idl_d[name] = _merge_idx(idl)
if not is_merged[name]:
is_merged[name] = (1 != len(set([len(idx) for idx in [*idl, new_idl_d[name]]])))
if 'man_grad' in kwargs:
deriv = np.asarray(kwargs.get('man_grad'))
@ -1148,13 +1136,11 @@ def derived_observable(func, data, array_mode=False, **kwargs):
if array_mode is True:
new_covobs_lengths = dict(set([y for x in [[(n, o.covobs[n].N) for n in o.cov_names] for o in raveled_data] for y in x]))
class _Zero_grad():
def __init__(self, N):
# self.grad = np.zeros(N)
self.grad = np.zeros((N, 1))
new_covobs_lengths = dict(set([y for x in [[(n, o.covobs[n].N) for n in o.cov_names] for o in raveled_data] for y in x]))
d_extracted = {}
g_extracted = {}
for name in new_sample_names: