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Test version to investigate recentering the deltas

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Simon Kuberski 2023-02-02 11:21:26 +01:00
parent cb31e681b8
commit 74fb9a0a74
1 changed files with 15 additions and 3 deletions
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18
pyerrors/obs.py
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@ -1073,7 +1073,7 @@ def _intersection_idx(idl):
return sorted(set.intersection(*[set(o) for o in idl]))
def _expand_deltas_for_merge(deltas, idx, shape, new_idx):
def _expand_deltas_for_merge(deltas, idx, shape, new_idx, shift):
"""Expand deltas defined on idx to the list of configs that is defined by new_idx.
New, empty entries are filled by 0. If idx and new_idx are of type range, the smallest
common divisor of the step sizes is used as new step size.
@ -1089,12 +1089,16 @@ def _expand_deltas_for_merge(deltas, idx, shape, new_idx):
Number of configs in idx.
new_idx : list
List of configs that defines the new range, has to be sorted in ascending order.
shift: double
Shift that is applied to each delta to properly recenter around the mean.
"""
if type(idx) is range and type(new_idx) is range:
if idx == new_idx:
return deltas
ret = np.zeros(new_idx[-1] - new_idx[0] + 1)
if abs(shift) > np.finfo(np.float64).eps:
deltas = np.array(deltas) + shift
for i in range(shape):
ret[idx[i] - new_idx[0]] = deltas[i]
return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))]) * len(new_idx) / len(idx)
@ -1165,6 +1169,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
new_r_values = {}
new_idl_d = {}
shift_d = {}
for name in new_sample_names:
idl = []
tmp_values = np.zeros(n_obs)
@ -1175,6 +1180,9 @@ def derived_observable(func, data, array_mode=False, **kwargs):
idl.append(tmp_idl)
if multi > 0:
tmp_values = np.array(tmp_values).reshape(data.shape)
shiftav = sum([item.r_values.get(name, item.value) * item.shape.get(name, 0) for item in raveled_data]) / sum([item.shape.get(name, 0) for item in raveled_data])
print(name, np.array([item.shape.get(name, 0) for item in raveled_data]) / sum([item.shape.get(name, 0) for item in raveled_data]))
shift_d[name] = [item.r_values[name] - shiftav if name in item.r_values else 0 for item in raveled_data]
new_r_values[name] = func(tmp_values, **kwargs)
new_idl_d[name] = _merge_idx(idl)
@ -1215,7 +1223,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
d_extracted[name] = []
ens_length = len(new_idl_d[name])
for i_dat, dat in enumerate(data):
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, )))
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))], 0).reshape(dat.shape + (ens_length, )))
for name in new_cov_names:
g_extracted[name] = []
zero_grad = _Zero_grad(new_covobs_lengths[name])
@ -1241,7 +1249,11 @@ def derived_observable(func, data, array_mode=False, **kwargs):
if name in obs.cov_names:
new_grad[name] = new_grad.get(name, 0) + deriv[i_val + j_obs] * obs.covobs[name].grad
else:
new_deltas[name] = new_deltas.get(name, 0) + deriv[i_val + j_obs] * _expand_deltas_for_merge(obs.deltas[name], obs.idl[name], obs.shape[name], new_idl_d[name])
shift = shift_d[name][j_obs[0]]
#shift = deriv[i_val + j_obs] * obs.r_values[name] - new_values[i_val]
print(shift_d, j_obs, shift_d[name][j_obs[0]],)
new_deltas[name] = new_deltas.get(name, 0) + deriv[i_val + j_obs] * _expand_deltas_for_merge(obs.deltas[name], obs.idl[name], obs.shape[name], new_idl_d[name], shift)
#new_deltas[name] = new_deltas.get(name, 0) + deriv[i_val + j_obs] * _expand_deltas_for_merge(obs.deltas[name], obs.idl[name], obs.shape[name], new_idl_d[name], 0)
new_covobs = {name: Covobs(0, allcov[name], name, grad=new_grad[name]) for name in new_grad}