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Merge branch 'develop' into documentation

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fjosw 2021-11-11 10:51:05 +00:00
commit ad1ac2b4d1
3 changed files with 53 additions and 39 deletions
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1
pyerrors/__init__.py
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@ -137,6 +137,7 @@ from .obs import *
from .correlators import *
from .fits import *
from . import dirac
from . import input
from . import linalg
from . import misc
from . import mpm

57
pyerrors/linalg.py
View file

@ -1,23 +1,25 @@
import numpy as np
from autograd import jacobian
import autograd.numpy as anp # Thinly-wrapped numpy
from .obs import derived_observable, CObs, Obs
from .obs import derived_observable, CObs, Obs, _merge_idx, _expand_deltas_for_merge, _filter_zeroes
from functools import partial
from autograd.extend import defvjp
def derived_array(func, data, **kwargs):
"""Construct a derived Obs according to func(data, **kwargs) of matrix value data
using automatic differentiation.
"""Construct a derived Obs for a matrix valued function according to func(data, **kwargs) using automatic differentiation.
Parameters
----------
func -- arbitrary function of the form func(data, **kwargs). For the
func : object
arbitrary function of the form func(data, **kwargs). For the
automatic differentiation to work, all numpy functions have to have
the autograd wrapper (use 'import autograd.numpy as anp').
data -- list of Obs, e.g. [obs1, obs2, obs3].
man_grad -- manually supply a list or an array which contains the jacobian
data : list
list of Obs, e.g. [obs1, obs2, obs3].
man_grad : list
manually supply a list or an array which contains the jacobian
of func. Use cautiously, supplying the wrong derivative will
not be intercepted.
"""
@ -30,25 +32,29 @@ def derived_array(func, data, **kwargs):
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])
raveled_data[i] = Obs([raveled_data[i] + np.zeros(first_shape)], [first_name], idl=[first_idl])
n_obs = len(raveled_data)
new_names = sorted(set([y for x in [o.names for o in raveled_data] for y in x]))
new_shape = {}
for i_data in raveled_data:
is_merged = len(list(filter(lambda o: o.is_merged is True, raveled_data))) > 0
reweighted = len(list(filter(lambda o: o.reweighted is True, raveled_data))) > 0
new_idl_d = {}
for name in new_names:
tmp = i_data.shape.get(name)
idl = []
for i_data in raveled_data:
tmp = i_data.idl.get(name)
if tmp is not None:
if new_shape.get(name) is None:
new_shape[name] = tmp
else:
if new_shape[name] != tmp:
raise Exception('Shapes of ensemble', name, 'do not match.')
idl.append(tmp)
new_idl_d[name] = _merge_idx(idl)
if not is_merged:
is_merged = (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])
else:
@ -71,8 +77,6 @@ def derived_array(func, data, **kwargs):
deriv = np.asarray(kwargs.get('man_grad'))
if new_values.shape + data.shape != deriv.shape:
raise Exception('Manual derivative does not have correct shape.')
elif kwargs.get('num_grad') is True:
raise Exception('Multi mode currently not supported for numerical derivative')
else:
deriv = jacobian(func)(values, **kwargs)
@ -83,7 +87,7 @@ def derived_array(func, data, **kwargs):
d_extracted[name] = []
for i_dat, dat in enumerate(data):
ens_length = dat.ravel()[0].shape[name]
d_extracted[name].append(np.array([o.deltas[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[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, )))
for i_val, new_val in np.ndenumerate(new_values):
new_deltas = {}
@ -95,12 +99,21 @@ def derived_array(func, data, **kwargs):
new_samples = []
new_means = []
for name in new_names:
new_samples.append(new_deltas[name])
new_idl = []
if is_merged:
filtered_names, filtered_deltas, filtered_idl_d = _filter_zeroes(new_names, new_deltas, new_idl_d)
else:
filtered_names = new_names
filtered_deltas = new_deltas
filtered_idl_d = new_idl_d
for name in filtered_names:
new_samples.append(filtered_deltas[name])
new_means.append(new_r_values[name][i_val])
final_result[i_val] = Obs(new_samples, new_names, means=new_means)
new_idl.append(filtered_idl_d[name])
final_result[i_val] = Obs(new_samples, filtered_names, means=new_means, idl=new_idl)
final_result[i_val]._value = new_val
final_result[i_val].is_merged = is_merged
final_result[i_val].reweighted = reweighted
return final_result

24
pyerrors/obs.py
View file

@ -829,7 +829,7 @@ class CObs:
return 'CObs[' + str(self) + ']'
def merge_idx(idl):
def _merge_idx(idl):
"""Returns the union of all lists in idl
Parameters
@ -856,7 +856,7 @@ def merge_idx(idl):
return list(set().union(*idl))
def expand_deltas_for_merge(deltas, idx, shape, new_idx):
def _expand_deltas_for_merge(deltas, idx, shape, new_idx):
"""Expand deltas defined on idx to the list of configs that is defined by new_idx.
New, empy 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.
@ -883,7 +883,7 @@ def expand_deltas_for_merge(deltas, idx, shape, new_idx):
return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))])
def filter_zeroes(names, deltas, idl, eps=Obs.filter_eps):
def _filter_zeroes(names, deltas, idl, eps=Obs.filter_eps):
"""Filter out all configurations with vanishing fluctuation such that they do not
contribute to the error estimate anymore. Returns the new names, deltas and
idl according to the filtering.
@ -976,7 +976,7 @@ def derived_observable(func, data, **kwargs):
tmp = i_data.idl.get(name)
if tmp is not None:
idl.append(tmp)
new_idl_d[name] = merge_idx(idl)
new_idl_d[name] = _merge_idx(idl)
if not is_merged:
is_merged = (1 != len(set([len(idx) for idx in [*idl, new_idl_d[name]]])))
@ -1038,13 +1038,13 @@ def derived_observable(func, data, **kwargs):
new_deltas = {}
for j_obs, obs in np.ndenumerate(data):
for name in obs.names:
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])
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])
new_samples = []
new_means = []
new_idl = []
if is_merged:
filtered_names, filtered_deltas, filtered_idl_d = filter_zeroes(new_names, new_deltas, new_idl_d)
filtered_names, filtered_deltas, filtered_idl_d = _filter_zeroes(new_names, new_deltas, new_idl_d)
else:
filtered_names = new_names
filtered_deltas = new_deltas
@ -1064,7 +1064,7 @@ def derived_observable(func, data, **kwargs):
return final_result
def reduce_deltas(deltas, idx_old, idx_new):
def _reduce_deltas(deltas, idx_old, idx_new):
"""Extract deltas defined on idx_old on all configs of idx_new.
Parameters
@ -1094,7 +1094,7 @@ def reduce_deltas(deltas, idx_old, idx_new):
pos = j
break
if pos < 0:
raise Exception('Error in reduce_deltas: Config %d not in idx_old' % (idx_new[i]))
raise Exception('Error in _reduce_deltas: Config %d not in idx_old' % (idx_new[i]))
ret[i] = deltas[j]
return np.array(ret)
@ -1124,7 +1124,7 @@ def reweight(weight, obs, **kwargs):
new_samples = []
w_deltas = {}
for name in sorted(weight.names):
w_deltas[name] = reduce_deltas(weight.deltas[name], weight.idl[name], obs[i].idl[name])
w_deltas[name] = _reduce_deltas(weight.deltas[name], weight.idl[name], obs[i].idl[name])
new_samples.append((w_deltas[name] + weight.r_values[name]) * (obs[i].deltas[name] + obs[i].r_values[name]))
tmp_obs = Obs(new_samples, sorted(weight.names), idl=[obs[i].idl[name] for name in sorted(weight.names)])
@ -1200,7 +1200,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
for name in sorted(set(obs1.names + obs2.names)):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
raise Exception('Shapes of ensemble', name, 'do not fit')
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):
@ -1306,7 +1306,7 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
for r_name in obs1.e_content[e_name]:
if r_name not in obs2.e_content[e_name]:
continue
idl_d[r_name] = merge_idx([obs1.idl[r_name], obs2.idl[r_name]])
idl_d[r_name] = _merge_idx([obs1.idl[r_name], obs2.idl[r_name]])
if idl_d[r_name] is range:
r_length.append(len(idl_d[r_name]))
else:
@ -1380,7 +1380,7 @@ def covariance3(obs1, obs2, correlation=False, **kwargs):
for name in sorted(set(obs1.names + obs2.names)):
if (obs1.shape.get(name) != obs2.shape.get(name)) and (obs1.shape.get(name) is not None) and (obs2.shape.get(name) is not None):
raise Exception('Shapes of ensemble', name, 'do not fit')
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
if (1 != len(set([len(idx) for idx in [obs1.idl[name], obs2.idl[name], _merge_idx([obs1.idl[name], obs2.idl[name]])]]))):
raise Exception('Shapes of ensemble', name, 'do not fit')
if not hasattr(obs1, 'e_names') or not hasattr(obs2, 'e_names'):