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added basic functionality for covobs
This commit is contained in:
Simon Kuberski
parent
1326e9c863
commit
30ba138558
2 changed files with 315 additions and 150 deletions
54
pyerrors/covobs.py
Normal file
54
pyerrors/covobs.py
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@ -0,0 +1,54 @@
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import numpy as np
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class Covobs:
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def __init__(self, mean, cov, name, pos=None, grad=None):
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""" Initialize Covobs object.
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Parameters
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----------
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mean : float
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Mean value of the new Obs
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cov : list or array
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2d Covariance matrix or 1d diagonal entries
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name : str
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identifier for the covariance matrix
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pos : int
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Position of the variance belonging to mean in cov.
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Is taken to be 1 if cov is 0-dimensional
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grad : list or array
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Gradient of the Covobs wrt. the means belonging to cov.
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"""
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self.cov = np.array(cov)
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if self.cov.ndim == 0:
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self.N = 1
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elif self.cov.ndim == 1:
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self.N = len(self.cov)
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self.cov = np.diag(self.cov)
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elif self.cov.ndim == 2:
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self.N = self.cov.shape[0]
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if self.cov.shape[1] != self.N:
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raise Exception('Covariance matrix has to be a square matrix!')
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else:
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raise Exception('Covariance matrix has to be a 2 dimensional square matrix!')
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self.name = name
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if grad is None:
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if pos is None:
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if self.N == 1:
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pos = 0
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else:
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raise Exception('Have to specify position of cov-element belonging to mean!')
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else:
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if pos > self.N:
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raise Exception('pos %d too large for covariance matrix with dimension %dx%d!' % (pos, self.N, self.N))
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self.grad = np.zeros((self.N, 1))
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self.grad[pos] = 1.
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else:
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self.grad = np.array(grad)
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self.value = mean
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def errsq(self):
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""" Return the variance (= square of the error) of the Covobs
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"""
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return float(np.dot(np.transpose(self.grad), np.dot(self.cov, self.grad)))
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129
pyerrors/obs.py
129
pyerrors/obs.py
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@ -6,6 +6,7 @@ from autograd import jacobian
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import matplotlib.pyplot as plt
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import numdifftools as nd
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from itertools import groupby
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from .covobs import Covobs
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class Obs:
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@ -37,11 +38,11 @@ class Obs:
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Dictionary for N_sigma values. If an entry for a given ensemble exists
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this overwrites the standard value for that ensemble.
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"""
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__slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
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'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma',
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'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
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'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
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'idl', 'is_merged', 'tag', '__dict__']
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#__slots__ = ['names', 'shape', 'r_values', 'deltas', 'N', '_value', '_dvalue',
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# 'ddvalue', 'reweighted', 'S', 'tau_exp', 'N_sigma',
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# 'e_dvalue', 'e_ddvalue', 'e_tauint', 'e_dtauint',
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# 'e_windowsize', 'e_rho', 'e_drho', 'e_n_tauint', 'e_n_dtauint',
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# 'idl', 'is_merged', 'tag', '__dict__']
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S_global = 2.0
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S_dict = {}
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@ -51,7 +52,7 @@ class Obs:
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N_sigma_dict = {}
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filter_eps = 1e-10
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def __init__(self, samples, names, idl=None, means=None, **kwargs):
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def __init__(self, samples, names, idl=None, means=None, covobs=None, **kwargs):
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""" Initialize Obs object.
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Parameters
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@ -67,7 +68,7 @@ class Obs:
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already subtracted from the samples
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"""
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if means is None:
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if means is None and not kwargs.get('empty', False):
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if len(samples) != len(names):
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raise Exception('Length of samples and names incompatible.')
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if idl is not None:
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@ -80,12 +81,21 @@ class Obs:
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if min(len(x) for x in samples) <= 4:
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raise Exception('Samples have to have at least 5 entries.')
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if kwargs.get('empty', False):
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self.names = []
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else:
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self.names = sorted(names)
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self.shape = {}
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self.r_values = {}
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self.deltas = {}
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if covobs is None:
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self.covobs = {}
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else:
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self.covobs = covobs
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self.idl = {}
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if not kwargs.get('empty', False):
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if idl is not None:
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for name, idx in sorted(zip(names, idl)):
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if isinstance(idx, range):
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@ -126,6 +136,10 @@ class Obs:
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for name in self.names:
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self._value += self.shape[name] * self.r_values[name]
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self._value /= self.N
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else:
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self._value = 0
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self.is_merged = {}
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self.N = 0
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self._dvalue = 0.0
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self.ddvalue = 0.0
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@ -220,7 +234,7 @@ class Obs:
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_parse_kwarg('N_sigma')
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for e, e_name in enumerate(self.e_names):
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if e_name not in self.covobs:
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r_length = []
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for r_name in e_content[e_name]:
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if isinstance(self.idl[r_name], range):
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@ -306,6 +320,11 @@ class Obs:
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self._dvalue += self.e_dvalue[e_name] ** 2
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self.ddvalue += (self.e_dvalue[e_name] * self.e_ddvalue[e_name]) ** 2
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else:
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self.e_dvalue[e_name] = np.sqrt(self.covobs[e_name].errsq())
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self.e_ddvalue[e_name] = 0
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self._dvalue += self.e_dvalue[e_name]**2
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self._dvalue = np.sqrt(self.dvalue)
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if self._dvalue == 0.0:
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self.ddvalue = 0.0
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@ -367,12 +386,15 @@ class Obs:
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if len(self.e_names) > 1:
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print(' Ensemble errors:')
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for e_name in self.e_names:
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if e_name not in self.covobs:
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if len(self.e_names) > 1:
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print('', e_name, '\t %3.8e +/- %3.8e' % (self.e_dvalue[e_name], self.e_ddvalue[e_name]))
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if self.tau_exp[e_name] > 0:
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print(' t_int\t %3.8e +/- %3.8e tau_exp = %3.2f, N_sigma = %1.0i' % (self.e_tauint[e_name], self.e_dtauint[e_name], self.tau_exp[e_name], self.N_sigma[e_name]))
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else:
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print(' t_int\t %3.8e +/- %3.8e S = %3.2f' % (self.e_tauint[e_name], self.e_dtauint[e_name], self.S[e_name]))
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else:
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print('', e_name, '\t %3.8e' % (self.e_dvalue[e_name]))
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if ens_content is True:
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if len(self.e_names) == 1:
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print(self.N, 'samples in', len(self.e_names), 'ensemble:')
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@ -380,6 +402,7 @@ class Obs:
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print(self.N, 'samples in', len(self.e_names), 'ensembles:')
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my_string_list = []
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for key, value in sorted(self.e_content.items()):
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if key not in self.covobs:
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my_string = ' ' + "\u00B7 Ensemble '" + key + "' "
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if len(value) == 1:
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my_string += f': {self.shape[value[0]]} configurations'
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@ -399,6 +422,8 @@ class Obs:
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sublist.append(my_substring)
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my_string += '\n' + '\n'.join(sublist)
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else:
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my_string = ' ' + "\u00B7 Covobs '" + key + "' "
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my_string_list.append(my_string)
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print('\n'.join(my_string_list))
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@ -1028,6 +1053,15 @@ def derived_observable(func, data, **kwargs):
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if isinstance(raveled_data[i], (int, float)):
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raveled_data[i] = Obs([raveled_data[i] + np.zeros(first_shape)], [first_name], idl=[first_idl])
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allcov = {}
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for o in raveled_data:
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for name in o.covobs:
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if name in allcov:
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if not np.array_equal(allcov[name], o.covobs[name].cov):
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raise Exception('Inconsistent covariance matrices for %s!' % (name))
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else:
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allcov[name] = o.covobs[name].cov
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n_obs = len(raveled_data)
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new_names = sorted(set([y for x in [o.names for o in raveled_data] for y in x]))
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@ -1100,14 +1134,25 @@ def derived_observable(func, data, **kwargs):
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for i_val, new_val in np.ndenumerate(new_values):
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new_deltas = {}
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new_grad = {}
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for j_obs, obs in np.ndenumerate(data):
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for name in obs.names:
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if name in obs.covobs:
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if name in new_grad:
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new_grad[name] += deriv[i_val + j_obs] * obs.covobs[name].grad
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else:
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new_grad[name] = deriv[i_val + j_obs] * obs.covobs[name].grad
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else:
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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])
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new_covobs = {name: Covobs(obs.covobs[name].value, obs.covobs[name].cov, obs.covobs[name].name, grad=new_grad[name]) for name in new_grad}
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new_samples = []
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new_means = []
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new_idl = []
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new_names_obs = []
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for name in new_names:
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if name not in new_covobs:
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if is_merged[name]:
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filtered_deltas, filtered_idl_d = _filter_zeroes(new_deltas[name], new_idl_d[name])
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else:
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@ -1117,7 +1162,13 @@ def derived_observable(func, data, **kwargs):
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new_samples.append(filtered_deltas)
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new_idl.append(filtered_idl_d)
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new_means.append(new_r_values[name][i_val])
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final_result[i_val] = Obs(new_samples, new_names, means=new_means, idl=new_idl)
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new_names_obs.append(name)
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final_result[i_val] = Obs(new_samples, new_names_obs, means=new_means, idl=new_idl)
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for name in new_covobs:
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final_result[i_val].names.append(name)
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final_result[i_val].shape[name] = 1
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final_result[i_val].idl[name] = []
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final_result[i_val].covobs = new_covobs
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final_result[i_val]._value = new_val
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final_result[i_val].is_merged = is_merged
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final_result[i_val].reweighted = reweighted
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@ -1603,3 +1654,63 @@ def merge_obs(list_of_obs):
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o.is_merged = {name: np.any([oi.is_merged.get(name, False) for oi in list_of_obs]) for name in o.names}
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o.reweighted = np.max([oi.reweighted for oi in list_of_obs])
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return o
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def covobs_to_obs(co):
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"""Make an Obs out of a Covobs
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Parameters
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----------
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co : Covobs
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Covobs to be embedded into the Obs
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"""
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o = Obs(None, None, empty=True)
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o._value = co.value
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o.names.append(co.name)
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o.covobs[co.name] = co
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o._dvalue = np.sqrt(co.errsq())
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o.shape[co.name] = 1
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o.idl[co.name] = []
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return o
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def create_Covobs(mean, cov, name, pos=None, grad=None):
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"""Make an Obs based on a Covobs
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Parameters
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----------
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mean : float
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Mean value of the new Obs
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cov : list or array
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2d Covariance matrix or 1d diagonal entries
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name : str
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identifier for the covariance matrix
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pos : int
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Position of the variance belonging to mean in cov.
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Is taken to be 1 if cov is 0-dimensional
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grad : list or array
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Gradient of the Covobs wrt. the means belonging to cov.
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"""
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return covobs_to_obs(Covobs(mean, cov, name, pos=pos, grad=grad))
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def create_Covobs_list(means, cov, name, grad=None):
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"""Make a list of Obs based Covobs
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Parameters
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----------
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mean : list of floats
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N mean values of the new Obs
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cov : list or array
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2d (NxN) Covariance matrix or 1d diagonal entries
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name : str
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identifier for the covariance matrix
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grad : list or array
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Gradient of the Covobs wrt. the means belonging to cov.
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"""
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ol = []
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for i in range(len(means)):
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ol.append(covobs_to_obs(Covobs(means[i], cov, name, pos=i, grad=grad)))
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if ol[0].covobs[name].N != len(means):
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raise Exception('You have to provide %d mean values!' % (ol[0].N))
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return ol
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