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https://github.com/fjosw/pyerrors.git
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Refined covobs implementation
This commit is contained in:
Simon Kuberski
parent
9db709a171
commit
950fb17c84
2 changed files with 196 additions and 147 deletions
131
pyerrors/obs.py
131
pyerrors/obs.py
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@ -38,11 +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', 'covobs', '__dict__']
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S_global = 2.0
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S_dict = {}
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@ -68,7 +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 and not kwargs.get('empty', False):
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if means is None and samples is not None:
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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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@ -81,10 +81,10 @@ 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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if names:
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self.names = sorted(names)
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else:
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self.names = []
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self.shape = {}
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self.r_values = {}
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@ -95,7 +95,7 @@ class Obs:
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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 samples is not None:
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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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@ -159,6 +159,14 @@ class Obs:
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def e_names(self):
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return sorted(set([o.split('|')[0] for o in self.names]))
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@property
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def cov_names(self):
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return sorted(set([o for o in self.covobs.keys()]))
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@property
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def mc_names(self):
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return sorted(set([o.split('|')[0] for o in self.names if o not in self.cov_names]))
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@property
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def e_content(self):
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res = {}
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@ -233,8 +241,7 @@ class Obs:
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_parse_kwarg('tau_exp')
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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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for e, e_name in enumerate(self.mc_names):
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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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@ -320,7 +327,7 @@ 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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for e_name in self.cov_names:
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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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@ -385,15 +392,14 @@ class Obs:
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print('Result\t %3.8e +/- %3.8e +/- %3.8e (%3.3f%%)' % (self.value, self.dvalue, self.ddvalue, percentage))
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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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for e_name in self.mc_names:
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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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for e_name in self.cov_names:
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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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@ -467,7 +473,7 @@ class Obs:
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raise Exception('Run the gamma method first.')
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fig = plt.figure()
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for e, e_name in enumerate(self.e_names):
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for e, e_name in enumerate(self.mc_names):
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plt.xlabel(r'$W$')
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plt.ylabel(r'$\tau_\mathrm{int}$')
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length = int(len(self.e_n_tauint[e_name]))
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@ -498,7 +504,7 @@ class Obs:
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"""Plot normalized autocorrelation function time for each ensemble."""
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if not hasattr(self, 'e_dvalue'):
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raise Exception('Run the gamma method first.')
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for e, e_name in enumerate(self.e_names):
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for e, e_name in enumerate(self.mc_names):
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plt.xlabel('W')
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plt.ylabel('rho')
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length = int(len(self.e_drho[e_name]))
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@ -520,7 +526,7 @@ class Obs:
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"""Plot replica distribution for each ensemble with more than one replicum."""
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if not hasattr(self, 'e_dvalue'):
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raise Exception('Run the gamma method first.')
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for e, e_name in enumerate(self.e_names):
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for e, e_name in enumerate(self.mc_names):
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if len(self.e_content[e_name]) == 1:
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print('No replica distribution for a single replicum (', e_name, ')')
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continue
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@ -546,7 +552,7 @@ class Obs:
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expand : bool
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show expanded history for irregular Monte Carlo chains (default: True).
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"""
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for e, e_name in enumerate(self.e_names):
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for e, e_name in enumerate(self.mc_names):
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plt.figure()
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r_length = []
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tmp = []
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@ -1055,7 +1061,7 @@ def derived_observable(func, data, **kwargs):
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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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for name in o.cov_names:
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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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@ -1137,7 +1143,7 @@ def derived_observable(func, data, **kwargs):
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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 obs.cov_names:
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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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@ -1231,6 +1237,8 @@ def reweight(weight, obs, **kwargs):
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"""
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result = []
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for i in range(len(obs)):
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if len(obs[i].cov_names):
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raise Exception('Error: Not possible to reweight an Obs that contains covobs!')
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if sorted(weight.names) != sorted(obs[i].names):
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raise Exception('Error: Ensembles do not fit')
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for name in weight.names:
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@ -1272,6 +1280,8 @@ def correlate(obs_a, obs_b):
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if sorted(obs_a.names) != sorted(obs_b.names):
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raise Exception('Ensembles do not fit')
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if len(obs_a.cov_names) or len(obs_b.cov_names):
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raise Exception('Error: Not possible to correlate Obs that contain covobs!')
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for name in obs_a.names:
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if obs_a.shape[name] != obs_b.shape[name]:
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raise Exception('Shapes of ensemble', name, 'do not fit')
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@ -1329,7 +1339,7 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
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dvalue = 0
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for e_name in obs1.e_names:
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for e_name in obs1.mc_names:
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if e_name not in obs2.e_names:
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continue
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@ -1349,6 +1359,13 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
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tau_combined = (obs1.e_tauint[e_name] + obs2.e_tauint[e_name]) / 2
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dvalue += gamma / e_N * (1 + 1 / e_N) / e_N * 2 * tau_combined
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for e_name in obs1.cov_names:
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if e_name not in obs2.cov_names:
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continue
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dvalue += float(np.dot(np.transpose(obs1.covobs[e_name].grad), np.dot(obs1.covobs[e_name].cov, obs2.covobs[e_name].grad)))
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if np.abs(dvalue / obs1.dvalue / obs2.dvalue) > 1.0:
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dvalue = np.sign(dvalue) * obs1.dvalue * obs2.dvalue
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@ -1420,9 +1437,9 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
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e_n_tauint = {}
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e_rho = {}
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for e_name in obs1.e_names:
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for e_name in obs1.mc_names:
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if e_name not in obs2.e_names:
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if e_name not in obs2.mc_names:
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continue
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idl_d = {}
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@ -1473,6 +1490,13 @@ def covariance2(obs1, obs2, correlation=False, **kwargs):
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dvalue += e_dvalue[e_name]
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for e_name in obs1.cov_names:
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if e_name not in obs2.cov_names:
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continue
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dvalue += float(np.dot(np.transpose(obs1.covobs[e_name].grad), np.dot(obs1.covobs[e_name].cov, obs2.covobs[e_name].grad)))
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if np.abs(dvalue / obs1.dvalue / obs2.dvalue) > 1.0:
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dvalue = np.sign(dvalue) * obs1.dvalue * obs2.dvalue
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@ -1610,6 +1634,8 @@ def merge_obs(list_of_obs):
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replist = [item for obs in list_of_obs for item in obs.names]
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if (len(replist) == len(set(replist))) is False:
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raise Exception('list_of_obs contains duplicate replica: %s' % (str(replist)))
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if any([len(o.cov_names) for o in list_of_obs]):
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raise Exception('Not possible to merge data that contains covobs!')
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new_dict = {}
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idl_dict = {}
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for o in list_of_obs:
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@ -1624,6 +1650,21 @@ def merge_obs(list_of_obs):
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return o
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def cov_Obs(means, cov, name, grad=None):
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"""Create an Obs based on mean(s) and a covariance matrix
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Parameters
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----------
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mean : list of floats or float
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N mean value(s) of the new Obs
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cov : list or array
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2d (NxN) Covariance matrix, 1d diagonal entries or 0d covariance
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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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def covobs_to_obs(co):
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"""Make an Obs out of a Covobs
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@ -1641,44 +1682,14 @@ def covobs_to_obs(co):
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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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if isinstance(means, (float, int)):
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means = [means]
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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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if len(ol) == 1:
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return ol[0]
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return ol
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@ -543,3 +543,41 @@ def test_import_jackknife():
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reconstructed_obs = pe.import_jackknife(my_jacks, 'test')
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assert my_obs == reconstructed_obs
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def test_covobs():
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val = 1.123124
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cov = .243423
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name = 'Covariance'
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co = pe.cov_Obs(val, cov, name)
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co.gamma_method()
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assert (co.dvalue == np.sqrt(cov))
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assert (co.value == val)
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do = 2 * co
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assert (do.covobs[name].grad[0] == 2)
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do = co * co
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assert (do.covobs[name].grad[0] == 2 * val)
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assert np.array_equal(do.covobs[name].cov, co.covobs[name].cov)
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pi = [16.7457, -19.0475]
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cov = [[3.49591, -6.07560], [-6.07560, 10.5834]]
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cl = pe.cov_Obs(pi, cov, 'rAP')
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pl = pe.misc.gen_correlated_data(pi, np.asarray(cov), 'rAPpseudo')
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def rAP(p, g0sq):
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return -0.0010666 * g0sq * (1 + np.exp(p[0] + p[1] / g0sq))
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for g0sq in [1, 1.5, 1.8]:
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oc = rAP(cl, g0sq)
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oc.gamma_method()
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op = rAP(pl, g0sq)
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op.gamma_method()
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assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
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assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
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assert(pe.covariance2(cl[0], cl[1]) == cov[1][0])
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do = cl[0] * cl[1]
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assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))
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