pyerrors/pyerrors/covobs.py

104 lines
3.5 KiB
Python

import numpy as np
class Covobs:
def __init__(self, mean, cov, name, pos=None, grad=None):
""" Initialize Covobs object.
Parameters
----------
mean : float
Mean value of the new Obs
cov : list or array
2d Covariance matrix or 1d diagonal entries
name : str
identifier for the covariance matrix
pos : int
Position of the variance belonging to mean in cov.
Is taken to be 1 if cov is 0-dimensional
grad : list or array
Gradient of the Covobs wrt. the means belonging to cov.
"""
self._set_cov(cov)
if '|' in name:
raise Exception("Covobs name must not contain replica separator '|'.")
self.name = name
if grad is None:
if pos is None:
if self.N == 1:
pos = 0
else:
raise Exception('Have to specify position of cov-element belonging to mean!')
else:
if pos > self.N:
raise Exception('pos %d too large for covariance matrix with dimension %dx%d!' % (pos, self.N, self.N))
self._grad = np.zeros((self.N, 1))
self._grad[pos] = 1.
else:
self._set_grad(grad)
self.value = mean
def errsq(self):
""" Return the variance (= square of the error) of the Covobs
"""
return float(np.dot(np.transpose(self.grad), np.dot(self.cov, self.grad)))
def _set_cov(self, cov):
""" Set the covariance matrix of the covobs
Parameters
----------
cov : list or array
Has to be either of:
0 dimensional number: variance of a single covobs,
1 dimensional list or array of lenght N: variances of multiple covobs
2 dimensional list or array (N x N): Symmetric, positive-semidefinite covariance matrix
"""
self._cov = np.array(cov)
if self._cov.ndim == 0:
self.N = 1
self._cov = np.diag([self._cov])
elif self._cov.ndim == 1:
self.N = len(self._cov)
self._cov = np.diag(self._cov)
elif self._cov.ndim == 2:
self.N = self._cov.shape[0]
if self._cov.shape[1] != self.N:
raise Exception('Covariance matrix has to be a square matrix!')
else:
raise Exception('Covariance matrix has to be a 2 dimensional square matrix!')
for i in range(self.N):
for j in range(i):
if not self._cov[i][j] == self._cov[j][i]:
raise Exception('Covariance matrix is non-symmetric for (%d, %d' % (i, j))
evals = np.linalg.eigvalsh(self._cov)
for ev in evals:
if ev < 0:
raise Exception('Covariance matrix is not positive-semidefinite!')
def _set_grad(self, grad):
""" Set the gradient of the covobs
Parameters
----------
grad : list or array
Has to be either of:
0 dimensional number: gradient w.r.t. a single covobs,
1 dimensional list or array of lenght N: gradient w.r.t. multiple covobs
"""
self._grad = np.array(grad)
if self._grad.ndim in [0, 1]:
self._grad = np.reshape(self._grad, (self.N, 1))
elif self._grad.ndim != 2:
raise Exception('Invalid dimension of grad!')
@property
def cov(self):
return self._cov
@property
def grad(self):
return self._grad