added basic functionality for covobs

pyerrors/covobs.py

@@ -0,0 +1,54 @@
+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.cov = np.array(cov)
+        if self.cov.ndim == 0:
+            self.N = 1
+        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!')
+        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.grad = np.array(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)))