Merge pull request #24 from s-kuberski/feature/covobs

Feature/covobs

pyerrors/covobs.py

@@ -20,18 +20,7 @@ class Covobs:
         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._set_cov(cov)
         if '|' in name:
             raise Exception("Covobs name must not contain replica separator '|'.")
         self.name = name
@@ -44,13 +33,43 @@ class Covobs:
             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.
+            self._grad = np.zeros((self.N, 1))
+            self._grad[pos] = 1.
         else:
-            self.grad = np.array(grad)
+            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):
+        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!')
+
+    def _set_grad(self, grad):
+        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

pyerrors/obs.py

@@ -1069,7 +1069,7 @@ def derived_observable(func, data, array_mode=False, **kwargs):
     for o in raveled_data:
         for name in o.cov_names:
             if name in allcov:
-                if not np.array_equal(allcov[name], o.covobs[name].cov):
+                if not np.allclose(allcov[name], o.covobs[name].cov):
                     raise Exception('Inconsistent covariance matrices for %s!' % (name))
             else:
                 allcov[name] = o.covobs[name].cov