Fix missing attribute '_from_state' in 'EmpiricalCovariance' (#1471)

* ADD: from_state allows int as covariance

* FIX: emp _from_state

* FIX: broken statefulness of update_many

* FIX: remove unexpected kwarg

* FIX: if statement nesting + redundant docstring entry

* FIX: wrong intendation + sort keys for shuffled samples

river/covariance/emp.py

@@ -14,7 +14,7 @@ class SymmetricMatrix(abc.ABC):
 
     @property
     @abc.abstractmethod
-    def matrix(self):
+    def matrix(self) -> dict:
         ...
 
     def __getitem__(self, key):
@@ -183,16 +183,38 @@ def update_many(self, X: pd.DataFrame):
             )
         }
 
+        self._update_from_state(n=n, mean=mean, cov=cov)
+
+    def _update_from_state(self, n: int, mean: dict, cov: float | dict):
+        """Update from state information.
+
+        Parameters
+        ----------
+        n
+            The number of data points.
+        mean
+            A dictionary of variable means.
+        cov
+            A dictionary of covariance or variance values.
+
+        Raises
+        ----------
+            KeyError: If an element in `mean` or `cov` is missing.
+        """
         for i, j in itertools.combinations(sorted(mean.keys()), r=2):
             try:
                 self[i, j]
             except KeyError:
                 self._cov[i, j] = stats.Cov(self.ddof)
+            if isinstance(cov, dict):
+                cov_ = cov.get((i, j), cov.get((j, i)))
+            else:
+                cov_ = cov
             self._cov[i, j] += stats.Cov._from_state(
                 n=n,
                 mean_x=mean[i],
                 mean_y=mean[j],
-                cov=cov.get((i, j), cov.get((j, i))),
+                cov=cov_,
                 ddof=self.ddof,
             )
 
@@ -201,9 +223,36 @@ def update_many(self, X: pd.DataFrame):
                 self[i, i]
             except KeyError:
                 self._cov[i, i] = stats.Var(self.ddof)
-            self._cov[i, i] += stats.Var._from_state(
-                n=n, m=mean[i], sig=cov[i, i], ddof=self.ddof
-            )
+            if isinstance(cov, dict):
+                cov_ = cov[i, i]
+            else:
+                cov_ = cov
+            self._cov[i, i] += stats.Var._from_state(n=n, m=mean[i], sig=cov_, ddof=self.ddof)
+
+    @classmethod
+    def _from_state(cls, n: int, mean: dict, cov: float | dict, *, ddof=1):
+        """Create a new instance from state information.
+
+        Parameters
+        ----------
+        cls
+            The class type.
+        n
+            The number of data points.
+        mean
+            A dictionary of variable means.
+        cov
+            A dictionary of covariance or variance values.
+        ddof
+            Degrees of freedom for covariance calculation. Defaults to 1.
+
+        Returns
+        ----------
+            cls: A new instance of the class with updated covariance matrix.
+        """
+        new = cls(ddof=ddof)
+        new._update_from_state(n=n, mean=mean, cov=cov)
+        return new
 
 
 class EmpiricalPrecision(SymmetricMatrix):