Implement marginal getter

src/centhesus/mst.py

@@ -121,3 +121,43 @@ def get_domain(self):
         domain = Domain.fromdict({**area_type_domain, **dimension_domain})
 
         return domain
+
+    def get_marginal(self, clique, flatten=True):
+        """
+        Retrieve the marginal table for a clique from the API.
+
+        This function also returns the metadata to "measure" the
+        marginal in the package that underpins the synthesis, `mbi`.
+
+        Parameters
+        ----------
+        clique : tuple of str
+            Tuple defining the columns of the clique to be measured.
+            Should be of the form `(col,)` or `(col1, col2)`.
+        flatten : bool
+            Whether the marginal should be flattened or not. Default is
+            `True` to work with `mbi`. Flattened marginals are NumPy
+            arrays rather than Pandas series.
+
+        Returns
+        -------
+        marginal : numpy.ndarray or pandas.Series
+            Marginal table. If `flatten` is True, this a flat array.
+            Otherwise, the indexed series is returned.
+        """
+
+        area_type = self.area_type or "nat"
+        dimensions = [
+            col for col in clique if col != area_type
+        ] or self.dimensions[0:1]
+
+        marginal = (
+            self.api.query_table(self.population_type, area_type, dimensions)
+            .groupby(list(clique))["count"]
+            .sum()
+        )
+
+        if flatten is True:
+            marginal = marginal.to_numpy().flatten()
+
+        return marginal