Fix mapping matrix type

Author: Marius1311

docs/notebooks/tutorials/spatial_mapping.ipynb

@@ -508,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -523,7 +523,7 @@
    ],
    "source": [
     "cmap.compute_neighbors(use_rep=\"X_harmony\", only_yx=True)\n",
-    "cmap.compute_mappping_matrix()"
+    "cmap.compute_mapping_matrix()"
    ]
   },
   {

docs/notebooks/tutorials/spatial_smoothing.ipynb

@@ -529,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -542,7 +542,7 @@
     }
    ],
    "source": [
-    "smap.compute_mappping_matrix(method=\"equal\")"
+    "smap.compute_mapping_matrix(method=\"equal\")"
    ]
   },
   {

src/cellmapper/model/cellmapper.py

@@ -249,7 +249,7 @@ def compute_neighbors(
         self.knn = Neighbors(np.ascontiguousarray(xrep), np.ascontiguousarray(yrep))
         self.knn.compute_neighbors(n_neighbors=n_neighbors, method=method, metric=metric, only_yx=only_yx)
 
-    def compute_mappping_matrix(
+    def compute_mapping_matrix(
         self,
         method: Literal["jaccard", "gaussian", "scarches", "inverse_distance", "random", "hnoca", "equal"] = "gaussian",
     ) -> None:
@@ -532,7 +532,7 @@ def fit(
         self.compute_neighbors(
             n_neighbors=n_neighbors, use_rep=use_rep, method=knn_method, metric=metric, only_yx=only_yx
         )
-        self.compute_mappping_matrix(method=mapping_method)
+        self.compute_mapping_matrix(method=mapping_method)
         if obs_keys is not None:
             self.transfer_labels(obs_keys=obs_keys, prediction_postfix=prediction_postfix)
         if obsm_keys is not None:

tests/conftest.py

@@ -177,7 +177,7 @@ def cmap(query_reference_adata):
 
     # Compute neighbors and mapping matrix
     cmap.compute_neighbors(n_neighbors=30, use_rep="X_pca", method="sklearn")
-    cmap.compute_mappping_matrix(method="gaussian")
+    cmap.compute_mapping_matrix(method="gaussian")
 
     return cmap
 

tests/test_query_to_reference_mapping.py

@@ -36,7 +36,7 @@ def test_expression_transfer(self, cmap, expected_expression_transfer_metrics):
         "method", ["gaussian", "scarches", "random", "inverse_distance", "jaccard", "hnoca", "equal"]
     )
     def test_compute_mapping_matrix_all_methods(self, cmap, method):
-        cmap.compute_mappping_matrix(method=method)
+        cmap.compute_mapping_matrix(method=method)
         assert cmap.mapping_matrix is not None
 
     @pytest.mark.parametrize("layer_key", ["X", "counts"])

tests/test_self_mapping.py

@@ -43,7 +43,7 @@ def test_all_operations_self_mapping(self, adata_pbmc3k):
 
         # Test with typical parameters
         cm.compute_neighbors(n_neighbors=5, use_rep="X_pca")
-        cm.compute_mappping_matrix(method="gaussian")
+        cm.compute_mapping_matrix(method="gaussian")
 
         # Test label transfer
         cm.transfer_labels(obs_keys="leiden")
@@ -83,7 +83,7 @@ def test_load_scanpy_distances(self, adata_spatial, n_neighbors):
         assert cm.knn.xx.n_neighbors + 1 == n_neighbors
 
         # Test the full pipeline with precomputed distances
-        cm.compute_mappping_matrix(method="gaussian")
+        cm.compute_mapping_matrix(method="gaussian")
         cm.transfer_labels(obs_keys="leiden")
 
         assert "leiden_pred" in cm.query.obs
@@ -142,7 +142,7 @@ def test_load_squidpy_distances(self, adata_spatial, squidpy_params):
             ).all()
 
         # Test the mapping pipeline
-        cm.compute_mappping_matrix(method="gaussian")
+        cm.compute_mapping_matrix(method="gaussian")
         cm.transfer_labels(obs_keys="leiden")
 
         assert "leiden_pred" in cm.query.obs
@@ -176,8 +176,8 @@ def test_load_distances_with_include_self(self, adata_spatial, include_self):
             assert i not in cm_without_self.knn.xx.indices[i]
 
         # Both should work with the rest of the pipeline
-        cm_with_self.compute_mappping_matrix(method="gaussian")
-        cm_without_self.compute_mappping_matrix(method="gaussian")
+        cm_with_self.compute_mapping_matrix(method="gaussian")
+        cm_without_self.compute_mapping_matrix(method="gaussian")
 
         # Compute label transfer for both
         cm_with_self.transfer_labels(obs_keys="leiden", prediction_postfix="with_self")
@@ -197,7 +197,7 @@ def test_self_mapping_without_rep(self, adata_pbmc3k):
 
         # Test with no representation provided
         cm.compute_neighbors(n_neighbors=5, use_rep=None, n_comps=10)
-        cm.compute_mappping_matrix(method="gaussian")
+        cm.compute_mapping_matrix(method="gaussian")
 
         # Verify joint PCA was computed
         assert "X_pca" in adata_pbmc3k.obsm