Resolves deprecation error

theislab · Jan 15, 2024 · 9311595 · 9311595
1 parent 216fd57
commit 9311595
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Showing 3 changed files with 8 additions and 8 deletions.
diff --git a/ncem/data.py b/ncem/data.py
@@ -247,7 +247,7 @@ def plot_degree_vs_dist(
             mean_d = [np.mean(degree) for degree in degrees]
             print(np.mean(mean_d))
             mean_degree += mean_d
-            distances += [np.int(dist * lateral_resolution)] * len(mean_d)
+            distances += [int(dist * lateral_resolution)] * len(mean_d)
 
         sns_data = pd.DataFrame(
             {

diff --git a/ncem/estimators/base_estimator.py b/ncem/estimators/base_estimator.py
@@ -307,7 +307,7 @@ def get_data(
         )
         if robustness:
             np.random.seed(robustness_seed)
-            n_images = np.int(len(self.data.img_celldata) * robustness)
+            n_images = int(len(self.data.img_celldata) * robustness)
             print(n_images)
             image_keys = list(
                 np.random.choice(
@@ -332,10 +332,10 @@ def get_data(
         if segmentation_robustness:
             node_fraction = segmentation_robustness[0]
             overflow_fraction = segmentation_robustness[1]
-            total_size = np.int(self.data.celldata.shape[0] * node_fraction)
+            total_size = int(self.data.celldata.shape[0] * node_fraction)
 
             for key, ad in self.data.img_celldata.items():
-                size = np.int(ad.shape[0] * node_fraction)
+                size = int(ad.shape[0] * node_fraction)
                 random_indices = np.random.choice(ad.shape[0], size=size, replace=False)
                 a = ad.obsp["adjacency_matrix_connectivities"].toarray()
                 err_ad = ad.copy()

diff --git a/ncem/interpretation/interpreter.py b/ncem/interpretation/interpreter.py
@@ -499,7 +499,7 @@ def _get_np_data(
         for k, v in nodes_idx.items():
             count = count + len(v)
 
-        with tqdm(total=np.int(count / self.n_eval_nodes_per_graph)) as pbar:
+        with tqdm(total=int(count / self.n_eval_nodes_per_graph)) as pbar:
             for _step, (x_batch, y_batch) in enumerate(ds):
                 target_batch, interaction_batch, sf_batch, node_covar_batch, g_batch = x_batch
                 target.append(target_batch.numpy().squeeze())
@@ -919,7 +919,7 @@ def get_sender_receiver_effects(self, params_type: str = "ols", significance_thr
         print("calculating inv fim.")
         fim_inv = get_fim_inv(x_design, y)
 
-        interaction_shape = np.int(self.n_features_0**2)
+        interaction_shape = int(self.n_features_0**2)
         params = params[:, self.n_features_0 : interaction_shape + self.n_features_0]
         is_sign, pvalues, qvalues = wald_test(
             params=params, fisher_inv=fim_inv, significance_threshold=significance_threshold
@@ -1902,7 +1902,7 @@ def _get_np_data(
     def get_sender_receiver_effects(self, params_type: str = "ols", significance_threshold: float = 0.05):
         data = {"target": self.data.celldata.obsm["node_types"], "proportions": self.data.celldata.obsm["proportions"]}
         target = np.asarray(dmatrix("target-1", data))
-        interaction_shape = np.int(self.n_features_0**2)
+        interaction_shape = int(self.n_features_0**2)
         interactions = np.asarray(dmatrix("target:proportions-1", data))
 
         y = self.data.celldata.X
@@ -1922,7 +1922,7 @@ def get_sender_receiver_effects(self, params_type: str = "ols", significance_thr
         is_sign, pvalues, qvalues = wald_test(
             params=params, fisher_inv=fim_inv, significance_threshold=significance_threshold
         )
-        interaction_shape = np.int(self.n_features_0**2)
+        interaction_shape = int(self.n_features_0**2)
         # subset to interaction terms
         is_sign = is_sign[self.n_features_0 : interaction_shape + self.n_features_0, :]
         pvalues = pvalues[self.n_features_0 : interaction_shape + self.n_features_0, :]